插电式混合动力城市公交大客车关键技术研究
|
摘要
插电式混合动力电动汽车依靠电池独立驱动可连续行驶20~80km，超出该续驶里程采用发动机与电混合驱动行驶，并可使用220V工频电源对动力电池组直接充电。此外，中国典型城市公交循环工况是启停与加减速频繁，行驶速度低，续驶里程短，发动机怠速时间长燃油效率低，排放污染大。由此，普遍认为在目前电池性能瓶颈下，由于插电式混合动力城市公交大客车兼顾混合动力与纯电动两类电动汽车的优点，适用于在城市公交工况下运行，是从传统燃油城市公交到纯电动城市公交过渡的最优方案之一。然而，插电式混合动力城市公交大客车在动力总成选型与匹配集成、整车能量管理控制、制动能量回收以及整车设计与制造等方面还存在诸多亟待解决的关键技术问题，导致目前在国内外城市公交大客车市场上还鲜有实车运行。
本课题围绕插电式混合动力城市公交大客车动力总成选型与匹配设计、整车控制策略、电池管理系统以及制动能量回收等关键技术开展算法与应用创新研究，具体地：
1）针对动力总成匹配计算与选型，基于中国典型城市公交循环工况需求，提出了一种基于NSGA-II改进多目标遗传算法的传动系统结构设计、参数匹配优化及选型方案。首先确定传动系统为并联式结构设计，并对传动系统参数进行匹配研究。进而基于NSGA-II改进多目标遗传算法对变速器各档位传动比进行最优化设计。最终给出传动系统部件的具体型号和参数。结果表明该方案在满足动力性需求的同时，显著提高了整车经济性指标。
2）针对整车能量管理控制，提出了一种基于规则的整车控制算法，结果表明该策略能保证整车动力性并改善燃油经济性。首先建立了基于CAN总线的整车分布式控制系统；然后设计了一套工况切换控制逻辑，以及一组纯电动机启动、纯发动机启动、纯电动运行、纯发动机运行、充电运行，电动机和发动机联合驱动运行以及再生制动等工况的控制算法；进一步建立了发动机外特性、电动机/发电机的转矩、功率与效率特性，以及电池充放电与荷电状态预测等仿真计算模型，建立了动力总成传动系统建模；最后基于开发的燃油经济性仿真程序完成了动力性与燃油经济性能评价。
3）针对电池管理系统，提出了一个基于V流程的电池管理系统中荷电状态（SOC）估计模块的快速开发方法。首先，基于Matlab/Simulink软件平台，完成电池建模与基于扩展卡尔曼滤波方法的SOC估计算法开发，通过离线仿真完成功能验证。进而，通过Matlab实时工作间（RTW）代码自动生成工具将算法的Simulink模块转化为目标代码，基于前期开发完成的MPC555嵌入式平台，完成快速原型开发。最后，对通过快速原型测试的算法代码进行实车搭载并在线标定，完成动力电池SOC估算功能模块的实际测试。结果表明，基于扩展卡尔曼滤波算法的SOC估计方法能够改善SOC估计精度，基于V流程的快速开发方法能够显著提高开发效率。
4）针对制动能量回收，提出一种回收能量最大化的复合制动控制策略，建立摩擦制动子系统与再生制动子系统的仿真模型，兼顾ECER13制动法规、电动机、锂离子电池与传动系统特性约束，开展典型制动工况下复合制动系统制动力分配控制策略仿真实验研究。结果表明，各典型制动工况下，复合制动系统能够在保证制动安全性的前提下最大限度的回收制动能量。
5）面向我国混合动力客车准入规范，开展了插电式混合动力城市公交大客车样车路测试验，测试指标包括百公里油耗、纯电动行驶里程、最高车速、加速时间、制动距离等。结果证实样车各项指标均合格，特别是燃油经济性优势更明显，表明本课题研究成果具有实用价值。
Plug-in hybrid electric vehicles (PHEV) can continuously run20km to80km solelydriven by the power battery. Beyond that range they can be driven by engine and the electricpower resource together. And besides, PHEV can use the220V50Hz power resource torecharge its batteries directly. On the other hand, the typical city bus driving cycle in China isthat start-stop and acceleration-deceleration work conditions are occurred frequently, drivingvelocity is usually low, driving range is often short, and the engines have to idle for a longtime with lower fuel-burning efficiency and heavy emission pollution. Therefore, consideringthe battery performance bottlenecks and due to the advantage that PHEV takes into accountthe advantages of hybrid electric vehicles (HEV) as well as of purely electric-driven vehicles,PHEV is suitable for the city bus work conditions; it is one of the optimal solutions for thetransition from traditional fuel-consumed city bus to the pure electric city bus. There are manyof unsolved key technical problems for PHEV so far which result that few real vehicles run inthe domestic and international city bus market, such as transmission selection and matching,energy management and control for the whole vehicle, brake energy recovery, and vehicledesign and manufacturing etc.
The research works are conducted for algorithm and application innovations focusing onthe key techniques on transmission selection and matching design, vehicle controllingstrategies, battery management systems and regenerative braking energy feedback.
1) As for transmission selection and matching issue, to fulfill the requirements of thetypical city bus driving cycle in China, based on the nondominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ), a design scheme is proposed for the transmission structure determination, forthe parameter matching and optimization, and for the component selection. Firstly thetransmission system is chosen a parallel structure design; the transmission system parametersare matched. And then, based on an improved NSGA-II multi-objective genetic algorithmeach gear transmission ratios are optimized. The concreted models and parameters of thecomponents for the transmission are proposed finally. The results show that the scheme canmeet the dynamic requirement, besides that it can significantly improve the economicindicators of the vehicle at the same time.
2) As for vehicle energy management and control, another kind of rule-based controlstrategy is proposed, it is proved that it can ensure vehicle dynamic and improve fuel-burningefficiency. Firstly, one CAN bus based distributed control system is built on. Secondly,control logics on work condition transition and control strategies on each work condition aremake out. The work conditions consist of pure motor start, pure engine start, running withpure electric, running with pure engine, charging, running co-driven by motor and engine, andregenerative braking. And then, simulation models are formulated such as the models forengine external characteristics, models for motor/generator torque, power and efficiencycharacteristics, models for battery charge and discharge, and models for state of chargeprediction. The system model for the whole powertrain is also drawn out. At finally, thedynamic performance and fuel economical performance are evaluated based on the fueleconomy simulation program we developed.
3) As for battery management system, a V-flow based rapid development method for thestate of charge (SOC) estimation module in battery management system is proposed. Firstly,based on Matlab/Simulink software platform a battery model and a SOC estimation algorithmbased extended Kalman filter are formulated, and their function are verified via offlinesimulations. And then, the Simulink blocks of the battery model and the estimation algorithmare translated into targeted codes by Matlab real-time workshop (RTW) automatic codegeneration tools. And based on pre-development MPC555embedded platform rapidprototyping developments are completed. Finally the codes verified by the rapid prototypingare downloaded into the prototype vehicle and are calibrated online there, the SOC estimationfunction module validation is finished at this actual test. The results demonstrate that theextended Kalman filter based SOC estimation algorithm improves the prediction accuracy,and the V-flow can result in improvement on development efficiency.
4) As for regenerative braking energy feedback, a hybrid braking control strategy is putforward which can maximize the regenerative brake energy. Simulation models for thefriction braking sub-system and the regenerative braking sub-system are established on.Considering the restrictions of the ECER13braking regulations, and of the characteristics ofthe motor, the lithium-ion batteries and the transmission, simulation experiments areconducted for the proposed braking force distribution and control strategy under all typical braking operation conditions. The results demonstrate that for all the typical braking operationconditions the composite braking system can effectively guarantee the braking safety andmaximize the recovery braking energy at the most degree.
5) According to China's hybrid city bus licensing authorization specification, actual roadtesting experiences are make on the developed plug-in hybrid city bus prototype vehicle,test indices include fuel consumption amount per100km, pure electric-driven mileage,maximum speed, acceleration time, braking distance, etc. The results demonstrate that all theindices are qualified, and the fuel economy advantage is especially obvious. It indicates thatthe results of this research have practical value.
本课题围绕插电式混合动力城市公交大客车动力总成选型与匹配设计、整车控制策略、电池管理系统以及制动能量回收等关键技术开展算法与应用创新研究，具体地：
1）针对动力总成匹配计算与选型，基于中国典型城市公交循环工况需求，提出了一种基于NSGA-II改进多目标遗传算法的传动系统结构设计、参数匹配优化及选型方案。首先确定传动系统为并联式结构设计，并对传动系统参数进行匹配研究。进而基于NSGA-II改进多目标遗传算法对变速器各档位传动比进行最优化设计。最终给出传动系统部件的具体型号和参数。结果表明该方案在满足动力性需求的同时，显著提高了整车经济性指标。
2）针对整车能量管理控制，提出了一种基于规则的整车控制算法，结果表明该策略能保证整车动力性并改善燃油经济性。首先建立了基于CAN总线的整车分布式控制系统；然后设计了一套工况切换控制逻辑，以及一组纯电动机启动、纯发动机启动、纯电动运行、纯发动机运行、充电运行，电动机和发动机联合驱动运行以及再生制动等工况的控制算法；进一步建立了发动机外特性、电动机/发电机的转矩、功率与效率特性，以及电池充放电与荷电状态预测等仿真计算模型，建立了动力总成传动系统建模；最后基于开发的燃油经济性仿真程序完成了动力性与燃油经济性能评价。
3）针对电池管理系统，提出了一个基于V流程的电池管理系统中荷电状态（SOC）估计模块的快速开发方法。首先，基于Matlab/Simulink软件平台，完成电池建模与基于扩展卡尔曼滤波方法的SOC估计算法开发，通过离线仿真完成功能验证。进而，通过Matlab实时工作间（RTW）代码自动生成工具将算法的Simulink模块转化为目标代码，基于前期开发完成的MPC555嵌入式平台，完成快速原型开发。最后，对通过快速原型测试的算法代码进行实车搭载并在线标定，完成动力电池SOC估算功能模块的实际测试。结果表明，基于扩展卡尔曼滤波算法的SOC估计方法能够改善SOC估计精度，基于V流程的快速开发方法能够显著提高开发效率。
4）针对制动能量回收，提出一种回收能量最大化的复合制动控制策略，建立摩擦制动子系统与再生制动子系统的仿真模型，兼顾ECER13制动法规、电动机、锂离子电池与传动系统特性约束，开展典型制动工况下复合制动系统制动力分配控制策略仿真实验研究。结果表明，各典型制动工况下，复合制动系统能够在保证制动安全性的前提下最大限度的回收制动能量。
5）面向我国混合动力客车准入规范，开展了插电式混合动力城市公交大客车样车路测试验，测试指标包括百公里油耗、纯电动行驶里程、最高车速、加速时间、制动距离等。结果证实样车各项指标均合格，特别是燃油经济性优势更明显，表明本课题研究成果具有实用价值。
Plug-in hybrid electric vehicles (PHEV) can continuously run20km to80km solelydriven by the power battery. Beyond that range they can be driven by engine and the electricpower resource together. And besides, PHEV can use the220V50Hz power resource torecharge its batteries directly. On the other hand, the typical city bus driving cycle in China isthat start-stop and acceleration-deceleration work conditions are occurred frequently, drivingvelocity is usually low, driving range is often short, and the engines have to idle for a longtime with lower fuel-burning efficiency and heavy emission pollution. Therefore, consideringthe battery performance bottlenecks and due to the advantage that PHEV takes into accountthe advantages of hybrid electric vehicles (HEV) as well as of purely electric-driven vehicles,PHEV is suitable for the city bus work conditions; it is one of the optimal solutions for thetransition from traditional fuel-consumed city bus to the pure electric city bus. There are manyof unsolved key technical problems for PHEV so far which result that few real vehicles run inthe domestic and international city bus market, such as transmission selection and matching,energy management and control for the whole vehicle, brake energy recovery, and vehicledesign and manufacturing etc.
The research works are conducted for algorithm and application innovations focusing onthe key techniques on transmission selection and matching design, vehicle controllingstrategies, battery management systems and regenerative braking energy feedback.
1) As for transmission selection and matching issue, to fulfill the requirements of thetypical city bus driving cycle in China, based on the nondominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ), a design scheme is proposed for the transmission structure determination, forthe parameter matching and optimization, and for the component selection. Firstly thetransmission system is chosen a parallel structure design; the transmission system parametersare matched. And then, based on an improved NSGA-II multi-objective genetic algorithmeach gear transmission ratios are optimized. The concreted models and parameters of thecomponents for the transmission are proposed finally. The results show that the scheme canmeet the dynamic requirement, besides that it can significantly improve the economicindicators of the vehicle at the same time.
2) As for vehicle energy management and control, another kind of rule-based controlstrategy is proposed, it is proved that it can ensure vehicle dynamic and improve fuel-burningefficiency. Firstly, one CAN bus based distributed control system is built on. Secondly,control logics on work condition transition and control strategies on each work condition aremake out. The work conditions consist of pure motor start, pure engine start, running withpure electric, running with pure engine, charging, running co-driven by motor and engine, andregenerative braking. And then, simulation models are formulated such as the models forengine external characteristics, models for motor/generator torque, power and efficiencycharacteristics, models for battery charge and discharge, and models for state of chargeprediction. The system model for the whole powertrain is also drawn out. At finally, thedynamic performance and fuel economical performance are evaluated based on the fueleconomy simulation program we developed.
3) As for battery management system, a V-flow based rapid development method for thestate of charge (SOC) estimation module in battery management system is proposed. Firstly,based on Matlab/Simulink software platform a battery model and a SOC estimation algorithmbased extended Kalman filter are formulated, and their function are verified via offlinesimulations. And then, the Simulink blocks of the battery model and the estimation algorithmare translated into targeted codes by Matlab real-time workshop (RTW) automatic codegeneration tools. And based on pre-development MPC555embedded platform rapidprototyping developments are completed. Finally the codes verified by the rapid prototypingare downloaded into the prototype vehicle and are calibrated online there, the SOC estimationfunction module validation is finished at this actual test. The results demonstrate that theextended Kalman filter based SOC estimation algorithm improves the prediction accuracy,and the V-flow can result in improvement on development efficiency.
4) As for regenerative braking energy feedback, a hybrid braking control strategy is putforward which can maximize the regenerative brake energy. Simulation models for thefriction braking sub-system and the regenerative braking sub-system are established on.Considering the restrictions of the ECER13braking regulations, and of the characteristics ofthe motor, the lithium-ion batteries and the transmission, simulation experiments areconducted for the proposed braking force distribution and control strategy under all typical braking operation conditions. The results demonstrate that for all the typical braking operationconditions the composite braking system can effectively guarantee the braking safety andmaximize the recovery braking energy at the most degree.
5) According to China's hybrid city bus licensing authorization specification, actual roadtesting experiences are make on the developed plug-in hybrid city bus prototype vehicle,test indices include fuel consumption amount per100km, pure electric-driven mileage,maximum speed, acceleration time, braking distance, etc. The results demonstrate that all theindices are qualified, and the fuel economy advantage is especially obvious. It indicates thatthe results of this research have practical value.
引文
[1]欧阳明高.我国节能与新能源汽车发展战略与对策[J].汽车工程,2006,28(4):317-321.
[2]万钢.中国“十五”电动汽车重大科技专项进展综述[J].中国科技产业,2006,(2):110-117.
[3]田振中,彭晗,薛海培.基于主成分和BP神经网络的汽车保有量预测[J].产业经济与管理,2008,10(上):173-175.
[4] Ramesh S. Remanufacturing for the automotive aftermarket-strategic factors: literature review andfuture research needs[J]. Journal of Cleaner Production,2009,17(13):1163-1174.
[5]兰召华,黄妙华.对国内外几个电动汽车发展计划及相关策略的研究[J].汽车研究与开发,2000,(5):43-48.
[6] Lenny B, Peter B, Osvaldo C, et al. Climate change2007: synthesis report, summary forpolicymakers[R]. Valencia, Spain: Intergovernmental Panel on Climate Change (IPCC),2007.
[7]何涛.电动汽车整车控制器软件设计及关键技术研究[D].北京:清华大学,2010.
[8]詹宜巨,陈清泉.电动车技术发展及前景展望[J].电气传动,1997,(5):40-44.
[9] Chan C C. The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles[J]. Proceedings of the IEEE,2007,95(4):704-718.
[10]Lam L T, Louey R. Development of ultra-battery for hybrid-electric vehicle applications[J]. Journal ofPower Sources,2006,158(2):1140–1148.
[11]Kelly K, Eudy L. Field operations program--overview of advanced technology transportation[R].Golden, CO, USA: National Renewable Energy Laboratory,2000.
[12]Yoo H J, Sul S-K, Park Y, Jeong J. System Integration and Power-Flow Management for a SeriesHybrid Electric Vehicle Using Supercapacitors and Batteries[J]. IEEE Transactions on IndustryApplications,2008,44(1):108–114.
[13]Schouten N J，Salman M A，Kheir N A．Energy management strategies for parallel hybrid vehiclesusing fuzzy logic[J]．Control Engineering Practice,2003,11(2):171-177.
[14]李兴虎．电动汽车概论[M]．北京:北京理工大学出版社,2005:167-216.
[15]颜增品.世界电动汽车用电池技术的发展现状[J].世界汽车,1997(10):9-11.
[16]刘剑雄,管前新.当前世界电动汽车发展概况[J].汽车技术,1997(5):56-59.
[17]熊建,管华.混合动力电动客车的发展及其产业化[J].客车技术与研究,2002,24(3):4-6.
[18]Bradley G, Gregory W, Giorgio R. Operation and control strategies for hybrid electric automobiles[C].SAE2000-01-1537,2000.
[19]张旸.混合动力客车动力系统设计及参数优化[D].合肥:合肥工业大学,2010.
[20]Fritz R K, Haresh K, Mark D, et al. Plug-In Hybrid Electric Vehicles: Promise, Issues and Prospects[A].EVS24International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium[C].2009:1-11.
[21]Environmental Assessment of Plug-In Hybrid Electric Vehicles, Volume1: Nationwide GreenhouseGas Emissions[R]. California, USA: Electric Power Research Institute (EPRI),2007.
[22]叶明,舒红,陈然.插电式混合动力客车工作模式切换控制研究[J].中国公路学报,2012,25(1):141-145.
[23]张松,吴光强,郑松林.插电式混合动力汽车能量管理策略多目标优化[J].同济大学学报(自然科学版),2011,39(7):1035-1039,1044.
[24]Rahman S A, Zhang N, Zhu J. A comparison on fuel economy and emissions for conventional hybridelectric vehicles and the UTS plug-in hybrid electric vehicle[A]. Proc.2nd Int. Conf. Comput. Autom.Eng.[c]. Singapore,2010:20–25.
[25]Fajri P, Asaei B. Plug-in hybrid conversion of a series hybrid electric vehicle and simulationcomparison[A]. Proc.11th Int. Conf. OPTIM[C]. Brasov, Romania,2008:287–292.
[26]Jin L, Zeng X, Wang W. The control strategy and cost analysis for series plug-in hybrid electricvehicle[A]. Proc.2nd ICACC[C]. Shenyang, China,2010:350–354.
[27]李绍梅. Plug-in混合动力汽车动力电池SOC估计研究[D].济南:山东大学,2010.
[28]戎品慈.混合动力汽车现状与发展[J].农业装备与车辆工程,2008,(7):5-8.
[29]陈树勇,陈全世,仇斌,等. Plug-In HEV的研究与发展[A].清洁汽车技术创新发展论坛[C],2008.
[30]舒红,聂天雄,邓丽君,等.插电式并联混合动力汽车模型预测控制[J].重庆大学学报,2011,34(5):36-41.
[31]Gonder J, Markel T. Energy management strategies for plug-in hybrid electric vehicle[A].2007SAEInternational World Congress[C]. Detroit, Michigan,2007.
[32]Axsen J, Kurani K S. The early U.S. market for PHEVs: anticipating consumer awareness, rechargepotential, design priorities and energy impacts[R]. Davis, U.S.A.: Institute of Transportation StudiesUniversity of California,2008.
[33]胡欢.插电式混合动力汽车现状[J].城市车辆,2009,(7):43-44.
[34]李锦,徐兆坤,许建昌.浅谈PHEV的发展现状及趋势[J].上海汽车,2009,(28):10-12.
[35]钮翔. Plug-in混合动力公交车整车综合控制策略研究[D].北京:北京理工大学,2008.
[36]Plug-In Hybrid Electric Sprinter Prototype Expansion Program[R]. California, USA: Electric PowerResearch Institute (EPRI),2004.
[37]Hybrid Electric School Bus Project[EB/OL]. Http://www.hybridschoolbus.org,2010-11-21.
[38]Michael M, Del P. Advanced Small Transit Vehicle Program[R].2006.
[39]美国能源部公开12插电式混合动力车的燃效试验结果[J].汽车与配件,2009,(22):14.
[40]夏文川.燃料电池系统电子控制单元[D].北京:清华大学,2005.
[41]刘原.32位车用控制器平台技术的研究与应用[D].北京:清华大学,2004.
[42]庄毅胜,黄妙华,周星亮.基于拓扑优化的电动游览车车身优化设计[J].轻型汽车技术,2009(7/8):10-14.
[43]刘碧军,杨林,朱建新,等.电动汽车高压电安全测试系统的研究[J].汽车工程,2005,27(4):274-277.
[44]牛礼民.辅助动力电动汽车整车匹配及电机控制系统研究[D].合肥:安徽农业大学,2005.
[45]孙立志,赵辉,陆永平,等.电动汽车中的电机驱动系统[J].电工电能新技术,1997,(4):14-19.
[46]Chan C C, Chau K T. An Overview of power Electronics in Electric Vehieles[J]. IEEE Transactions onIndustrial Electronics,1997,44(1):3-13.
[47]张承宁,孙逢春,余晓江.电动汽车的几种驱动系统比较[J].兵工学报(坦克装甲车与发动机分册),1995,(4):36-39.
[48]Wang J P, Cao B G, Cheng Q S. Combined state of charge estimator for electric vehicle battery pack[J].Control Engineering Practice,2007,15(12):1569-1576.
[49]黄文华,韩晓东,陈全世,等.电动汽车SOC估计算法与电池管理系统的研究[J].汽车工程,2007,29(3):198-202.
[50]Cai C H, Du D, Liu Z Y. Battery State-of-Charge (SoC) Estimation Using Adaptive Neuro-FuzzyInference System[A].The IEEE International Conferenee on FuzzySystems[C]. ST.LOUISMO,USA,2003:234-240.
[51]Fellner C, Newman J. High-power batteries for use in hybrid vehicles[J]. Journal of Power Sources,2000,85:29-236.
[52]Kennedy B, Patterson D, Camilleri S. Use of lithium-ion batteries in electric vehicles[J]. Journal ofPower Sources,2000,90:156-162.
[53]魏学哲,孙泽昌,邹广楠.模块化的HEV锂离子电池管理系统[J].汽车工程,2004,26(6):629-631.
[54]徐梁飞，卢兰光，李建秋，等．燃料电池混合动力系统建模及能量管理算法仿真［J］．机械工程学报，2009，45(1):141-147．
[55]李卫民.混合动力汽车控制系统与能量管理策略研究[D].上海:上海交通大学,2008.
[56]Powers W F, Nicastri P R. Automotive vehicle control challenges in the21st century[J]. ControlEngineering Practice,2000,8(6):605-618.
[57]吴晓刚,王旭东,毛亮.ISG型混合动力汽车能量管理模糊控制的研究[J].汽车工程,2011,33(7):558-562,568.
[58]王伟华,王庆年,曾小华.并联混合动力汽车自适应控制策略［J］．汽车工程，2009，31(9):824-838．
[59]白中浩，王耀南，曹立波．混合动力电动汽车能量自适应模糊控制研究［J］．汽车工程，2005，27(4):389-391．
[60]邓元望,龚金科,王耀南.轻度混合控制策略下混合动力电动汽车能量优化与仿真[J].中国公路学报,2008,21(6):114-120.
[61]张欣,刘溧,于海生.混合动力电动汽车制动系统回馈特性仿真[J].中国公路学报,2006,19(3):111-116.
[62]叶敏,安强,程博,等.电动汽车主辅电源能量回馈研究[J].系统仿真学报,2007,29(23):5434-5437.
[63]王军,熊冉,杨振迁.纯电动大客车制动能量回收系统控制策略研究[J].汽车工程,2009,31(10):932-937.
[64]李玉芳,林逸,何洪文,等.电动汽车再生制动控制算法研究[J].汽车工程,2007,29(11):1059-1062.
[65]张毅,杨林,朱建新,等.电动汽车能量回馈的整车控制[J].汽车工程,2005,27(1):24-27.
[66]Ahn J K, Jung K H, Kim D H, et al. Analysis of a regenerative braking system for Hybrid ElectricVehicles using an Electro-Mechanical Brake[J]. International Journal of Automotive Technology,2009,10(2):229-234.
[67]徐明辉.重型商用车气动AMT故障诊断技术研究[D].北京:北京理工大学,2003.
[68]Liao Chenglin, Zhang Junzhi, Lu Qingchun. Coordinated powertrain control method for shiftingprocess of automated mechanical transmission in the hybrid electric vehicle[J]. Chinese Journal ofMechanical Engineering,2005,41(12):37-41.
[69]彭涛,陈全世,田光宇,等.并联混合动力电动汽车动力系统的参数匹配[J].机械工程学报,2003,39(2):69-73.
[70]郑维.混合动力汽车动力总成参数匹配方法与控制策略的研究[D].哈尔滨:哈尔滨工业大学,2010.
[71]杨为琛.混合电动公交车总体匹配、仿真及其控制系统研究[D].北京:北京理工大学,2002.
[72]初亮.混合动力总成的控制算法和参数匹配研究[D].长春:吉林大学,2002.
[73]节能与新能源汽车节油率与最大电功率比检验大纲[S].中国工业和信息化部，2009.
[74]关于开展节能与新能源汽车示范推广试点工作的通知[EB/OL].http://www.gov.cn/zwgk/2009-02/05/content_1222338.htm,2013-02-20.
[75]公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289.
[76]Schaffer J D. Some experiments in machine learning using vector evaluated genetic algorithms[D].Nashville, TN: Vanderbilt Univ.,1985.
[77]Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: Formulation,discussion and generalization[A]. Proceedings of the Fifth International Conference of GeneticAlgorithms[C]. San Mateo, CA: Morgan Kaufmann,1993:416-423.
[78]Horn J, Nafpliotis N, Goldberg D E. A Niched Pareto Genetic Algorithm for MultiobjectiveOptimization[A]. Proceedings of the First IEEE Conference on Evolutionary Computation[C].Piscataway, NJ: IEEE Service Center,1994:82-87.
[79]Srinivas N, Deb K. Multiobjective function optimization using nondominated sorting geneticalgorithms[J]. Evol. Comput.,1995,2(3):221–248.
[80]钟勇,钟志华,余群明,等.电动汽车CAN总线通用协议的应用研究[J].汽车工程,2006,28(5):422-426,438.
[81]李永强,宋希庚,薛冬新. CAN局域网及J1939协议在货车和客车上的运用[J].汽车工程,2003,25(4):377-340.
[82]舒红,秦大同,胡建军.混合动力汽车控制策略研究现状及发展趋势[J].重庆大学学报（自然科学版）,2001,24(6):28-31.
[83]Pusca R, Ait-Amirat Y, Berthon A, et al. Advanced control applied to hybrid electrical vehicle[A].Proceedings of the19th International Electric Vehicle Symposium[C]. Busan, Korea,2002:604-615.
[84]Barsali S, Ceraolo M, Possenti A. Techniques to control the electricity generation in a series hybridelectric vehicle[J]. IEEE Transactions on Energy Conversion,2002,17(2):260-266.
[85]林歆悠,孙冬野.新型混联式混合动力客车实时优化控制策略[J].中国公路学报,2012,25(5):152-158.
[86]程莺,冯能莲,李克强,等. ADVISOR混合动力电动汽车仿真系统的二次开发及应用[J].汽车工程,2004,26(3):249-252.
[87]曾小华,王庆年,李骏,等.基于ADVISOR2002混合动力汽车控制策略模块开发[J].汽车工程,2004,26(4):394-396,416.
[88]张翔,赵韩,钱立军,等.电动汽车仿真软件ADVISOR[J].汽车研究与开发,2003,103(4):14-16.
[89]张为,王伟达,余贵珍,等.基于V流程的驱动防滑控制系统控制器设计与试验[J].农业机械学报,2009,40(12):30-36.
[90]邹红明,丁能根,王伟达,等. ABS“V模式”开发中的快速控制器样件制作与硬件在环仿真的研究[J].汽车工程,2009,31(4):357-361.
[91]刘晓康，詹琼华，何葵，等.dSPACE快速控制原型在电池管理系统中的应用[J].电源技术，2006，30（9）：753-756.
[92]李建秋,田光宇,卢青春,等.利用V型开发模式研制燃料电池混合动力客车的整车控制器[J].机械工程学报,2005,41(12):30-36.
[93]万加富,李迪.基于MATLAB/SIMULINK模型的复杂测控制系统设计方法[J].计算机应用研究,2008,25(4):1016-1019.
[94]刘思久,孙莹.基于MATLAB/RTW的控制系统一体化设方法[J].哈尔滨理工大学学报,2004,9(5):29-32.
[95]朱庆林,王庆年,曾小华,等.基于V模式的混合动力汽车多能源动力总成控制器开发平台[J].吉林大学学报(工学版),2007,37(6):1242-1246.
[96]叶险.基于模型的汽油机电控系统快速原型的研究[D].成都:西华大学,2012.
[97]dSPACE产品手册[Z].北京：九州恒润有限公司,2007.
[98]Duma R, Dobra P. Rapid prototyping of control systems using embedded target for TI C2000DSP[A].Mediterranean Conference on Control and Automation[C]. Athens: IEEE Press,2007:1-5.
[99]方正,张淇淳,齐玉成.基于DSP的快速控制原型系统[J].东北大学学报(自然科学版),2009,30(8):1069-1073.
[100] Strobel M. Rapid control prototyping of automatic climate control systems[J]. AdvancedMicrosystems for Automotive Applications,2003,2(3):387-407.
[101]韩利竹,王华. Matlab电子仿真与应用[M].北京:国防工业出版社,2003:124-166.
[102] Mathworks Inc. Real time workshop user’s guide[M]. Natic: Mathworks Inc,2000:242-259.
[103]齐振恒,孙中杰,李涛. RTW嵌入式代码自动生成机制与代码结构分析[J].计算机测量与控制.2010.18(3):639-642.
[104]李强,王民钢,杨尧.快速原型中Simulink模型的代码自动生成[J].电子测量技术,2009,32(2):28-30.
[105]赵彦斌,钟再敏.基于代码自动生成技术的汽车电子实时控制软件开发[J].计算机辅助工程,2008,17(3):36-40.
[106]杭勇,刘学瑜.快速原型工具在高压共轨柴油机控制系统开发中的应用[J].现代车用动力,2004,(4):22-25.
[107] MicroAutoBox Installation and Configuration Guide for Release3.4[M]．Paderborn：dSPACEGmbH,2002.
[108]刘学瑜，施光林，范永健，等．dSPACE实时仿真系统在高压共轨ECU开发中的应用[J]，现代车用动力,2003,(1):20-22．
[109]刘浩,谢桦,姜久春,等.纯电动汽车用锂离子电池SOC估算方案的研究[J].冶金电气,2010,29(12):54-58.
[110]劳力.动力蓄电池管理系统SOC算法研究[D].北京:北京交通大学,2007.
[111] Plett G. Extended Kalman filtering for battery management systems of LiPB-based HEV batteryPacks. Part2: Modeling and identifieation[J]. Journal of power Sources,2004,134(2):277-292.
[112] Sabine P, Marion P, Andreas J. Methods for state-of-charge determination and theirapplications[J]. J Power Sources,2001,96(1):113-120.
[113]林成涛,王军平,陈全世.电动汽车SOC估计方法原理与应用[J].电池,2004,34(5):376-378.
[114]毛群辉,滕召胜,方亮,等.基于UKF的电动汽车锂电池SOC估计方法[J].测控技术,2010,29(3):89-91.
[115]林成涛,仇斌,陈全世.电动汽车电池功率输入等效电路模型的比较研究[J].汽车工程,2006,28(3):229-234.
[116]夏超英，张术，孙宏涛.基于推广卡尔曼滤波算法的S O C估算策略[J].电源技术,2007,31(5):414-417.
[117]袁方伟,陈思忠.电动汽车电池管理系统的研究[J].汽车研究与开发,2003,(3):41-44.
[118]廖晓军,周红丽,钟志华等.电池管理系统国内外现状及其未来发展趋势[J].汽车工程,2006,28(10):961-964.
[119] SimPowerSystems[EB/OL].http://www.mathworks.cn/products/simpower/,2012-8-26.
[120]范波,田晓辉,马建伟.基于EKF的动力锂电池SOC状态预测[J].电源技术,2010,34(8):797-799.
[121] Motorola, Inc. Motorola MPC555/565User’s Manual[Z]. Motorola Literature Distribution,2000.
[122]欧洲经济委员会（ECE）汽车标准法规中文译本——有关M、N和O类车辆制动认证的统一规定[S].2001.
[123] Yimin Gao, Liping Chen, Mehrdad Ehsani. Investigation of the Effectiveness of RegenerativeBraking for EV and HEV[J]. SAE paper,1999-01-2910.
[124] Yimin Gao, Mehrdad Ehsani. Electrinic Braking System of EV and HEV Integration ofRegenerative Braking, Automatic Braking Forces Control and ABS [J]. SAE paper,2001-01-2478.
[125] GB/T19754-2005重型混合动力电动汽车能量消耗量试验方法[S].北京：中国标准出版社,2005.
[2]万钢.中国“十五”电动汽车重大科技专项进展综述[J].中国科技产业,2006,(2):110-117.
[3]田振中,彭晗,薛海培.基于主成分和BP神经网络的汽车保有量预测[J].产业经济与管理,2008,10(上):173-175.
[4] Ramesh S. Remanufacturing for the automotive aftermarket-strategic factors: literature review andfuture research needs[J]. Journal of Cleaner Production,2009,17(13):1163-1174.
[5]兰召华,黄妙华.对国内外几个电动汽车发展计划及相关策略的研究[J].汽车研究与开发,2000,(5):43-48.
[6] Lenny B, Peter B, Osvaldo C, et al. Climate change2007: synthesis report, summary forpolicymakers[R]. Valencia, Spain: Intergovernmental Panel on Climate Change (IPCC),2007.
[7]何涛.电动汽车整车控制器软件设计及关键技术研究[D].北京:清华大学,2010.
[8]詹宜巨,陈清泉.电动车技术发展及前景展望[J].电气传动,1997,(5):40-44.
[9] Chan C C. The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles[J]. Proceedings of the IEEE,2007,95(4):704-718.
[10]Lam L T, Louey R. Development of ultra-battery for hybrid-electric vehicle applications[J]. Journal ofPower Sources,2006,158(2):1140–1148.
[11]Kelly K, Eudy L. Field operations program--overview of advanced technology transportation[R].Golden, CO, USA: National Renewable Energy Laboratory,2000.
[12]Yoo H J, Sul S-K, Park Y, Jeong J. System Integration and Power-Flow Management for a SeriesHybrid Electric Vehicle Using Supercapacitors and Batteries[J]. IEEE Transactions on IndustryApplications,2008,44(1):108–114.
[13]Schouten N J，Salman M A，Kheir N A．Energy management strategies for parallel hybrid vehiclesusing fuzzy logic[J]．Control Engineering Practice,2003,11(2):171-177.
[14]李兴虎．电动汽车概论[M]．北京:北京理工大学出版社,2005:167-216.
[15]颜增品.世界电动汽车用电池技术的发展现状[J].世界汽车,1997(10):9-11.
[16]刘剑雄,管前新.当前世界电动汽车发展概况[J].汽车技术,1997(5):56-59.
[17]熊建,管华.混合动力电动客车的发展及其产业化[J].客车技术与研究,2002,24(3):4-6.
[18]Bradley G, Gregory W, Giorgio R. Operation and control strategies for hybrid electric automobiles[C].SAE2000-01-1537,2000.
[19]张旸.混合动力客车动力系统设计及参数优化[D].合肥:合肥工业大学,2010.
[20]Fritz R K, Haresh K, Mark D, et al. Plug-In Hybrid Electric Vehicles: Promise, Issues and Prospects[A].EVS24International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium[C].2009:1-11.
[21]Environmental Assessment of Plug-In Hybrid Electric Vehicles, Volume1: Nationwide GreenhouseGas Emissions[R]. California, USA: Electric Power Research Institute (EPRI),2007.
[22]叶明,舒红,陈然.插电式混合动力客车工作模式切换控制研究[J].中国公路学报,2012,25(1):141-145.
[23]张松,吴光强,郑松林.插电式混合动力汽车能量管理策略多目标优化[J].同济大学学报(自然科学版),2011,39(7):1035-1039,1044.
[24]Rahman S A, Zhang N, Zhu J. A comparison on fuel economy and emissions for conventional hybridelectric vehicles and the UTS plug-in hybrid electric vehicle[A]. Proc.2nd Int. Conf. Comput. Autom.Eng.[c]. Singapore,2010:20–25.
[25]Fajri P, Asaei B. Plug-in hybrid conversion of a series hybrid electric vehicle and simulationcomparison[A]. Proc.11th Int. Conf. OPTIM[C]. Brasov, Romania,2008:287–292.
[26]Jin L, Zeng X, Wang W. The control strategy and cost analysis for series plug-in hybrid electricvehicle[A]. Proc.2nd ICACC[C]. Shenyang, China,2010:350–354.
[27]李绍梅. Plug-in混合动力汽车动力电池SOC估计研究[D].济南:山东大学,2010.
[28]戎品慈.混合动力汽车现状与发展[J].农业装备与车辆工程,2008,(7):5-8.
[29]陈树勇,陈全世,仇斌,等. Plug-In HEV的研究与发展[A].清洁汽车技术创新发展论坛[C],2008.
[30]舒红,聂天雄,邓丽君,等.插电式并联混合动力汽车模型预测控制[J].重庆大学学报,2011,34(5):36-41.
[31]Gonder J, Markel T. Energy management strategies for plug-in hybrid electric vehicle[A].2007SAEInternational World Congress[C]. Detroit, Michigan,2007.
[32]Axsen J, Kurani K S. The early U.S. market for PHEVs: anticipating consumer awareness, rechargepotential, design priorities and energy impacts[R]. Davis, U.S.A.: Institute of Transportation StudiesUniversity of California,2008.
[33]胡欢.插电式混合动力汽车现状[J].城市车辆,2009,(7):43-44.
[34]李锦,徐兆坤,许建昌.浅谈PHEV的发展现状及趋势[J].上海汽车,2009,(28):10-12.
[35]钮翔. Plug-in混合动力公交车整车综合控制策略研究[D].北京:北京理工大学,2008.
[36]Plug-In Hybrid Electric Sprinter Prototype Expansion Program[R]. California, USA: Electric PowerResearch Institute (EPRI),2004.
[37]Hybrid Electric School Bus Project[EB/OL]. Http://www.hybridschoolbus.org,2010-11-21.
[38]Michael M, Del P. Advanced Small Transit Vehicle Program[R].2006.
[39]美国能源部公开12插电式混合动力车的燃效试验结果[J].汽车与配件,2009,(22):14.
[40]夏文川.燃料电池系统电子控制单元[D].北京:清华大学,2005.
[41]刘原.32位车用控制器平台技术的研究与应用[D].北京:清华大学,2004.
[42]庄毅胜,黄妙华,周星亮.基于拓扑优化的电动游览车车身优化设计[J].轻型汽车技术,2009(7/8):10-14.
[43]刘碧军,杨林,朱建新,等.电动汽车高压电安全测试系统的研究[J].汽车工程,2005,27(4):274-277.
[44]牛礼民.辅助动力电动汽车整车匹配及电机控制系统研究[D].合肥:安徽农业大学,2005.
[45]孙立志,赵辉,陆永平,等.电动汽车中的电机驱动系统[J].电工电能新技术,1997,(4):14-19.
[46]Chan C C, Chau K T. An Overview of power Electronics in Electric Vehieles[J]. IEEE Transactions onIndustrial Electronics,1997,44(1):3-13.
[47]张承宁,孙逢春,余晓江.电动汽车的几种驱动系统比较[J].兵工学报(坦克装甲车与发动机分册),1995,(4):36-39.
[48]Wang J P, Cao B G, Cheng Q S. Combined state of charge estimator for electric vehicle battery pack[J].Control Engineering Practice,2007,15(12):1569-1576.
[49]黄文华,韩晓东,陈全世,等.电动汽车SOC估计算法与电池管理系统的研究[J].汽车工程,2007,29(3):198-202.
[50]Cai C H, Du D, Liu Z Y. Battery State-of-Charge (SoC) Estimation Using Adaptive Neuro-FuzzyInference System[A].The IEEE International Conferenee on FuzzySystems[C]. ST.LOUISMO,USA,2003:234-240.
[51]Fellner C, Newman J. High-power batteries for use in hybrid vehicles[J]. Journal of Power Sources,2000,85:29-236.
[52]Kennedy B, Patterson D, Camilleri S. Use of lithium-ion batteries in electric vehicles[J]. Journal ofPower Sources,2000,90:156-162.
[53]魏学哲,孙泽昌,邹广楠.模块化的HEV锂离子电池管理系统[J].汽车工程,2004,26(6):629-631.
[54]徐梁飞，卢兰光，李建秋，等．燃料电池混合动力系统建模及能量管理算法仿真［J］．机械工程学报，2009，45(1):141-147．
[55]李卫民.混合动力汽车控制系统与能量管理策略研究[D].上海:上海交通大学,2008.
[56]Powers W F, Nicastri P R. Automotive vehicle control challenges in the21st century[J]. ControlEngineering Practice,2000,8(6):605-618.
[57]吴晓刚,王旭东,毛亮.ISG型混合动力汽车能量管理模糊控制的研究[J].汽车工程,2011,33(7):558-562,568.
[58]王伟华,王庆年,曾小华.并联混合动力汽车自适应控制策略［J］．汽车工程，2009，31(9):824-838．
[59]白中浩，王耀南，曹立波．混合动力电动汽车能量自适应模糊控制研究［J］．汽车工程，2005，27(4):389-391．
[60]邓元望,龚金科,王耀南.轻度混合控制策略下混合动力电动汽车能量优化与仿真[J].中国公路学报,2008,21(6):114-120.
[61]张欣,刘溧,于海生.混合动力电动汽车制动系统回馈特性仿真[J].中国公路学报,2006,19(3):111-116.
[62]叶敏,安强,程博,等.电动汽车主辅电源能量回馈研究[J].系统仿真学报,2007,29(23):5434-5437.
[63]王军,熊冉,杨振迁.纯电动大客车制动能量回收系统控制策略研究[J].汽车工程,2009,31(10):932-937.
[64]李玉芳,林逸,何洪文,等.电动汽车再生制动控制算法研究[J].汽车工程,2007,29(11):1059-1062.
[65]张毅,杨林,朱建新,等.电动汽车能量回馈的整车控制[J].汽车工程,2005,27(1):24-27.
[66]Ahn J K, Jung K H, Kim D H, et al. Analysis of a regenerative braking system for Hybrid ElectricVehicles using an Electro-Mechanical Brake[J]. International Journal of Automotive Technology,2009,10(2):229-234.
[67]徐明辉.重型商用车气动AMT故障诊断技术研究[D].北京:北京理工大学,2003.
[68]Liao Chenglin, Zhang Junzhi, Lu Qingchun. Coordinated powertrain control method for shiftingprocess of automated mechanical transmission in the hybrid electric vehicle[J]. Chinese Journal ofMechanical Engineering,2005,41(12):37-41.
[69]彭涛,陈全世,田光宇,等.并联混合动力电动汽车动力系统的参数匹配[J].机械工程学报,2003,39(2):69-73.
[70]郑维.混合动力汽车动力总成参数匹配方法与控制策略的研究[D].哈尔滨:哈尔滨工业大学,2010.
[71]杨为琛.混合电动公交车总体匹配、仿真及其控制系统研究[D].北京:北京理工大学,2002.
[72]初亮.混合动力总成的控制算法和参数匹配研究[D].长春:吉林大学,2002.
[73]节能与新能源汽车节油率与最大电功率比检验大纲[S].中国工业和信息化部，2009.
[74]关于开展节能与新能源汽车示范推广试点工作的通知[EB/OL].http://www.gov.cn/zwgk/2009-02/05/content_1222338.htm,2013-02-20.
[75]公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289.
[76]Schaffer J D. Some experiments in machine learning using vector evaluated genetic algorithms[D].Nashville, TN: Vanderbilt Univ.,1985.
[77]Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: Formulation,discussion and generalization[A]. Proceedings of the Fifth International Conference of GeneticAlgorithms[C]. San Mateo, CA: Morgan Kaufmann,1993:416-423.
[78]Horn J, Nafpliotis N, Goldberg D E. A Niched Pareto Genetic Algorithm for MultiobjectiveOptimization[A]. Proceedings of the First IEEE Conference on Evolutionary Computation[C].Piscataway, NJ: IEEE Service Center,1994:82-87.
[79]Srinivas N, Deb K. Multiobjective function optimization using nondominated sorting geneticalgorithms[J]. Evol. Comput.,1995,2(3):221–248.
[80]钟勇,钟志华,余群明,等.电动汽车CAN总线通用协议的应用研究[J].汽车工程,2006,28(5):422-426,438.
[81]李永强,宋希庚,薛冬新. CAN局域网及J1939协议在货车和客车上的运用[J].汽车工程,2003,25(4):377-340.
[82]舒红,秦大同,胡建军.混合动力汽车控制策略研究现状及发展趋势[J].重庆大学学报（自然科学版）,2001,24(6):28-31.
[83]Pusca R, Ait-Amirat Y, Berthon A, et al. Advanced control applied to hybrid electrical vehicle[A].Proceedings of the19th International Electric Vehicle Symposium[C]. Busan, Korea,2002:604-615.
[84]Barsali S, Ceraolo M, Possenti A. Techniques to control the electricity generation in a series hybridelectric vehicle[J]. IEEE Transactions on Energy Conversion,2002,17(2):260-266.
[85]林歆悠,孙冬野.新型混联式混合动力客车实时优化控制策略[J].中国公路学报,2012,25(5):152-158.
[86]程莺,冯能莲,李克强,等. ADVISOR混合动力电动汽车仿真系统的二次开发及应用[J].汽车工程,2004,26(3):249-252.
[87]曾小华,王庆年,李骏,等.基于ADVISOR2002混合动力汽车控制策略模块开发[J].汽车工程,2004,26(4):394-396,416.
[88]张翔,赵韩,钱立军,等.电动汽车仿真软件ADVISOR[J].汽车研究与开发,2003,103(4):14-16.
[89]张为,王伟达,余贵珍,等.基于V流程的驱动防滑控制系统控制器设计与试验[J].农业机械学报,2009,40(12):30-36.
[90]邹红明,丁能根,王伟达,等. ABS“V模式”开发中的快速控制器样件制作与硬件在环仿真的研究[J].汽车工程,2009,31(4):357-361.
[91]刘晓康，詹琼华，何葵，等.dSPACE快速控制原型在电池管理系统中的应用[J].电源技术，2006，30（9）：753-756.
[92]李建秋,田光宇,卢青春,等.利用V型开发模式研制燃料电池混合动力客车的整车控制器[J].机械工程学报,2005,41(12):30-36.
[93]万加富,李迪.基于MATLAB/SIMULINK模型的复杂测控制系统设计方法[J].计算机应用研究,2008,25(4):1016-1019.
[94]刘思久,孙莹.基于MATLAB/RTW的控制系统一体化设方法[J].哈尔滨理工大学学报,2004,9(5):29-32.
[95]朱庆林,王庆年,曾小华,等.基于V模式的混合动力汽车多能源动力总成控制器开发平台[J].吉林大学学报(工学版),2007,37(6):1242-1246.
[96]叶险.基于模型的汽油机电控系统快速原型的研究[D].成都:西华大学,2012.
[97]dSPACE产品手册[Z].北京：九州恒润有限公司,2007.
[98]Duma R, Dobra P. Rapid prototyping of control systems using embedded target for TI C2000DSP[A].Mediterranean Conference on Control and Automation[C]. Athens: IEEE Press,2007:1-5.
[99]方正,张淇淳,齐玉成.基于DSP的快速控制原型系统[J].东北大学学报(自然科学版),2009,30(8):1069-1073.
[100] Strobel M. Rapid control prototyping of automatic climate control systems[J]. AdvancedMicrosystems for Automotive Applications,2003,2(3):387-407.
[101]韩利竹,王华. Matlab电子仿真与应用[M].北京:国防工业出版社,2003:124-166.
[102] Mathworks Inc. Real time workshop user’s guide[M]. Natic: Mathworks Inc,2000:242-259.
[103]齐振恒,孙中杰,李涛. RTW嵌入式代码自动生成机制与代码结构分析[J].计算机测量与控制.2010.18(3):639-642.
[104]李强,王民钢,杨尧.快速原型中Simulink模型的代码自动生成[J].电子测量技术,2009,32(2):28-30.
[105]赵彦斌,钟再敏.基于代码自动生成技术的汽车电子实时控制软件开发[J].计算机辅助工程,2008,17(3):36-40.
[106]杭勇,刘学瑜.快速原型工具在高压共轨柴油机控制系统开发中的应用[J].现代车用动力,2004,(4):22-25.
[107] MicroAutoBox Installation and Configuration Guide for Release3.4[M]．Paderborn：dSPACEGmbH,2002.
[108]刘学瑜，施光林，范永健，等．dSPACE实时仿真系统在高压共轨ECU开发中的应用[J]，现代车用动力,2003,(1):20-22．
[109]刘浩,谢桦,姜久春,等.纯电动汽车用锂离子电池SOC估算方案的研究[J].冶金电气,2010,29(12):54-58.
[110]劳力.动力蓄电池管理系统SOC算法研究[D].北京:北京交通大学,2007.
[111] Plett G. Extended Kalman filtering for battery management systems of LiPB-based HEV batteryPacks. Part2: Modeling and identifieation[J]. Journal of power Sources,2004,134(2):277-292.
[112] Sabine P, Marion P, Andreas J. Methods for state-of-charge determination and theirapplications[J]. J Power Sources,2001,96(1):113-120.
[113]林成涛,王军平,陈全世.电动汽车SOC估计方法原理与应用[J].电池,2004,34(5):376-378.
[114]毛群辉,滕召胜,方亮,等.基于UKF的电动汽车锂电池SOC估计方法[J].测控技术,2010,29(3):89-91.
[115]林成涛,仇斌,陈全世.电动汽车电池功率输入等效电路模型的比较研究[J].汽车工程,2006,28(3):229-234.
[116]夏超英，张术，孙宏涛.基于推广卡尔曼滤波算法的S O C估算策略[J].电源技术,2007,31(5):414-417.
[117]袁方伟,陈思忠.电动汽车电池管理系统的研究[J].汽车研究与开发,2003,(3):41-44.
[118]廖晓军,周红丽,钟志华等.电池管理系统国内外现状及其未来发展趋势[J].汽车工程,2006,28(10):961-964.
[119] SimPowerSystems[EB/OL].http://www.mathworks.cn/products/simpower/,2012-8-26.
[120]范波,田晓辉,马建伟.基于EKF的动力锂电池SOC状态预测[J].电源技术,2010,34(8):797-799.
[121] Motorola, Inc. Motorola MPC555/565User’s Manual[Z]. Motorola Literature Distribution,2000.
[122]欧洲经济委员会（ECE）汽车标准法规中文译本——有关M、N和O类车辆制动认证的统一规定[S].2001.
[123] Yimin Gao, Liping Chen, Mehrdad Ehsani. Investigation of the Effectiveness of RegenerativeBraking for EV and HEV[J]. SAE paper,1999-01-2910.
[124] Yimin Gao, Mehrdad Ehsani. Electrinic Braking System of EV and HEV Integration ofRegenerative Braking, Automatic Braking Forces Control and ABS [J]. SAE paper,2001-01-2478.
[125] GB/T19754-2005重型混合动力电动汽车能量消耗量试验方法[S].北京：中国标准出版社,2005.