Characterization of cycle-to-cycle variations in a natural gas spark
lp4054充电芯片
600mA Standalone LinearLi-Ion Battery Charger withThermal Regulation in ThinSOTGeneral DescriptionThe LP4054 is a complete constant-current/constant- voltage linear charger for single cell lithium-ion batteries. Its ThinSOT package and low external component count make the LP4054 ideally suited for portable applications. Furthermore, the LP4054 is specifically designed to work within USB power specifications.No external sense resistor is needed, and no blocking diode is required due to the internal MOSFET architecture. Thermal feedback regulates the charge current to limit the die temperature during high power operation or high ambient temperature. The charge voltage is fixed at 4.2V, and the charge current can be programmed externally with a single resistor. The LP4054 automatically terminates the charge cycle when the charge current drops to 1/10th the programmed value after the final float voltage is reached. When the input supply (wall adapter or USB supply) is removed, the LP4054 automatically enters a low current state, dropping the battery drain current to less than 2µA. The LP4054 can be put into shutdown mode, reducing he supply current to 25µA.Other features include charge current monitor, undervoltage lockout, automatic recharge and a status pin to indicate charge termination and the presence of an input voltage.Ordering InformationLP4054 □□□PackageB5:SOT23-5 FeaturesProgrammable Charge Current Up to 800mANo MOSFET, Sense Resistor or Blocking Diode RequiredComplete Linear Charger in ThinSOT Package forSingle Cell Lithium-ion BatteriesConstant-Current/Constant-Voltage Operation with Thermal Regulation to Maximize ChargeRate Without Risk of OverheatingCharges Single Cell Li-Ion Batteries Directly from USB PortPreset 4.2V Charge Voltage with ± 1% Accuracy Charge Current Monitor Output for Gas GaugingAutomatic RechargeSoft-Start Limits Inrush CurrentAvailable in 5-Lead SOT-23 Package2.9V Trickle Charge ThresholdC/10 Charge Termination25mA Supply Current in Shutdown2.9V Trickle Charge ThresholdApplicationsPortable Media Players/MP3 playersCellular and Smart mobile phoneCharging Docks and CradlesBluetooth ApplicationsMarking InformationPlease see website.F:PB-FreeLP4054LP4054LP4054LP4054LP4054PINLP4054DESCRIPTIONCHRG 1 Open-Drain Status OutputGND 2Ground BAT 3 Charge Current Output VCC4Positive Input Supply VoltagePROG 5Charge Current Program, Charge Current Monitor andShutdown Pin.Pin FunctionsCHRG (PIN 1):Open-Drain Charge Status Output. When the battery is charging, the CHRG pin is pulled low by an internal N-channel MOSFET. When the charge cycle is completed, a weak pull-down of approximately 12µA is connected to the CHRG pin, indicating a “AC present” condition. When the LP4054 detects an undervoltage lockout condition, CHRG is forced high impedance.GND (PIN 2): Ground. BAT (PIN 3):Charge Current Output. Provides charge current to the battery and regulates the final float voltage to 4.2V. An internal precision resistor divider from this pin sets the float voltage which is disconnected in shutdown mode. VCC (PIN 4): Positive Input Supply Voltage. Provides power to the charger. V CC can range from 4.35V to 6.5V and should be bypassed with at least a 1µF capacitor. When V CC drops to within 30mV of the BAT pin voltage, the LP4054 enters shutdown mode, dropping IBAT to less than 2µA.PROG (PIN 5): Charge Current Program, Charge Current Monitor and Shutdown Pin. The charge current is programmed by connecting a 1% resistor, R PROG , to ground. When charging in constant-current mode, this pin servos to 1V. In all modes, the voltage on this pin can be used to measure the charge current using the following formula:The PROG pin can also be used to shutdown the charger. Disconnecting the program resistor from ground allows a 3µA current to pull the PROG pin high. When it reaches the 1.94V shutdown threshold voltage, the charger enters shutdown mode, charging stops and the input supply current drops to 25µA. This pin is also clamped to approximately 2.4V. Driving this pin to voltages beyond the clamp voltage will draw currents as high as1.5mA. Reconnecting R PROG to ground will return the charger to normal operation.LP4054 (TSOT23-5)Absolute Maximum Ratings(Over 0C ≤TJ ≤125°C and recommended supply voltage)Note 1: Absolute Maximum Ratings are those values beyond which the life of the device may be impaired.Note 2: The LP4054 are guaranteed to meet performance specifications from 0℃ to 70℃. Specifications over the -40℃ to 85℃ operating temperature range are assured by design, characterization and correlation with statistical process controls.Note 3: See Thermal Considerations.Note 4: Supply current includes PROG pin current (approximately 100µA) but does not include any current delivered to the battery through the BAT pin (approximately 100mA).Note 5: This parameter is not applicable to the LP4054X.Note 6: I TERM is expressed as a fraction of measured full charge current with indicated PROG resistor.OperationThe LP4054 is a single cell lithium-ion battery charger using a constant-current/constant-voltage algorithm. It can deliver up to 800mA of charge current (using a good thermal PCB layout) with a final float voltage accuracy of ± 1%. The LP4054 includes an internal P-channel power MOSFET and thermal regulation circuitry. No blocking diode or external current sense resistor is required; thus, the basic charger circuit requires only two external components. Furthermore, the LP4054 is capable of operating from a USB power source.Normal Charge CycleA charge cycle begins when the voltage at the V CC pin rises above the UVLO threshold level and a 1% program resistor is connected from the PROG pin to ground or when a battery is connected to the charger output. If the BAT pin is less than 2.9V, the charger enters trickle charge mode. In this mode, the LP4054 supplies approximately 1/10 the programmed charge current to bring the battery voltage up to a safe level for full current charging. (Note: The LP4054X does not include this trickle charge feature).When the BAT pin voltage rises above 2.9V, the charger enters constant-current mode, where the programmed charge current is supplied to the battery. When the BAT pin approaches the final float voltage (4.2V), the LP4054 enters constant-voltage mode and the charge current begins to decrease. When the charge current drops to 1/10 of the programmed value, the charge cycle ends.Programming Charge CurrentThe charge current is programmed using a single resistor from the PROG pin to ground. The battery charge current is 1000 times the current out of the PROG pin. The program resistor and the charge current are calculated using the following equations:The charge current out of the BAT pin can be determined at any time by monitoring the PROG pin voltage using the following equation:Charge TerminationA charge cycle is terminated when the chargecurrent falls to 1/10th the programmed value after the final float voltage is reached. This condition is detected by using an internal, filtered comparator to monitor the PROG pin. When the PROG pin voltage falls below 100mV for longer than tTERM (typically 1ms), charging is terminated. The charge current is latched off and the LP4054 enters standby mode, where the input supply current drops to 200µA.When charging, transient loads on the BAT pin can cause the PROG pin to fall below 100mV for short periods of time before the DC charge current has dropped to 1/10th the programmed value. The 1ms filter time (T TERM ) on the termination comparator ensures that transient loads of this nature do not result in premature charge cycle termination. Once the average charge current drops below 1/10th the programmed value, the LP4054 terminates the charge cycle and ceases to provide any current through the BAT pin. In this state, all loads on the BAT pin must be supplied by the battery.The LP4054 constantly monitors the BAT pin voltage in standby mode. If this voltage drops below the 4.05V recharge threshold (V RECHRG ), another charge cycle begins and current is once again supplied to the battery. To manually restart a charge cycle when in standby mode, the input voltage must be removed and reapplied, or the charger must be shut down and restarted using the PROG pin. Figure 7 shows the state diagram of a typical charge cycle.Charge Status Indicator(CHRG)The charge status output has three different states: strong pull-down(~10mA), weak pull-down (~12µA) and high impedance. The strong pull-down state indicates that the LP4054 is in a charge cycle. Once the charge cycle has terminated , the pin state is determined by undervoltage lockout conditions. A weak pull-down indicates that V CC meets the UVLO conditions and the LP4054 is ready to charge. High impedance indicates that the LP4054 is in undervoltage lockout mode: either V CC is less than 100mV above the BAT pin voltage or insufficient voltage is applied to the V CC pin. A microprocessor can be used to distinguish betweenthese three states –this method is discussed in the Applications Information section.Charge TerminationAn internal thermal feedback loop reduces the programmed charge current if the die temperature attempts to rise above a preset value of approximately 120℃. This feature protects the LP4054 from excessive temperature and allows the user to push the limits of the power handling capability of a given circuit board without risk of damaging the LP4054. The charge current can be set according to typical (not worst-case) ambient temperature with the assurance that the charger will automatically reduce the current in worst-case conditions. ThinSOT power considerations are discussed further in the Applications Information section.Undervoltage Lockout (UVLO)An internal undervoltage lockout circuit monitors the input voltage and keeps the charger in shutdown mode until V CC rises above the undervoltage lockout threshold .The UVLO circuit has a built-in hysteresis of 200mV. Furthermore, to protect against reverse current in the power MOSFET, the UVLO circuit keeps the charger in shutdown mode if V CC falls to within 30mV of the battery voltage. If the UVLO comparator is tripped, the charger will not come out of shutdown mode until V CC rises 100mV above the battery voltage.Manual ShutdownAt any point in the charge cycle, the LP4054 can be put into shutdown mode by removing RPROG thus floating the PROG pin. This reduces the battery drain current to less than 2µA and the supply current to less than 50µA. A new charge cycle can be initiated by reconnecting the program resistor. In manual shutdown, the CHRG pin is in a weakpull-down state as long as VCC is high enough to exceed the UVLO conditions. The CHRG pin is in a high impedance state if the LP4054 is in under voltage lockout mode: either VCC is within 100mV of the BAT pin voltage or insufficient voltage is applied to the VCC pin.Automatic RechargeOnce the charge cycle is terminated, the LP4054 continuously monitors the voltage on the BAT pin using a comparator with a 2ms filter time (T RECHARGE). A charge cycle restarts when the battery voltage falls below 4.05V (which corresponds to approximately 80% to 90% battery capacity). This ensures that the battery is kept at or near a fully charged condition and eliminates the need for periodic charge cycle initiations. CHRG output enters a strong pull-down state during recharge cycles.Application InformationStability ConsiderationsThe constant-voltage mode feedback loop is stable without an output capacitor provided a battery is connected to the charger output. With no battery present, an output capacitor is recommended to reduce ripple voltage. When using high value, low ESR ceramic capacitors, it is recommended to add a 1Ω resistor in series with the capacitor. No series resistor is needed if tantalum capacitors are used. In constant-current mode, the PROG pin is in the feedback loop, not the battery. The constant-current mode stability is affected by the impedance at the PROG pin. With no additional capacitance on the PROG pin, the charger is stable with program resistor values as high as 20k. However,additional capacitance on this node reduces the maximum allowed program resistor. The pole frequency at the PROG pin should be kept above 100kHz. Therefore, if the PROG pin is loaded with a capacitance, PROG, the following equation can be used to calculate the maximum resistance value for RPROG:Average, rather than instantaneous, charge current may be of interest to the user. For example, if a switching power supply operating in low current mode is connected in parallel with the battery, the average current being pulled out of the BAT pin is typically of more interest than the instantaneous current pulses. In such a case, a simple RC filter can be used on the PROG pin to measure the average battery current as shown in Figure 8. A 10k resistor has been added between the PROG pin and the filter capacitor to ensure stability.Power DissipationThe conditions that cause the LP4054 to reduce chargecurrent through thermal feedback can be approximated byconsidering the power dissipated in the IC. Nearly all of thispower dissipation is generated by the internalMOSFET—this is calculated to be approximately:P D =(V CC －V BAT ) • I BATwhere P D is the power dissipated, V CC is the input supply voltage, V BAT is the battery voltage and I BAT is the charge current. The approximate ambient temperature at which the thermal feedback begins to protect the IC is:T A =120℃-P D θJAT A =120℃-(V CC -V BAT ) • I BAT • θJAExample: An LP4054 operating from a 5V USB supply is programmed to supply 400mA full-scale current to a discharged Li-Ion battery with a voltage of 3.75V. Assuming θJA is 150℃/W (see Board Layout Considerations ), the ambient temperature at which the LP4054 will begin to reduce the charge current is approximately:T A =120℃-(5V-3.75V) • (400mA) • 150℃/W T A =120℃-0.5W • 150℃/W =120℃-75℃ T A =45℃The LP4054 can be used above 45℃ ambient, but the charge current will be reduced from 400mA. Theapproximate current at a given ambient temperature can be approximated by:Using the previous example with an ambient temperature of 60℃, the charge current will be reducedto approximately:Moreover, when thermal feedback reduces the chargecurrent, the voltage at the PROG pin is also reducedproportionally as discussed in the Operation section. Itis important to remember that LP4054 applications donot need to be designed for worst-case thermalconditions since the IC will automatically reduce powerdissipation when the junction temperature reaches approximately 120℃.Thermal Considerations Because of the small size of the ThinSOTpackage, it is very important to use a good thermal PC board layout to maximize the available charge current. The thermal path for the heat generated by the IC is from the die to the copper lead frame, through the package leads, (especially the ground lead) to the PC board copper. The PC board copper is the heat sink. The footprint copper pads should be as wide asFigure 8. Isolating Capacitive Load on PROG Pin and Filteringpossible and expand out to larger copper areas tospread and dissipate the heat to the surroundingambient. Feedthrough vias to inner or backside copperlayers are also useful in improving the overall thermalperformance of the charger .Other heat sources on theboard, not related to the charger , must also beconsidered when designing a PC board layout becausethey will affect overall temperature rise and themaximum charge current.Increasing Thermal Regulation CurrentReducing the voltage drop across the internal MOSFETcan significantly decrease the power dissipation in theIC. This has the effect of increasing the currentdelivered to the battery during thermal regulation.One method is by dissipating some of the powerthrough an external component, such as aresistor or diode.Example: An LP4054 operating from a 5V wall adapteris programmed to supply 800mA full-scale current to adischarged Li-Ion battery with a voltage of 3.75V.Assuming θJA is 125℃/W, the approximate chargecurrent at an ambient temperature of 25℃ is:By dropping voltage across a resistor in series with a 5Vwall adapter (shown in Figure 9), the on-chip powerdissipation can be decreased, thus increasing thethermally regulated charge current.:Solving for I BAT using the quadratic formaula2Using R CC = 0.25Ω, V S = 5V, V BAT = 3.75V, T A = 25℃and θJA = 125℃/W we can calculate the thermallyregulated charge current to be:While this application delivers more energy to thebattery and reduces charge time in thermal mode, itmay actually lengthen charge time in voltage mode ifVCC becomes low enough to put the LP4054 intodropout.This technique works best when RCC values areminimized to keep component size small and avoiddropout. Remember to choose a resistor with adequatepower handling capability.VCC Bypass CapacitorMany types of capacitors can be used for inputbypassing, however, caution must be exercised whenusing multilayer ceramic capacitors. Because of theself-resonant and high Q characteristics of some typesof ceramic capacitors, high voltage transients can begenerated under some start-up conditions, such asconnecting the charger input to a live powersource .Adding a 1.5Ω resistor in series with an X5Rceramic capacitor will minimize start-up voltageCharge Current Soft-StartThe LP4054 includes a soft-start circuit to minimize theinrush current at the start of a charge cycle. When acharge cycle is initiated, the charge current ramps fromzero to the full-scale current over a period of approximately 100µs. This has the effect of minimizingthe transient current load on the power supply duringstart-up.CHRG Status Output PinThe CHRG pin can provide an indication that the input voltage is greater than the undervoltage lockout threshold level. A weak pull-down current of approximately 12µA indicates that sufficient voltage isapplied to VCC to begin charging. When a dischargedbattery is connected to the charger, the constant currentportion of the charge cycle begins and the CHRG pin pulls to ground. The CHRG pin can sink up to 10mA to drive an LED that indicates that a charge cycle is in progress. When the battery is nearing full charge, the charger enters the constant-voltage portion of the charge cycle and the charge current begins to drop. When the charge current drops below 1/10 of the programmed current, the charge cycle ends and the strong pull-down is replaced by the 12µA pull-down, indicating that the charge cycle has ended. If the input voltage is removed or drops below the under voltagelockout threshold, the CHRG pin becomes high impedance. Figure 10 shows that by using two different value pull-up resistors, amicro-processor can detect all three states from this pin.To detect when the LP4054 is in charge mode, force the digital output pin (OUT) high and measure the voltage at the CHRG pin. The N-channel MOSFET will pull thepin voltage low even with the 2k pull-up resistor. Once the charge cycle terminates, the N-channel MOSFET is turned off and a 12µA current source is connected to the CHRG pin. The IN pin will then be pulled high by the 2k pull-up resistor. To determine if there is a weak pull-down current, the OUT pin should be forced to ahigh impedance state. The weak current source will pull the IN pin low through the 800k resistor; if CHRG is high impedance, the IN pin will be pulled high, indicating that the part is in a UVLO state. Reverse Polarity Input Voltage ProtectionIn some applications, protection from reverse polarity voltage on VCC is desired .If the supply voltage is highenough, a series blocking diode can be used. In other cases, where the voltage drop must be kept low a P-channel MOSFET can be used (as shown in Fig 11.) USB and Wall Adapter Power The LP4054 allows charging from both a wall adapter and a USB port. Figure 12 shows an example of how to combine wall adapter and USB power inputs. AP-channel MOSFET, MP1,is used to prevent back conducting into the USB port when a wall adapter is present and a Schottky diode, D1, is used to preventUSB power loss through the 1k pull-down resistor. Typically a wall adapter can supply more current thanthe 500mA-limited USB port. Therefore, an N-channel MOSFET, MN1, and extra 10k program resistor are used to increase the charge current to 600mA when the wall adapter is present.Packaging Information。
外文文献—遗传算法
附录I 英文翻译第一部分英文原文文章来源：书名：《自然赋予灵感的元启发示算法》第二、三章出版社：英国Luniver出版社出版日期：2008Chapter 2Genetic Algorithms2.1 IntroductionThe genetic algorithm (GA), developed by John Holland and his collaborators in the 1960s and 1970s, is a model or abstraction of biolo gical evolution based on Charles Darwin’s theory of natural selection. Holland was the first to use the crossover and recombination, mutation, and selection in the study of adaptive and artificial systems. These genetic operators form the essential part of the genetic algorithm as a problem-solving strategy. Since then, many variants of genetic algorithms have been developed and applied to a wide range of optimization problems, from graph colouring to pattern recognition, from discrete systems (such as the travelling salesman problem) to continuous systems (e.g., the efficient design of airfoil in aerospace engineering), and from financial market to multiobjective engineering optimization.There are many advantages of genetic algorithms over traditional optimization algorithms, and two most noticeable advantages are: the ability of dealing with complex problems and parallelism. Genetic algorithms can deal with various types of optimization whether the objective (fitness) functionis stationary or non-stationary (change with time), linear or nonlinear, continuous or discontinuous, or with random noise. As multiple offsprings in a population act like independent agents, the population (or any subgroup) can explore the search space in many directions simultaneously. This feature makes it ideal to parallelize the algorithms for implementation. Different parameters and even different groups of strings can be manipulated at the same time.However, genetic algorithms also have some disadvantages.The formulation of fitness function, the usage of population size, the choice of the important parameters such as the rate of mutation and crossover, and the selection criteria criterion of new population should be carefully carried out. Any inappropriate choice will make it difficult for the algorithm to converge, or it simply produces meaningless results.2.2 Genetic Algorithms2.2.1 Basic ProcedureThe essence of genetic algorithms involves the encoding of an optimization function as arrays of bits or character strings to represent the chromosomes, the manipulation operations of strings by genetic operators, and the selection according to their fitness in the aim to find a solution to the problem concerned. This is often done by the following procedure:1) encoding of the objectives or optimization functions; 2) defining a fitness function or selection criterion; 3) creating a population of individuals; 4) evolution cycle or iterations by evaluating the fitness of allthe individuals in the population,creating a new population by performing crossover, and mutation,fitness-proportionate reproduction etc, and replacing the old population and iterating again using the new population;5) decoding the results to obtain the solution to the problem. These steps can schematically be represented as the pseudo code of genetic algorithms shown in Fig. 2.1.One iteration of creating a new population is called a generation. The fixed-length character strings are used in most of genetic algorithms during each generation although there is substantial research on the variable-length strings and coding structures.The coding of the objective function is usually in the form of binary arrays or real-valued arrays in the adaptive genetic algorithms. For simplicity, we use binary strings for encoding and decoding. The genetic operators include crossover,mutation, and selection from the population.The crossover of two parent strings is the main operator with a higher probability and is carried out by swapping one segment of one chromosome with the corresponding segment on another chromosome at a random position (see Fig.2.2).The crossover carried out in this way is a single-point crossover. Crossover at multiple points is also used in many genetic algorithms to increase the efficiency of the algorithms.The mutation operation is achieved by flopping the randomly selected bits (see Fig. 2.3), and the mutation probability is usually small. The selection of anindividual in a population is carried out by the evaluation of its fitness, and it can remain in the new generation if a certain threshold of the fitness is reached or the reproduction of a population is fitness-proportionate. That is to say, the individuals with higher fitness are more likely to reproduce.2.2.2 Choice of ParametersAn important issue is the formulation or choice of an appropriate fitness function that determines the selection criterion in a particular problem. For the minimization of a function using genetic algorithms, one simple way of constructing a fitness function is to use the simplest form F = A−y with A being a large constant (though A = 0 will do) and y = f(x), thus the objective is to maximize the fitness function and subsequently minimize the objective function f(x). However, there are many different ways of defining a fitness function.For example, we can use the individual fitness assignment relative to the whole populationwhere is the phenotypic value of individual i, and N is the population size. The appropriateform of the fitness function will make sure that the solutions with higher fitness should be selected efficiently. Poor fitness function may result in incorrect or meaningless solutions.Another important issue is the choice of various parameters.The crossover probability is usually very high, typically in the range of 0.7~1.0. On the other hand, the mutation probability is usually small (usually 0.001 _ 0.05). If is too small, then the crossover occurs sparsely, which is not efficient for evolution. If the mutation probability is too high, the solutions could still ‘jump around’ even if the optimal solution is approaching.The selection criterion is also important. How to select the current population so that the best individuals with higher fitness should be preserved and passed onto the next generation. That is often carried out in association with certain elitism. The basic elitism is to select the most fit individual (in each generation) which will be carried over to the new generation without being modified by genetic operators. This ensures that the best solution is achieved more quickly.Other issues include the multiple sites for mutation and the population size. The mutation at a single site is not very efficient, mutation at multiple sites will increase the evolution efficiency. However, too many mutants will make it difficult for the system to converge or even make the system go astray to the wrong solutions. In reality, if the mutation is too high under high selection pressure, then the whole population might go extinct.In addition, the choice of the right population size is also very important. If the population size is too small, there is not enough evolution going on, and there is a risk for the whole population to go extinct. In the real world, a species with a small population, ecological theory suggests that there is a real danger of extinction for such species. Even the system carries on, there is still a danger of premature convergence. In a small population, if a significantly more fit individual appears too early, it may reproduces enough offsprings so that they overwhelm the whole (small) population. This will eventually drive the system to a local optimum (not the global optimum). On the other hand, if the population is too large, more evaluations of the objectivefunction are needed, which will require extensive computing time.Furthermore, more complex and adaptive genetic algorithms are under active research and the literature is vast about these topics.2.3 ImplementationUsing the basic procedure described in the above section, we can implement the genetic algorithms in any programming language. For simplicity of demonstrating how it works, we have implemented a function optimization using a simple GA in both Matlab and Octave.For the generalized De Jong’s test function where is a positive integer andr > 0 is the half length of the domain. This function has a minimum of at . For the values of , r = 100 and n = 5 as well as a population size of 40 16-bit strings, the variations of the objective function during a typical run are shown in Fig. 2.4. Any two runs will give slightly different results dueto the stochastic nature of genetic algorithms, but better estimates are obtained as the number of generations increases.For the well-known Easom functionit has a global maximum at (see Fig. 2.5). Now we can use the following Matlab/Octave to find its global maximum. In our implementation, we have used fixedlength 16-bit strings. The probabilities of crossover and mutation are respectivelyAs it is a maximization problem, we can use the simplest fitness function F = f(x).The outputs from a typical run are shown in Fig. 2.6 where the top figure shows the variations of the best estimates as they approach while the lower figure shows the variations of the fitness function.% Genetic Algorithm (Simple Demo) Matlab/Octave Program% Written by X S Yang (Cambridge University)% Usage: gasimple or gasimple(‘x*exp(-x)’);function [bestsol, bestfun,count]=gasimple(funstr)global solnew sol pop popnew fitness fitold f range;if nargin<1,% Easom Function with fmax=1 at x=pifunstr=‘-cos(x)*exp(-(x-3.1415926)^2)’;endrange=[-10 10]; % Range/Domain% Converting to an inline functionf=vectorize(inline(funstr));% Generating the initil populationrand(‘state’,0’); % Reset the random generatorpopsize=20; % Population sizeMaxGen=100; % Max number of generationscount=0; % counternsite=2; % number of mutation sitespc=0.95; % Crossover probabilitypm=0.05; % Mutation probabilitynsbit=16; % String length (bits)% Generating initial populationpopnew=init_gen(popsize,nsbit);fitness=zeros(1,popsize); % fitness array% Display the shape of the functionx=range(1):0.1:range(2); plot(x,f(x));% Initialize solution <- initial populationfor i=1:popsize,solnew(i)=bintodec(popnew(i,:));end% Start the evolution loopfor i=1:MaxGen,% Record as the historyfitold=fitness; pop=popnew; sol=solnew;for j=1:popsize,% Crossover pairii=floor(popsize*rand)+1; jj=floor(popsize*rand)+1;% Cross overif pc>rand,[popnew(ii,:),popnew(jj,:)]=...crossover(pop(ii,:),pop(jj,:));% Evaluate the new pairscount=count+2;evolve(ii); evolve(jj);end% Mutation at n sitesif pm>rand,kk=floor(popsize*rand)+1; count=count+1;popnew(kk,:)=mutate(pop(kk,:),nsite);evolve(kk);endend % end for j% Record the current bestbestfun(i)=max(fitness);bestsol(i)=mean(sol(bestfun(i)==fitness));end% Display resultssubplot(2,1,1); plot(bestsol); title(‘Best estimates’); subplot(2,1,2); plot(bestfun); title(‘Fitness’);% ------------- All sub functions ----------% generation of initial populationfunction pop=init_gen(np,nsbit)% String length=nsbit+1 with pop(:,1) for the Signpop=rand(np,nsbit+1)>0.5;% Evolving the new generationfunction evolve(j)global solnew popnew fitness fitold pop sol f;solnew(j)=bintodec(popnew(j,:));fitness(j)=f(solnew(j));if fitness(j)>fitold(j),pop(j,:)=popnew(j,:);sol(j)=solnew(j);end% Convert a binary string into a decimal numberfunction [dec]=bintodec(bin)global range;% Length of the string without signnn=length(bin)-1;num=bin(2:end); % get the binary% Sign=+1 if bin(1)=0; Sign=-1 if bin(1)=1.Sign=1-2*bin(1);dec=0;% floating point.decimal place in the binarydp=floor(log2(max(abs(range))));for i=1:nn,dec=dec+num(i)*2^(dp-i);enddec=dec*Sign;% Crossover operatorfunction [c,d]=crossover(a,b)nn=length(a)-1;% generating random crossover pointcpoint=floor(nn*rand)+1;c=[a(1:cpoint) b(cpoint+1:end)];d=[b(1:cpoint) a(cpoint+1:end)];% Mutatation operatorfunction anew=mutate(a,nsite)nn=length(a); anew=a;for i=1:nsite,j=floor(rand*nn)+1;anew(j)=mod(a(j)+1,2);endThe above Matlab program can easily be extended to higher dimensions. In fact, there is no need to do any programming (if you prefer) because there are many software packages (either freeware or commercial) about genetic algorithms. For example, Matlab itself has an extra optimization toolbox.Biology-inspired algorithms have many advantages over traditional optimization methods such as the steepest descent and hill-climbing and calculus-based techniques due to the parallelism and the ability of locating the very good approximate solutions in extremely very large search spaces.Furthermore, more powerful new generation algorithms can be formulated by combiningexisting and new evolutionary algorithms with classical optimization methods.Chapter 3Ant AlgorithmsFrom the discussion of genetic algorithms, we know that we can improve the search efficiency by using randomness which will also increase the diversity of the solutions so as to avoid being trapped in local optima. The selection of the best individuals is also equivalent to use memory. In fact, there are other forms of selection such as using chemical messenger (pheromone) which is commonly used by ants, honey bees, and many other insects. In this chapter, we will discuss the nature-inspired ant colony optimization (ACO), which is a metaheuristic method.3.1 Behaviour of AntsAnts are social insects in habit and they live together in organized colonies whose population size can range from about 2 to 25 millions. When foraging, a swarm of ants or mobile agents interact or communicate in their local environment. Each ant can lay scent chemicals or pheromone so as to communicate with others, and each ant is also able to follow the route marked with pheromone laid by other ants. When ants find a food source, they will mark it with pheromone and also mark the trails to and from it. From the initial random foraging route, the pheromone concentration varies and the ants follow the route with higher pheromone concentration, and the pheromone is enhanced by the increasing number of ants. As more and more ants follow the same route, it becomes the favoured path. Thus, some favourite routes (often the shortest or more efficient) emerge. This is actually a positive feedback mechanism.Emerging behaviour exists in an ant colony and such emergence arises from simple interactions among individual ants. Individual ants act according to simple and local information (such as pheromone concentration) to carry out their activities. Although there is no master ant overseeing the entire colony and broadcasting instructions to the individual ants, organized behaviour still emerges automatically. Therefore, such emergent behaviour is similar to other self-organized phenomena which occur in many processes in nature such as the pattern formation in animal skins (tiger and zebra skins).The foraging pattern of some ant species (such as the army ants) can show extraordinary regularity. Army ants search for food along some regular routes with an angle of about apart. We do not know how they manage to follow such regularity, but studies show that they could move in an area and build a bivouac and start foraging. On the first day, they forage in a random direction, say, the north and travel a few hundred meters, then branch to cover a large area. The next day, they will choose a different direction, which is about from the direction on the previous day and cover a large area. On the following day, they again choose a different direction about from the second day’s direction. In this way, they cover the whole area over about 2 weeks and they move out to a different location to build a bivouac and forage again.The interesting thing is that they do not use the angle of (this would mean that on the fourth day, they will search on the empty area already foraged on the first day). The beauty of this angle is that it leaves an angle of about from the direction on the first day. This means they cover the whole circle in 14 days without repeating (or covering a previously-foraged area). This is an amazing phenomenon.3.2 Ant Colony OptimizationBased on these characteristics of ant behaviour, scientists have developed a number ofpowerful ant colony algorithms with important progress made in recent years. Marco Dorigo pioneered the research in this area in 1992. In fact, we only use some of the nature or the behaviour of ants and add some new characteristics, we can devise a class of new algorithms.The basic steps of the ant colony optimization (ACO) can be summarized as the pseudo code shown in Fig. 3.1.Two important issues here are: the probability of choosing a route, and the evaporation rate of pheromone. There are a few ways of solving these problems although it is still an area of active research. Here we introduce the current best method. For a network routing problem, the probability of ants at a particular node to choose the route from node to node is given bywhere and are the influence parameters, and their typical values are .is the pheromone concentration on the route between and , and the desirability ofthe same route. Some knowledge about the route such as the distance is often used so that ,which implies that shorter routes will be selected due to their shorter travelling time, and thus the pheromone concentrations on these routes are higher.This probability formula reflects the fact that ants would normally follow the paths with higher pheromone concentrations. In the simpler case when , the probability of choosing a path by ants is proportional to the pheromone concentration on the path. The denominator normalizes the probability so that it is in the range between 0 and 1.The pheromone concentration can change with time due to the evaporation of pheromone. Furthermore, the advantage of pheromone evaporation is that the system could avoid being trapped in local optima. If there is no evaporation, then the path randomly chosen by the first ants will become the preferred path as the attraction of other ants by their pheromone. For a constant rate of pheromone decay or evaporation, the pheromone concentration usually varies with time exponentiallywhere is the initial concentration of pheromone and t is time. If , then we have . For the unitary time increment , the evaporation can beapproximated by . Therefore, we have the simplified pheromone update formula:where is the rate of pheromone evaporation. The increment is the amount of pheromone deposited at time t along route to when an ant travels a distance . Usually . If there are no ants on a route, then the pheromone deposit is zero.There are other variations to these basic procedures. A possible acceleration scheme is to use some bounds of the pheromone concentration and only the ants with the current global best solution(s) are allowed to deposit pheromone. In addition, certain ranking of solution fitness can also be used. These are hot topics of current research.3.3 Double Bridge ProblemA standard test problem for ant colony optimization is the simplest double bridge problem with two branches (see Fig. 3.2) where route (2) is shorter than route (1). The angles of these two routes are equal at both point A and pointB so that the ants have equal chance (or 50-50 probability) of choosing each route randomly at the initial stage at point A.Initially, fifty percent of the ants would go along the longer route (1) and the pheromone evaporates at a constant rate, but the pheromone concentration will become smaller as route (1) is longer and thus takes more time to travel through. Conversely, the pheromone concentration on the shorter route will increase steadily. After some iterations, almost all the ants will move along the shorter route. Figure 3.3 shows the initial snapshot of 10 ants (5 on each route initially) and the snapshot after 5 iterations (or equivalent to 50 ants have moved along this section). Well, there are 11 ants, and one has not decided which route to follow as it just comes near to the entrance.Almost all the ants (well, about 90% in this case) move along the shorter route.Here we only use two routes at the node, it is straightforward to extend it to the multiple routes at a node. It is expected that only the shortest route will be chosen ultimately. As any complex network system is always made of individual nodes, this algorithms can be extended to solve complex routing problems reasonably efficiently. In fact, the ant colony algorithms have been successfully applied to the Internet routing problem, the travelling salesman problem, combinatorial optimization problems, and other NP-hard problems.3.4 Virtual Ant AlgorithmAs we know that ant colony optimization has successfully solved NP-hard problems such asthe travelling salesman problem, it can also be extended to solve the standard optimization problems of multimodal functions. The only problem now is to figure out how the ants will move on an n-dimensional hyper-surface. For simplicity, we will discuss the 2-D case which can easily be extended to higher dimensions. On a 2D landscape, ants can move in any direction or , but this will cause some problems. How to update the pheromone at a particular point as there are infinite number of points. One solution is to track the history of each ant moves and record the locations consecutively, and the other approach is to use a moving neighbourhood or window. The ants ‘smell’ the pheromone concentration of their neighbourhood at any particular location.In addition, we can limit the number of directions the ants can move by quantizing the directions. For example, ants are only allowed to move left and right, and up and down (only 4 directions). We will use this quantized approach here, which will make the implementation much simpler. Furthermore, the objective function or landscape can be encoded into virtual food so that ants will move to the best locations where the best food sources are. This will make the search process even more simpler. This simplified algorithm is called Virtual Ant Algorithm (VAA) developed by Xin-She Yang and his colleagues in 2006, which has been successfully applied to topological optimization problems in engineering.The following Keane function with multiple peaks is a standard test functionThis function without any constraint is symmetric and has two highest peaks at (0, 1.39325) and (1.39325, 0). To make the problem harder, it is usually optimized under two constraints:This makes the optimization difficult because it is now nearly symmetric about x = y and the peaks occur in pairs where one is higher than the other. In addition, the true maximum is, which is defined by a constraint boundary.Figure 3.4 shows the surface variations of the multi-peaked function. If we use 50 roaming ants and let them move around for 25 iterations, then the pheromone concentrations (also equivalent to the paths of ants) are displayed in Fig. 3.4. We can see that the highest pheromoneconcentration within the constraint boundary corresponds to the optimal solution.It is worth pointing out that ant colony algorithms are the right tool for combinatorial and discrete optimization. They have the advantages over other stochastic algorithms such as genetic algorithms and simulated annealing in dealing with dynamical network routing problems.For continuous decision variables, its performance is still under active research. For the present example, it took about 1500 evaluations of the objective function so as to find the global optima. This is not as efficient as other metaheuristic methods, especially comparing with particle swarm optimization. This is partly because the handling of the pheromone takes time. Is it possible to eliminate the pheromone and just use the roaming ants? The answer is yes. Particle swarm optimization is just the right kind of algorithm for such further modifications which will be discussed later in detail.第二部分中文翻译第二章遗传算法2.1 引言遗传算法是由John Holland和他的同事于二十世纪六七十年代提出的基于查尔斯·达尔文的自然选择学说而发展的一种生物进化的抽象模型。
OB2279 DataSheet
GENERAL DESCRIPTIONOB2279 is a highly integrated current mode PWM control IC optimized for high performance, low standby power and cost effective offline flyback converter applications. PWM switching frequency at normal operation is externally programmable and trimmed to tight range. At no load or light load condition, the IC operates in extended ‘burst mode’ to minimize switching loss. Lower standby power and higher conversion efficiency is thus achieved. VDD low startup current and low operating current contribute to a reliable power on startup design with OB2279. A large value resistor could thus be used in the startup circuit for reduced power loss. The internal slope compensation improves system large signal stability and reduces the possible sub-harmonic oscillation at high PWM duty cycle output. Leading-edge blanking on current sense input removes the signal glitch due to snubber circuit diode reverse recovery and greatly reduces the externalcomponent count and system cost in the design.OB2279 offers comprehensive protection coverageincluding Cycle-by-Cycle current limiting(OCP),VDD Under Voltage Lockout(UVLO), VDD OverVoltage Protection(OVP), VDD Clamp, Gate Clamp,Over Load protection(OLP) and Over Temperatureprotection (OTP), etc.Different latch shutdown options are offered onOB2279 in different device version. V version has OVP Latch shutdown. T version supports both OVP and OTP latch shutdown. L version provides all OVP, OTP and OLP latch shutdown control. Excellent EMI performance is achieved with On-Bright proprietary frequency shuffling technique together with soft switching control at the totem pole gate drive output. Tone energy at below 20KHZ is minimized in operation. Consequently, audio noise is eliminated during operation.OB2279 is offered in SOP-8 and DIP-8 packages.FEATURES■ On-Bright Proprietary Frequency Shuffling Technology for Improved EMI Performance ■ Power On Soft Start■ Extended Burst Mode Control For Improved Efficiency and Minimum Standby Power Design ■ Audio Noise Free Operation■ External Programmable PWM Switching Frequency ■ Internal Synchronized Slope Compensation■ Low VIN/VDD Startup Current(3uA) and Low Operating Current (2.3mA)■ Leading Edge Blanking on Current Sense Input ■ Complete Protection Coverage with selective protections for Latch Shutdown o VDD Over Voltage Protection(OVP) – LatchShutdown o Over Temperature Protection(OTP) – Auto recovery or Latch Shutdowno Over Load Protection. (OLP) – Auto recovery or Latch Shutdown o VDD Under Voltage Lockout with Hysteresis (UVLO) o Gate Output Voltage Clamp (16.5V) o Built-in OCP Compensation to Achieve Minimum OPP Variation over Universal AC Input Range. APPLICATIONSOffline AC/DC flyback converter for ■ Adaptor ■ Notebook Adaptor ■ LCD Monitor/TV/PC/Set-Top Box PowerSupplies■ Open-frame SMPS ■ Printer Power TYPICAL APPLICATIONOn -B ri g h tC o nf i de n ti al ToH ig hr ayGENERAL INFORMATIONPin ConfigurationThe pin map of OB2279 in DIP8 and SOP8 package is shown as below.Ordering Information Part Number Description OB2279AP-V DIP8, V version with OVPLatchOB2279AP-T DIP8, T version withOVP/OTP latchOB2279AP-L DIP8, L version withOVP/OTP/OLP latchOB2279CP-V SOP8, V version with OVPlatchOB2279CP-T SOP8, T version withOVP/OTP latchOB2279CP-LSOP8, L version with OVP/OTP/OLP latchNote: All Devices are offered in Pb-free Package if not otherwisenoted.Package Dissipation Rating PackageR θJA (°C/W)DIP8 90 SOP8 150Absolute Maximum RatingsParameter Value VDD Clamp Voltage 35 V VDD Clamp Continuous Current10 mA V FB Input Voltage -0.3 to 7V V SENSE Input Voltage to Sense Pin-0.3 to 7V V RT Input Voltage to RT Pin -0.3 to 7V V RI Input Voltage to RI Pin -0.3 to 7V Min/Max Operating Junction Temperature T J-20 to 150 o C Min/Max Storage Temperature T stg-55 to 150 o C Lead Temperature (Soldering, 10secs)260 o C Note: Stresses beyond those listed under “absolute maximumratings” may cause permanent damage to the device. These are stress ratings only, functional operation of the device at these or any other conditions beyond those indicated under “recommended operating conditions” is not implied. Exposure to absolute maximum-rated conditions for extended periods may affect device reliability.On -B ri g h tC o nf i de n ti al ToH ig hr ayMarking InformationTERMINAL ASSIGNMENTSPin Num Pin Name I/O Description 1 GND P Ground 2 FB I Feedback input pin. PWM duty cycle is determined by voltage level into thispin and current-sense signal level at Pin 6.3 VIN I Connected through a large value resistor to rectified line input for Startupand line voltage sensing.4 RI I Internal Oscillator frequency setting pin. A resistor connected between RIand GND sets the PWM frequency.5 RT I Dual function pin. Either connected through a NTC resistor to GND for overtemperature shutdown control or used as latch shutdown control input.6 SENSE I Current sense input pin. Connected to MOSFET current sensing resistornode.7 VDD P DC power supply pin. 8 GATE O Totem-pole gate drive output for power MOSFET.On -B ri g h tCo nf i de n ti al ToH ig hr ayBLOCK DIAGRAMRECOMMENDED OPERATING CONDITIONSymbol Parameter Min Max UnitVDD VDD Supply Voltage 11.5 25 V RI RI Resistor Value 100 133 Kohm T A Operating Ambient Temperature-20 85o COn -B ri gh tC o nf i de n ti al ToH ig hr ayELECTRICAL CHARACTERISTICS(T A = 25O C if not otherwise noted)Symbol Parameter Test Conditions Min Typ Max Unit Supply Voltage (VDD) I_VDD_Startup VDD Start up Current VDD =15V, RI=100K Measure current into VDD3 20 uAI_VDD_Ops Operation Current VDD =16V, RI=100Kohm, V FB =3V2.3 mAUVLO(Enter) VDD Under Voltage Lockout Enter8.8 9.8 10.8 VUVLO(Exit) VDD Under Voltage Lockout Exit (Startup)15.5 16.5 17.5 VOVP(Latch)VDD Over Voltage Latch Trigger26.5 28 29.5 VOVP(De-Latch) VDD Latch Release Voltage Threshold7.5 VI(Vdd)_latch VDD bleeding current at latch shutdown when VDD = 9V45 uA T D _OVP VDD OVP Debounce timeRI = 100Kohm 80 uSecV DD _Clamp V DD Zener Clamp Voltage RI = 100 Kohm, I(V DD ) = 5 mA35 VT_Softstart Soft Start Time 3 mSec Feedback Input Section(FB Pin)A VCS PWM Input Gain ΔV FB /ΔV cs2.8 V/V V FB_Open V FB Open Voltage VDD = 16V 6.2VI FB _Short FB pin short circuit current Short FB pin to GND, measure current0.75 mAV TH _0D Zero Duty Cycle FB Threshold Voltage VDD = 16V, RI=100Kohm0.95 VV TH _BM Burst Mode FB Threshold Voltage1.6 VV TH _PL Power Limiting FB Threshold Voltage4.4 VT D _PL Power limiting Debounce Time VDD = 16V, RI=100Kohm80 mSecZ FB _IN Input Impedance 9.0 Kohm Current Sense Input(Sense Pin) T_blanking Sense Input Leading Edge Blanking TimeRI = 100Kohm 300 nSecZ SENSE _IN Sense Input Impedance30 KohmT D _OC Over Current Detection and Control DelayCL=1nf at GATE, RI=100Kohm 70 nSecV TH _OC_0 Current LimitingThreshold at NoCompensationVDD = 16V, I(VIN) = 0uA, RI=100Kohm 0.85 0.90 0.95 V On -B ri g h tC o nf i de n ti al ToH ig hr ayV TH _OC_1 Current Limiting Threshold at CompensationVDD = 16V, I(VIN) = 150uA, RI=100Kohm 0.80 VOscillator F OSC Normal Oscillation FrequencyRI = 100Kohm 60 65 70 KHZ∆f_TempCentral Frequency Temperature Stability VDD = 16V, RI=100Kohm, -20oC to100 o C3 % ∆f_VDDCentral Frequency Voltage Stability VDD = 12-28V, RI=100Kohm3 % RI_range Operating RI Range 50 100 250 Kohm V_RI_open RI open voltage VDD = 16V 2.0 V F_BM Burst Mode Base Frequency VDD = 16V, RI=100Kohm20 KHZGate Drive Output VOL Output Low Level VDD = 16V, Io = 20 mA 0.3 V VOH Output High Level VDD = 16V, Io = 20 mA 11 V VG_Clamp Output Clamp Voltage Level16.5 VT_r Output Rising Time VDD = 16V, CL = 1nf 120 nSec T_f Output Falling Time VDD = 16V, CL = 1nf 50 nSec Over Temperature Protection I_RT Output Current of RT pin VDD = 16V, RI=100Kohm70 uAV TH _OTP OTP Threshold Voltage VDD = 16V, RI=100Kohm1.015 1.065 1.115 VV TH _OTP_off (Version V Only) OTP Recovery Threshold Voltage VDD = 16V, RI=100Kohm 1.165 V V TH _RT_latch (Version V Only) RT Input Latch Threshold Voltage 0.6 V T D _OTP OTP De-bounce Time VDD = 16V, RI=100Kohm100 uSecV_RT_Open RT Pin Open Voltage VDD = 16V, RI=100Kohm3.7 VFrequency Shuffling∆f_OSCFrequency Modulation range /Base frequencyRI =100Kohm -3 3 % Freq_Shuffling Shuffling Frequency RI = 100Kohm 32 HZOn -B ri gh tC o nf i de n ti al ToH ig hr ayri g ToigOPERATION DESCRIPTIONOB2279 is a highly integrated PWM controller IC optimized for offline flyback converter applications with requirement in latch shutdown or auto recovery. The extended burst mode control greatly reduces the standby power consumption and helps the design easily meet the international power conservation requirements. z Startup Current and Start up ControlStartup current of OB2279 is designed to be very low so that VDD could be charged up above UVLO(exit) threshold level and device starts up quickly. A large value startup resistor can therefore be used to minimize the power loss yet reliable startup in application. For a typical AC/DC adaptor with universal input range design, a 2 M Ω, 1/8 Wstartup resistor could be used together with a VDDcapacitor to provide a fast startup and yet lowpower dissipation design solution.z Operating CurrentThe Operating current of OB2279 is low at 2.3mA.Good efficiency is achieved with OB2279 lowoperating current together with extended burstmode control schemes.z Frequency shuffling for EMI improvementThe frequency Shuffling/jittering (switchingfrequency modulation) is implemented in OB2279.The oscillation frequency is modulated with ainternally generated random source so that the toneenergy is evenly spread out. The spread spectrumminimizes the conduction band EMI and thereforeeases the system design in meeting stringent EMIrequirement.z Burst Mode OperationAt zero load or light load condition, most of thepower dissipation in a switching mode powersupply is from switching loss on the MOSFETtransistor, the core loss of the transformer and theloss on the snubber circuit. The magnitude ofpower loss is in proportion to the number ofswitching events within a fixed period of time.Reducing switching events leads to the reductionon the power loss and thus conserves the energy.OB2279 self adjusts the switching mode accordingto the loading condition. At from no load tolight/medium load condition, the FB input dropsbelow burst mode threshold level. Device entersBurst Mode control. The Gate drive output switchesonly when VDD voltage drops below a preset leveland FB input is active to output an on state.Otherwise the gate drive remains at off state tominimize the switching loss thus reduce the standby power consumption to the greatest extend. The nature of high frequency switching also reduces the audio noise at any loading conditions. z Oscillator Operation A resistor connected between RI and GND sets the constant current source to charge/discharge theinternal cap and thus the PWM oscillator frequency is determined. The relationship between RI and switching frequency follows the below equation within the specified RI in Kohm range at nominal loading operational condition. )()(6500Khz Kohm RI F OSC = z Current Sensing and Leading Edge Blanking (LEB) Cycle-by-Cycle current limiting is offered in OB2279 current mode PWM control. The switch current is detected by a sense resistor into the sense pin. An internal leading edge blanking circuit chops off the sense voltage spike at initial MOSFET on state due to snubber diode reverse recovery so that the external RC filtering on sense input is no longer needed. The current limit comparator is disabled and cannot turn off the external MOSFET during the blanking period. The PWM duty cycle is determined by the current sense input voltage and the FB input voltage. z Internal Synchronized Slope Compensation Built-in slope compensation circuit adds voltage ramp onto the current sense input voltage for PWM generation. This greatly improves the close loop stability at CCM and prevents the sub-harmonic oscillation and thus reduces the output ripple voltage. z Over Temperature Protection with Latch Shutdown(Only to T and L version) A NTC resistor in series with a regular resistor should connect between RT and GND for temperature sensing and protection. NTC resistor value becomes lower when the ambient temperature rises. With the fixed internal current I RT flowing through the resistors, the voltage at RT pin becomes lower at high temperature. The internal OTP circuit is triggered and shutdown the MOSFET when the sensed input voltage is lower than V TH _OTP. OTP is a latched shutdown. On -B ri g h tC o nf i de n ti al ToH ig hr ayz RT Pin Used as Latch Shutdown InputControlRT pin could also be used as a control input to implement system latch shutdown function.An example is to implement system OVP protection with a latch shutdown function through a photo coupler and affiliated circuits. When OVP detection signal connected to RT is lower than V TH _OTP for Version T/L device, (or V TH _OT_Latch for Version V), OB2279 controls system into latch shutdown. The recovery of the AC/DC system could only start by resetting internal latch when VDD voltage drops below VDD_De-latch value. This could be achieved by unplugging/re-plugging of AC source in AC start-up configuration.z Gate DriveOB2279 Gate is connected to the Gate of an external MOSFET for power switch control. Too weak the gate drive strength results in higher conduction and switch loss of MOSFET while too strong gate drive output compromises the EMI.Good tradeoff is achieved through the built-in totem pole gate drive design with right output strength and dead time control. The low idle loss and good EMI system design is easier to achieve with this dedicated control scheme. An internal 16.5V clamp is added for MOSFET gate protection at higher than expected VDD input.z Protection Controls Good system reliability is achieved with OB2279’s rich protection features including Cycle-by-Cycle current limiting (OCP), Over Load Protection (OLP) with auto-recovery(V and T version) or latch shutdown(L version), over temperature protection (OTP) with auto-recovery(V version) or latch shutdown(T and L version), on-chip VDD over voltage protection (OVP) with latch shutdown and under voltage lockout (UVLO).VDD OVP protection is a latched shutdown in OB2279.The OCP threshold value is self adjusted lower at higher current into VIN pin. This OCP threshold slope adjustment helps to compensate the increased output power limit at higher AC voltage caused by inherent Over-Current sensing and control delay. A constant output power limit is achieved with recommended OCP compensation scheme.At output overload condition, FB voltage is set higher. When FB input exceeds power limit threshold value for more than 80mS, control circuit reacts to turnoff the power MOSFET. This is so called OLP shutdown. It is either auto-recovery or latched shutdown depending on version of OB2279. Similarly, control circuit shutdowns the power MOSFET when an Over Temperature condition is detected. This shutdown is either auto-recovery or latched depending on version of OB2279 been used. VDD is supplied with transformer auxiliary winding output. It is clamped when VDD is higher than 35V. MOSFET is shut down when VDD drops below UVLO(enter) limit and device enters power on start-up sequence thereafter.On -B ri g h tC o nf i de n ti al ToH ig hr ayPACKAGE MECHANICAL DATA8-Pin Plastic DIPOn -B ri g h tC o nf i de n ti al ToH ig hr ay8-Pin Plastic SOPO n -B r i g h t C o nf i d e n t i a l ToH ig hr ayIMPORTANT NOTICERIGHT TO MAKE CHANGESOn-Bright Electronics Corp. reserves the right to make corrections, modifications, enhancements, improvements and other changes to its products and services at any time and to discontinue any product or service without notice. Customers should obtain the latest relevant information before placing orders and should verify that such information is current and complete.WARRANTY INFORMATIONOn-Bright Electronics Corp. warrants performance of its hardware products to the specifications applicable at the time of sale in accordance with its standard warranty. Testing and other quality control techniques are used to the extent it deems necessary to support this warranty. Except where mandated by government requirements, testing of all parameters of each product is not necessarily performed.On-Bright Electronics Corp. assumes no liability for application assistance or customer product design. Customers are responsible for their products and applications using On-Bright’s components, data sheet and application notes. To minimize the risks associated with customer products and applications, customers should provide adequate design and operating safeguards.LIFE SUPPORTOn-Bright Electronics Corp.’s products are not designed to be used as components in devices intended to support or sustain human life. On-bright Electronics Corp. will not be held liable for any damages or claims resulting from the use of its products in medical applications.MILITARYOn-Bright Electronics Corp.’s products are not designed for use in military applications. On-Bright Electronics Corp. will not be held liable for any damages or claims resulting from the use of its products in military applications.On -B ri g h tC o nf i de n ti al ToH ig hr ay。
锂离子电池基础科学问题(Ⅷ)——负极材料
万方数据万方数据万方数据万方数据万方数据万方数据万方数据万方数据锂离子电池基础科学问题(Ⅷ)——负极材料作者：罗飞， 褚赓， 黄杰， 孙洋， 李泓， LUO Fei， CHU Geng， HUANG Jie， SUN Yang， LI Hong作者单位：中国科学院物理研究所,北京,100190刊名：储能科学与技术英文刊名：Energy Storage Science and Technology年，卷(期)：2014,3(2)1.Armand M;Murphy D;Broadhead J Materials for Advanced Batteries 19802.Garreau M;Thevenin J;Fekir M On the processes responsible for the degradation of the aluminum lithium electrode used as anode material in lithium aprotic electrolyte batteries 1983(3-4)3.Yazami R;Touzain P A reversible graphite-lithium negative electrode for electrochemical generators 1983(3)4.Tarascon J MorSe6:A new solid-state electrode for secondary lithium batteries 1985(9)5.Scrosati B Non aqueous lithium cells 1981(11)6.Abraham K Ambient temperature secondary lithium batteries using LiA1 lithium insertion anodes 19877.Hrold A Recherches sur les composes d'insertion du graphite 1955(7-8)8.Dey A;Sullivan B The electrochemical decomposition of propylene carbonate on graphite 1970(2)9.SONY Non-aqueous electrolyte secondary cell 198910.Nagaura T;Tozawa K Lithium ion rechargeable battery 199011.Endo M;Kim C;Nishimura K Recent development of carbon materials for Li ion batteries 2000(2)12.Mabuchi A A survey on the carbon anode materials for rechargeable lithiumbatteries 199413.Yamaura J;Ozaki Y;Morita A High voltage,rechargeable lithium batteries using newly-developed carbon for negative electrode material 1993(1)14.Tarascon J M;Armand M Issues and challenges facing rechargeable lithium battefies 2001(6861)15.Van S W;gcrosati B Advances in Lithium-Ion Batteries 200216.Kang B;Ceder G Battery materials for ultrafast charging and diseharging 2009(7235)17.Armand M;Tarascon J M Building better batteries 2008(7179)18.Jansen A;Kahaian A;Kepler K Development of a high-power lithium-ion battery 199919.Smith K;Wang C Y Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles 2006(1)20.Zhang X;Ross P;Kostecki R Diagnostic characterization of high power lithium-ion batteries for use in hybrid electric vehicles 2001(5)21.Zhou H H;Ci L C;Liu C Y Progress in studies of the electrode materials for Li ion batteries 1998(1)22.Hao R R;Fang X Y;Niu S C Chemistry of the Elements (Ⅲ) 199823.Ohzuku T;Ueda A;Yamamoto N Zero-strain insertion material of Li(Li1/3Ti5/3)O4 for rechargeable lithium cells 1995(5)24.Woo K C;Mertwoy H;Fischer J Experimental phase diagram of lithium-intercalated graphite 1983(12)25.Dahn J Phase diagram of LixC6 1991(17)26.Nalamova V;Guerard D;Lelaurain M X-ray investigation of highly saturated Li-graphite intercalation compound 1995(2)27.Feng Z Z;Song S Q Preparation and application of mesophase pitch 201328.Honda H;Yamada Y Meso-carbon microbeads 197329.Xu B;Chen E Intermediate development phase carbon microbeads (MCMB),properties and applications 1996(3)30.Niu Y J;Zhang H G;ZhouA M Non-Ferrous Progress:1996-2005 200731.Choi W C;Byun D;Lee J K Electrochemical characteristics of silver-and nickel-coated synthetic graphite preparedby a gas suspension spray coating method for the anode of lithium secondary batteries 2004(2)32.Lee H Y;Baek J K;Lee S M Effect of earbon coating on elevated temperature performance of graphite as lithium-ion battery anode material 2004(1)33.Tanaka H;Osawa T;Moriyoshi Y Improvement of the anode performance of graphite particles through surface modification in RF thermal plasma 2004(1)34.Guoping W;Bolan Z;Min Y A modified graphite anode with high initial efficiency and excellent cycle life expectation 2005(9)35.Lee J H;Lee S;Paik U Aqueous processing of natural graphite particulates for lithium-ion battery anodes andtheir electrochemical performance 2005(1)36.Yamauchi Y;Hino T;Ohzeki K Gas desorption behavior of graphite anodes used for lithium ion secondary batteries 2005(6)37.Zhao X;Hayner C M;Kung M C In-plane vacancy-enabled high-power Si-graphene composite electrode for lithium-ion batteries 2011(6)38.王广驹世界石墨生产,消费及国际贸易 2006(1)39.Jonker G H Magnetic compounds with perovskite structure Ⅳ conducting and non-conducting compounds 195640.Murphy D;Cava R;Zahurak S Ternary LixTiO2 phases from insertion reactions 198341.Ferg E;Gummow R;De K A Spinel anodes for lithium-ion batteries 1994(11)42.Robertson A;Trevino L;Tukamoto H New inorganic spinel oxides for use as negative electrode materials in future lithium-ion batteries 199943.Peramunage D;Abraham K Preparation of micron-sized Li4Ti5O12 and its electrochemistry in polyacrylonitrile electrolyte-based lithium cells 1998(8)44.Julien C;Massot M;Zaghib K Structural studies of Li4/3Me5/3O4 (Me=Ti,Mn) electrode materials:Local structure and electrochemical aspects 2004(1)45.Scharner S;Weppner W;Schmid B E Evidence of two-phase formation upon lithium insertion into the Li1.33Ti1.67O4 spinel 1999(3)46.Zaghib K;Simoneau M;Armand M Electrochemical study of Li4Ti5O12 as negative electrode for Li-ion polymer rechargeable batteries 199947.Pecharroman C;Amarilla J Thermal evolution of infrared vibrational properties of Li4/3Ti5/3O4 measured by specular reflectance 2000(18)48.Guerfi A;Charest P;Kinoshita K Nano electronically conductive titanium-spinel as lithium ion storage negative electrode 2004(1)49.Gao L;Qiu W;Zhao H L Lithiated titanium complex oxide as negative electrode 2005(1)50.Bach S;Pereira R J;Baffier N Electrochemical properties of sol-gel Li4/3Ti5/3O4 199951.Kavan L;Grtzel M Facile synthesis of nanocrystalline Li4Ti5O12 (spinel) exhibiting fast Li insertion 2002(2)52.Hao Y;Lai Q Y;Liu D Synthesis by citric acid sol-gel method and electrochemical properties of Li4Ti5O12 anode material for lithium-ion battery 2005(2-3)53.王虹微波法制备钛酸锂的方法 200854.白莹一种用于锂二次电池负极材料尖晶石钛酸锂的制各方法 200655.Li J;Tang Z;Zhang Z Controllable formation and electrochemical properties of one-dimensional nanostructured spinel Li4Ti5O12 2005(9)56.杨立一种应用于锂离子电池的钛酸锂负极材料的制备方法中国 200857.Huang S;Wen Z;Zhu X Effects of dopant on the electrochemical performance of Li4Ti5O12 as electrode material for lithium ion batteries 2007(1)58.Tian B;Xiang H;Zhang L Niobium doped lithium titanate as a high rate anode material for Li-ion batteries2010(19)59.Huang Y;Qi Y;Jia D Synthesis and electrochemical properties of spinel Li4Ti5Ol2-xClx anode materials forlithium-ion batteries 2012(5)60.Venkateswarlu M;Chen C;Do J Electrochemical properties of nano-sized Li4Ti5O12 powders synthesized by a sol-gel process and characterized by X-ray absorption spectroscopy 2005(1)61.Cai R;Yu X;Liu X Li4Ti5O12/Sn composite anodes for lithium-ion batteries:Synthesis and electrochemical performance 2010(24)62.Yuan T;Yu X;Cai R Synthesis of pristine and carbon-coated Li4Ti5O12 and their low-temperature electrochemical performance 2010(15)63.Hu X;Lin Z;Yang K Effects of carbon source and carbon content on electrochemical performances of Li4Ti5O12/C prepared by one-step solid-state reaction 2011(14)64.Martha S K;Haik O;Borgel V Li4Ti5O12/LiMnPO4 lithium-ion battery systems for load leveling application 2011(7)65.Huang K L;Wang Z X;Liu S Q Lithium-Ion Battery Technology and Key Principles 200866.Xu K;Wang X Y;Xiao L X Lithium Ion Battery 200267.Wang Q;Li H;Chen L Novel spherical microporous carbon as anode material for Li-ion batteries 200268.Li H;Wang Q;Shi L Nanosized SnSb alloy pinning on hard non-graphitic carbon spherules as anode materials for aLi ion battery 2002(1)69.Hu J;Li H;Huang X Influence of micropore structure on Li-storage capacity in hard carbon spherules 2005(11)70.Fey G T K;Chen C L High-capacity carbons for lithium-ion batteries prepared from rice husk 200171.Yin G P;Zhou D R;Xia B J Preparation of phosphorus-doped carbon and its performance Lithium intercalation2000(4)72.Schnfelder H H;Kitoh K;Nemoto H Nanostructure criteria for lithium intercalation in non-doped and phosphorus-doped hard carbons 1997(2)73.Buiel E;Dahn J Li-insertion in hard carbon anode materials for Li-ionbatteries 1999(1)74.Rosamaria F;Ulrich V S;Dahn J R Studies of lithium intercalation into carbons using nonaqueous electrochemical-cells 1990(7)75.Stevens D;Dahn J The mechanisms of lithium and sodium insertion in carbon materials 2001(8)76.Bonino F;Brutti S;Piana M Structural and electrochemical studies of a hexaphenylbenzene pyrolysed soft carbon as anode material in lithium batteries 2006(17)77.Guo M;Wang J C;Wu L B Study of carbon nanofibers as negative materials for Li-ion batteries 2004(5)78.Sato Y;Kikuchi Y;Kawai T Characteristics of coke carbon modified with mesophase-pitch as a negative electrodefor lithium ion batteries 199979.Yoshio M;Tsumura T;Dimov N Electrochemical behaviors of silicon based anode material 2005(1)i S C Solid lithium-silicon electrode 197681.Sharma R A;Seefurth R N Thermodynamic properties of the lithium-silicon system 1976(12)82.Seefurth R N;Sharma R A Investigation of lithium utilization from a lithium-silicon electrode 1977(8)83.Seefurth R N;Sharma R A Dependence of lithium-silicon electrode potential and lithium utilization on reference electrode location 1980(5)84.Wen C J;Huggins R A Chemical diffusion in intermediate phases in the lithium-silicon system 1981(3)85.Boukamp B A;Lesh G C;Huggins R A All-solid lithium electrodes with mixed-conductor matrix 1981(4)86.Weydanz W J;Wohlfahrt M M;Huggins R A A room temperature study of the binary lithium-silicon and the ternary lithium-chromium-silicon system for use in rechargeable lithium batteries 199987.Gao B;Sinha S;Fleming L Alloy formation in nanostructured silicon 2001(11)88.Li H;Huang X J;Chen L Q A high capacity nano-Si composite anode material for lithium rechargeable batteries 1999(11)89.Li H;Huang X J;Chen L Q The crystal structural evolution of nano-Si anode caused by lithium insertion and extraction at room temperature 2000(1-4)90.Limthongkul P;Jang Y I;Dudney N J Electrochemically-driven solid-state amorphization in lithium-silicon alloys and implications for lithium storage 2003(4)91.Hatchard T D;Dahn J R In situ XRD and electrochemical study of the reaction of lithium with amorphous silicon 2004(6)92.Key B;Bhattacharyya R;Grey C P Real-time NMR investigations of structural changes in silicon electrodes for lithium-ion batteries 2009(26)93.Key B;Morcrette M;Grey C P Pair distribution function analysis and solid State NMR studies of silicon electrodes for lithium ion batteries:Understanding the (De) lithiation mechanisms 2011(3)94.Beaulieu L Y;Hatchard T D;Bonakdarpour A Reaction of Li with alloy thin films studied by in situ AFM 2003(11)95.Baggetto L;Danilov D;Notten P H L Honeycomb-structured silicon:Remarkable morphological changes induced by electrochemical (De)lithiation 2011(13)96.Lee S W;Mcdowell M T;Choi J W Anomalous shape changes of silicon nanopillars by electrochemical lithiation2011(7)97.Lee S W;Mcdowell M T;Berla L A Fracture of crystalline silicon nanopillars during electrochemical lithium insertion 2012(11)98.He Y;Yu X Q;Wang Y H Alumina-coated patterned amorphous silicon as the anode for a lithium-ion battery with high coulombic effficiency 2011(42)99.He Y;Wang Y H;Yu X Q Si-Cu thin film electrode with kirkendall voids structure for lithium-ion batteries2012(12)100.He Y;Yu X Q;Li G Shape evolution of patterned amorphous and polycrystalline silicon microarray thin film electrodes caused by lithium insertion and extraction 2012101.Wang Y;He Y;Xiao R Investigation of crack patterns and cyclic performance of Ti-Si nanocomposite thin film anodes for lithium ion batteries 2012102.Notten P H L;Roozeboom F;Niessen R A H3-D integrated all-solid-state rechargeable batteries 2007(24)103.Baggetto L;Oudenhoven J F M;Van D T On the electrochemistry of an anode stack for all-solid-state 3D-integrated batteries 2009(1)104.Chan C K;Ruffo R;Hong S S Surface chemistry and morphology of the solid electrolyte interphase on silicon nanowire lithium-ion battery anodes 2009(2)105.Zheng J Y;Zheng H;Wang R An investigation on the sold electrolyte interphase of silicon anode for Li-ion batteries through force curve method 2013(6)106.Zhang X W;Patil P K;Wang C S Electrochemical performance of lithium ion battery,nano-silicon-based,disordered carbon composite anodes with different microstructures 2004(2)107.Chan C K;Ruffo R;Hong S S Structural and electrochemical study of the reaction of lithium with silicon nanowires 2009(1)108.Cui L F;Ruffo R;Chan C K Crystalline-amorphous core-shell silicon nanowires for high capacity and high current battery electrodes 2009(1)109.Mcdowell M T;Lee S W;Ryu I Novel size and surface oxide effects in silicon nanowires as lithium battery anodes 2011(9)110.Ryu I;Choi J W;Cui Y Size-dependent fracture of Si nanowire battery anodes 2011(9)111.Xu W L;Vegunta S S S;Flake J C Surface-modified silicon nanowire anodes for lithium-ion batteries 2011(20) 112.Yue L;Wang S Q;Zhao X Y Nano-silicon composites using poly (3,4-ethylenedioxythiophene):Poly (styrenesulfonate) as elastic polymer matrix and carbon source for lithium-ion battery anode 2012(3)113.Zang J L;Zhao Y P Silicon nanowire reinforced by single-walled carbon nanotube and its applications to anti-pulverization electrode in lithium ion battery 2012(1)114.Yoshio M;Wang H Y;Fukuda K Carbon-coated Si as a lithium-ion battery anode material 2002(12)115.Qu J;Li H Q;henry J J Self-aligned Cu-Si core-shell nanowire array as a high-performance anode for Li-ion batteries 2012116.Jia H P;Gao P F;Yang J Novel three-dimensional mesoporous silicon for high power lithium-ion battery anode material 2011(6)117.Yao Y;Mcdowell M T;Ryu I Interconnected silicon hollow nanospheres for lithium-ion battery anodes with long cycle life 2011(7)118.Fu K;Yildiz O;Bhanushali H Aligned carbon nanotube-silicon sheets:A novel nano-architecture for flexiblelithium ion battery electrodes 2013(36)119.Min J H;Bae Y S;Kim J Y Self-organized artificial SEI for improving the cycling ability of silicon-basedbattery anode materials 2013(4)120.Choi N S;Yew K H;Lww K Y Effect of fluoroethylene carbonate additive on interfacial properties of silicon thin-film electrode 2006(2)121.Chakrapani V;Rusli F;Filler M A Quaternary ammonium ionic liquid electrolyte for a silicon nanowire-based lithium ion battery 2011(44)122.Etacheri V;Haik O;Goffer Y Effect of fluoroethylene carbonate (FEC) on the performance and surface chemistry of Si-nanowire Li-ion battery anodes 2011(1)123.Buddie M C High performance silicon nanoparticle anode in fluoroethylene carbonate-based electrolyte for Li-ion batteries 2012(58)124.Profatilova I A;Stock C;Schmitz A Enhanced thermal stability of a lithiated nano-silicon electrode by fluoroethylene carbonate and vinylene carbonate 2013125.Leung K;Rempe S B;Foster M E Modeling electrochemical decomposition of fluoroethylene carbonate on silicon anode surfaces in lithium ion batteries 2014(3)126.Kovalenko I;Zdyrko B;Magasinski A A major constituent of brown algae for use in high-capacity Li-ion batteries 2011(6052)127.Ryou M H;Kim J;Lee I Mussel-inspired adhesive binders for high-performance silicon nanoparticle anodes in lithium-ion batteries 2012(11)128.Li J;Lewis R;Dahn J Sodium carboxymethyl cellulose a potential binder for Si negative electrodes for Li-ion batteries 2007(2)129.Bridel J S;Azais T;Morcrette M Key parameters governing the reversibility of Si/carbon/CMC electrodes for Li-ion batteries 2009(3)130.Mazouzi D;Lestriez B;Roue L Silicon composite electrode with high capacity and long cycle life 2009(11)131.Guo J C;Wang C S A polymer scaffold binder structure for high capacity silicon anode of lithium-ion battery 2010(9)132.Liu W R;Yang M H;Wu H C Enhanced cycle life of Si anode for Li-ion batteries by using modified elastomeric binder 2005(2)133.Park H K;Kong B S;Oh E S Effect of high adhesive polyvinyl alcohol binder on the anodes of lithium ionbatteries 2011(10)134.Magasinski A;Zdyrko B;Kovalenko I Toward efficient binders for Li-ion battery Si-based anodes:Polyacrylic acid 2010(11)135.Yun J B;Soo K J;Tae L K Aphoto-cross-linkable polymeric binder for silicon anodes in lithium ion batteries 2013(31)136.Han Z J;Yabuuchi N;Hashimoto S Cross-linked poly (acrylic acid) with polycarbodiimide as advanced binder for Si/graphite composite negative electrodes in Li-ion batteries 2013(2)137.Koo B;Kim H;Cho Y A highly cross-linked polymeric binder for high-performance silicon negative electrodes in lithium ion batteries 2012(35)138.Bae J;Cha S H;Park J A new polymeric binder for silicon-carbon nanotube composites in lithium ion battery 2013(7)139.Yim C H;Abu L Y;Courtel F M High capacity silicon/graphite composite as anode for lithium-ion batteries using low content amorphous silicon and compatible binders 2013(28)140.Erk C;Brezesinski T;Sommer H Toward silicon anodes for next-generation lithium ion batteries:A comparative performance study of various polymer binders and silicon nanopowders 2013(15)141.Kim J S;Choi W;Cho K Y Effect of polyimide binder on electrochemical characteristics of surface-modified silicon anode for lithium ion batteries 2013142.Li J;Christensen L;Obrovac M Effect of heat treatment on Si electrodes using polyvinylidene fluoride binder 2008(3)143.Kim Y L;Sun Y K;Lee S M Enhanced electrochemical performance of silicon-based anode material by using current collector with modified surface morphology 2008(13)144.Guo J C;Sun A;Wang C S A porous silicon-carbon anode with high overall capacity on carbon fiber current collector 2010(7)145.Choi J Y;Lee D J;Lee Y M Silicon nanofibrils on a flexible current collector for bendable lithium-ion battery anodes 2013(17)146.Hang T;Nara H;Yokoshima T Silicon composite thick film electrodeposited on a nickel micro-nanocones hierarchical structured current collector for lithium batteries 2013147.Luais E;Sakai J;Desploban S Thin and flexible silicon anode based on integrated macroporous silicon film onto electrodeposited copper current collector 2013148.Tang X X;Liu W;Ye B Y Preparation of current collector with blind holes and enhanced cycle performance of silicon-based anode 2013(6)149.Kim H;Han B;Choo J Three-dimensional porous silicon particles for use in high-performance lithium secondary batteries 2008(52)150.Bang B M;Kim H;Song H K Scalable approach to multi-dimensional bulk Si anodes via metal-assisted chemical etching 2011(12)151.Kasavajjula U;Wang C;Appleby A J Nano-and bulk-silicon-based insertion anodes for lithium-ion secondary cells 2007(2)152.Magasinski A;Dixon P;Hertzberg B High-performance lithium-ion anodes using a hierarchical bottom-up approach 2010(4)153.Liu G;Xun S;Vukmirovic N Polymers with tailored electronic structure for high capacity lithium battery electrodes 2011(40)154.Chan C K;Peng H;Liu G High-performance lithium battery anodes using silicon nanowires 2007(1)155.Idota Y;Kubota T;Matsufiti A Tin-based amorphous oxide:A high-capacity lithium-ion-storage material 1997(5317)156.Courtney I A;Dahn J Key factors controlling the reversibility of the reaction of lithium with SnO2 and Sn2BPO6 glass 1997(9)157.Li H;Huang X J;Chen L Q Direct imaging of the passivating film and microstructure of nanometer-scale SnO anodes in lithium rechargeable batteries 1998(6)158.Liu W;Huang X J;Wang Z Studies of stannic oxide as an anode material for lithium-ion batteries 1998(1)159.Li H;Wang Z;Chen L Research on advanced materials for Li-ion batteries 2009(45)160.David M New materials extend Li-ion performance 2006(5)161.Ogisu K R&D activities & results for sony batteries 2005162.索尼公司索尼成功开发3.5 A·h高容量锂离子电池"Nexelion" 2011163.Dahn J;Mar R;Abouzeid A Combinatorial study of Sn1-xCox (0《x《 0.6) and (Sn0 55Co0 45)1-yCy (0《 y《 0 5)alloy negative electrode materials for Li-ion battaries 2006(2)164.Todd A;Mar R;Dahn J Tin-transition metal-carbon systems for lithium-ion battery negative electrodes 2007(6) 165.Ferguson P;Martine M;Dunlap R Structural and electrochemical studies of (SnxCo1-x)60C40 alloys prepared by mechanical attriting 2009(19)166.Ferguson P;Rajora M;Dunlap R(Sn0.5Co0 5)1-yCy alloy negative electrode materials prepared by mechanical attriting 2009(3)167.Ferguson P;ToddA;Dahn J Comparison of mechanically alloyed and sputtered tin-cobalt-carbon as an anode material for lithium-ion batteries 2008(1)168.Hassoun J;Mulas G;Panero S Ternary Sn-Co-C Li-ion battery electrode material prepared by high energy ball milling 2007(8)vela P;Nacimiento F;Ortiz G F Sn-Co-C composites obtained from resorcinol-formaldehyde gel as anodes in lithium-ion batteries 2010(1)170.Liu B;Abouimrane A;Ren Y New anode material based on SiO-SnxCoyCz for lithium batteries 2012(24)171.Zhong X C;Jiang F Q;Xin P A Preparation and electrochemical performance of Sn-Co-C composite as anode material for Li-ion batteries 2009(1)172.Yang S;Li Q;Shen D Influence of Fe on electrochemical performance of SnxCoy/C anode materials 2011(2)173.Shaobin Y;Ding S;Qiang L Synthesis and electrochemical properties of Sno.35-0 5xCoo 35-0 5xZnxCo 3o composite 2010(1)174.YangSB;ShenD;WuXG Effects of Cu on structures and electrochemical properties of Sn-Co/C composite 2012(4)175.Cui W;Wang F;Wang J Nanostructural CoSnC anode prepared by CoSnO3 with improved cyclability for high-performance Li-ion batteries 2011(13)176.Li M Y;Liu C L;Shi M R Nanostructure Sn-Co-C composite lithium ion battery electrode with unique stability and high electrochemical performance 2011(8)177.Xin L;Jing Y X;Hai L Z Synthesis and properties of Sn30Co30C40 ternary alloy anode material for lithium ion battery 2013(7)178.Lee S I;Yoon S;Park C M Reaction mechanism and electrochemical characterization of a Sn-Co-C composite anodefor Li-ion batteries 2008(2)179.Fauteux D;Koksbang R Rechargeable lithium battery anodes:Alternatives to metallic lithium 1993(1)180.Rahner D;Machill S;Schlorb H Intercalation materials for lithium rechargeable batteries 1996181.Besenhard J;Hess M;Komenda P Dimensionally stable Li-alloy electrodes for secondary batteries 1990182.Maxfield M;Jow T;Gould S Composite electrodes containing conducting polymers and Li alloys 1988(2)183.Winter M;Besenhard J O Electrochemical lithiation of tin and tin-based intermetallics and composites 1999(1) 184.Du C W;Chen Y B;Wu M S Advances in lithium-ion battery anode materials for non-carbon 2000185.Wu Y P;Wan C R Study on materials for lithium-ion batteries tin-based negative 1999(3)186.Kepler K D;Vaughey J T;Thackeray M M LixCu6Sn5(0《x《13):An intermetallic insertion electrode for rechargeable lithium batteries 1999(7)187.Mao O;Dunlap R;Dahn J Mechanically alloyed Sn-Fe(-C) powders as anode materials for Li-ion batteries:Ⅰ.TheSn2Fe-C system 1999(2)rcher D;Beaulieu L;Macneil D In situ X-ray study of the electrochemical reaction of Li with η'-Cu6Sn52000(5)189.Li H;Zhu G;Huang X Synthesis and electrochemical performance of dendrite-like nanosized SnSb alloyprepared by co-precipitation in alcohol solution at low temperature 2000(3)190.Kim H;Kim Y J;Kim D Mechanochemical synthesis and electrochemical characteristics of Mg2Sn as an anode material for Li-ion batteries 2001(1)191.Wang L;Kitamura S;Sonoda T Electroplated Sn-Zn alloy electrode for Li secondary batteries 2003(10)192.Yin J;Wada M;Yoshida S New Ag-Sn alloy anode materials for lithium-ion batteries 2003(8)193.Tamura N;Fujimoto M;Kamino M Mechanical stability of Sn-Co alloy anodes for lithium secondary batteries2004(12)194.Wang L;Kitamura S;Obata K Multilayered Sn-Zn-Cu alloy thin-film as negative electrodes for advanced lithium-ion batteries 2005(2)195.Beauleiu L;Hewitt K;Turner R The electrochemical reaction of Li with amorphous Si-Sn alloys 2003(2)196.Besenhard J;Yang J;Winter M Will advanced lithium-alloy anodes have a chance in lithium-ion batteries 1997(1) 197.Yang J;Winter M;Besenhard J Small particle size multiphase Li-alloy anodes for lithium-ionbatteries 1996(1) 198.Mukaibo H;Sumi T;Yokoshima T Electrodeposited Sn-Ni alloy film as a high capacity anode material for lithium-ion secondary batteries 2003(10)199.Photo F Nonaqueous secondary battery 1995200.Photo F Nonaqueous secondary battery 1995201.Goodenough J;Manthiram A;James A Lithium insertion compounds 1988202.Aydinol M;Kohan A;Ceder G Abinitio calculation of the intercalation voltage of lithium-transition-metal oxide electrodes for rechargeable batteries 1997(2)203.三星SDI株式会社用于非水电解液电池的负极活性材料,其制备方法和非水电解液电池 2005204.Song J H;Park H J;Kim K J Electrochemical characteristics of lithium vanadate,Li1+xVO2,new anode materials for lithium ion batteries 2010(18)205.Chang J J Synthesis and electrochemical:Properties of lithium-ion battery anode material Li1+xVO2 2012206.Armstrong A R;Lyness C;Panchmatia P M The lithium intercalation process in the low-voltage lithium battery anode Li1+xV1-xO2 2011(3)207.Chen H;Xiang K X;Hu Z L Synthesis and electrochemical performance of new anode materials Li1.1V0 9O2 forlithium ion batteries 2012(5)208.Choi N S;Kim J S;Yin R Z Electrochemical properties of lithium vanadium oxide as an anode material for lithium-ion battery 2009(2)zzari M;Scrosati B A cyclable lithium organic electrolyte cell based on two intercalation electrodes 1980(3) 210.Dipietro B;Patriarco M;Scrosati B On the use of rocking chair configurations for cyelabte lithium organic electrolyte batteries 1982(2)211.Ktakata H O;Meri T;Koshita N Procedures of the symposium onprimary and secondary lithium batteries 1988212.Poizot P;Laurelle S;Grugeon S Nano-sized ttansition-metal oxides as negative-electrode materials for lithium-ion batteries 2000(6803)213.Debart A;Dupont L;Poizot P A transmission electron microscopy study of the reactivity mechanism of tailor-made CuO particles toward lithium 2001(11)214.Dedryvere R;Laruelle S;Grugeon S Contribution of X-ray photoelectron spectroscopy to the study of the electrochemical reactivity of CoO toward lithium 2004(6)215.Xin C;Naiqing Z;Kening S3d transition-metal oxides as anode micro/nano-materials for lithium ion batteries 2011(10)216.Li H;Richter G;Maier J Reversible formation and decomposition of LiF clusters using transition metal fluorides as precursors and their application in rechargeable Li batteries 2003(9)217.Badway F;Mansour A;Pereira N Structure and electrochemistry of copper fluoride nanocomposites utilizing mixed conducting matrices 2007(17)218.Dbart A;Dupont L;Patrice R Reactivity of transition metal (Co,Ni,Cu) sulphides versus lithium:The intriguing case of the copper sulphide 2006(6)219.Gillot F;Boyanov S;Dupont L Electrochemical reactivity and design of NiP2 negative electrodes for secondary Li-ion batteries 2005(25)220.Pereira N;Dupont L;Tarascon J Electrochemistry of Cu3N with lithium a complex system with parallel processes 2003(9)221.Zhang W M;Wu X L;Hu J S Carbon coated Fe3O4 nanospindles as a superior anode material for lithium-ion batteries 2008(24)222.Rahman M;Chou S L;Zhong C Spray pyrolyzed NiO-C nanocomposite as an anode material for the lithium-ion battery with enhanced capacity retention 2010(40)223.Wang Y;Zhang H J;Lu L Designed functional systems from peapod-like Co@carbon to Co3O4@carbon nanocomposites 2010(8)224.Zhou G;Wang D W;Li F Graphene-wrapped Fe3O4 anode material with improved reversible capacity and cyclicstability for lithium ion batteries 2010(18)225.Wang Y;Zhang L Simple synthesis of CoO-NiO-C anode materials for lithium-ion batteries and investigation on its electrochemical performance 2012226.Zhang P;Guo Z;Kang S Three-dimensional Li2O-NiO-CoO composite thin-film anode with network structure forlithium-ion batteries 2009(1)227.Zhu X J;Guo Z P;Zhang P Highly porous reticular tin-cobalt oxide composite thin film anodes for lithium ion batteries 2009(44)228.Wang C;Wang D;Wang Q Fabrication and lithium storage performance of three-dimensional porous NiO as anode for lithium-ion battery 2010(21)229.Xia Y;Zhang W;Xiao Z Biotemplated fabrication of hierarchically porous NiO/C composite from lotus pollen grains for lithium-ion batteries 2012(18)230.Yu Y;Chen C H;Shi Y A tin-based amorphous oxide composite with a porous,spherical,multideck-cage morphology as a highly reversible anode material for lithium-ion batteries 2007(7)231.Li F;Zou Q Q;Xia Y Y Co-loaded graphitable carbon hollow spheres as anode materials for lithium-ion battery 2008(2)232.Wu Z S;Ren W;Wen L Graphene anchored with Co3O4 nanoparticles as anode of lithium ion batteries with enhanced reversible capacity and cyclic performance 2010(6)引用本文格式：罗飞.褚赓.黄杰.孙洋.李泓.LUO Fei.CHU Geng.HUANG Jie.SUN Yang.LI Hong锂离子电池基础科学问题(Ⅷ)——负。
等容吸附热焓计算
25. Figure S15. IAST selectivities of CO2 over N2 in 1a at different mixture composition at 273 K (a) and 298 K (b). 26.Figure S16. IAST selectivites of CO2 over H2 in 1a at different mixture compositions as a function of total pressure at 273 K (a) and 298 K (b). 27. Figure S17. Gas cycling experiment for 1a under a mixed CO2–N2 (15:85 v/v) flow and a pure N2 flow at a constant temperature of 303 K for 35 cycles. 28. Figure S18. An enlargment of five cycles―TG-DSC curves from cycle 5th to cycle 9th. 29. Figure S19. The IR spectra of the as-synthesized sample (a) and acetoneexchanged one (b). 30. Table S10. The weight change for the special cycle in the gas cycling experiment. 31. Table S11. High-pressure excess sorption and total sorption data of 1a.
Contents:
国际大电网会议标准清单
Thyristor controlled voltage regulators Parts 1
and 2
New optical access technology The automation of new and substations： why and how
existing
Optimization of power transmission capability
12. TB 078-1994
13. TB 082-1994 14. TB 086-1994 15. TB 092-1995
16. TB 093-1995
17. TB 097-1995 18. TB 112-1997
标准名称
CIGRE Technical Brochure on Grid Integration of Wind Generation（2009）
Conformance testing guideline for
communication in substations
Static synchronous compensator (STATCOM)
for arc furnace and flicker compensation
Modeling of gas turbines and steam turbines in
47. TB 234 48. TB 235 -49. TB 236 -2003 50. TB 237-2003 51. TB 238-2003 52. TB 239 53. TB 240-2004 54. TB 241 55. TB 242-2004 56. TB 245 57. TB 246 -2004 58. TB 247 -
植物学专有名词
前沿·热点Research Hot/Frontiers种系发生Phylogeny生命起源Origins of Life群落多样性Community diversity生物多样性Biodiversity外来物种Alien species物种共存Species coexistence生态系统健康Ecosystem HealthDNA条形码DNA barcoding信号转导Signal transduction细胞周期调控Cell cycle regulation细胞增殖cell proliferation细胞凋亡Cell Apoptosis生物钟Biological clock生物适应性Biochemical adaptation网络生物学Network biology生物控制论Biological Cybernetics染色体重排Chromosomal rearrangements 进化论The theory of evolution自然选择natural selection人工选择artificial selection细胞分裂cell division选择育种selective breeding遗传漂变Genetic drift种系发生phylogeny脱氧核糖核酸DNA生命起源Origin of life古生物学paleontology种系遗传学phylogenetics表型学phenetics共同祖先common descent物种形成Speciation古生菌archaea灵长类primates板块构造论plate tectonics无树大草原savannah寄生parasitism共生symbiosis生命之树Tree of life遗传分类学cladistics开放系统open system动态平衡dynamic equilibrium查尔斯·达尔文Charles Darwin动物Animals植物plants真菌fungi原生生物protists细菌bacteria原核生物prokaryote昆虫类insects病毒viruses多细胞生物multicellular organism热血动物warm-blood生态系统ecosystems系统发生树Phylogenetic tree新陈代谢metabolism遗传学Genetics遗传heredity变异variation核苷酸nucleotides氨基酸amino acids遗传密码genetic code泛生论pangenesis染色体chromosomes遗传连锁genetic linkage螺旋结构helical structure信使RNAmessenger RNA氨基酸序列amino acid sequence遗传的特征 Features of inheritance孟德尔Gregor Mendel等位基因alleles纯合子homozygote杂合子heterozygote基因型genotype表现型phenotype显性dominance隐性recessiveness不完全显性incomplete dominance共显性codominance有性繁殖sexual reproduction谱系图pedigree charts异位显性epistasis遗传力heritability遗传的分子基础 Molecular basis for inheritance 腺嘌呤adenine胞嘧啶cytosine鸟嘌呤guanine胸腺嘧啶thymine双螺旋double helix碱基对base pairs染色质chromatin核小体nucleosomes组蛋白histone单倍体haploid二倍体diploid性连锁遗传病sex-linked disorder无性生殖asexual reproduction有丝分裂mitosis配子gametes生殖细胞germ cells连锁图linkage map分子生物学中心法则central dogma of molecular biology 镰状细胞性贫血sickle-cell anemia核糖体RNArRNA转移核糖核酸tRNA微RNAmicroRNA苯（丙）酮尿症phenylketonuria转录因子Transcription factors大肠杆菌Escherichia coli色氨酸tryptophan负反馈negative feedback阻遏物repressor胞间信号intercellular signals副突变paramutation遗传改变Genetic change诱变mutagenesis紫外辐射UV radiationDNA修复DNA repair减数分裂meiosis适合度fitness果蝇Drosophila群体遗传学Population genetics适应adaptation分子钟molecular clock进化树evolutionary trees模式生物model organisms医学遗传学Medical genetics孟德尔随机化Mendelian randomization同源基因orthologues药物遗传学pharmacogenetics连接酶ligase分子克隆molecular cloning重组DNArecombinant DNA遗传多样性genetic diversity生物量biomass分子生物学Molecular biology分子筛molecular sieve核糖核酸RNA蛋白质的生物合成protein biosynthesis 生物大分子biomolecules复制replication翻译translation转录transcription克隆clone聚合酶链式反应PCR质粒plasmid载体vector启动子元件promoter elements抗生素耐性antibiotic resistance结合conjugation转导transduction转染transfection电穿孔electroporation显微注射microinjection三级结构tertiary structure定量多聚酶链反应QPCR电泳electrophoresis毛细管作用capillary action胚胎干细胞株embryonic stem cell lines SDS-PAGEnorthern blot放射自显影autoradiography增强子Enhancer抗体antibody蔗糖梯度sucrose gradient粘度测定法viscometry保护生物学Conservation biology生物多样性biodiversity种群动态population dynamics稀有种rare species自然保护运动conservation movement 灭绝extinction脊椎动物vertebrates无脊椎动物invertebrates人口过剩overpopulation砍伐森林deforestation放牧过度overgrazing刀耕火种法slash and burn应用生态学Applied ecology濒危物种Endangered species环境保护论Environmentalism迁地保护Ex-situ conservation基因污染Genetic pollution就地保护In situ conservation发育生物学Developmental Biology细胞生长cell growth形态发生morphogenesis分化differentiation染色体畸变chromosomal aberrations个体发生ontogeny细胞凋亡apoptosis先天性疾病congenital disorders内稳态homeostasis神经胚形成neurulation胚胎发生embryogenesis性别决定sex determination原肠胚形成gastrulation适应能力adaptive capacity衰老senescence细胞信号传导Cell signaling信号转导Signal transduction器官发生organogenesis胚胎发生embryogenesis形态发生morphogenesis生态学Ecology次级生产力secondary productivity捕食predationecozones食物链food chains大气圈atmosphere生物群落biocoenosis分解者Decomposers互利共生mutualism光合作用photosynthesis初级生产者primary producers初级消费者primary consumers三级消费者tertiary consumers食肉动物carnivores杂食动物omnivores生境biotope生物地球化学循环biogeochemical cycle氮循环nitrogen cycle陆地生态系统terrestrial ecosystems森林生态系统forest ecosystems水圈hydrosphere生物圈biosphere岩石圈lithosphere生态位ecological niche海洋生态系统marine ecosystems生态危机Ecological crisis血亲consanguinity植物学Botany/Plant Biology(Plant Science)真菌类fungi被子植物angiosperms藻类algae双子叶植物dicotyledon单子叶植物monocot裸子植物gymnosperm分类学taxonomy形态学morphology藓类Mosses苔类liverworts园艺学Horticulture花粉Pollen植物病理学Phytopathology植物生理学Plant physiology草本植物Herbs温室气体greenhouse gas地衣类lichens水循环water cycle古植物学家Paleobotanists果菜类fruit vegetable食性层次trophic level食品安全food security作物育种plant breeding人类植物学Ethnobotany遗传学定律genetic laws四氢大麻酚tetrahydrocannabinol跳跃基因jumping genes细胞生物学Cell biology显微镜microscope核糖体ribosomes细胞质cytoplasm膜蛋白membrane proteins内质网endoplasmic reticulum高尔基体Golgi apparatus细胞骨架cytoskeletal线粒体mitochondria细胞核nucleus叶绿体chloroplasts溶酶体lysosomes主动运输Active transportmRNA剪接mRNA splicing自吞噬Autophagy纤毛Cilia微管microtubule鞭毛Flagella脂质双层Lipid bilayerImmunostaining原位杂交In situ hybridization免疫沉淀反应Immunoprecipitation免疫组化immunohistochemistry生命周期life cycles神经生物学Neurobiology计算神经科学computational neuroscience 认知神经科学cognitive neuroscience行为神经学behavioral neuroscience生物精神病学biological psychiatry神经病学neurology神经心理学neuropsychology神经心理学neuropsychology树状突dendrites光受体photoreceptors动作电位action potentialsquid giant axon突触synapses神经系统nervous system多巴胺Dopamine去甲肾上腺素Norepinephrine肾上腺素Epinephrine黑色素Melanin神经递质neurotransmitters褪黑激素MelatoninP物质Substance P兴奋性突触后电位EPSP抑制性突触后电位IPSP神经元neuron听力系统auditory system嗅觉系统olfactory system视觉系统visual system胶质细胞glial cell神经板neural plate系统生物学Systems BiologyDNA微阵列DNA microarrays蛋白质组学Proteomics质谱测定法mass spectrometry高性能液体色谱HPLC phosphoproteomics糖蛋白质组学glycoproteomics代谢物组学Metabolomics Biomicsprocess calculi信息提取information extraction转录组学transcriptomics代谢组学metabolomics酶动力学enzyme kinetics糖组学Glycomics生物学的其他学科Other fields太空生物学Astrobiology微生物学Microbiology古生物学Paleontology寄生虫学Parasitology生理学Physiology动物学Zoology。
薛定谔—麦克斯韦尔方程径向解的存在性和多重性(英文)
In 1887, the German physicist Erwin Schrödinger proposed a radial solution to the Maxwell-Schrödinger equation. This equation describes the behavior of an electron in an atom and is used to calculate its energy levels. The radial solution was found to be valid for all values of angular momentum quantum number l, which means that it can describe any type of atomic orbital.The existence and multiplicity of this radial solution has been studied extensively since then. It has been shown that there are infinitely many solutions for each value of l, with each one corresponding to a different energy level. Furthermore, these solutions can be divided into two categories: bound states and scattering states. Bound states have negative energies and correspond to electrons that are trapped within the atom; scattering states have positive energies and correspond to electrons that escape from the atom after being excited by external radiation or collisions with other particles.The existence and multiplicity of these solutions is important because they provide insight into how atoms interact with their environment through electromagnetic radiation or collisions with other particles. They also help us understand why certain elements form molecules when combined together, as well as why some elements remain stable while others decay over time due to radioactive processes such as alpha decay or beta decay.。
Materials Characterization
Materials Characterization Materials characterization is a crucial aspect of scientific research and development, allowing researchers to understand the properties and behavior of materials at the atomic and molecular levels. By analyzing the structure, composition, and properties of materials, scientists can gain valuable insightsinto their performance and potential applications in various industries. This process involves a combination of techniques such as microscopy, spectroscopy, and diffraction to provide a comprehensive understanding of the material under study. One of the key reasons why materials characterization is essential is its role in quality control and assurance. By thoroughly analyzing the composition andstructure of materials, researchers can ensure that they meet the required specifications and standards for a particular application. This is particularly important in industries such as aerospace, automotive, and electronics, where the performance and reliability of materials are critical for safety and functionality. Without proper characterization, manufacturers run the risk of producing substandard products that could lead to costly recalls and potential safety hazards. Furthermore, materials characterization plays a crucial role in the development of new materials with enhanced properties and performance. By understanding the structure-property relationships of materials, researchers can design and engineer materials with specific characteristics tailored to meet the demands of modern technology. This has led to the development of advancedmaterials such as carbon nanotubes, graphene, and shape memory alloys, which have revolutionized various industries and opened up new possibilities for innovation. In addition to quality control and material development, materialscharacterization also plays a vital role in failure analysis and forensic investigations. When materials fail in real-world applications, it is essential to identify the root cause of the failure to prevent future incidents. By using techniques such as scanning electron microscopy and X-ray diffraction, researchers can analyze the fracture surfaces and microstructures of failed materials to determine the mechanisms of failure. This information is invaluable for improving the design and performance of materials in various applications. Moreover, materials characterization is essential for environmental and sustainabilityconsiderations. By understanding the environmental impact of materials throughout their lifecycle, researchers can develop sustainable materials with minimal ecological footprint. Techniques such as life cycle assessment and environmental impact analysis allow scientists to evaluate the environmental impact of materials from raw material extraction to end-of-life disposal. This holistic approach to materials characterization is crucial for promoting sustainable practices and reducing the environmental impact of industrial processes. Overall, materials characterization plays a critical role in scientific research, technological advancement, and industrial applications. By providing valuable insights into the properties and behavior of materials, researchers can optimize their performance, develop new materials, and ensure quality control in manufacturing processes. The interdisciplinary nature of materials characterization, combining physics, chemistry, and engineering, highlights its importance in advancing our understanding of materials and driving innovation in various industries. As technology continues to evolve, the demand for advanced materials with tailored properties will only increase, making materials characterization an indispensable tool for researchers and engineers alike.。
OB2211, OB2212 Datasheet
GENERAL DESCRIPTIONOB2211/OB2212 series is an offline PWM Power switch for low power AC/DC charger and adaptor applications. It operates in primary-side sensing and regulation, thus, opto-coupler and TL431 is eliminated. It achieves excellent Constant Voltage (CV) and Constant Current (CC) performance by built-in constant current and constant voltage control, as shown in the figure below. OB2211/OB2212 operates with flyback converter in DCM mode. The pulse frequency modulation (PFM) is used in CC control. In CV control, multi-mode operations are utilized to achieve high performance and efficiency: fixed frequency mode at large load conditions; frequency reduction mode atlight/medium load; ‘Extended burst mode’ atNo/light load. OB2211/OB2212 offers complete protection coverage with auto-recovery features including Cycle-by-Cycle current limiting, VDD over-voltage clamp and UVLO, and Power on soft start. Excellent EMI performance is achieved with On-Bright proprietary frequency shuffling technique together with soft switching control at the totem pole gate FEATURESPrimary-side Sensing and Regulation WithoutTL431 and Opto-couplerMulti-mode Operation for High Efficiency Programmable CV and CC RegulationFrequency Shuffling and Adjustable Gate Drive Greatly Improving EMIPower on Soft-start Time (4ms)“Extended Burst Mode Control” for Improved Efficiency and Minimum Standby Design Built-in Leading Edge Blanking Cycle-by-Cycle Current LimitingVDD Under Voltage Lockout with Hysteresis (UVLO)Gate Output Maximum Voltage Clamp (16V) Auto-restart in Short Circuit APPLICATIONSLow Power AC/DC offline SMPS for Cell Phone ChargerDigital Cameras Charger Small Power AdaptorAuxiliary Power for PC, TV etc. Linear Regulator/RCC Replacement OB2211 is offered in SOP8 package.OB2212 is offered in DIP8 packageTYPICAL APPLICATIONGENERAL INFORMATION Pin ConfigurationOB2211 is offered in SOP8OB2212 is offered in DIP8The pin map is shown as below.Ordering InformationPart Number DescriptionOB2211CP SOP8,Pb-freeOB2211CPA SOP8, Pb-free, T&ROB2212AP DIP8,Pb-freeNote: All Devices are offered in Pb-free Package if not otherwisenoted.Package Dissipation RatingPackage RθJA (°C/W)SOP8 150DIP8 90Absolute Maximum RatingsParameter ValueDrain Voltage (off state)-0.3V to 650VVDD Voltage -0.3 to 38 VVDDG Voltage -0.3 to 38 VVDD Zener Clamp ContinuousCurrent10 mACS Input Voltage-0.3 to 7VINV Input Voltage-0.3 to 7VMin/Max Operating JunctionTemperature T J-20 to 150 o CMin/Max Storage TemperatureT stg-55 to 150 oCLead Temperature (Soldering,10secs)260 oCNote: Stresses beyond those listed under “absolute maximumratings” may cause permanent damage to the device. These arestress ratings only, functional operation of the device at these orany other conditions beyond those indicated under “recommendedoperating conditions” is not implied. Exposure to absolutemaximum-rated conditions for extended periods may affect devicereliability.Marking InformationTERMINAL ASSIGNMENTSPin Num Pin Name I/O Description1 VDDG P Internal Gate Driver Power Supply2 VDD P IC DC power supply Input3 INV I Inverting input of error amplifier (EA). Connected to resistor divider fromprimary sensing winding reflecting output voltage. PWM duty cycle isdetermined by EA output and current sense signal at pin 4.4 CS I Current sense input5/6 Drain O HV MOSFET Drain Pin. The Drain pin is connected to the primary lead ofthe transformerGround8 GND POutput Power Table230VAC±15% 90-264VAC ProductOpen Frame1 Open Frame1OB2211 10W 8WOB2212 20W 12W Notes:1. Maximum practical continuous power in an open frame design with sufficient drain pattern as a heat sink, at 50℃ ambient.BLOCK DIAGRAMRECOMMENDED OPERATING CONDITIONSymbol Parameter Min Max UnitVoltage 12 23 V VDD VDDSupplyT A Operating Ambient Temperature -20 85 o CELECTRICAL CHARACTERISTICS(T A = 25O C , VDD=VDDG=16V, if not otherwise noted)Symbol Parameter Test Conditions Min Typ Max Unit Supply Voltage (VDD) Section I DD ST Standby current Start up current, VDD=13V 5 10 uAI DD op Operation Current Operation supply current INV=1.25V, CS=0V, VDD=VDDG=20V- 1.02.0 mA UVLO(ON) VDD Under Voltage Lockout Enter7.5 8.5 10.0 VUVLO(OFF) VDD Under Voltage Lockout Out13.5 14.7 16.0 VV DD _clamp I DD =10mA 38 V Current Sense Input SectionT LEB LEB time 540 ns Vth_oc V TH _OC_test800 830 860 mV Td_oc Propagation delay 150 ns Z SENSE _IN Input Impedance 50 Kohm T_ss Soft start time 4 ms Oscillator Section (CV) Fosc normal mode frequency48 50 52 KHz Fosc_Burst Green-Mode min.Frequency22 KHz△f/FoscFrequency shuffling range+/-4 % CC Section VDD=16V VDDG=16V INV=0V (minimum frequen 12.2 KHz VDD=16V VDDG=16V INV=0.35V 14.7 KHz VDD=16V VDDG=16V INV=0.53V 22.1 KHz VDD=16V VDDG=16V INV=0.71V 29.3 KHzVDD=16V VDDG=16V INV=0.89V36.4 KHz VDDH=16V VDDG=16V INV=1.07V43.2 KHz FccOscillation Frequency VDDH=16V VDDG=16V INV=1.25V50.1 KHz Error Amplifier section Vref_EA Reference voltage for EA1.23 1.25 1.27 VGdc DC gain of the EA 50 dB GBW Unity gain bandwidth 10 kHz Power MOSFET SectionOB2211 650BVdss MOSFET Drain-Source Breakdown VoltageOB2212 600V OB2211 12 15RDS(on) Static Drain to Source On Resistance OB2212 9.9 12 ΏCHARACTERIZATION PLOTSOPERATION DESCRIPTIONOB2211 and OB2212 are cost effective PWM Switch optimized for off-line low power switching mode power supply applications including battery chargers and AC adaptors in sub 20W range. It operates in primary side sensing and regulation, thus opto-coupler and TL431 are not required. Proprietary CC control and built-in error amplifier can achieve a good CC/CV performance.z Startup Current and Start up ControlStartup current of OB2211/2 is designed to be very low so that VDD could be charged up above UVLO threshold level and device starts up quickly. A large value startup resistor can therefore be used to minimize the power loss yet reliable startup in application. For AC/DC adaptor with universal input range design, a 1.2 M Ω, 1/8 W startup resistor could be used together with a VDD capacitor to provide a fast startup and yet low power dissipation design solution.z Operating CurrentThe Operating current of OB2211/2 is as low as 1mA. Good efficiency is achieved with the low operating current together with ‘Extended burst mode’ control features.z Soft StartOB2211/2 features an internal 4ms soft start to soften the electrical stress occurring in the power supply during startup. It is activated during the power on sequence. As soon as VDD reaches UVLO(OFF), the peak current is gradually increased from nearly zero to the maximum clamping level 0.77V. Every restart is followed by a soft start.z CC/CV OperationOB2211/2 is designed to produce an approximate CV/CC output characteristic as shown in the figure 1. In charger applications, a discharged batteryoperates on the CC portion of the curve until almost fully charged and then smoothly switches to the CVportion of the curve. In an AC adapter, the normal operation occurs only on the CV portion of the curve, the CC portion provides over current protection and auto-restart short circuit protection. In CV operation, the output voltage is sensed on the primary side and the sensed signal controls the duty cycle through a built-in error amplifier (EA).Figure 1z Error Amplifier (EA)Connected to a resistor divider from the primary side sensing winding, the inverting input of the Error Amplifier (EA) is compared to an internal reference voltage of 1.25V to regulate the output voltage. The EA output is internally connected to the PWM generator and controls the duty cycle.z Primary Side Sensing and RegulationFigure 2 shows the simplified schematic of Flyback converter using primary side sensing and regulation. The voltage signal reflecting the output voltage from a feedback winding in the primary side is used to monitor and regulate the output voltage. The relationship of the DC voltage of the feedback winding and the output voltage is expressed as:()21F F o FB V V V N V −+⋅= (1)Where N is the turn ratio between the feedback winding and the output winding, V F1 is the forward drop voltage of the rectified diode, D1, and V F2is the forward drop voltage of the rectified diode, D2.FB INV V R R R V ⋅+=212(2)()()21212F F o V V V N R R R Vo −+⋅⋅+=(3)Figure 2In the regulation, V INV is regulated to 1.25V. V F1 of Diode D1 changes with the output current thus the load conditions, and V F2 is always constant regardless of output current, therefore, the regulation of output voltage, Vo, is affected by V F1 as shown in equation 3. To reduce the nonlinear effect of V F1, the N ratio needs is chosen to be small.z Loop CompensationTo provide good line and load regulation and dynamic response, a capacitor used for loop compensation is connected to pin INV as shown in Figure 3，where Req, Ceq and Roeq is respectively reflected effective ESR resistance, output capacitance and load resistance from the output winding to the primary side winding. The resistance Req and capacitance Ceq form a zero. The resistance Roeq and capacitance Ceq form a pole. The small signal transfer function of EA compensation circuit is obtained by:()1211221211C R R s C R s R R R A v v FBC +++⋅+⋅=ΛΛ(4)z Constant Current (CC) ModeTo achieve constant current (CC) at different output voltage, the Flyback converter is designed to operate in DCM mode and the switching frequency changes with output voltage as INV s V k f ⋅= and it is low clamped to12KHz, therefore the switching frequency is proportional to the output voltage as V INV is approximately the replica of output voltage, Vo .Figure 4 illustrates the key waveforms of the Flyback converter operating in discontinuous current mode (DCM). During Q1 on-time, theenergy can be stored in the primary inductor of the transformer; the load current is supplied from the output capacitor. The peak current of the primary side ramps up to I PK at the end of the on-time:on minpk T L V I ⋅=(5) In OB2211/2, the peak current at CC mode is always constant regardless of voltage level of V INV . The energy stored in the primary inductance Lm at the end of Ton is expressed by the current Ipk:()22pk m I L E ⋅=(6)where RsV I oc th pk _=, Rs is the current sensingresistor connecting to pin CS.During Q1 turns off, the energy stored in the primary winding is transferred to the secondary and output. The power drawn from the input AC supply is expressed by:()22pk s m I f L P ⋅⋅=(7)Assuming 100% efficiency, we can get the following equation at different output voltage,()o o pk s m I V I f L ×=⋅⋅⋅22(8)The output current is further expressed as()022V I f L I pk s m o ×⋅⋅⋅=(9)Since fs is linear proportional to Vo as described, therefore output current Io is kept constant and it depends only on L m and I pk .Fig. 4 The waveforms of Flyback converter in DCMz Extended Burst Mode OperationAt light load or zero load condition, most of the power dissipation in a switching mode power supply is from switching loss, the core loss of the transformer and the loss on the snubber circuit. The magnitude of power loss is in proportion to the switching frequency. Lower switching frequency leads to the reduction on the power loss and thus conserves the energy.OB2211/2 self adjusts the switching frequency according to the loading condition. The switch frequency is reduced at light/no load condition to improve the conversion efficiency. At light load/no load condition, the output of the Error amplifier (EA) drops below the burst mode threshold level and device enters Burst Mode control. The frequency control also eliminates the audio noise at any loading conditions.z Oscillator OperationThe switching frequency of OB2211/2 is internally fixed at 50KHZ. No external frequency setting components are required for PCB design simplification.z Frequency shuffling for EMI improvement The frequency Shuffling/jittering (switching frequency modulation) is implemented in OB2211/2. The oscillation frequency is modulated with a pseudo random source so that the tone energy is spread out. The spread spectrum minimizes the conduction band EMI and therefore eases the system design.z Current Sensing and Leading Edge BlankingCycle-by-Cycle current limiting is offered in OB2211/2 current mode PWM control. The switch current is detected by a sense resistor into the CS pin. An internal leading edge blanking circuit chops off the sensed voltage spike at initial internal power MOSFET on state due to snubber diode reverse recovery and surge gate current of internal power MOSFET so that the external RC filtering on sense input is no longer needed. The current limiting comparator is disabled and cannot turn off the internal power MOSFET during the blanking period. The PWM duty cycle is determined by the current sense input voltage and the EA output voltage.z DriveThe internal power MOSFET in OB2211/2 is driven by a dedicated gate driver for power switch control. Too weak the gate drive strength results in higher conduction and switch loss of MOSFET while too strong gate drive results the compromise of EMIA good tradeoff is achieved through the built-in totem pole gate design with right output strength and dead time control. The low idle loss and good EMI system design is easier to achieve with this dedicated control scheme.In addition to the gate drive control scheme mentioned, the gate drive strength can also be adjusted externally by a resistor connected between VDD and VDDG, the falling edge of the Drain output can be well controlled. It provides great flexibility for system EMI design.z Protection ControlGood power supply system reliability is achieved with its rich protection features including Cycle-by-Cycle current limiting (OCP), and VDD over voltage clamp, Power on Soft Start, Under Voltage Lockout on VDD (UVLO).With On-Bright Proprietary technology, the OCP is line voltage compensated to achieve constant output power limit over the universal input voltage range.VDD is supplied by transformer auxiliary winding output. It is clamped when VDD is higher than 30V. The output of OB2211 is shut down when VDD drops below UVLO(ON) limit and Switcher enters power on start-up sequence thereafter.PACKAGE MECHANICAL DATADimensions In Millimeters Dimensions In Inches SymbolMin Max Min MaxA 1.350 1.750 0.053 0.069A1 0.100 0.250 0.004 0.010 A2 1.300 1.550 0.051 0.061b 0.330 0.510 0.013 0.020c 0.170 0.250 0.006 0.010D 4.700 5.150 0.185 0.203E 3.800 4.000 0.150 0.157E1 5.800 6.200 0.228 0.244e 1.270 (BSC) 0.050 (BSC)L 0.400 1.270 0.016 0.050 θ 0º 8º 0º 8ºDimensions In Millimeters Dimensions In InchesSymbolMin Max Min MaxA 3.710 4.310 0.146 0.1700.500 0.020 A1A2 3.200 3.600 0.126 0.142B 0.350 0.650 0.014 0.026 B1 1.524 (BSC) 0.060 (BSC)C 0.200 0.360 0.008 0.014D 9.000 9.500 0.354 0.374E 6.200 6.600 0.244 0.260 E1 7.320 7.920 0.288 0.312e 2.540 (BSC) 0.100 (BSC)L 3.000 3.600 0.118 0.142 E2 8.200 9.000 0.323 0.354IMPORTANT NOTICERIGHT TO MAKE CHANGESOn-Bright Electronics Corp. reserves the right to make corrections, modifications, enhancements, improvements and other changes to its products and services at any time and to discontinue any product or service without notice. Customers should obtain the latest relevant information before placing orders and should verify that such information is current and complete.WARRANTY INFORMATIONOn-Bright Electronics Corp. warrants performance of its hardware products to the specifications applicable at the time of sale in accordance with its standard warranty. Testing and other quality control techniques are used to the extent it deems necessary to support this warranty. Except where mandated by government requirements, testing of all parameters of each product is not necessarily performed.On-Bright Electronics Corp. assumes no liability for application assistance or customer product design. Customers are responsible for their products and applications using On-Bright’s components, data sheet and application notes. To minimize the risks associated with customer products and applications, customers should provide adequate design and operating safeguards.LIFE SUPPORTOn-Bright Electronics Corp.’s products are not designed to be used as components in devices intended to support or sustain human life. On-bright Electronics Corp. will not be held liable for any damages or claims resulting from the use of its products in medical applications.MILITARYOn-Bright Electronics Corp.’s products are not designed for use in military applications. On-Bright Electronics Corp. will not be held liable for any damages or claims resulting from the use of its products in military applications.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Characterization of cycle-to-cycle variations in a natural gas spark ignitionengineM.Reyes a ,⇑,F.V.Tinaut a ,B.Giménez a ,A.Pérez ba Department of Energy and Fluid Mechanics Engineering,University of Valladolid,Paseo del Cauce s/n,E-47011Valladolid,Spain bCIDAUT Foundation,Parque Tecnológico de Boecillo p.209,E-47151Boecillo-Valladolid,Spainh i g h l i g h t sg r a p h i c a l a b s t r a c ta r t i c l e i n f o Article history:Received 4July 2014Received in revised form 15September 2014Accepted 23September 2014Available online 23October 2014Keywords:Cycle-to-cycle variations Natural gasCombustion diagnosis Genetic algorithm Spark ignition enginea b s t r a c tIn this work a study of the influence of the fuel/air equivalence ratio and engine rotational speed on the cycle-to-cycle variations in combustion in a natural gas spark ignition engine is presented.The study con-siders both classic estimators of cyclic dispersion and a new one,based on the burned mass and burning rate.The engine experimental conditions were as follows:Intake pressure 0.5bar,while fuel/air equiva-lence ratio was changed from 1.0to 0.63,and engine rotational speed was varied from 1000rpm to 2500rpm.For each equivalence ratio and engine speed,a diagnosis model is used to process the exper-imentally obtained combustion pressure data in order to provide combustion relevant results such as the mass burning rate at a cycle level.A procedure based on the use of genetic algorithms is used to obtain a very accurate and objective (without human intervention)adjustment of the optimum parameters needed for combustion diagnosis:angular positioning and pressure offset of the pressure register,dynamic compression ratio,and heat transfer coefficients.The model allows making the diagnosis of ser-ies of 830consecutive engine cycles in an automatic way,increasing the objectivity of the combustion diagnosis.The paper focuses on using the values of the mass fraction burned computed from the pressure register and especially on the analysis of the combustion cycle to cycle variation in the natural gas fuelled engine.A new indicator for the study of cycle-to-cycle variations is proposed,i.e.the standard deviation of the mass fraction burning rate.The values of this new indicator are compared with other classic indi-cators,showing the same general trends.However,a deeper insight is provided on the combustion cyclic variation when the values of the new indicator are plotted asa function of the mass fraction burned,since this allows analyzing the cyclic variation along the combustion development in each cycle from a mass fraction burned of zero to one,with arelevant value at mass fraction burned of 0.5.More important is that the consideration of the dependence of the combustion variables (density,flame front surface,/10.1016/j.fuel.2014.09.1210016-2361/Ó2014Elsevier Ltd.All rights reserved.⇑Corresponding author.E-mail address:miriam.reyes@uva.es (M.Reyes).combustion speed)on the mass fraction burned allows ensemble averaging of all registered cycles for each value of mass fraction burned.This permits using the ensemble averaged mass fraction burning rate as an estimator of combustion speed.The analysis of the general trends of cyclic dispersion when engine speed and equivalence ratio are modified(1000,1750and2500rpm;0.7,0.8,0.9and1.0)indicate that cycle-to-cycle variations show, as expected,a strong dependence on the engine rotational speed,increasing the variation with engine rpm.However,when the standard deviation of mass fraction burning rate is plotted as a function of mass fraction burned,there is a linear dependence on engine rpm,but only a very weak dependence on equivalence ratio.This means that the proposed estimator of cyclic dispersion is sensitive to onlyflow turbulent intensity and not to equivalence ratio.Ó2014Elsevier Ltd.All rights reserved.1.IntroductionBecause of concerns for the environment protection and energy shortages,much effort has been concentrated on the utilization of alternative fuels in internal combustion engines(ICE).Alternative fuels are clean when they are compared to conventional ones derived from petroleum in ICE.Natural gas(NG)is considered to be a possible alternative fuel due to its higher octane number and properties.NG is a mixture of different gases where methane is its main component(75–98%of methane,0.5–13%of ethane and0–2.6%of propane[1]).NG combustionsion than that of conventional fuels because theof NG is less complex,together with theevaporation[2].The high octane number of NGand130)allows the engine to operate at highbecause it gives a high anti-knocking potential[3].In general,combustion in spark-ignition engineserably from cycle to cycle[4].Many studies havein order tofind the main causes of this effect[5,6].are associated with considerable variations incombustion duration[7].The effect of cyclicdescribed by Litak et al.[8,9].These variationsin the mean effective torque as much as20%[10].Cyclic dispersion has been classically evaluatedcessing of the maximum pressure(p max)and themaximum pressure is reached(a P max)[11].It haswith the variation in the heat released during[12,13].Recently it also has been studied incompression ignition(HCCI)engine processes ignition engines[15]and also by using CFD simulations[16].A tra-ditional[17–22]estimator of the cycle-to-cycle variation is the Coef-ficient of Variation in Indicated Mean Pressure,COV IMEP.In this paper,the authors propose complementary considering the varia-tion of the mass fraction burning rate of each individual cycle to characterize cyclic variation,as explained later(Fig.1).Cycle-to-cycle dispersion studies carried out using combustion diagnosis require a lot of effort because each cycle requires adjust-ing some parameters such as pressure offset,angular positioning and others,before an accurate analysis can be performed.This implies that cyclic dispersion studies conducted by manual or tradi-NomenclatureA f sphericalflame front area(m2)c m mean piston velocity(m/s)CR compression ratio(–)E errorHCCI homogeneous charge compression ignition ICE internal combustion engineIMEP indicated mean pressure(Pa)m total mass(kg)MFB mass fraction burned(–)MFBR mass fraction burning rate(1/°or1/s)NG natural gasp pressure(Pa)p m motored engine pressure(Pa)R j universal gas constantS c combustion speed(m/s)T temperature(K)TDC top dead center u j internal energy(J/m3)V volume(m3)V(a)cylinder volume for each crank angle(m3)Greeka crank angle(°or rad)U fuel/air equivalence ratioðu¼_m sto air=_m airÞl mean valueq density(kg/m3)r standard deviationSubscriptsb burnedmax maximumu unburned,freshwos Woschni’sFig.1.Classical and proposed ways of analyzing cycle to cycle variation outline.M.Reyes et al./Fuel140(2015)752–761753improved by the use of genetic algorithms in order to determine some parameters needed for an accurate diagnosis.For each cycle, the parameters of the diagnosis(as pressure offset and angular position of the pressure data)are adjusted by the genetic algorithm techniques.This complete model is run over six series of830con-secutive engine pressure data.More details about the model can be found in the work of Reyes et al.[24].Genetic algorithms werefirstly introduced by Holland[25]. Genetic algorithms belong to a group of heuristic mathematical techniques generally used to solve optimization problems that are known as evolutionary algorithms.Since its introduction, genetic algorithms have been widely used to solve diverse optimi-zation problems in differentfields of thermodynamic andfluid sci-ence[26,27].Turbulence speeds up the combustion process;this raises the velocity of combustion an order of magnitude above the laminar combustion velocity[28].Some authors[29,30]have investigated the turbulence in the combustion chamber using different param-eters and techniques to characterize the turbulent intensity using fluid velocity measurements in a point of the combustion chamber.A study of the velocityfield is carried out by using the ensemble-averaging approach[31].Theflame front position can be calculated as a function of the geometry and the volume of the burned mass.For afixed equiva-lence ratio,the MFB(Mass Fraction Burned)and the volume of burned mass are related.In consequence for afixed equivalence ratio a given value of MFB indicates the position of theflame front and is not dependent of the crankshaft angle if the minor piston displacements are neglected.In the present study,the concept of ensemble averaged values is applied to the combustion velocity for different values of MFB in order to analyze the phenomenon of cycle-to-cycle variation,see Fig.1.The main objective of this work is to evaluate the relative influ-ence of the equivalence fuel/air ratio and the engine rotational speed on the cycle-to-cycle variations produced in a single cylinder 2.Methodology2.1.Experimental apparatus and procedureThe tests were performed in a single-cylinder,four-stroke,air cooled MINSEL M380engine coupled to an asynchronous machine with a constant engine rotation speed.This engine was originally designed to be a compression ignition engine,withflat cylinder head and a bowl-in piston combustion chamber.A number of changes were made to transform it into a spark ignition engine. The original injector was substituted by a spark plug and a modifi-cation in the piston was carried out in order to transform the com-bustion chamber and to reduce the original compression ratio.The specifications of the cylinder are:80mm bore,75mm stroke,and 11.4compression ratio.This engine was coupled to a5.5kW LEROY SOMER asynchro-nous machine that was used for motoring and braking,see Fig.2. The engine was instrumented for the measurement of mean engine performance values to determine when it is stabilized,such us, intake and exhaust pressures,and intake,exhaust,cylinder head and oil temperatures.When the engine is stabilized in a certain operating point,the instantaneous pressure in the combustion chamber is registered and stored in a Yokogawa DL750Scopecord-er.IMEP at each operating point is then calculated from the instan-taneous pressure plot.The experiments have been carried out in two different stages. In thefirst stage,engine rotational speed was maintained at 1500rpm and intake pressure was set at0.5bar,the angle of spark ignition was set to obtain the maximum brake torque for each test, and the fuel/air equivalence ratio(U)was varied from0.63to1.0. In the second stage of the experimentation,intake pressure was also set around0.5bar and fuel/air equivalence ratio was varied from0.7to1.0,while engine rotational speed was modified from 1000rpm to2500rpm,to discern the effect of the engine rota-tional speed on the cyclic dispersion.Fig.2.Schematic diagram of the engine setup. 754M.Reyes et al./Fuel140(2015)752–761The crankshaft angle was measured using a free end AVL360C.03 angular encoder.This encoder has600marks per revolution:i.e.a 0.6degrees resolution and also a single pulse per revolution signal. In order to synchronize the pressure signal with the crankshaft angle,the option of an external clock was activated in the Scopecorder.2.1.2.Massflow set and measurementThe fuel used during the experiments was natural gas(NG).The inlet mixture of NG and air was made by using two BROOKS ther-mal massflow controllers.These controllers are equipped with a proportional valve and an actuator.Therefore,the massflow rate could be measured and controlled at once.A5853S model was used for the air and a5851S model was used for the NG.The mix-ture of the NG and air was formed in the inlet manifold,so the level of premixing was high and almost constant.2.2.Models employed2.2.1.Thermodynamic modelA one zone,zero dimensional thermodynamic model has been used to perform the diagnosis of premixed combustion from the experimentally measured in-cylinder pressure.The model solves the energy conservation equation during the closed valves interval by using the ideal gas state equation to relate pressure,volume and temperature(Eqs.(1)and(2)).U jÀU jÀ1¼_Q WmÁD tÀ1=2ðp jþp jÀ1ÞðV jÀV jÀ1Þð1ÞpjV j¼mR j T jð2ÞThe subscript j refers to a time point,while jÀ1refers to theprevious one.U is total internal energy,_Q Wmis the average heat transferred through the walls,calculated using Woschni’s heat transfer coefficient,p is the pressure,V is volume,R is the ideal gas constant and T is the absolute temperature.The total trapped mass(m)is constant during all the cycle because the leakageflow is neglected.IMEP,p max and a max are easily found from experimen-tal data,while the fraction of mass burned(MFB-mass fraction burned)obtained as the ratio between the mass burned until a par-ticular crankangle and the total trapped mass is the most relevant model result.Previous equations are solved by using the following procedure: For the initial angle,total mass is unburned and is determined by the massflow meters data and incremented with a mass of resid-uals predicted using a gas exchange process model for the test con-ditions.The composition of the unburned and burned mass is known because the equivalence ratio and residuals are known and complete combustion is assumed.The temperature is calcu-lated with the ideal gas state equation.The specific internal energy in the burned u b and unburned u u zones are calculated with a thermal estate equation,as a function of the composition and temperature,with the correlations pub-lished in the NIST tables[32]where sensible and formation energy are included.The total internal energy of the cylinder contents is weighted with the mass fraction burned(Eq.(3)).UðTÞ¼mðMFBÁu bðTÞþð1ÀMFBÞÁu uðTÞÞð3ÞIn order to calculate the next angle situation,an increment of burned mass is assumed.An error in terms of energy is computed with Eq.(1).This is iteratively solved to adjust the increment of burned mass to satisfy the energy equation.Heat transferred through the walls is computed by means of Woschni’s expression as shown in Eq.(4).h¼0:013ÁdÀ0:2pÁp0:8ÁTÀ0:53ðk wos12:28c mþk wos2Á0:00324V d T icpicðpÀp mÞÞ0:8ð4Þwhere h is the coefficient of heat transfer,d p is the diameter of the piston,c m is the mean piston speed.T ic and p ic are the temperature and pressure when the intake valve closes and p m is the motored engine pressure.The parameters k wos1and k wos2are multipliers that have to be adjusted.More details of the thermodynamic model can be found in Reyes et al.[24].2.2.2.Genetic algorithmWith the purpose of correctly process the pressure data with the above mentioned thermodynamic model,parameters as exper-imental pressure offset,crank angle positioning,compression ratio and others should be adjusted.In this work the authors use an application of genetic algorithms to solve the positioning of the pressure diagram and to adjust the rest of parameters of the engine that in any cases are difficult to know precisely.Genetic algorithms are based on Darwin’s Theory of Evolution: the best adapted individuals are selected to survive and to be the progenitors of the subsequently generation.Thus by this selection, the best genetic combination is transmitted to the next generation of individuals.This procedure,repeated numerous generations, ends with a new,better-adapted individual.This method is pro-grammed to obtain the most precise combustion diagnosis for each engine cycle.This ensures that the best determination of MFB is obtained for each cycle.Any genetic algorithm used to solve an optimization problem has several concepts in common:individual, codification,fitness function,selection and genetic operators.The total individuals of the population are,N2parand they are obtained with all the combinations of pairs of N par individuals. The crossover function is applied to the total individuals to obtain the initial population.The criterion to select the best adapted indi-viduals is done using thefitness function Z(applied to the MFB cal-culated with the diagnostic model for each individual codification).Each individual is characterized by its codification.The param-eters that must be adjusted by the algorithm are:Angular position, pressure offset,compression ratio and a multiplier of the heat transfer coefficient(k wos1and k wos2),calculated using Woschni’s correlation[33].The evaluation criterion to select the better adapted individuals is afitness function which is applied to the result of diagnostic model,run with the parameters of each indi-vidual.The general objective of the optimization procedure is to reduce the error of thefitness function,both for motored(mass fraction burned zero)and combustion(mass fraction burned unity) conditions.Detailed information of the crossover function and the Z function can be found in Reyes et al.[24]where a full description of the genetic algorithm is presented with supplementary information.3.Results and discussion3.1.Data analysis and diagnostic methodologyTo perform a research about the cycle-to-cycle variations,an elevated number of engine cycles must be studied for each exper-imental set of conditions(i.e.regime,load,etc).Some authors choose between120and2000cycles[4,8,11,34]to obtain a repre-sentative sample.With the purpose of obtaining a correct diagno-sis,parameters related to the experimental measurement process, i.e.angular positioning,heat transfer multipliers,compression ratio and pressure offset,must be determined,if possible not including any bias.M.Reyes et al./Fuel140(2015)752–761755756M.Reyes et al./Fuel140(2015)752–7610.63.Inlet pressure was kept constant(0.5bar),while engine speedTable1Values of k wos1and k wos1obtained after the second execution offraction burned MFB obtained from the analysis of combustion pressure in series of830engine cycles fortest.The best values of pressure offset and the parameters k wos1 and k wos2for each test point are then obtained by the genetic algo-rithm(Table1.).Pressure offset is again left free.In a third and last step,all the genetic algorithm parameters are fixed to the previously obtained values,except for the pressure off-set,which is always left free.3.2.Cycle to cycle dispersion studies3.2.1.Influence of the fuel/air equivalence ratioCyclic dispersion analysis has been carried out over six series of 830consecutive cycles,varying the equivalence ratio.The results of MFB obtained from all pressure data are presented in Fig.3, where830cycles are considered for each equivalence ratio(all cases at1500rpm).The low slope at the end of combustion,from FMQ%0.95to the end of combustion,is due to the shape of the combustion chamber.When theflame front enters the small gap between the piston and the cylinder head cools down and this reduces its combustion velocity;probably a part of this mixture is burned more slowly in a secondary combustion process.An initial estimation of the importance of cycle-to-cycle varia-tion is made with the coefficient of variation(COV)of the indicated mean pressure[18,19],calculated as the ratio of the standard devi-ation and the mean value(Eq.(5)).COV IMEP¼rIMEPlIMEPÁ100%ð5Þl IMEP ¼1N cX N ci¼1x ið6Þr IMEP¼1X N cðx iÀl IMEPÞ2"#1=2ð7ÞThe results of the COV of indicated mean pressure(IMEP),and of other three estimators of cyclic variation(explained below) are shown in Fig.4.It is accepted[6],that,in order to ensure regular running of the engine,the coefficient of variation of the IMEP must be smaller than 5%.Notice that this limit is not reached in any of the tests,including the leanest operating condition(U=0.63)that is near the lean limit.As the full diagnosis is performed for each cycle,other values relative to combustion development can be computed,in order to check the influence of the equivalence ratio on the cyclic disper-sion.Thus the second estimator for cyclic variation is the COV of the maximum combustion pressure(p max).The mean value and standard deviation of the values of p max corresponding to all cycles are obtained and the coefficient of variation COV pmax is computed similarly as for the IMEP and is also represented in Fig.4.The third estimator is the variation in combustion duration(D a c).This is defined as the angular interval from10%to90%of MFB.Similarly as for the other variables,the COV D a c has been computed and is plotted in Fig.4.Finally a fourth estimator of the cyclic variability is calculated from the diagnosis results:The value of the coefficient of variation COV MFBR of the mass fraction burning rate(MFBR)for a given value of the mass fraction burned.For this latter,the authors have cho-sen a representative value of50%of mass fraction burned.Then the COV(MFBR0.5)is represented also in Fig.4.Notice that IMEP,p max and D a c are magnitudes that take into account how the combustion pressure has evolved from the begin-ning of combustion and include averaged information of the his-tory of the cycle.They can be considered as cycle integrated values(this is strictly true for IMEP,and qualitatively right for p max and D a c).In contrast,MFBR for a given MFB is a single value rep-resentative of combustion rate.This makes the COV of MFBR higher than the ones obtained for the cycle integrated values.As can be seen in Fig.4,the COV of all the estimators present the same tendency with the equivalence ratio,showing that leanerFig.4.Values of coefficient of variation(COV)of the four combustion variables six different equivalence ratios considered(1500rpm).Fig.5.Values of standard deviation r of the four indicators for the six different equivalence ratios(1500rpm).M.Reyes et al./Fuel140(2015)752–761757that has not a clear tendency with thetive independence of equivalence ratio is next section.3.2.2.Relationship between ensemble averaged rate and combustion velocityIt is interesting to express the the turbulent combustion velocity,S c ,see d ðMFB ða ÞÞd a¼dm b ða Þmd a¼1m d ðm b ða ÞÞdt dt d ad ðMFB ða ÞÞd a ¼MFBR ða Þ¼_m b ða Þm 1x _mb ða Þ¼q u ða ÞA f ða ÞSc ða ÞMFBR ða Þ¼q u ða ÞA f ða ÞxS c ða Þwhere q u is the unburned density,A f is the the turbulent combustion velocity (the three variables for a given value of crank angle a ),m is the total mass and x =d a /dt is the engine angular velocity (constant in each test).For a specific crank angle a ,q u and A f may have slightly different values from cycle to cycle.Considering all cycles of a given operating point,when the amount of burned mass is the same (i.e.the same value of MFB),if we assume that the piston position varies only slightly during combustion,q u and A f can be considered similar for all the cycles (because pressure and temperatures must be similar,since the mass burned and the volume are similar).Under this assumption,the ensemble average value of MFBR (ðMFBR Þfor each cycle i turns out to be proportional to the average value of S c ,as can be expressed in Eq.(13),where the summations are extended to the values of each cycle (defined by i )up to N c ,the number of cycles The turbulent fluctuation of the combustion speed r (S c )can then be estimated from the standard deviation of the burning rate r (MFBR),since from Eq.(13)it can be seen that they are propor-tional for each value of MFB,Eq.(14).Fig.6.Ensemble averaged values of mass fraction burning rates MFBR as a function of mass fraction burned for each equivalence ratio (1500rpm).Fig.7.Standard deviation of mass fraction burning rate r MFBR as a function of mass fraction burned MFB for each equivalence ratio (1500rpm).050100150200250300350MFB M F B R (1/s )Φ = 1Φ = 0.9Φ = 0.8Φ = 0.7(i) 1000 rpm0100200300400500600MFB M F B R (1/s )Φ = 1Φ = 0.9Φ = 0.8Φ = 0.7(ii) 1750 rpm0.20.40.60.810.20.40.60.81758rðMFBRÞ¼q u A fm xrðS cÞð14ÞFor all cycles and equivalence ratios studied,the ensemble aver-age of MFBR,ðMFBRÞfor each MFB was computed,calculating the mean value and the standard deviation for each test(Eq.(15)).rðMFBRÞ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1cX N ci¼1½MFBRðMFB;iÞÀMFBÞ 2v uu tð15ÞThe results are shown in Fig.6where the ensemble average of the MFBR as a function of MFB is represented with the equivalence ratio as independent parameter.Each line represents the ensemble average in phase for each value of MFB of the830cycles of a test. Fig.6shows that the influence of the equivalence ratio on burning rate is clear,since higher equivalence ratios cause have higher val-ues of MFBR,as can be expected due to the influence of the laminar combustion velocity on the turbulent combustion velocity and then on MFBR through Eq.(13).Another interesting result is shown in Fig.7where the standard deviation of the MFBR(r MFBR,Eq.(15))is represented for each MFB.It can be observed that in this case there is almost no influence of equivalence ratio for the tested mixtures.This result is in agree-ment with the trend of r(MFBR0.5)presented in Fig.5.This is important and can be stated as:When the burning rate is plotted as a function of the mass fraction burned,only the influence of tur-bulence remains,while the influence of equivalence ratio(through laminar combustion speed)is almost negligible.On the contrary, the other estimators of cyclic dispersion(IMEP,p max and D a c),that we have named‘‘integrated,’’retain the dependence on equiva-lence ratio(as shown in Fig.5).For that reason,the type of repre-sentation of relevant variables as functions of mass fraction burned is useful to better identify the origin of the cyclic variation.bined effects of the engine rotational speed and equivalence ratioThe tests in previous sections were carried out at afixed engine rotational speed of1500rpm.This section focuses on the effects of engine speed on the combustion behavior and cyclic variations at certain equivalence ratios.Prior to these experiments the engine piston was changed and is slightly different from that used in the previous section,that means,the compression ratio is higher, 11.4.Fig.8presents results of the ensemble average of the MFBR as a function the MFB values for different engine speeds (1000rpm,1750rpm and2500rpm)with four equivalence ratios. According to Eq.(12),is an estimator of the combustion speed.The unit of MFBR is changed to a time basis(1/s)to elimi-nate the bias of the engine speed on the results(notice the change of scale of vertical axes).As can be seen,for each engine speed,the, MFBR increases with the equivalence ratio due to its effect on lam-inar combustion speed.The effect of engine speed increases,MFBR although less than linearly.For example,a change of engine speed from1000to2500rpm(a factor of2.5)results in a change on, MFBR of about2.2.To identify the combined effect of engine speed and equivalence ratio on cyclic dispersion,Fig.9shows the standard deviation of the MFBR as a function of MFB for three different rotational engine speeds when the fuel/air equivalence ratio is varied from0.7to1.0.M.Reyes et al./Fuel140(2015)752–761759。