Rapid Prototyping of ACC Algorithms with Virtual Human and Plant Models

SAE TECHNICAL PAPER SERIES2002-01-0516Rapid Prototyping of ACC Algorithms with Virtual Human and Plant ModelsMingyu Wang, Lin-Jie Huang and Charles A. ArchibaldDelphi Harrison Thermal SystemsReprinted From: Progress in Climate Control Technologies (SP–1679)SAE 2002 World Congress Detroit, Michigan March 4-7, 2002400 Commonwealth Drive, Warrendale, PA 15096-0001 U.S.A. Tel: (724) 776-4841 Fax: (724) 776-5760The appearance of this ISSN code at the bottom of this page indicates SAE’s consent that copies of the paper may be made for personal or internal use of specific clients. This consent is given on the condition, however, that the copier pay a per article copy fee through the Copyright Clearance Center, Inc. Operations Center, 222 Rosewood Drive, Danvers, MA 01923 for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. 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A process is available by which discussions will be printed with the paper if it is pu blished in SAE Transactions. For permission to publish this paper in full or in part, contact the SAE Publications Group. Persons wishing to submit papers to be considered for presentation or publication through SAE should send the manuscript or a 300 word abstract of a proposed manuscript to: Secretary, Engineering Meetings Board, SAE.Printed in USA2002-01-0516Rapid Prototyping of ACC Algorithms with Virtual Human and Plant ModelsMingyu Wang, Lin-Jie Huang and Charles A. ArchibaldDelphi Harrison Thermal SystemsCopyright © 2002 Society of Automotive Engineers, Inc.ABSTRACTDevelopment of Automatic Climate Control (ACC) systems requires an exorbitant amount of environmental tunnel testing for proper calibration of the control algorithms. This adds tremendous cost and lead-time to vehicle development programs. As Original Equipment Manufacturers (OEM) gear up to implement accelerated vehicle development programs, suppliers are expected to provide the needed technologies in support of such programs. The present paper introduces the Delphi Digital Platform of Virtual Thermal Comfort Engineering (VTCE) and Transient AC System Simulation for the development of ACC control algorithm. With the Digital Platform, significant cost and time reductions can be realized.system hardware development matures, the control algorithm is prepared to be ported into the controller microprocessor. Since preliminary control algorithm design concepts will have been scrutinized during the virtual development stage, limited environmental tunnel work will be needed to fine-tune the control algorithm. The end result is reduced tunnel and road test time as well as the associated cost. Effective implementation of such a Digital Platform depends on being able to model the pertinent subsystems mathematically and have them simulated digitally. These subsystems include the passenger compartment and the HVAC AC systems. For advanced control algorithm development, mathematical models for human comfort and human reaction also need to be developed. The Delphi Digital Platform is composed of four component technologies. These include the Virtual Thermal Comfort Model, Transient AC System Simulation, MatLab/Simulink control algorithm prototyping, and Altia Virtual Control Head generation. The Transient AC System Simulation provides the instantaneous AC system response to control inputs or vehicle operating condition changes, while the CFD or lumped parameter based Virtual Thermal Comfort Model is expected to provide the passenger compartment thermal environment and the human perception of the thermal environment. MatLab/Simulink is used for rapid control algorithm prototyping. The control head virtual prototyping will be performed by the Altia package.INTRODUCTIONDevelopment of Automatic Climate Control algorithms depends heavily on environmental tunnel testing and field trip testing for control strategy validation and calibration. It is recognized that this adds to the vehicle development time and cost. As the product cycle shortens and pricing pressure increases, it has become imperative for suppliers and OEM’s to search for ways of cost reduction and lead-time reduction. As is well known, the use of math tools and computers can significantly impact product development time and the associated cost. The present paper introduces the Delphi Digital Platform for the development of the ACC algorithms. The concept of the Digital Platform is based on the idea that preliminary control algorithm development work can be carried out virtually through computer simulations prior to actually taking the prototype vehicle into the environmental tunnel [1]. This allows the introduction of elements of concurrent engineering into the traditional serial process of first developing the HVAC subsystem hardware and subsequently the control algorithm prior to taking the vehicle into the environmental tunnel for calibration. Instead, control algorithms can be developed in parallel or in advance of the HVAC subsystem hardware by working in a simulated system environment. As the HVAC sub-DELPHI DIGITAL PLATFORM OVERALL DESIGNTYPICAL CURRENT DEVELOPMENT ENVIRONMENT The traditional approach for developing real time embedded control systems for automotive applications (see Figure 1a) involves a serial process that is time consuming and problematic in maintenance of design data. A systems / controls engineer develops a control specification that is ultimately interpreted by a software engineer in developing code. In parallel, the actual controller isdesigned and fabricated as well as other physical pieces of the system. Once software and hardware merge into the initial prototype, the controls engineer and software engineer can start to debug the system. Through an iterative process, a final working system is debugged, refined and calibrated.Environment tunnels offer good repeatability of the conditions that can be controlled, but stop short of being perfect simulators of real world conditions. Cross or tail wind situations, as well as full spectrum solar load simulation are challenges to most facilities. Additionally, these facilities are expensive to operate and have limited availability. Often during the endeavor, the software and the control specification will become out of sync, with only the software containing the up to date data. Later, when the control specification is updated, there is a risk that it still may not accurately reflect the final implementation. This can easily result from the last minute efforts of delivering a working production system. VIRTUAL DEVELOPMENT WORLD OVERVIEWAlgorithm SpecificationExecutable Specification Debug ErrorsSoftware SpecificationAlogorithm Testing & CalibrationSoftware Code Debug ErrorsAuto-Code GenerationSoftware & Hardware IntegrationSoftware & Hardware IntegrationSoftware & Hardware TestingSystem Testing & CalibrationSystem VerificationTo improve the development process, a revised plan has been developed as shown in Figure 1b. Overall there are fewer steps in this process vs. the typical approach, but the key improvement is that system/software defects are caught earlier in the process. The auto-code generation of software eliminates any errors that may occur by the software engineer when the control specification is translated into a software specification and finally code. This should result in higher quality and less development time. Cost savings stem from:Completed SystemCompleted System• • • •Earlier detection and correction of defects. Elimination of manual effort of generating software specifications and code. Reduced needs for test facilities. Reduction of man-hours in testing.(a) Typical(b) Virtual HVAC control systems typically have multiple feedback paths. There are paths that directly measure system parameters through physical sensors indicating how well the system is controlling to projected targets to achieve comfort. Additionally, there is the subjective feedback of the vehicle occupant through the man-machine interface of the control head. This feedback is ultimately a reflection the occupant’s comfort. To help achieve the Virtual HVAC Embedded Systems Development Process, a framework of tools is being developed as shown in Figure 2. This simulation environment is composed of several tools working in harmony to provide a repeatable rapid prototyping environment for HVAC embedded control systems and predicting system performance capabilities. There are three main functional areas of the tool set which simulate the vehicle plant model, human occupant and the HVAC controls.Figure 1. Embedded System Development Process The elapsed time for these activities can be great. Typical HVAC system development involves testing through three seasons of environmental changes and allowing for various vehicle temperatures to equalize with the environment prior to individual tests. Actual active testing may be completed within an hour while the vehicle may have to be parked for twelve hours prior to testing. Vehicle availability for testing can be a significant impediment. Hardware failures and software defects are also negative impacts. Development is typically done on road and in environmental tunnels. Road testing can be performed any time once the project vehicle is functional. The main drawbacks are repeatability of the tests and risk of accidents. Obviously the control of the environment is beyond the engineer’s scope; differences of temperature, solar load, wind velocity, humidity, and traffic will impact test results.Auto-code Generation ToolsVehicle Plant ModelVehicle ControllerVirtual Controlsfound in the following sections. The resultant comfort data is used to determine how the Virtual Human will adjust the climate control system to maximize comfort. This is achieved by generating the “desired” changes to temperature set point, mode and blower speeds.Virtual HumanVehicle HVAC Control HeadVirtual Development WorldReal WorldVehicle Plant ModelFigure 2. Delphi Climate Control Development System This framework provides the opportunity to perform total closed loop simulation of the control system with feedback from both vehicle systems and occupants. The simulation environment interfaces with the real world through auto-code generation tools by producing C code from the control models. The resultant C code is compiled and loaded into the actual vehicle target control for final system calibration and verification. HIGH LEVEL IMPLEMENTATION The virtual development tools are composed of a mixture of commercially available tools and custom developed applications. The Vehicle Plant Model is composed of several tools that will be described in detail in a later section. The commercially available tools used in implementing it include Boeing’s EASY5 Simulation package and Fluent CFD. The Virtual Controls are implemented in MathWork’s MatLab/Simulink/Stateflow control simulation environment and Altia Design/FacePlate. Custom applications are used to define the Virtual Human. Figure 3 depicts how the flow of data between the applications transpires. Two control feedback loops for the Control System are highlighted. The first is the set of data flowing between the Automatic Air Conditioning Control System and Vehicle Plant Model. This provides for a positive feedback of direct control of various system devices. Typical data exchanged between the Control System Simulation and the Vehicle Plant Model is shown in Table 1. A second feedback path for the control system exists through the loop from the Automatic Air Conditioning Control System, Vehicle Plant Model, Virtual Human, Automatic Air Conditioning Control Head and back to the AAC Control System. The purpose of this path is to provide for the subjective behavior of the occupant. This behavior would reflect possible changes to the control head based on simulated comfort. The Vehicle Plant Model provides the Virtual Human with the necessary parameters to determine comfort levels. This data includes discharge temperatures, air velocity, and delivery locations. Comfort levels are calculated for various body segments. Details on this tool can beThermal Comfort SimulationAutomatic Air Conditioning Control SystemComfort Reaction Model Virtual HumanAutomatic Air Conditioning Control HeadVirtual ControlsVirtual Development WorldKey: Interface Signals Control Feedback LoopFigure 3. Delphi Virtual Development World Table 1. Data Exchange Between Control System and Vehicle Plant Model Data Compressor Speed Cabin Air Temperature Coolant Temperature Temperature Door Position Air Inlet Door Position Compressor Clutch Status Purpose Control Input Direct Control Feedback Control Input Control Output Control Output Control Output From Vehicle Plant Model Vehicle Plant Model Vehicle Plant Model Control System Control System Control System To Control System Control System Control System Vehicle Plant Model Vehicle Plant Model Vehicle Plant ModelThe flow of data from the Automatic Air Conditioning Control System to the Automatic Air Conditioning Control Head represents diagnostic codes and response confirmation data for possible display. A major benefit from this suite of tools is testing of the overall system. Test conditions can be set up in seconds by setting initial conditions instead of hours waiting for vehicles to soak to environmental conditions. Actual tests are performed in minutes instead of hours and arerepeatable. Data can be automatically collected and graphed to enhance system understanding when debugging. Difficult boundary conditions can be tested to ensure proper system behavior. These boundary conditions can involve controlling in or near undesirable regions that could cause damage to system components. This is another advantage for virtual components where there are no risks of being damaged. Algorithms are developed using real engineering units instead of vague programming count values that are often difficult for non-software engineers to comprehend. The application layer is developed to be independent of low level hardware I/O definitions; for example a temperature door position is expressed as a percentage of the full hot position instead of an A/D count value from a feedback potentiometer. This approach has the benefit of generating a generic algorithm that can be applied to similar systems where basic low level I/O definitions change, but the desired system functionality remains the same.The modeling of HVAC system controls with these tools has just recently started. The ability to diagram the system and see it visually instead of a thick specification has helped to define new control systems. The ability to focus on sub-system interfaces has resulted in clean welldefined system models. It is expected that the next steps of auto-code generation and in vehicle testing will be fruitful. VIRTUAL CONTROLS: AUTOMATIC AIR CONTROL HEAD Tools from Altia were chosen to develop virtual control heads. The simple control head shown in Figure 4 was developed using these tools. Altia customers have used these tools to simulate control panels from aircrafts to cars to consumer electronics [9]. The packages can be used as stand alone tools or integrated with Simulink. Altia Design is a comprehensive package that provides for developing libraries of custom control interfaces as well as complete control panels. Altia FacePlate focuses on designing the control panels utilizing existing libraries. These tools provide for the simulation of the appearance, color, lighting and logic of the control head, key parameters for most control heads.VIRTUAL CONTROL ALGORITHM PROTOTYPING AND CONTROL HEAD GENERATIONVIRTUAL CONTROLS: AUTOMATIC AIR CONTROL SYSTEM Using MatLab [2, 3] as the basis of control system development is a natural choice given the success the automotive industry is having applying the tools to powertrain and chassis controls [4, 5, 6]. Key players in the industry have formed a team called “MAAB” (The MathWorks Automotive Advisory Board). The tool is also widely used in universities teaching controls classes. The MathWorks has an active program with third party companies, who develop tools to enhance the capabilities of MatLab. MatLab can act as a simulation backplane, providing easy interfacing with external tools. Interfacing with the Boeing EASY5 [7] (plant model simulation), Altia Design (control head simulation) and Altia FacePlate (control head simulation) has been accomplished in simulation activities at Delphi Harrison Thermal Systems. The use of MatLab provides the hooks for using the right tool for the right job. The benefits of using MatLab/Simulink include: • Graphical environment provides ease of use for the system/control engineer. • Control scenarios can be developed to be portable. • Auto-code tools exist from The MathWorks, dSpace [8] and others. • Models become executable specifications since they define the desired transfer function of the system and are the basis for target code. • Simulation runs can be compared against real world executions for system verification.Figure 4. Experimental Virtual HVAC Control Head Altia Benefits: • Ties into mobile multi-media development. • Multiple concepts can be quickly mocked up and reviewed. • Models can be used as an executable specification. • Up front testing of control head logic, before hardware implementation. • Virtual system testing reflects real world through visualization of the human interface providing a “natural” way to observe system behavior [10]. • Models can be executed in a run time only package and delivered to the customer for approval before hardware fabrication.•As part of a quote package, control heads can be virtually mocked up and delivered.Similarly to the work with MatLab, the use of these tools has just begun. It is expected that there will be a payback from it, which will be reported on in the future.It is interesting to note that the transient AC analysis can also be used for the optimization of the AC system component design. Instead of optimizing at a particular AC steady state operating point, Hendricks [15] demonstrated that better overall system performance can be achieved through optimizing components design over the average of a transient vehicle driving cycle. However, the requirements imposed by ACC algorithm development are more stringent. To be used as a physical plant model, the AC simulation software must be able to provide data updates at millisecond time intervals. Additionally, the AC system components must exhibit proper time constants given a step change to the system operating conditions. The software must also be able to interface with the control algorithm development environment (or software platform) such that proper message passing can be established. The Transient AC System Model presented here is developed to meet the variety of needs as outlined above. Primarily, however, it is intended to help accelerate the process of ACC control algorithm development. Once integrated with the control algorithm development software such as MatLab/Simulink, control strategies can be modeled and evaluated within the virtual environment without having to go to the environmental tunnel, thus reducing the development time and cost. The Delphi Transient AC System Model is built with the EASY5 [7] software platform that was developed by The Boeing Company to analyze various dynamic systems. Its multiphase library, among other libraries, supports the development of the mobile AC system model [20]. TRANSIENT AC SYSTEM AND COMPONENTS MODELING Figure 5 shows the overall system design for the Transient AC Model. The intent is to implement the vapor compression cycle as given in Figure 6 with emphasis on the transient system behavior. The system model is composed of four main components, including the compressor, condenser, orifice tube, and the evaporator. These components carry out the four processes found in the vapor compression cycle: • • • • Compression of low temperature but dry vapor Condensation of high temperature vapor into liquid through cooling Isenthalpic expansion of the condensed liquid into low temperature, low pressure vapor with high liquid content Vaporization of the liquid content through evaporation, which cools the air that circulates into the passenger compartment for climate control.TRANSIENT AC SYSTEMAir Conditioning System analysis software has been used extensively by Tier 1 supplier to help with the optimization of AC system design [11-18]. The optimization is done in various stages of a product development cycle to improve performance and reduce development cost. AC system analysis is also performed during the early phase of a vehicle development program by both suppliers and OEM’s. Suppliers use analysis to establish the technical validity of a quoted system while OEM’s use system analysis to make technical assessment of the many systems proposed by suppliers. Most of the above mentioned analysis might be categorized as performance analysis. There are accepted operating conditions in the Mobile Air Conditioning industry for the assessment of AC system performance. These include Idle, 30, 50, and 70 mph Outside Air conditions, Soak and Cooldown, Idle Recirculated Air conditions, etc. There is a trend presently to use a combination of those points as a test and analysis procedure to gauge the performance of an AC system. The many needs of the performance analysis, however, can generally be met by steady state system analysis software. Even the Soak and Cooldown type of analysis can be addressed by using a steady state AC system analysis software with a transient passenger compartment model. At Delphi Harrison Thermal Systems, steady state performance analyses have been carried out routinely for the last 15 years. Procedures have been developed for the characterization of components and subsystems. A database for component performance maps has been established in support of various steady state system analyses. Lately, the need for system analysis has outpaced the steady state analysis software’s capability. Assessment of AC systems impact on energy efficiency and emission requires transient analysis of the AC system in a vehicle environment during a particular driving cycle such as the Supplemental Federal Testing Procedure (SFTP) SCO3 [15]. Even in performance analysis, more emphasis has been given to composite driving cycles such as City Traffic, which cannot be easily simulated by steady state analysis software due to the transient nature of the frequent stops and starts. Other AC system performance targets, such as compressor head pressure spike during vehicle drive-away from traffic lights [19], AC system discharge temperature variation during drive-away, etc., can only be analyzed using transient analysis software.Figure 7 shows the evaporator model. The evaporator is modeled with two primary components and three supporting components. The two primary components describe the refrigerant side and the airside respectively, and together they ensure the energy conservation between the airside and the refrigerant side. The supporting components provide for the customizing information for a specific evaporator. These include the geometry of the evaporator such as the tube length, direct and indirect heat transfer areas, hydraulic diameters of the refrigerant side and the airside, etc. The refrigerant side two-phase heat transfer coefficient and the airside heat transfer coefficient are each calculated with correlations established through dissipation testing. Figure 8 gives the condenser model. The condenser modeling approach is similar to that for the evaporator. There are two primary components maintaining the transient energy balance between the airside and the refrigerant side. The secondary components provide the geometry and the heat transfer coefficients. The difference is, of course, in the heat transfer correlations themselves and the way condensate is handled. While the evaporator primary component needs to comprehend the water condensate formation and its impact on heat transfer, there is no such requirement for the condenser. Figure 9 shows how the compressor is modeled. It is composed of the main compressor component model along with input elements. The compressor model is a marriage of the EASY5 multiphase compressor component with the Delphi compressor specific isentropic efficiency and volumetric efficiency maps. The efficiency maps are obtained through the calorimeter test of thecompressor in the laboratory. The flow rate in the compressor model is calculated by using the following equation [21]:! m = V d ⋅ crpm ⋅ η v ⋅ ρ suc(1)where Vd is the compressor displacement, crpm the compressor speed, ηv the volumetric efficiency and ρsuc the suction density. The compressor work for process CD in Figure 6 is calculated based on the inlet state point and the outlet pressure. The isentropic compression work is first calculated and subsequently the isentropic efficiency is applied to obtain the actual work of compression. As with the volumetric efficiency, the isentropic efficiency is obtained through its map. The main function of the orifice tube is to realize the isenthalpic expansion in order to achieve the low temperature level desired for climate control [11,12, 22]. In the simulation environment, the function of the orifice tube model is to predict the refrigerant flow rate given the upstream state and the downstream pressure. The orifice tube model implemented is a variation of the model proposed by Kim and O’Neal [23] and is customized for the present application. The refrigerant flow rate is calculated via the following equation with the tube size, upstream and downstream pressure, upstream subcooling or quality as inputs:! m = Ctp As 2 gρ Pup − P f()(2)where Ctp is the two-phase quality correction factor and Pf is the adjusted downstream pressure, As the crosssectional area, and ρ the inlet refrigerant density.CondAirFlowVolumeRefrigerant LineCondenserRefrigerant LineOrifice TubeFluid PropertiesSimulated and Integration Information accumulated CPU times Gear and AdamsMass Manager for Closed SystemEvaporatorRefrigerant LineAccumulatorCompressorEvapAirFlowPR1RL3 CompressorRPMFigure 5. Transient AC System ModelOne-pole, Normally Closed Switch Parameter Input VALPA 19.8Pressure, kPaD’ Atan t con sDEvapAirFlowAir Secondary Side for EvaporatorN-Node EvaporatorHR2ENC BEVA HTCs=Parallel RibEVR HTCEnthalpy, kJ/KGFigure 6. Vapor Compression CycleFigure 7. Evaporator Component ModelEVA HTCCTC18mm2.5KCND REF HTCN-Node CondenserCompressor (2-chamber)One-pole, Normally Closed SwitchComp Speed 1 5s+1CompressorRPMComp_RPMAir Secondary Side for CondenserOne-pole, Normally Closed SwitchCondAirFlowFigure 8. Condenser ModelFigure 9. Compressor ModelEXAMPLES OF APPLICATION The Transient AC System Model has been validated for various steady state simulation conditions such the 30, 50, 70mph AC operating points. Validation testing has also been performed for various ramp-up and rampdown profiles in airflow and compressor speed. In the following, we will discuss the validation performed with the SC03 driving cycle, which is part of the Supplemental Federal Test Procedures. Figure 10 gives the SC03 driving profile in terms of the vehicle and compressor speed as recorded during an environmental tunnel test. A driver-aid computer program is used to monitor the vehicle speed and provide feedback to the driver so that the SC03 cycle can be followed with accuracy. The tunnel air speed is controlled to follow the vehicle speed. To better validate the ACsystem, a slight variation is made to the SC03 cycle by keeping the windows closed instead of down. During the tunnel testing, various instrumentations are used to record the AC system response. These include the pressures, temperatures, and refrigerant flow rate at various points around the AC loop. Air temperatures into and out of the heat exchangers are recorded by thermal couple grids. Temperatures from the passenger compartment are also recorded with thermal couples. In order to simulate the performance of the AC system during the SC03 cycle, which requires the AC system to be run in the recirculation mode, a passenger compartment model is constructed that can predict the thermal response of the passenger compartment to air conditioning inputs such as the cooling air flow rate and its temperature. The passenger compartment model subse-。