(PVRP) and the Multi-Depot Vehicle Routing Problem (MDVRP).

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车辆路径问题

车辆路径问题
禁忌搜寻法的主要步骤
14.2 单中心非满载送货车辆路径问题启发式算法
14.2.1 禁忌搜寻法简介
5. 停止准则 停止准则是整个演算过程结束的条件,通常使用以下四种准则: (1)预设最大迭代次数; (2)目标函数值持续未改善的次数; (3)预设允许CPU最长的执行时间; (4)预设可接受的目标函数值。
禁忌搜寻法的主要步骤
14.2 单中心非满载送货车辆路径问题启发式算法
14.2.1 禁忌搜寻法简介
4. 免禁准则 当一个移步为禁忌,但是若此一移步被允许,可以使得目前所搜寻到的目标函数值得以改善时,则接受此一移步,免禁准则的目的就是用来释放原本禁忌的状态,在求解过程中能逃脱局部最优解的局限。
14.1 物流配送车辆优化调度的概述
目前有关VRP的研究已经可以表示为:给定一个或多个中心(中心车库)一个车辆集合和一个顾客集合,车辆和顾客各有自己的属性,每辆车都有容量,所载的货物不能超过它的容量。
地址特性包括:车场数目、需求类型、作业要求。 车辆特性包括:车辆数量、载重量约束、可运载品种约束、运行路线约束、工作时间约束。 问题的其他特性。 目标函数可能是总成本极小化,或者极小化最大作业成本,或者最大化准时作业。
14.2 单中心非满载送货车辆路径问题启发式算法
14.2.2 问题描述与符号表示
问题中的参数做以下定义: V:需求点集合 O:物流配送中心 K:货车的容量 qi:配送点i的需求量 cij:配送点i到配送点j的距离
添加标题
14.1 物流配送车辆优化调度的概述
旅行商问题
带容量约束的车辆路线问题
带时间窗的车辆路线问题
收集和分发问题
多车型车辆路线问题
优先约束车辆路线问题

车辆路径问题专题—VehicleRoutingProblem

车辆路径问题专题—VehicleRoutingProblem

Periodic VRP (PVRP)
• In classical VRPs, typically the planning period is a single day. In the case of the Period Vehicle Routing Problem (PVRP), the classical VRP is generalized by extending the planning period to M days. • We define the problem as follows: Objective: The objective is to minimize the vehicle fleet and the sum of travel time needed to supply all customers. Feasibility: A solution is feasible if all constraints of VRP are satisfied. Furthermore a vehicle may not return to the depot in the same day it departs. Over the M-day period, each customer must be visited at least once.
Capacitated VRP (CPRV)
• CVRP is a VRP in which a fixed fleet of delivery vehicles of uniform capacity must service known customer demands for a single commodity from a common depot at minimum transit cost. That is, CVRP is like VRP with the additional constraint that every vehicles must have uniform capacity of a single commodity. We can find below a formal description for the CVRP: • Objective: The objective is to minimize the vehicle fleet and the sum of travel time, and the total demand of commodities for each route may not exceed the capacity of the vehicle which serves that route. • Feasibility: A solution is feasible if the total quantity assigned to each route does not exceed the capacity of the vehicle which services the route.

车辆路径问题概念、模型与算法(五星推荐)

车辆路径问题概念、模型与算法(五星推荐)

总的说来,精确性算法基于严格的数学手段,在可 以求解的情况下,其解通常要优于人工智能算法。
但由于引入严格的数学方法,计算量一般随问题规
模的增大呈指数增长,因而无法避开指数爆炸问题,
从而使该类算法只能有效求解中小规模的确定性 VRP,并且通常这些算法都是针对某一特定问题设 计的,适用能力较差,因此在实际中其应用范围很有 限。
一般第一阶段常用构造算法,在第二阶段常用的改 进技术有2-opt(Lin,1965),3-opt(Lin Kernighan,1973)和Or-opt (Or,1976)交换法,这是一 种在解的邻域中搜索,对初始解进行某种程度优化 的算法,以改进初始解。
在两阶段法求解过程中,常常采用交互式优化技术, 把人的主观能动作用结合到问题的求解过程中,其 主要思想是:有经验的决策者具有对结果和参数的 某种判断能力,并且根据知识直感,把主观的估计 加到优化模型中去。这样做通常会增加模型最终实 现并被采用的可能性。
2023最新整理收集 do something
车辆路径问题概念、模型及算法
1、定义
车辆路径问题(VRP)一般定义为:对一系列装货点 和卸货点,组织适当的行车线路,使车辆有序地通 过它们,在满足一定的约束条件(如货物需求量、 发送量、交发货时间、车辆容量限制、行驶里程限 制、时间限制等)下,达到一定问题的目标(如路程 最短、费用最少、时间尽量少、使用车辆数尽量少 等)。
网络流算法(Network Flow Approach)
图论中的一种理论与方法,研究网络上的一类最优化 问题 。1955年 ,T.E.哈里斯在研究铁路最大通量时首 先提出在一个给定的网络上寻求两点间最大运输量的 问题。1956年,L.R. 福特和 D.R. 富尔克森等人给出了 解决这类问题的算法,从而建立了网络流理论。所谓 网络或容量网络指的是一个连通的赋权有向图 D= (V、 E、C) , 其中V 是该图的顶点集,E是有向边(即弧)集, C是弧上的容量。此外顶点集中包括一个起点和一个终 点。网络上的流就是由起点流向终点的可行流,这是 定义在网络上的非负函数,它一方面受到容量的限制, 另一方面除去起点和终点以外,在所有中途点要求保 持流入量和流出量是平衡的。

考研翻译硕士必备热词:排放门丑闻

考研翻译硕士必备热词:排放门丑闻

凯程翻译硕士考研指导第 1 页 共 1 页 考研翻译硕士必备热词:排放门丑闻 翻译硕士考研对于单词量的要求还是很高的,其单词量要求和专业八级不相上下,也就是说,需要掌握的单词总量是很大,下面是凯程考研整理的考研翻译硕士必备热词——"排放门"丑闻。

"排放门"丑闻scandal over rigging of vehicle emissions testsV olkswagen will name Matthias Mueller, the head of its Porsche sports car brand, as its chief executive as it tries to recover from a scandal over its rigging of US vehicle emissions tests, a source close to the matter said.消息人士称,大众将指定大众麾下跑车业务部门保时捷的负责人马提亚斯·穆勒出任新执行长,以试图恢复元气。

此前,大众在美国所售部分柴油车陷入排放检测造假丑闻。

穆勒此前就被人们认为是最有希望接替文德恩的人选(has been widely tipped to succeed Martin Winterkorn)。

此番"排放门",即在部分柴油车排放检测中造假(cheat emissions tests on diesel cars),成为大众78年历史上最严重的危机(the biggest business crisis in Volkswagen's 78-year history)。

大众汽车将面临重振消费者及经销商信心的艰苦战役(a battle to restore the confidence of customers and motor dealers)。

Adaptive energy management of a plug-in hybrid electric vehicle混合动力汽车能量管理

Adaptive energy management of a plug-in hybrid electric vehicle混合动力汽车能量管理

Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamicprogrammingShuo Zhang a ,Rui Xiong a ,b ,⇑aNational Engineering Laboratory for Electric Vehicles,School of Mechanical Engineering,Beijing Institute of Technology,No.5South Zhongguancun Street,Haidian District,Beijing 100081,China bCollaborative Innovation Center of Electric Vehicles in Beijing,Beijing Institute of Technology,No.5South Zhongguancun Street,Haidian District,Beijing 100081,Chinah i g h l i g h t sThe hierarchical control strategy has been proposed for the multiple energy sources. Three typical driving patterns have been classified with the fuzzy logic controller. A driving pattern recognition method was developed with the fuzzy logic controller. DP was used to develop suboptimal control strategies for different driving blocks. Adaptive energy management method for a plug-in HEV has been proposed and verified.a r t i c l e i n f o Article history:Received 24April 2015Received in revised form 26May 2015Accepted 1June 2015Available online 16June 2015Keywords:Plug-in hybrid electric vehicle Hybrid energy-storage system Multi-scaleEnergy managementDriving pattern recognition Dynamic programminga b s t r a c tTo achieve the optimal energy allocation for the engine-generator,battery and ultracapacitor of a plug-in hybrid electric vehicle,a novel adaptive energy management strategy has been proposed.Three efforts have been made.First,the hierarchical control strategy has been proposed for multiple energy sources from a multi-scale view.The upper level is for regulating the energy between the engine-generator and hybrid energy-storage system,while the lower level is for the battery and ultracapacitor.Second,a driving pattern recognition based adaptive energy management approach has been proposed.This approach uses a fuzzy logic controller to classify typical driving cycles into different driving patterns and to identify the real-time driving pattern.Dynamic programming has been employed to develop opti-mal control strategies for different driving blocks,and it is helpful for realizing the adaptive energy man-agement for real-time driving cycles.Third,to improve the real-time and robust performance of the energy management,the previous 100s duration of historical information has been determined to iden-tify a real-time driving pattern.Finally,an adaptive energy management strategy has been proposed.The simulation results indicate that the proposed energy management strategy has better fuel efficiency than the original and conventional dynamic programming-based control strategies.Ó2015Elsevier Ltd.All rights reserved.1.IntroductionEnergy shortages,environmental concerns over air pollution,and the prospect of the global warming support the need for fur-ther development of plug-in hybrid electric vehicles (HEVs).Plug-in HEVs have become more and more popular due to their excellent fuel economy and relatively low cost [1,2].However,pos-sessing both a highly specific energy density for long driving ranges and a highly specific power for deep and shallow dis-charge/charge cycles is difficult for current batteries [3].Because an ultracapacitor has a high power density and can be used as a power buffer during climbing,braking or acceleration,the combi-nation of lithium-ion batteries and ultracapacitors is an efficient solution to prolong the battery service life by optimizing its oper-ating range [4–7].1.1.Literature reviewA few topologies of a hybrid battery/ultracapacitor energy-storage system (HESS)have been proposed and can be roughly classified into four types from the control perspective./10.1016/j.apenergy.2015.06.0030306-2619/Ó2015Elsevier Ltd.All rights reserved.⇑Corresponding author at:National Engineering Laboratory for Electric Vehicles,School of Mechanical Engineering,Beijing Institute of Technology,No.5South Zhongguancun Street,Haidian District,Beijing 100081,China.Tel.:+86(10)68914070;fax:+86(10)68940589.E-mail address:rxiong@ (R.Xiong).They include directly connecting the battery and ultracapacitor in parallel[4],connecting the battery with a DC/DC converter in series before connecting with the ultracapacitor in parallel[5],connecting the ultracapacitor with a DC/DC converter in series before connect-ing with the battery in parallel[7À9],and connecting the battery and the ultracapacitor each with a DC/DC converter in series before they are connected in parallel[7].In addition to these types,Ali Emadi has proposed a new HESS topology in which the battery does not provide power unless its terminal voltage is higher than that of the ultracapacitor[8].However,the required voltage level of the ultracapacitor is twice as much as that of the battery,which is unsup-portable in some applications.Based on our previous research expe-rience on the systematical evaluation results for the four HESS topologies in Ref.[9],the second topology that the battery pack con-nects with a DC/DC converter in series before it is connected with the ultracapacitor pack in parallel has been selected for this study.This type has the potential to fully exert the dynamic performance of the ultracapacitor by avoiding the current/power impact of the bat-tery and to extend the calendar life of the HESS.To achieve optimal energy/power management for HEVs and plug-in HEVs,a number of strategies have been developed[10–12].The rule based strategy is the most direct and widely used method due to its easy implementation and high calculation effi-ciency[13–15].Jalil N et al.have proposed a rule-based strategy to determine the power split between the battery and engine for a ser-ies hybrid electric vehicle[15].To further improve the performance of the energy management system for hybrid electric vehicles,sev-eral optimal energy/power management methods have been pro-posed,such as methods based on a fuzzy logic approach[16]and an equivalent consumption minimization strategy[17].However, with the development of intelligent algorithms,multiple advanced algorithms such as neural network[18],particle swarm optimiza-tion[19],simulated annealing[20],model predictive control[21], and dynamic programming(DP)[22,23]have been widely employed to develop various advanced adaptive/online energy management systems and optimal strategies.With a prior knowledge of the driv-ing cycle,DP-based methods have the ability to locate the global optimal control strategy.However,the actual future driving cycles can hardly be known in advance.In this case,the DP-based strategies cannot be used for an online energy management.Few energy management methods have been conducted for more than two energy sources[13–28].Most publications have investigated control strategies for EVs and HEVs powered by the battery and engine or the battery and ultracapacitor[24–28]. Specifically,for a series plug-in HEV with HESS,the optimization allocation problem for electricity energy among the battery,ultra-capacitor and engine-generator has not been solved effectively.1.2.Motivation and innovationThe purpose of this study is to propose an adaptive energy man-agement approach via driving pattern recognition and dynamic pro-gramming and to improve the energy management efficiency for a plug-in HEV with a HESS.To avoid the adverse effects of the optimal result against unknown cycles,the driving pattern recognition(DPR) method has been employed to classify and train the typical driving patterns.With the DP algorithm,the micro-control strategies for dif-ferent classified driving patterns can be developed in a systematic ing the fuzzy control algorithm-based predictive approach, the current driving pattern can be recognized with a period of histor-ical driving information.To realize the optimal energy allocation between the engine-generator and HESS with less computational cost,a hierarchical control strategy has been proposed for three energy sources from a multi-scale view.The proposed energy man-agement strategy has been verified and evaluated by a combined driving cycle and Japanese10–15mode driving anization of the paperThis paper is organized as follows.Section2describes the con-figuration of the plug-in HEV and the original control strategy. Then,the classification of driving blocks,construction of DPR,sub-system modeling,DP formulation and energy management system are illustrated in Section3.The verification and evaluation of the proposed method are reported in Section4,and conclusions are drawn in Section5.2.Plug-in hybrid electric vehicle configuration2.1.Vehicle configurationThe structure of the researched target is illustrated in Fig.1. The electricity power of the plug-in HEV comes from two parts: the HESS and assistance power unit(APU).The APU consists of an80kW permanent magnetic generator and a 1.9L gasoline engine,and the rated power of the APU is75kW.Detailed mod-eling of the APU and HESS are introduced in Section3.2.The tar-get vehicle is an electric bus and its essential parameters are presented in Table1.2.2.Hierarchical energy management for the plug-in HEVThe energy management system of the plug-in HEV can be divided into two layers.The upper level is for controlling the energy between the APU and HESS,and the lower level is for con-trolling the energy between the battery and ultracapacitor.2.2.1.Upper level control strategyThe main objective of the energy management is to minimize the operation cost of the plug-in HEV.For optimizing the alloca-tion of energy/power between the HESS and APU,a systematic energy management strategy is necessary.The original control strategy of the researched plug-in HEV is a typical charge deplet-ing(CD)and charge sustaining(CS)method.Itfirst operates the plug-in HEV with the CD mode,which is similar to a pure electric vehicle,and then operates the plug-in HEV with the CS mode once its state-of-charge(SoC)level hit the lower threshold,which is similar to a traditional hybrid electric vehicle.The detailed original control rules in the CS model are identified by the required power of the plug-in HEV-P n and they are described by the following conditions.Condition I:P n<0.The HESS absorbs as much energy as possible,and the excess energy is consumed by the traditional mechanical braking system. It is noted that the APU is turned off in condition I.Condition II:P n P75kW.The output power of APUÀP APU will maintain to its rated power (75kW),and the insufficient power will be supported by the HESS.Condition III:06P n675kWIf the SoC of the battery pack(z b)is bigger than its lower thresh-old(z b,min),the HESS will provide the total required power and the APU will be in the off stage.If the SoC of the battery pack(z b)is smaller than its lower threshold(z b,min),the APU will work in the rated power condi-tion and the redundant power will be used to charge the HESS to its predetermined level.S.Zhang,R.Xiong/Applied Energy155(2015)68–78692.2.2.Lower level control strategyThe power management between the battery pack and ultraca-pacitor pack is implemented via a rule based control strategy with the following rules[28].Condition I:P HESS<0When the required power of HESS(P HESS)is negative,the ultra-capacitor pack will absorb as much braking energy as possible until its SoC is bigger than its upper threshold(z uc,max),and then the bat-tery is allowed to absorb the remaining energy.Condition II:P HESS P0If06P HESS<30kW,z b>0.201and z uc<0.85,then P b=30kW and P uc=P HESSÀP b.If06P HESS<30kW,z b>0.201and z uc P0.85,then P b=P HESS and P uc=0.If P HESS P30kW,z b>0.201and z uc P0.515,then P b=30kW and P uc=P HESSÀP b.If P HPESS P30kW,z b>0.201and z uc<0.515,then P b=P HESS and P uc=0.If P HESS P0kW,z b60.201and z uc P0.515,then P b=0and P uc=P HESS.If P HESS P0kW,z b60.201and z uc<0.515,then P b=0and P uc=0.where z b denotes the SoC of the battery pack,z uc denotes the SoC of the ultracapacitor pack,P b denotes the output power of the battery pack,and P uc denotes the output power of the ultracapac-itor pack.3.Decomposition of the drive cycles3.1.Driving blocks classificationThe traditional DPR method based power management approaches tend to use the existing unbroken driving cycles to classify the driving blocks and then develop the control strategies by recognizing a whole driving cycle[29–32].However,for a given driving cycle,these methods usually contain several types of driv-ing blocks that have been neglected in these methods.Fig.2illus-trates that different types of drive patterns may have similar driving blocks and that the same driving cycle may have different driving blocks.Thus,the control strategy developed by a whole driving cycle can hardly ensure the optimal vehicle performance. To overcome the drawback,a novel classification method that has the ability to classify the driving block into several groups has been proposed.The driving blocks from three typical drive cycles are plotted in Fig.2,which includes the Chinese Bus Driving Cycle(CBDC), ECEÀEUDC drive cycle and MANHATTAN drive cycle.For a deter-minate driving cycle,the number of description parameters may be as high as62[32].Too many parameters may unnecessarily bias the calculation.The average speed has been reported as the unique parameter to use in Ref.[33].This study considers the average and maximum speed of each block as its classification parameters.The calculation method for the average speed and maximum speed is displayed below:V ai¼Zv dt=tð1ÞV max i¼maxðv j;j¼1;2...kÞð2Þwhere V ai denotes the average speed of each driving block,i denotes the index of the driving blocks,and V maxi denotes the maximum speed of each driving block.The fuzzy logic controller is employed to classify the drive blocks and identify the driving types for the DPR process.The con-troller consists of four parts(as displayed in Fig.3),including fuzzi-fication,rule base,fuzzy reasoning and defuzzification.The fuzzification part is used to fuzzify the input values and these fuzzified values will be converted into the output fuzzy values through the fuzzy reasoning block based on the rule base part. Then,the output fuzzy variable will be defuzzified by theTable1Basic parameters of the plug-in HEV.Name Value UnitVehicle loaded mass M16,500kgEfficiency of the transmission system g00.9/Rolling resistance coefficient f0.011/Windward area A ar 6.6m2Air resistance coefficient C ar0.55/Gravitational acceleration g9.81m/s2Correction coefficient of rotating mass d 1.03/70S.Zhang,R.Xiong/Applied Energy155(2015)68–78defuzzification block.The details of the fuzzy logic controller oper-ation process are displayed below:(1)FuzzificationFuzzy sets represent the linguistic terms,and the linguistic terms of the input variables and output variable are set to Low ÀLevel (low speed driving pattern),Middle ÀLevel (medium speed driving pat-tern)and High ÀLevel (high speed driving pattern).It is worth noting that,for the low speed driving pattern,the maximum velocity is less than 25km/h,and the average velocity less than 15km/h.For the medium speed driving pattern,the maximum velocity is between 25km/h and 45km/h,and the average velocity is between 15km/h and 25km/h.For the high speed driving pattern,the maxi-mum velocity is greater than 45km/h,and the average velocity is greater than 25km/h.The input variables are fuzzified by member-ship functions as shown in Fig.4.Once the average speed and max-imum speed of a driving block are known,we can determine the memberships (Lower Àlevel l L (v ),Middle Àlevel l M (v )and High Àlevel l H (v ),where v denotes the input variables)through membership functions.After comparing these values,we can finally obtain the fuzzification results,which are the fuzzy sets.Rule baseFuzzificationOutput driving patternDrive block12i 8128 V ...V ...V max(,...)mn mn mn imn mn R R R R R R R R R R =⎧⎨=⎩S.Zhang,R.Xiong /Applied Energy 155(2015)68–7871(2)Rule baseThe rule base displayed in Table 2shows that the fuzzy logic is a typical type of the A +B ?C (if A and B,then C)mode,where A denotes the fuzzy sets of average speed,B denotes the fuzzy sets of maximum speed and C denotes the fuzzy sets of driving block pattern.The reasoning process is based on the Mamdani fuzzy theory [34].From each rule shown in Table 2,we can obtain a correspond-ing fuzzy relation matrix R i by the cross-product of A i and B i .The fuzzy relation matrix R can be obtained by the combination of the fuzzy relation matrix R i using the following equation:R ¼R 1V R 2...V R i ...V R 8R mn ¼max ðR 1mn ;R 2mn ...R imn :R 8mn Þð3Þwhere m and n (m =1,2,3and n =1,2,3)denote the index of matrix elements for R i and R .(3)Fuzzy reasoningWhen we obtain the fuzzification results of the input variables (A 1and B 1)and the fuzzy relation matrix R ,we can locate the driv-ing types using the following equation:C 1¼ðA 1ÂB 1Þ Rð4Þwhere C 1denotes the fuzzy set for the output variable.(4)DefuzzificationThe results obtained by Eq.(4)from a fuzzy set,which is not applicable under real conditions.Thus,we should convert the fuzzy set into a known driving pattern.The biggest subordinate principle is employed to locate the driving block pattern.According to this principle,the driving block pattern is located to the value whose membership is the biggest one in the domain of discourse.Fig.5shows the classification results.From Fig.5we can observe that most of the driving blocks have similar profiles in the rearranged drive cycles.Three drive cycles will be used for the power manage-ment design during the following DP process.3.2.Modeling for subsystemsThe correspondingly optimal control strategy can be developed through analyzing the DP optimization results under different types of driving conditions.By combining these strategies together with the proposed DPR process,a suboptimal solution for the energy management of the plug-in HEV can be achieved.Before the implementation of the DP formulation process,we should first build the subsystem models.Considering that the DP process relies on the state equations of the plug-in HEV powertrain system,if the total order number of the system is too high,the computational cost is usually unacceptable.In this way,simplified backward sim-ulation models for the HESS,APU and vehicle are developed.It is noting that the parameters of the battery pack,ultracapacitor pack and DC/DC converter in the HESS were determined by the Ref.[9].The models are described as follows.(1)APU modelAs an independent power unit in the series powertrain,the engine and generator are mechanically decoupled from the drive-line.The APU operates according to the maximum efficiency line.The optimal fuel rate line of the APU is derived from the APU effi-ciency map from a combination of the efficiency map between the engine and generator.The optimal fuel rate line of the APU system is shown in Fig.6(a).In this study,we neglect the APU transience influence and calculate the fuel consumption of the engine by its static operating points.Once the output power of APU is deter-mined,we can obtain the fuel consumption using the following equation:_m f ¼f ðP APU Þð5Þwhere _mf denotes fuel consumption and f ðP APU Þdescribes the map-ping function between the output power of APU and engine fuel rate according to the combined optimal fuel rate line presented in Fig.6(a).Fig.6(b)shows the fuel consumption when the APU output is 1kJ of energy under different output power conditions.From Fig.6(b)we can observe that the efficiency of the APU will increase with increasing output power,and when the output power reaches 75kW,the efficiency is at its highest.Thus,as the rated power and maximum power of APU,75kW is the best operation point of the APU.(2)Ultracapacitor model The dynamic property of the ultracapacitor is recognized as a series connection of a resistance R c (0.0756X )and an idealTable 2Rule base for the driving pattern recognition.V a ÀLowV a ÀMiddle V a ÀHigh V max ÀLow Low Middle –V max ÀMiddle Low Middle High V max ÀHighMiddleMiddleHigh72S.Zhang,R.Xiong /Applied Energy 155(2015)68–78capacitor (maximum voltage is 576V).The operation behavior of the ultracapacitor is illustrated by the following equation:U ct ¼U co ÀR c i c ð6Þwhere U ct is terminal voltage of ultracapacitor,U co denotes the ideal capacitor voltage and i c denotes the output current.(3)Battery modelTo execute the optimization and analyze the dynamic features of the battery,we need a ‘‘discrete-time cell dynamic model’’that relates the SoC to its voltage.Based on our research experience in battery control and state estimation [2,35],a classical lumped parameters battery model,the Thevenin model,has been selected for this study.Its electrical behavior can be expressed as the following:_U D ¼À1D D U D þ1D i L U t ¼U oc ÀU D Ài L R i(ð7Þwhere U oc denotes the open circuit voltage (OCV)of a battery,R i denotes the series resistance,and R D and C D denote the diffusion resistance and diffusion capacitance,respectively.The parameter i L denotes the load current (positive indicates discharging and neg-ative indicates charging),U D denotes the diffusion voltage and U t denotes terminal voltage.It is worth noting that the lithium-ion battery cell with graphite anodes and nickel–manganese–cobalt oxide (NMC)cathodes is used in this study,and its upper and lower cutoff voltages are 4.2V and 3.0V,respectively.Each cell has a nominal capacity of 79A h and nominal voltage of 3.7V.It is noted that the battery pack consists of 135lithium-ion battery cells con-nected in series.Thus,the nominal capacity and voltage of the bat-tery pack are 79A h and 499.5V,respectively.The total energy of the battery pack is 39kW h according to the energy requirement of the target vehicle.The parameters of the battery model have been obtained by the parameter identification method based on the recursive least squares filter described in Ref.[35].(4)DC/DC converter modelTo analyze the dynamic behavior of the HESS,we first need a model for the DC/DC converter.Table 3shows an efficiency map n (i DC ,P DC )to describe the operating behavior of the DC/DC con-verter,where i DC and P DC denote the output current and output power of the DC/DC converter,respectively.The current points contain four levels:i DC =10A,i DC =50A,i DC =100A,and i DC P 150A.The power points contain five levels:P DC =10kW;P DC =20kW;P DC =30kW;P DC =40kW;and P DC P 50kW.The rated power of the DC/DC converter is 30kW.(5)Vehicle and transmission modelIn this study we only consider the vehicle’s longitudinal dynam-ics,namely,the vehicle is modeled as a point-mass.The required power (P n )of the plug-in HEV can be calculated from Eq.(8)[36].Its transmission is modeled as a fixed efficiency g as shown in Eq.(8).P n ¼u a g Mgf cos ðb Þ3600þMg sin ðb Þ3600þC ar A ar 76140u 2a þd M 3600d u ad tð8Þwhere u a denotes the vehicle velocity and b represents the grade of the road.3.3.Formulation of the DP algorithmBased on the above models,the DP algorithm can be used to locate the optimal power distribution ratio between the APU and HESS.According to Bellman’s optimization theory,a numerical-based DP approach is adopted and the state equation of the battery model and ultracapacitor model can be generally expressed by the following equation:x ðk þ1Þ¼f ðx ðk Þ;u ðk ÞÞð9Þwhere x (k )denotes the state vector of system and u (k )denotes con-trol variable (power increment P iAPU ).The parameter x (k )can be cal-culated by the following equation:x ðk Þ¼P APU z b z uc U D ½ ð10ÞIt is obvious that we can reduce the state order by removing the output power of the APU from the state equation and selecting the output power as the control variable,but this reduction will lead to drastic fluctuations in the output power of the APU from the lowest working point directly to the highest working point;it is not reasonable.To reduce the dimensions of the optimization problem and improve the control efficiency for the multi-power system of the plug-in HEV,the energy management of the HESS employs the rule-based strategy,which has been illustrated in Section 2.2;this strategy is helpful for reducing the order of the state equation and calculation burden.Then,the control variable has been simplified to the power increment of the APU (P iAPU )and once the output power of the APU is provided then the output power of HESS will be determined according to the required power of the plug-in HEV.The detailed state equations of the APU,battery and ultracapac-itor are described by the following equation:P APU ðk þ1Þ¼P APU ðk ÞþP iAPU ðk Þð11ÞU D ðk þ1Þz b ðk þ1Þ¼exp ðÀD t =R D C D Þ001U D ðk Þz b ðk Þþð1Àexp ðÀD t =R D C D ÞÞR DÀ1=3600Q bi L ðk Þð12Þz uc ðk þ1Þ¼z uc ðk ÞÂQ c Ài cQ cð13Þwhere Q b and Q c denote the nominal capacity of the battery packand ultracapacitor pack,respectively,i L and i C denote the output current of the battery pack and the ultracapacitor pack respectively,and D t denotes the calculation step,which herein is set to1s .The optimization target is to locate the optimal control variable P iAPU to minimize a cost function (minimum usage costs),as dis-played below:J ¼X N À1k ¼0L ðx ðk Þ;u ðk ÞÞ¼X N À1k ¼0½L fuel ðx ðk Þ;u ðk ÞÞþL e ðx ðk Þ;u ðk ÞÞ L fuel ðx ðk Þ;u ðk ÞÞ¼_m ðk ÞM fuel L e ðx ðk Þ;u ðk ÞÞ¼i L ðk ÞU t ðk ÞM e8>>>><>>>>:ð14Þwhere N denotes the duration of the driving cycle,L denotes the instantaneous cost which represents usage costs,L fuel and L e denote the fuel and electric cost,respectively,and M fuel and M e denote the current fuel and electric price,respectively.It is noted that,M fuel and M e are set to 8.9908RMB per liter and 0.799RMB per KWH,respectively (according to the data on Dec.62014,in Beijing).During the optimization process,the following inequalityTable 3Efficiency map of the DC/DC converter [9].n (i DC ,P DC )10kW 20kW 30kW 40kW P 50kW 10A 92%95%97%95%94%50A 91%93%96%93%92%100A 88%91%95%92%91%P 150A82%89%92%91%90%S.Zhang,R.Xiong /Applied Energy 155(2015)68–7873constraints are necessary to ensure safe and reasonable operation of the APU and HPS:z b ;min 6z b ðk Þ6z b ;max z uc ;min 6z uc ðk Þ6z uc ;max j zuc ;end Àz uc ;start j 60:5%i L ;min 6i L ðk Þ6i L ;max i c ;min 6i c ðk Þ6i c ;max U t ;min 6U t ðk Þ6U t ;max8>>>>>>>><>>>>>>>>:ð15Þwhere z b,min and z b,max denote the lower and upper bounds of thebattery SoC,U t,min and U t,max denote the lower and upper con-strains of the battery terminal voltage,i L,min and i L max denote the lower and upper constrains of the battery current,and i c,min and i c,max denote the bounds of the ultracapacitor current.The param-eters z uc,start and z uc,end denote the start and end values of the SoC of the ultracapacitor pack during the optimization process.It is worth noting that less than 0.5%of the difference between the start and end SoC of the ultracapacitor pack indicates the energy consump-tion of ultracapacitor pack can be approximated to zero.In this study,z b,min and z b,max are set to 0.2and 1,respectively,U t,min and U t,max are set to be 405V and 567V,respectively,i L,min and i L,max are set to À158A (À2C)and 158A (2C),respectively,and i c,min and i c,max are set to À500A and 500A,respectively.According to the dynamic optimization theory of Bellman,we first need to solve the problem from the last stage and then we extend the problem to solve for the last two stages and last three stages until all of the stages are included.Under this condition the optimization problem has been decomposed into a sequence of minimization problems as shown below.Step N À1:J ÃN À1ðx ðN À1ÞÞ¼min u ðN À1Þ½L ðx ðN À1Þ;u ðN À1ÞÞþG ðx ðN ÞÞð16ÞStep k ,for 06k <N À1:J Ãk ðx ðk ÞÞ¼min u ðk Þ½L ðx ðk Þ;u ðk ÞÞþJ Ãk þ1ðx ðk þ1ÞÞð17Þwhere J k *(x (k ))denotes the optimal value function at state x (k )start-ing from k th time stage to the last stage.The operation of the opti-mization process is subject to constraints presented in Eq.(15).Because the APU and HESS are nonlinear systems,the DP optimiza-tion process has been implemented with some approximations.Quantization and interpolation are used to solve Eq.(17)numeri-cally.At each step,the function J k (x (k ))is only calculated at the grid points of the state variables.The values of J k *(x (k ))in Eq.(17)and G (x (N ))in Eq.(16)will be determined through linear interpolation when the next state does not fall on a quantized value.3.4.Power management design frameworkThe detailed development process of the adaptive energy man-agement for the plug-in HEV with the DPR and DP is illustrated in Fig.7.It mainly consists of four parts:driving cycle classification,system modeling,DP processing and DPR process.The detailed operation for each part has been described in the above sections.First,three typical drive cycles are used to train the representa-tive driving blocks with the fuzzy logic algorithm based on the driving cycle classification method.The driving cycle classification process is used to rearrange the driving blocks into several types of new cycles.Its classification is implemented with the average speed and maximum speed.Driving pattern recognitionOriginal driving cycleReal driving cycle Classified driving blocksFuzzy logic based classification module Fuzzy logic based DPR74S.Zhang,R.Xiong /Applied Energy 155(2015)68–78。

美国政府《联邦自动驾驶汽车政策》解读与探讨

美国政府《联邦自动驾驶汽车政策》解读与探讨

25美国政府《联邦自动驾驶汽车政策》解读与探讨Interpreting and discussing for the Federal Automated Vehicles Policy摘要│自动驾驶汽车是当前最热门的技术,制定相应的发展政策也受到政府、企业、消费者等相关各方的关注。

本文介绍了美国政府发布的全球首个无人驾驶汽车政策文件——《联邦自动驾驶汽车政策》,其内容包括:政策目的、自动驾驶等级定义,政策组成包括了自动驾驶汽车性能指南、统一的州政策、现行国家公路交通安全管理局监管法规工具、将来可采用的新监管工具,以及政策下一步工作。

总结了政策成果,包括将安全监管作为核心,鼓励创新、建立规范的决策程序,以及政策困难。

认为美国的政策相关内容值得中国不断地跟踪分析。

关键词│美国、政策、法规、自动驾驶汽车、无人驾驶汽车文章编号│2096-255X(2018)01—0025—06中图分类号│T-013/017 文献标识码│AAbstract │The automated vehicles are the most popular technical field and the related development policies are concerned by governments, businesses, consumers and stakeholders widely. The paper introduced United States Government issued the world's first self-driving car policy——“Federal Automated Vehicles Policy”,including the policy objectives and Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. Components of the Policy include “Vehicle Performance Guidance for Automated Vehicles”, Model State Policy, Current Regulatory Tools and Modern Regulatory Tools, and next steps of the Policy. Summarizes the results of policy, including safety regulating as a core, prompting innovation, establishing the normal rule-making procedures and policy difficulties. The paper deems the content related to the Policy is worth continuing to track and analyze.Keywords │United States, policy, regulation, Automated Vehicle, State、self-driving car文/陈燕申1 陈思凯2 (1中国城市规划设计研究院 研究员,北京 100037; 2美国普渡大学(西拉法叶)工程学院博士研究生)Yanshen Chen Sikai Chen0 引言美国交通运输部(DOT)于2016年9月20日公布了全球首个无人驾驶汽车(self-driving cars)政策文件《联邦自动驾驶汽车政策》(Federal Automated Vehicles Policy,简称《政策》)[1]。

Petri网在制造系统建模与仿真中的应用_段波

Petri⽹在制造系统建模与仿真中的应⽤_段波Petr i⽹在制造系统建模与仿真中的应⽤3段波1,赵稳庄2,仉树军1(1总参陆航研究所,北京101121;2中国科学院西安光学精密机械研究所,西安710072)摘要:介绍制造系统的概念以及制造系统建模与仿真的⽬的和意义,阐述Petri⽹的内涵,同时引出⾯向对象的Petri⽹的定义。

依据⾯向对象的Petri⽹的理论,给出制造系统的建模过程,最后将统⼀建模语⾔(UML)融⼊到制造系统的建模与仿真中,通过实例进⼀步阐明了Petri⽹在制造系统建模与仿真中的应⽤。

关键词:制造系统;建模与仿真;Petri⽹;⾯向对象中图分类号:TP39117 ⽂献标识码:A ⽂章编号:1671—3133(2008)08—0024—04Petr i nets for m odeli n g of manufactur i n g system sDuan Bo1,Zhao W en2zhuang2,Zhang Shu2jun1(1A r my Aviati on Research I nstitute,Beijing101121,CHN;2Xiπan I nstitute of Op tics and Precisi on Mechanics of CAS,Xiπan710072,CHN)Abstract:I ntr oduced the concep t of manufacturing syste m,and pur pose and meaning of modeling and si mulating.Expounded the connotati on of Petri nets,educing definiti on of object2oriented Petri nets.Recommended the si m ulating course of manufacturing syste m according as the theoretics of object2oriented Petri nets.Illu m inated Petri nets f or modeling of manufacturing syste m s mak2 ing use of instance.Key words:M anufacturing syste m;Modeling and si m ulating;Petri nets;Object2O riented(O2O)0 引⾔制造是⼀种产⽣具有出售优势、由实体和服务所构成的产品的⾏为。

基于虚拟试验场的牵引车动态载荷研究

2024年第1期27doi:10.3969/j.issn.1005-2550.2024.01.005 收稿日期:2023-10-27基于虚拟试验场的牵引车动态载荷研究王庆华1,王丽荣2,陈小华2,李蒙然1,黄刚1(1.国家汽车质量检验检测中心(襄阳),襄阳441004;2. 北京福田戴姆勒汽车有限公司,北京 101400)摘 要:基于Adams软件的虚拟试验场动态载荷分解技术在乘用车耐久性能开发领域广泛应用。

对于重卡车型,由于车辆模型复杂、参数有限且测试难度大,虚拟试验场技术的应用推广受到限制。

搭建某牵引车整车多体动力学模型及虚拟试验场仿真环境,同时采集试验场工况下的实车载荷谱数据并与虚拟试验场动力学仿真分析提取的动态载荷进行对比。

使用相对伪损伤比值、频谱分析等评估比利时、扭曲路、搓板路等典型路面工况下仿真与实测载荷谱数据的差异。

结果表明:基于虚拟试验场的动态载荷提取技术可应用于牵引车车型且可实现较高的精度,是一种获取试验场耐久工况载荷谱的有效方法。

关键词:虚拟试验场;载荷分解;路面模型;牵引车中图分类号:U467 文献标识码:A 文章编号:1005-2550(2024)01-0027-07Research on Dynamic Load of Tractor Based on VPGWANG Qing-hua1, WANG Li-rong2, CHEN Xiao-hua2, LI Meng-ran1, HUANG Gang1(1.National Automobile Quality Inspection and T est Center (Xiangyang), Xiangyang 441004,China; 2. Beijing Foton Daimler Automobile Co., Ltd, Beijing 101400, China)Abstract: The dynamic load decomposition technology of VPG based on Adams is widely applied in the field of passenger car durability performance development. For heavytruck, the application and promotion of VPG are limited due to the complexity of vehiclemodels, limited parameters, and high RLDA testing difficulty. The complete vehicle multi-body dynamics model of a tractor and virtual proving ground simulation environment arebuilt based on Adams. The real vehicle load data acquisition of the proving ground eventswas carried out and compared with the dynamic loads extracted from dynamic simulationanalysis of the virtual proving ground to verify the model accuracy and load accuracy.Relative pseudo damage ratio, RMS value ratio, and spectrum analysis were used to evaluatethe differences between simulated and measured load data under typical road conditionssuch as Belgium, twisted roads, and washboard roads. It is proved that The dynamic loadextraction technology based on virtual proving ground can be applied to tractor models andachieve high accuracy, which is an effective method for obtaining the load data of provingground durability events.Key Words: Virtual Proving Ground; Load Extraction; Road Model; Tractor随着高精度路面扫描和轮胎力学模型建模等技术快速发展,基于虚拟试验场(V i r t u a l Proving Ground)的动态载荷提取技术在车型开发早期阶段即可开展,可有效缩短开发周期和试验成本[1-4]。

交通流

Network impacts of a road capacity reduction:Empirical analysisand model predictionsDavid Watling a ,⇑,David Milne a ,Stephen Clark baInstitute for Transport Studies,University of Leeds,Woodhouse Lane,Leeds LS29JT,UK b Leeds City Council,Leonardo Building,2Rossington Street,Leeds LS28HD,UKa r t i c l e i n f o Article history:Received 24May 2010Received in revised form 15July 2011Accepted 7September 2011Keywords:Traffic assignment Network models Equilibrium Route choice Day-to-day variabilitya b s t r a c tIn spite of their widespread use in policy design and evaluation,relatively little evidencehas been reported on how well traffic equilibrium models predict real network impacts.Here we present what we believe to be the first paper that together analyses the explicitimpacts on observed route choice of an actual network intervention and compares thiswith the before-and-after predictions of a network equilibrium model.The analysis isbased on the findings of an empirical study of the travel time and route choice impactsof a road capacity reduction.Time-stamped,partial licence plates were recorded across aseries of locations,over a period of days both with and without the capacity reduction,and the data were ‘matched’between locations using special-purpose statistical methods.Hypothesis tests were used to identify statistically significant changes in travel times androute choice,between the periods of days with and without the capacity reduction.A trafficnetwork equilibrium model was then independently applied to the same scenarios,and itspredictions compared with the empirical findings.From a comparison of route choice pat-terns,a particularly influential spatial effect was revealed of the parameter specifying therelative values of distance and travel time assumed in the generalised cost equations.When this parameter was ‘fitted’to the data without the capacity reduction,the networkmodel broadly predicted the route choice impacts of the capacity reduction,but with othervalues it was seen to perform poorly.The paper concludes by discussing the wider practicaland research implications of the study’s findings.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionIt is well known that altering the localised characteristics of a road network,such as a planned change in road capacity,will tend to have both direct and indirect effects.The direct effects are imparted on the road itself,in terms of how it can deal with a given demand flow entering the link,with an impact on travel times to traverse the link at a given demand flow level.The indirect effects arise due to drivers changing their travel decisions,such as choice of route,in response to the altered travel times.There are many practical circumstances in which it is desirable to forecast these direct and indirect impacts in the context of a systematic change in road capacity.For example,in the case of proposed road widening or junction improvements,there is typically a need to justify econom-ically the required investment in terms of the benefits that will likely accrue.There are also several examples in which it is relevant to examine the impacts of road capacity reduction .For example,if one proposes to reallocate road space between alternative modes,such as increased bus and cycle lane provision or a pedestrianisation scheme,then typically a range of alternative designs exist which may differ in their ability to accommodate efficiently the new traffic and routing patterns.0965-8564/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.tra.2011.09.010⇑Corresponding author.Tel.:+441133436612;fax:+441133435334.E-mail address:d.p.watling@ (D.Watling).168 D.Watling et al./Transportation Research Part A46(2012)167–189Through mathematical modelling,the alternative designs may be tested in a simulated environment and the most efficient selected for implementation.Even after a particular design is selected,mathematical models may be used to adjust signal timings to optimise the use of the transport system.Road capacity may also be affected periodically by maintenance to essential services(e.g.water,electricity)or to the road itself,and often this can lead to restricted access over a period of days and weeks.In such cases,planning authorities may use modelling to devise suitable diversionary advice for drivers,and to plan any temporary changes to traffic signals or priorities.Berdica(2002)and Taylor et al.(2006)suggest more of a pro-ac-tive approach,proposing that models should be used to test networks for potential vulnerability,before any reduction mate-rialises,identifying links which if reduced in capacity over an extended period1would have a substantial impact on system performance.There are therefore practical requirements for a suitable network model of travel time and route choice impacts of capac-ity changes.The dominant method that has emerged for this purpose over the last decades is clearly the network equilibrium approach,as proposed by Beckmann et al.(1956)and developed in several directions since.The basis of using this approach is the proposition of what are believed to be‘rational’models of behaviour and other system components(e.g.link perfor-mance functions),with site-specific data used to tailor such models to particular case studies.Cross-sectional forecasts of network performance at specific road capacity states may then be made,such that at the time of any‘snapshot’forecast, drivers’route choices are in some kind of individually-optimum state.In this state,drivers cannot improve their route selec-tion by a unilateral change of route,at the snapshot travel time levels.The accepted practice is to‘validate’such models on a case-by-case basis,by ensuring that the model—when supplied with a particular set of parameters,input network data and input origin–destination demand data—reproduces current mea-sured mean link trafficflows and mean journey times,on a sample of links,to some degree of accuracy(see for example,the practical guidelines in TMIP(1997)and Highways Agency(2002)).This kind of aggregate level,cross-sectional validation to existing conditions persists across a range of network modelling paradigms,ranging from static and dynamic equilibrium (Florian and Nguyen,1976;Leonard and Tough,1979;Stephenson and Teply,1984;Matzoros et al.,1987;Janson et al., 1986;Janson,1991)to micro-simulation approaches(Laird et al.,1999;Ben-Akiva et al.,2000;Keenan,2005).While such an approach is plausible,it leaves many questions unanswered,and we would particularly highlight two: 1.The process of calibration and validation of a network equilibrium model may typically occur in a cycle.That is to say,having initially calibrated a model using the base data sources,if the subsequent validation reveals substantial discrep-ancies in some part of the network,it is then natural to adjust the model parameters(including perhaps even the OD matrix elements)until the model outputs better reflect the validation data.2In this process,then,we allow the adjustment of potentially a large number of network parameters and input data in order to replicate the validation data,yet these data themselves are highly aggregate,existing only at the link level.To be clear here,we are talking about a level of coarseness even greater than that in aggregate choice models,since we cannot even infer from link-level data the aggregate shares on alternative routes or OD movements.The question that arises is then:how many different combinations of parameters and input data values might lead to a similar link-level validation,and even if we knew the answer to this question,how might we choose between these alternative combinations?In practice,this issue is typically neglected,meaning that the‘valida-tion’is a rather weak test of the model.2.Since the data are cross-sectional in time(i.e.the aim is to reproduce current base conditions in equilibrium),then in spiteof the large efforts required in data collection,no empirical evidence is routinely collected regarding the model’s main purpose,namely its ability to predict changes in behaviour and network performance under changes to the network/ demand.This issue is exacerbated by the aggregation concerns in point1:the‘ambiguity’in choosing appropriate param-eter values to satisfy the aggregate,link-level,base validation strengthens the need to independently verify that,with the selected parameter values,the model responds reliably to changes.Although such problems–offitting equilibrium models to cross-sectional data–have long been recognised by practitioners and academics(see,e.g.,Goodwin,1998), the approach described above remains the state-of-practice.Having identified these two problems,how might we go about addressing them?One approach to thefirst problem would be to return to the underlying formulation of the network model,and instead require a model definition that permits analysis by statistical inference techniques(see for example,Nakayama et al.,2009).In this way,we may potentially exploit more information in the variability of the link-level data,with well-defined notions(such as maximum likelihood)allowing a systematic basis for selection between alternative parameter value combinations.However,this approach is still using rather limited data and it is natural not just to question the model but also the data that we use to calibrate and validate it.Yet this is not altogether straightforward to resolve.As Mahmassani and Jou(2000) remarked:‘A major difficulty...is obtaining observations of actual trip-maker behaviour,at the desired level of richness, simultaneously with measurements of prevailing conditions’.For this reason,several authors have turned to simulated gaming environments and/or stated preference techniques to elicit information on drivers’route choice behaviour(e.g. 1Clearly,more sporadic and less predictable reductions in capacity may also occur,such as in the case of breakdowns and accidents,and environmental factors such as severe weather,floods or landslides(see for example,Iida,1999),but the responses to such cases are outside the scope of the present paper. 2Some authors have suggested more systematic,bi-level type optimization processes for thisfitting process(e.g.Xu et al.,2004),but this has no material effect on the essential points above.D.Watling et al./Transportation Research Part A46(2012)167–189169 Mahmassani and Herman,1990;Iida et al.,1992;Khattak et al.,1993;Vaughn et al.,1995;Wardman et al.,1997;Jou,2001; Chen et al.,2001).This provides potentially rich information for calibrating complex behavioural models,but has the obvious limitation that it is based on imagined rather than real route choice situations.Aside from its common focus on hypothetical decision situations,this latter body of work also signifies a subtle change of emphasis in the treatment of the overall network calibration problem.Rather than viewing the network equilibrium calibra-tion process as a whole,the focus is on particular components of the model;in the cases above,the focus is on that compo-nent concerned with how drivers make route decisions.If we are prepared to make such a component-wise analysis,then certainly there exists abundant empirical evidence in the literature,with a history across a number of decades of research into issues such as the factors affecting drivers’route choice(e.g.Wachs,1967;Huchingson et al.,1977;Abu-Eisheh and Mannering,1987;Duffell and Kalombaris,1988;Antonisse et al.,1989;Bekhor et al.,2002;Liu et al.,2004),the nature of travel time variability(e.g.Smeed and Jeffcoate,1971;Montgomery and May,1987;May et al.,1989;McLeod et al., 1993),and the factors affecting trafficflow variability(Bonsall et al.,1984;Huff and Hanson,1986;Ribeiro,1994;Rakha and Van Aerde,1995;Fox et al.,1998).While these works provide useful evidence for the network equilibrium calibration problem,they do not provide a frame-work in which we can judge the overall‘fit’of a particular network model in the light of uncertainty,ambient variation and systematic changes in network attributes,be they related to the OD demand,the route choice process,travel times or the network data.Moreover,such data does nothing to address the second point made above,namely the question of how to validate the model forecasts under systematic changes to its inputs.The studies of Mannering et al.(1994)and Emmerink et al.(1996)are distinctive in this context in that they address some of the empirical concerns expressed in the context of travel information impacts,but their work stops at the stage of the empirical analysis,without a link being made to net-work prediction models.The focus of the present paper therefore is both to present thefindings of an empirical study and to link this empirical evidence to network forecasting models.More recently,Zhu et al.(2010)analysed several sources of data for evidence of the traffic and behavioural impacts of the I-35W bridge collapse in Minneapolis.Most pertinent to the present paper is their location-specific analysis of linkflows at 24locations;by computing the root mean square difference inflows between successive weeks,and comparing the trend for 2006with that for2007(the latter with the bridge collapse),they observed an apparent transient impact of the bridge col-lapse.They also showed there was no statistically-significant evidence of a difference in the pattern offlows in the period September–November2007(a period starting6weeks after the bridge collapse),when compared with the corresponding period in2006.They suggested that this was indicative of the length of a‘re-equilibration process’in a conceptual sense, though did not explicitly compare their empiricalfindings with those of a network equilibrium model.The structure of the remainder of the paper is as follows.In Section2we describe the process of selecting the real-life problem to analyse,together with the details and rationale behind the survey design.Following this,Section3describes the statistical techniques used to extract information on travel times and routing patterns from the survey data.Statistical inference is then considered in Section4,with the aim of detecting statistically significant explanatory factors.In Section5 comparisons are made between the observed network data and those predicted by a network equilibrium model.Finally,in Section6the conclusions of the study are highlighted,and recommendations made for both practice and future research.2.Experimental designThe ultimate objective of the study was to compare actual data with the output of a traffic network equilibrium model, specifically in terms of how well the equilibrium model was able to correctly forecast the impact of a systematic change ap-plied to the network.While a wealth of surveillance data on linkflows and travel times is routinely collected by many local and national agencies,we did not believe that such data would be sufficiently informative for our purposes.The reason is that while such data can often be disaggregated down to small time step resolutions,the data remains aggregate in terms of what it informs about driver response,since it does not provide the opportunity to explicitly trace vehicles(even in aggre-gate form)across more than one location.This has the effect that observed differences in linkflows might be attributed to many potential causes:it is especially difficult to separate out,say,ambient daily variation in the trip demand matrix from systematic changes in route choice,since both may give rise to similar impacts on observed linkflow patterns across re-corded sites.While methods do exist for reconstructing OD and network route patterns from observed link data(e.g.Yang et al.,1994),these are typically based on the premise of a valid network equilibrium model:in this case then,the data would not be able to give independent information on the validity of the network equilibrium approach.For these reasons it was decided to design and implement a purpose-built survey.However,it would not be efficient to extensively monitor a network in order to wait for something to happen,and therefore we required advance notification of some planned intervention.For this reason we chose to study the impact of urban maintenance work affecting the roads,which UK local government authorities organise on an annual basis as part of their‘Local Transport Plan’.The city council of York,a historic city in the north of England,agreed to inform us of their plans and to assist in the subsequent data collection exercise.Based on the interventions planned by York CC,the list of candidate studies was narrowed by considering factors such as its propensity to induce significant re-routing and its impact on the peak periods.Effectively the motivation here was to identify interventions that were likely to have a large impact on delays,since route choice impacts would then likely be more significant and more easily distinguished from ambient variability.This was notably at odds with the objectives of York CC,170 D.Watling et al./Transportation Research Part A46(2012)167–189in that they wished to minimise disruption,and so where possible York CC planned interventions to take place at times of day and of the year where impacts were minimised;therefore our own requirement greatly reduced the candidate set of studies to monitor.A further consideration in study selection was its timing in the year for scheduling before/after surveys so to avoid confounding effects of known significant‘seasonal’demand changes,e.g.the impact of the change between school semesters and holidays.A further consideration was York’s role as a major tourist attraction,which is also known to have a seasonal trend.However,the impact on car traffic is relatively small due to the strong promotion of public trans-port and restrictions on car travel and parking in the historic centre.We felt that we further mitigated such impacts by sub-sequently choosing to survey in the morning peak,at a time before most tourist attractions are open.Aside from the question of which intervention to survey was the issue of what data to collect.Within the resources of the project,we considered several options.We rejected stated preference survey methods as,although they provide a link to personal/socio-economic drivers,we wanted to compare actual behaviour with a network model;if the stated preference data conflicted with the network model,it would not be clear which we should question most.For revealed preference data, options considered included(i)self-completion diaries(Mahmassani and Jou,2000),(ii)automatic tracking through GPS(Jan et al.,2000;Quiroga et al.,2000;Taylor et al.,2000),and(iii)licence plate surveys(Schaefer,1988).Regarding self-comple-tion surveys,from our own interview experiments with self-completion questionnaires it was evident that travellersfind it relatively difficult to recall and describe complex choice options such as a route through an urban network,giving the po-tential for significant errors to be introduced.The automatic tracking option was believed to be the most attractive in this respect,in its potential to accurately map a given individual’s journey,but the negative side would be the potential sample size,as we would need to purchase/hire and distribute the devices;even with a large budget,it is not straightforward to identify in advance the target users,nor to guarantee their cooperation.Licence plate surveys,it was believed,offered the potential for compromise between sample size and data resolution: while we could not track routes to the same resolution as GPS,by judicious location of surveyors we had the opportunity to track vehicles across more than one location,thus providing route-like information.With time-stamped licence plates, the matched data would also provide journey time information.The negative side of this approach is the well-known poten-tial for significant recording errors if large sample rates are required.Our aim was to avoid this by recording only partial licence plates,and employing statistical methods to remove the impact of‘spurious matches’,i.e.where two different vehi-cles with the same partial licence plate occur at different locations.Moreover,extensive simulation experiments(Watling,1994)had previously shown that these latter statistical methods were effective in recovering the underlying movements and travel times,even if only a relatively small part of the licence plate were recorded,in spite of giving a large potential for spurious matching.We believed that such an approach reduced the opportunity for recorder error to such a level to suggest that a100%sample rate of vehicles passing may be feasible.This was tested in a pilot study conducted by the project team,with dictaphones used to record a100%sample of time-stamped, partial licence plates.Independent,duplicate observers were employed at the same location to compare error rates;the same study was also conducted with full licence plates.The study indicated that100%surveys with dictaphones would be feasible in moderate trafficflow,but only if partial licence plate data were used in order to control observation errors; for higherflow rates or to obtain full number plate data,video surveys should be considered.Other important practical les-sons learned from the pilot included the need for clarity in terms of vehicle types to survey(e.g.whether to include motor-cycles and taxis),and of the phonetic alphabet used by surveyors to avoid transcription ambiguities.Based on the twin considerations above of planned interventions and survey approach,several candidate studies were identified.For a candidate study,detailed design issues involved identifying:likely affected movements and alternative routes(using local knowledge of York CC,together with an existing network model of the city),in order to determine the number and location of survey sites;feasible viewpoints,based on site visits;the timing of surveys,e.g.visibility issues in the dark,winter evening peak period;the peak duration from automatic trafficflow data;and specific survey days,in view of public/school holidays.Our budget led us to survey the majority of licence plate sites manually(partial plates by audio-tape or,in lowflows,pen and paper),with video surveys limited to a small number of high-flow sites.From this combination of techniques,100%sampling rate was feasible at each site.Surveys took place in the morning peak due both to visibility considerations and to minimise conflicts with tourist/special event traffic.From automatic traffic count data it was decided to survey the period7:45–9:15as the main morning peak period.This design process led to the identification of two studies:2.1.Lendal Bridge study(Fig.1)Lendal Bridge,a critical part of York’s inner ring road,was scheduled to be closed for maintenance from September2000 for a duration of several weeks.To avoid school holidays,the‘before’surveys were scheduled for June and early September.It was decided to focus on investigating a significant southwest-to-northeast movement of traffic,the river providing a natural barrier which suggested surveying the six river crossing points(C,J,H,K,L,M in Fig.1).In total,13locations were identified for survey,in an attempt to capture traffic on both sides of the river as well as a crossing.2.2.Fishergate study(Fig.2)The partial closure(capacity reduction)of the street known as Fishergate,again part of York’s inner ring road,was scheduled for July2001to allow repairs to a collapsed sewer.Survey locations were chosen in order to intercept clockwiseFig.1.Intervention and survey locations for Lendal Bridge study.around the inner ring road,this being the direction of the partial closure.A particular aim wasFulford Road(site E in Fig.2),the main radial affected,with F and K monitoring local diversion I,J to capture wider-area diversion.studies,the plan was to survey the selected locations in the morning peak over a period of approximately covering the three periods before,during and after the intervention,with the days selected so holidays or special events.Fig.2.Intervention and survey locations for Fishergate study.In the Lendal Bridge study,while the‘before’surveys proceeded as planned,the bridge’s actualfirst day of closure on Sep-tember11th2000also marked the beginning of the UK fuel protests(BBC,2000a;Lyons and Chaterjee,2002).Trafficflows were considerably affected by the scarcity of fuel,with congestion extremely low in thefirst week of closure,to the extent that any changes could not be attributed to the bridge closure;neither had our design anticipated how to survey the impacts of the fuel shortages.We thus re-arranged our surveys to monitor more closely the planned re-opening of the bridge.Unfor-tunately these surveys were hampered by a second unanticipated event,namely the wettest autumn in the UK for270years and the highest level offlooding in York since records began(BBC,2000b).Theflooding closed much of the centre of York to road traffic,including our study area,as the roads were impassable,and therefore we abandoned the planned‘after’surveys. As a result of these events,the useable data we had(not affected by the fuel protests orflooding)consisted offive‘before’days and one‘during’day.In the Fishergate study,fortunately no extreme events occurred,allowing six‘before’and seven‘during’days to be sur-veyed,together with one additional day in the‘during’period when the works were temporarily removed.However,the works over-ran into the long summer school holidays,when it is well-known that there is a substantial seasonal effect of much lowerflows and congestion levels.We did not believe it possible to meaningfully isolate the impact of the link fully re-opening while controlling for such an effect,and so our plans for‘after re-opening’surveys were abandoned.3.Estimation of vehicle movements and travel timesThe data resulting from the surveys described in Section2is in the form of(for each day and each study)a set of time-stamped,partial licence plates,observed at a number of locations across the network.Since the data include only partial plates,they cannot simply be matched across observation points to yield reliable estimates of vehicle movements,since there is ambiguity in whether the same partial plate observed at different locations was truly caused by the same vehicle. Indeed,since the observed system is‘open’—in the sense that not all points of entry,exit,generation and attraction are mon-itored—the question is not just which of several potential matches to accept,but also whether there is any match at all.That is to say,an apparent match between data at two observation points could be caused by two separate vehicles that passed no other observation point.Thefirst stage of analysis therefore applied a series of specially-designed statistical techniques to reconstruct the vehicle movements and point-to-point travel time distributions from the observed data,allowing for all such ambiguities in the data.Although the detailed derivations of each method are not given here,since they may be found in the references provided,it is necessary to understand some of the characteristics of each method in order to interpret the results subsequently provided.Furthermore,since some of the basic techniques required modification relative to the published descriptions,then in order to explain these adaptations it is necessary to understand some of the theoretical basis.3.1.Graphical method for estimating point-to-point travel time distributionsThe preliminary technique applied to each data set was the graphical method described in Watling and Maher(1988).This method is derived for analysing partial registration plate data for unidirectional movement between a pair of observation stations(referred to as an‘origin’and a‘destination’).Thus in the data study here,it must be independently applied to given pairs of observation stations,without regard for the interdependencies between observation station pairs.On the other hand, it makes no assumption that the system is‘closed’;there may be vehicles that pass the origin that do not pass the destina-tion,and vice versa.While limited in considering only two-point surveys,the attraction of the graphical technique is that it is a non-parametric method,with no assumptions made about the arrival time distributions at the observation points(they may be non-uniform in particular),and no assumptions made about the journey time probability density.It is therefore very suitable as afirst means of investigative analysis for such data.The method begins by forming all pairs of possible matches in the data,of which some will be genuine matches(the pair of observations were due to a single vehicle)and the remainder spurious matches.Thus, for example,if there are three origin observations and two destination observations of a particular partial registration num-ber,then six possible matches may be formed,of which clearly no more than two can be genuine(and possibly only one or zero are genuine).A scatter plot may then be drawn for each possible match of the observation time at the origin versus that at the destination.The characteristic pattern of such a plot is as that shown in Fig.4a,with a dense‘line’of points(which will primarily be the genuine matches)superimposed upon a scatter of points over the whole region(which will primarily be the spurious matches).If we were to assume uniform arrival rates at the observation stations,then the spurious matches would be uniformly distributed over this plot;however,we shall avoid making such a restrictive assumption.The method begins by making a coarse estimate of the total number of genuine matches across the whole of this plot.As part of this analysis we then assume knowledge of,for any randomly selected vehicle,the probabilities:h k¼Prðvehicle is of the k th type of partial registration plateÞðk¼1;2;...;mÞwhereX m k¼1h k¼1172 D.Watling et al./Transportation Research Part A46(2012)167–189。

汽车试验场数字化综合管理平台应用

10AUTO TIMEFRONTIER DISCUSSION | 前沿探讨1 前言随着社会的进步,我国汽车工业飞速发展,汽车试验场作为汽车行业的伴生行业,近年来也在不断发展壮大,除研发机构外,各大型整车企业、轮胎及零部件企业,纷纷建设自己的试验场,既保证企业研发试验需求,同时对外经营。

随着汽车行业“四化”的要求,对汽车试验场的信息化、智能化也提出了更高的要求。

通过结合5G 、高精定位等先进技术,利用数字化手段,搭建汽车试验场运营管理平台,全面提升试验场运营及试验过程管理能力,提高汽车企业车辆产品的研发效率。

2 智能化汽车试验场要求■ 建立试验场与企业其他数字化平台的集成,在整个试制试验技术、业务的要求下进行试验场数字化的建设。

■ 移动端与电脑端相结合的方式,自助查询场地信息、试验预约记录、服务记录和驾驶资质等。

■ 动态的场地管理,动态更新场地占用情况,自动排程,辅助调度,计划变更提醒,违约识别,风险预警等。

■ 统一的数据管理,统一的场地试验数据调度管理,统一的设备运行状态管理,完善的统计分析功能等。

■ 试验场道闸远程控制,道闸自动授权,道闸容量和通行记录的综合管理,道闸与LED 屏显联动等。

■ 试验场运行车辆精准监控,3D 高精度地图上的车辆实时定位监控。

3 汽车试验场综合管理体系为了保障试验场运营的安全与高效,满足试验场智能化的要求,需要建立一套完整的汽车试验场运营管理体系,集运营管理、试验管理、综合服务管理三个维度于一体,实现包括场地运营管理、试验服务管理、经营管理、场地预约管理、试验数据管理、安全管理、人员管理、综合保障等八部分内容的管理,完成汽车试验场数字化综合管理。

4 汽车试验场综合管理平台方案平台架构如图,方案感知通过层设备网联形式,数据层通过对接、采集等形式形成数据中台提供服务,展示层结合PC 端、移动端APP 、大屏不同展现方式,实现试验场数字化综合管控。

汽车试验场数字化综合管理平台应用徐昊 刘芳 贺怡中汽数据(天津)有限公司 天津市 300300摘 要: 随着汽车行业“四化”的要求,各个整车企业对研发试制领域的管理要求越来越高,因此加快试制试验环节的数字化建设成为汽车行业发展趋势。

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AN EXACT ALGORITHM FOR PERIOD AND MULTI-DEPOTVEHICLE ROUTING PROBLEMSAristide Mingozzi and Andrea VallettaDepartment of Mathematics, University of Bologna, Bologna, ItalyAbstractThis paper presents an exact method for two important generalizations of the classical Capacitated Vehicle Routing problem (CVRP): the Period Vehicle Routing Problem (PVRP) and the Multi-Depot Vehicle Routing Problem (MDVRP).The VRP is defined on a graph G=(V, A), where V={0,1,…,n} is the vertex set and A={(i,j):i,j U V, i≠j} is the arc set. The distinguished vertex 0 represents the depot where m identical vehicles of capacity Q are located. Each vertex i∈V\{0} corresponds to a customer and has a non negative demand q i. With each a rc (i,j) ∈A is associated a cost d ij. The CVRP consists of designing one route for each vehicle such that: (i) each route starts and ends at the depot; (ii) each customer is visited exactly once; (ii) the total demand of the customers visited by a route does not exceed Q and (iv) the total route cost is minimized.The PVRP consists of designing a set of routes for each day of a given planning period of p days. Each customer may require k (say) visits during this period and these visits may only occur in one of a given number of allowable k-day combinations. For example, a customer may require to be visited twice during a 5-day period imposing that these visits take place on Monday-Thursday or Monday-Friday or Tuesday-Friday. The PVRP consists of simultaneously selecting a day-combination for each customer and VRP designing the vehicle routes by solving a VRP for each day of the planning period so that each customer is visited the required number of times, all constraints are satisfied and the sum of the route costs is minimized. The PVRP has many practical applications in the areas of grocery distribution (Carter et al (1996), Golden and Wasil(1987)) and refuse collection (Beltrami and Bodin(1974), Russel and Igo(1979).All papers on the PVRP reported in the literature present heuristic methods. Early heuristic algorithms were proposed by Beltrami and Bodin (1974) and Russel and Igo (1979). More sophisticated heuristics were presented by Christofides and Beasley (1984), Tan and Beasley (1984), Russel and Gribbin (1991), Gaudioso and Paletta (1992), Chao, Golden and Wasil (1995) and Cordeau, Gendreau and Laporte (1997). The tabu search proposed by Cordeau, Gendreau and Laporte (1997) is the best heuristic currently published as the computational results on test problems from the literature show that this method outperforms all other heuristics.At our knowledge, no exact method was proposed in the literature to solve the PVRP.The MDVRP is defined on a single day as the VRP but the vehicles operate from different depots and each route must start and end at the same depot.Laporte et al (1984) and Laporte et al.(19888) describe two exact branch and bound algorithms for solving the symmetric and asymmetric version of the MDVRP, respectively. Heuristic algorithms which extend standard CVRP procedures were presented by Tillman and Hering (1971), Tillman and Cain(1972), Wren and Holliday(1972), Gillet and Johnson(1976), Raft (1982). Better heuristic methods were proposed by Chao, Golden and Wasil (1993) and Renaud, Laporte and Boctor (1996).The MDVRP can be viewed as a special case of the PVRP where each day of the planning period corresponds to a different depot and each customer requires to be visited once in one of the days of the period.In this paper we describe an integer programming formulation and an exact method for solving both the PVRP and the MDVRP. The exact method involves the computation of a valid lower bound by means of an additive procedure which combines different relaxations of the integer formulation to derive an effective feasible solution of the dual of the LP-relaxation of the integer programm. The dual solution is used to generate a reduced integer program which can be solved to optimality by an integer programming solver as CPLEX.The computational results on both PVRP and MDVRP test problems from the literature show the effectiveness of the proposed methods.ReferencesE. J. Beltrami and L. D. Bodin, (1974). Networks and vehicle routing for municipal waste collection, Networks4,65-94.M. W. Carter, J. M. Farolden, G. Laporte and J. Xu, (1996). Solving an integrated logistics problem arising in grocery distribution. INFOR34, 290-306.I. M. Chao, B. L. Golden and E. A. Wasil, (1993). A new heuristic for the multi-depot vehicle routing problem that improves upon best-known results. Am .J. Math. Mgmt. Sci. 13, 371-406.I. M. Chao, B. L. Golden and E. A. Wasil, (1995). An improved heuristic for the period vehicle routing problem. Networks26, 22-44.N. Christofides and J. E. Beasley, (1984). The period routing problem, Networks 14, 237-256.J. F. Cordeau, M. Gendreau and G. Laporte, (1997). A tabu search heuristic for periodic and multi-depot vehicle routing problems, Networks30, 105-119.M. Gaudioso and G. Paletta, (1992). A heuristic for the periodic vehicle routing problem. Trans. Sci.26, 86-92.B. E. Gillet and J. G. Johnson, (1976). Multi-terminal vehicle-dispatch algorithm. Omega4, 711-718.G. Laporte, Y. Nobert and D. Arpin, (1984). Optimal solutions to capacitated multi-depot vehicle routing problem. Congress. Num.44, 283-292.G. Laporte, Y. Nobert and S. Taillefer, (1988). Solving a family of multi-depot vehicle routing and location-routing problems. Trans. Sci.22, 161-172.O. M. Raft, (1982). A modular algorithm for an extended vehicle scheduling problem. Eur. J. Oper. Res.11, 67-76.J. Renaud, G. Laporte and F. F. Boctor, (1996), A tabu search heuristic for the nulti-depot vehicle routing problem. Comput. Oper. Res.23, 229-235.R. A. Russel and D. Gribbin, (1991). A multiphase approach to the period routing problem. Networks 21,747-765.R. A. Russel and W. Igo,(1979). An assigment routing problem. Networks 9, 1-17.C. C. R. Tan and J. E. Beasley, (1984). A heuristic algorithm for the period routing problem. Omega12, 497-504.F.A.Tillman and T. M. Cain, (1972). An upper bound algorithm for the single and multiple terminal delivery problem. Mgmt. Sci. 18, 664-682.F.A.Tillman and R. W Hering, (1971). A study for look-ahead procedure for solving the multiterminal delivery problem. Trans. Res.5, 225-229.A.Wren and A. Holliday, (1972), Computer scheduling of vehicles from one or more depots to a number of delivery points. Oper. Res. Q. 23, 333-344.。

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