纯电动公交客车加速踏板驾驶特性辅助优化策略研究
|
摘要
降低运行能耗，延长续驶里程，对于发展纯电动公交客车，应对能源和环境问题，具有重要意义。鉴于司机驾驶行为对整车运行能耗有很大影响，本文深入研究驾驶行为对能耗的影响规律，并提出加速踏板的驾驶特性优化算法，对于降低电动公交客车运行能耗，具有重要的理论意义和实用价值。
本文根据课题组提出的公交线路全程多尺度综合规划基本思想，提出了一套实现能量规划、速度规划和驾驶规划的实施方法。根据公交客车进出站的能耗特性，重点提出了基于模型预测的加速踏板驾驶策略优化模型，通过对司机驾驶行为的主动管理，有效降低电动公交客车线路运行能耗。
研究开发了基于GPRS网络的电动公交客车运行无线远程监控标定系统，发展了电动公交客车主控制器软硬件。主控制器和无线远程监控标定系统可按照标准协议相互通信，既有效采集车辆线路运行的基本信息，又可实现对加速踏板驾驶策略数据区的远程配置。开发了无线远程通信品质控制算法，改善了数据传送的连续性和准确性，为客车线路运行特性分析和控制策略验证试验提供了保障。
依托无线远程监控平台，采集了天津市600路电动公交客车的大量运行数据，系统分析了其工况运行和能耗特征。结果表明，客车出站过程所消耗的能量占到线路总能耗的47%以上。由于司机驾驶行为的差异，导致出站能耗差异可达到28.9%，其根本原因在于，出站过程中加速踏板开度分布影响了电机工况点在效率平面的运行路径。另外，进站过程中司机对车速的预测准确性，对滑行能量回收有决定性的影响。通过对进出站过程中加速踏板的合理规划，减少其加速度的离散程度，实现对电机工况轨迹的优化，是降低电动公交客车能耗的有效途径。
系统研究了与能耗相关的司机行为特征时域变量（包括踏板开度、车速、加速度和加速度变化率等参数）的分布情况。根据加速踏板开度信号曲线的倒频谱特征，研究建立了用于司机驾驶行为特征辨识的高斯混合模型(GMM)算法，准确度达到93%以上。构建了基于RBF神经网络的虚拟驾驶员模型，搭建了基于dSPACE的硬件在环仿真实时测试环境。
研究提出了基于多维线性空间匹配的扭矩预测方法，依据当前已行驶的片段，在司机驾驶特征向量多维空间中匹配出相似的历史向量轨迹，由此给出司机的扭矩需求，其预测精度达到88%以上。以实际车速与目标车速的偏差最小化为优化目标，对加速踏板的驾驶过程进行规划。仿真表明，驾驶规划降低了行车加速度的离散程度，使能耗改善10.5%。实车验证表明，在司机驾驶操作未受影响的前提下，能耗降低了7.2%。
The research of decreasing energy consumption and extension of driving range isa key problem to the development of the pure electric buses besides confronting theenergy crisis and the environmental problems. However, due to inappropriate driverbehaviors the practical endurance mileage of the electric buses are significantlyreduced. In this research, the relationship between the difference of driver behaviorand energy consumption was investigated specifically and it was focused to improvethe practical endurance mileage based on the real-time optimization of driving abilityof the acceleration pedal.
The energy optimization algorithm of multi-level programming for the electricbuses, comprised of the driving mission energy programming，the bus station speedprogramming and the preview distance acceleration pedal programming respectively,were put forward, and the pedal programming algorithm was focused to improve theenergy consumption.
A data acquisition system based on GPRS network was developed to meet theneed of wireless real-time data recording and remote control variables configuration.An vehicle control unit was developed to drive the electic bus which cancommunicate with the data acquisition system in high communication quality underthe assurance of the wireless communication quality strategy.
The relationship between the difference of the driver behavior and energyconsumption was studied through analyzing the main driving fragment of the drivingcycles of the route600electric bus in Tianjin. It shows that47%of the energy wasconsumed in the process of out station and the difference reached28.9%between thedrivers. The essential reason of the difference was the deviaton of the motoroperating path in the efficiency plane which was the result of acceleration pedal usedin different way. The distribution of acceleration pedal was useful to experss theaccuracy of vehicle speed prediction and the tendency of deceleration of drivers in theinlet parking process. The more energy was recycled in the inlet parking process, thehigher efficiency of the energy recovery in the whole driving cycle. An effectivemanagement to the motor operating path in the out station process is very helpful to the improvement of the whole energy consumption besides the minimization of theacceleration dispersion.
Several key variables describing the characteristics of driver behavior weredefined, and an effective Gaussian Mixture Model (GMM) identification model of thedrivers, with accuracy higher than93%, was established by extracting the cepstrum ofthe pedal signal besides the key variables mentioned before. A hardware-in-the-loopsimulation model was set up based on dSPACE platform which the key part is thevirtual driver model with real characteristics of the driver based on the Radial BasisFunction (RBF) network.
A vector set of driving characteristics of an electric bus driver was defined and amultidimensional linear space was used to describe the running track of the drivingstatus. The torque demand was predicted against historical running tracks withaccuracy of over88%in the driving characteristics vector set of the driver which wasidentified before. The pedal programming algorithm was constructed based on thefuture torque demand prediction. A model predictive control strategy was developedto obtain active management of the driver behavior, in order to minimize the deviationbetween the current and target car speed.
Simulation results showed that the acceleration dispersion of the electric bus wasreduced and10.5%of the energy was saved. Experimental results showed that thedriver was not significantly influenced by the regulating process, and7.2%of theenergy was saved.
本文根据课题组提出的公交线路全程多尺度综合规划基本思想，提出了一套实现能量规划、速度规划和驾驶规划的实施方法。根据公交客车进出站的能耗特性，重点提出了基于模型预测的加速踏板驾驶策略优化模型，通过对司机驾驶行为的主动管理，有效降低电动公交客车线路运行能耗。
研究开发了基于GPRS网络的电动公交客车运行无线远程监控标定系统，发展了电动公交客车主控制器软硬件。主控制器和无线远程监控标定系统可按照标准协议相互通信，既有效采集车辆线路运行的基本信息，又可实现对加速踏板驾驶策略数据区的远程配置。开发了无线远程通信品质控制算法，改善了数据传送的连续性和准确性，为客车线路运行特性分析和控制策略验证试验提供了保障。
依托无线远程监控平台，采集了天津市600路电动公交客车的大量运行数据，系统分析了其工况运行和能耗特征。结果表明，客车出站过程所消耗的能量占到线路总能耗的47%以上。由于司机驾驶行为的差异，导致出站能耗差异可达到28.9%，其根本原因在于，出站过程中加速踏板开度分布影响了电机工况点在效率平面的运行路径。另外，进站过程中司机对车速的预测准确性，对滑行能量回收有决定性的影响。通过对进出站过程中加速踏板的合理规划，减少其加速度的离散程度，实现对电机工况轨迹的优化，是降低电动公交客车能耗的有效途径。
系统研究了与能耗相关的司机行为特征时域变量（包括踏板开度、车速、加速度和加速度变化率等参数）的分布情况。根据加速踏板开度信号曲线的倒频谱特征，研究建立了用于司机驾驶行为特征辨识的高斯混合模型(GMM)算法，准确度达到93%以上。构建了基于RBF神经网络的虚拟驾驶员模型，搭建了基于dSPACE的硬件在环仿真实时测试环境。
研究提出了基于多维线性空间匹配的扭矩预测方法，依据当前已行驶的片段，在司机驾驶特征向量多维空间中匹配出相似的历史向量轨迹，由此给出司机的扭矩需求，其预测精度达到88%以上。以实际车速与目标车速的偏差最小化为优化目标，对加速踏板的驾驶过程进行规划。仿真表明，驾驶规划降低了行车加速度的离散程度，使能耗改善10.5%。实车验证表明，在司机驾驶操作未受影响的前提下，能耗降低了7.2%。
The research of decreasing energy consumption and extension of driving range isa key problem to the development of the pure electric buses besides confronting theenergy crisis and the environmental problems. However, due to inappropriate driverbehaviors the practical endurance mileage of the electric buses are significantlyreduced. In this research, the relationship between the difference of driver behaviorand energy consumption was investigated specifically and it was focused to improvethe practical endurance mileage based on the real-time optimization of driving abilityof the acceleration pedal.
The energy optimization algorithm of multi-level programming for the electricbuses, comprised of the driving mission energy programming，the bus station speedprogramming and the preview distance acceleration pedal programming respectively,were put forward, and the pedal programming algorithm was focused to improve theenergy consumption.
A data acquisition system based on GPRS network was developed to meet theneed of wireless real-time data recording and remote control variables configuration.An vehicle control unit was developed to drive the electic bus which cancommunicate with the data acquisition system in high communication quality underthe assurance of the wireless communication quality strategy.
The relationship between the difference of the driver behavior and energyconsumption was studied through analyzing the main driving fragment of the drivingcycles of the route600electric bus in Tianjin. It shows that47%of the energy wasconsumed in the process of out station and the difference reached28.9%between thedrivers. The essential reason of the difference was the deviaton of the motoroperating path in the efficiency plane which was the result of acceleration pedal usedin different way. The distribution of acceleration pedal was useful to experss theaccuracy of vehicle speed prediction and the tendency of deceleration of drivers in theinlet parking process. The more energy was recycled in the inlet parking process, thehigher efficiency of the energy recovery in the whole driving cycle. An effectivemanagement to the motor operating path in the out station process is very helpful to the improvement of the whole energy consumption besides the minimization of theacceleration dispersion.
Several key variables describing the characteristics of driver behavior weredefined, and an effective Gaussian Mixture Model (GMM) identification model of thedrivers, with accuracy higher than93%, was established by extracting the cepstrum ofthe pedal signal besides the key variables mentioned before. A hardware-in-the-loopsimulation model was set up based on dSPACE platform which the key part is thevirtual driver model with real characteristics of the driver based on the Radial BasisFunction (RBF) network.
A vector set of driving characteristics of an electric bus driver was defined and amultidimensional linear space was used to describe the running track of the drivingstatus. The torque demand was predicted against historical running tracks withaccuracy of over88%in the driving characteristics vector set of the driver which wasidentified before. The pedal programming algorithm was constructed based on thefuture torque demand prediction. A model predictive control strategy was developedto obtain active management of the driver behavior, in order to minimize the deviationbetween the current and target car speed.
Simulation results showed that the acceleration dispersion of the electric bus wasreduced and10.5%of the energy was saved. Experimental results showed that thedriver was not significantly influenced by the regulating process, and7.2%of theenergy was saved.
引文
[1].国际能源署，全球能源展望报告，2011
[2].王海艳，中国能源警世通言:六成石油将依赖进口，南方都市报，2008
[3].魏曙光，去年我国石油对外依存度升至51.3%，证券时报，2009
[4].欧阳明高，发展节能环保汽车实现能源转型与产业振兴，全国政协十一届二次会议，2009
[5]. Green Car congress. http://www.greencarcongress.com/electric battery/index.html.
[6]. Energy Information Administration, Official Engergy Statistics from the U.S.Government.http://www.eia.doe.gov/oil_gas/petroleum/info_glance/petroleum.html.
[7].陈清泉，孙逢春，现代电动车技术，北京：北京理工大学出版社，2002.96~97
[8]. Michael H. The Electric Car,The Institution of Electrical Engineers,2001
[9]. Baldasano, J. Soriano, C. Boada. Emission Inventory for Greenhouse Gases in theCity of Barcelona. Atmospheric Environment,1999,33(23):59~65
[10].天相投资顾问有限公司，汽车行业2009年研究报告,2009
[11].万沛霖，电动汽车的关键技术，北京：北京理工大学出版社，1998
[12]. Mehrdad Ehsani,Yimin Gao.Modern Electric hybrid Electric and Fuel CellVehiucles:Fundamentals,Theory, and Design, CRC Press,2010.124~125
[13]. Joel C. McCall, Mohan M. Trivedi Driver Behavior and Situation Aware BrakeAssistance for Intelligent Vehicles. Proceedings of the IEEE.2007,95(2):374~387
[14]. Arif Mehmood. Application of System Dynamics in Car-Following Models.Journal of Transportation Engineering.2003,129(6):625~634
[15]. Mingyuan Bian. Road Condition Estimation for Automotive Anti-Skid ControlSystem Based on BP Neural Network. Proceedings of the IEEE InternationalConference on Mechatronics&Automation.2005:1017~1022
[16].周东华，叶银忠，现代故障诊断与容错控制，北京：清华大学出版社，2000
[17].李俊松，基于神经网络的混合动力汽车故障诊断研究：[硕士学位论文]，武汉；武汉理工大学，2003
[18]. Mohamed El Hachemi Benbouzid. Advanced Fault-Tolerant Control ofInduction-Motor Drives for EV/HEV Traction Applications: From Conventionalto Modern and Intelligent Control Techniques. IEEE Transactions on VehicleTechnology,2007,56(2):519~528
[19]. J. A. Autonino, J. Rusek, M. Riera. Case Histories in large motor: Diagnosis ofElectromechanical Faults through Extraction of Characteristic ComponentsDuringthe Startup. IEEE International Symposium on Semped Cracow,2007:161~166
[20]. Bellini, Alberto, Filippetti, Fiorenzo. On-field Experience with Online Diagnosisof Large Induction Motors Cage Failures Using MCSA. IEEE Trans. on IndustryApplications.2002,38(4):1045~1053
[21]. R. Ong, J. H. Dymond, R. D. Findlay. Bearing Damage Analysis in a LargeOil-ring-lubricated Induction Machine. IEEE Transaction on IndustrialElectronics.2000,47(5):1085~1089
[22].朱建新，郑荣良，电动汽车高压电安全诊断与控制策略的研究，汽车工程，2007，29（4）：308~312
[23].刘晓俊，电池故障智能诊断系统的研究与实现：[硕士学位论文]，北京；北京邮电大学，2010
[24].张又伟，基于学习算法的公交电动汽车故障诊断模型及应用研究：[硕士学位论文]，北京；北京交通大学，2007
[25]. X. Zhang, P. N. Ross. Diagnostic Characterization of High Power Lithium-IonBatteries for Use in Hybrid Electric Vehicles. Journal of The ElectrochemicalSociety,2001,148(5):463~470
[26]. Chris Bingham, Power Energy Storage Technologies and EnergyManagement.3th Advanced Engine Control Symposium,TianJin,2010
[27]. Phatiphat Thounthong, Control Strategy of Fuel Cell/Supercapacitors HybridPower Sources for Electric Vehicle. Journal of Power Sources,2006(158):806~814
[28]. Ke Li,Chenghui Zhang,Naxin Cui.Am Improved Energy Optimization ControlStrategy for Electric Vehicle Drive System.Chinese Control and DecisionConferenee,Chinese.2008:2244~2249
[29].白志峰，张传伟，电动汽车驱动与再生制动的鲁棒控制，西安交通大学学报，2005，39（3）：256~260
[30]. Chia-Cheng Wueng, Yi-Hsiang Yang. An Improved Regenerative BrakingControl Strategy and System for Dual Motor Electric Vehicle. The25thInternational Battery, Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010.138~142
[31]. Y. Gao, L. Chen, and M. Ehsani, Investigation of the effectiveness ofregenerative braking for EV and HEV,SAE Journal, SP-1466,1999.1999-01-2901
[32]. Y. Gao and M. Ehsani, Electronic Braking System of EV and HEV-integrationof Regenerative Braking, automatic braking force control and ABS, inProceedings of the SAE2001Future Transportation Technology Conference,2001.Paper No.2001-01-2478,
[33]. Kim, D., Oh, K., Yeo, H. Operation and Brake Force Distribution Algorithm for a4WD HEV.20th Electric Vehicle Symposium,2003.1214~1219
[34]. Jeongmin Kim, Sangmok Lee. Analysis of Regenerative Braking Patterns forHybrid Electric Vehicles. The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010.3458~3462
[35]. F. Wang, and B. Zhuo, Regenerative Braking Strategy for Hybrid ElectricVehicles based on Regenerative Torque Optimization Control, Proc. IMechE,Part D: J. Automobile Engineering,2008,222(12):499~513,
[36]. H. Yeo, H. Kim, Hardware-in-the-loop Simulation of Regenerative Braking for aHybrid Electric Vehicle, Proc. IMechE, Part D: J. Automobile Engineering,2002,216(15):855~864
[37]. C. Albrichsfeld, and J. Karner, Brake System for Hybrid and Electric Vehicles,SAE,2009.2009-01-1217,
[38]. Jeongmin Kim, Sangmok Lee. Analysis of regenerative braking patterns forhybrid electric vehicles. The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010.2489~2493
[39]. J. Fox, R. Roberts, C. Baier-Welt, L. M. Ho, L. Lacraru, and B. Gombert,Modeling and Control of a Single Motor Electronic Wedge Brake, SAE paper2007-01-0866,2007
[40]. B. Cho, J. Oh, and W. Lee, Modeling of Pulse Width Modulation PressureControl System for Automatic Transmission, SAE paper2002-01-1257,2002.
[41]. Yimin Gao, Liang Chu and M. Ehsani, Design and control principles of hybridbraking system for EV, HEV and FCV, Vehicle Power and PropulsionConference,2007.384~391
[42]. S. R. Cikanek and K. E. Bailey, Electric Vehicle Braking Systems, InternationalElectric Vehicle Symposium,1997.1458~1463
[43]. N. Mutoh, Y. Hayano, H. Yahagi and K. Takita, Electric braking control methodsfor electric vehicles with independently driven front and rear wheels,IEEE Trans.Industrial Electronics,2007,54(15):1168~1176
[44]. H. Yeo and H. Kim, Regenerative Braking Algorithm for a Hybrid ElectricVehicle with CVT Ratio Control,Proc. IMechE, Part D:1AutomobileEngineering,2006,220(4):1589~1600
[45]. L. Hellgren, E. Jonasson.Maximisation of Brake Energy Regeneration in aHybrid Electric Parallel Car,Electric and Hybrid Vehicles,2007,5:95~121
[46]. Zhong Z.M, Sun Z.C. A Fuzzy Logic based Regenerative Braking Regulation fora Fuel Cell Bus. In Proceedings of the IEEE International Conference onVehicular Electronics and Safety,2006.22-25
[47]. Zolot, Markel, Pesaran, A. Analysis of Fuel Cell Vehicle Hybridization andImplications for Energy Storage Devices. In Proceedings of the4th AdvancedAutomotive Battery Conference,2004.121~124
[48]. Zhang, C.W, Bai, Z.F. Study on Regenerative Braking of Electric Vehicle.InProceedings of the4th International Power Electronics and Motion ControlConference,2004,2.836~839
[49]. Binggang Cao,Zhifeng Bai. Research on Control for Regenerative Braking ofElectric Vehicle.IEEE Conference on Vehicluar Elecltronics andSafety,2005.92-97
[50]. S.DELPRAT. Optimal Control of a Parallerl Powertrain: from GlobalOptimization to Real Control Strategy, IEEE,2002.1345~1350
[51]. Kyoungcheol Oh, Hyunsoo Kim,Optimal Power Distribution Control for ParallelHybrid Electric Vehicles, Vehicular Electronics and Safety, InternationalConference,2005.79~85
[52]. Delprat Sebastien.Control Strategy Optimization for an Hybrid ParallelPowertrain，Proceedings of the American Control Conference Arlington,2001.25~27
[53]. Ilya Kolmanovskyt, Irina Siverguina, Bob Lygoe. Optimization of PowertrainOperating Policy for Feasibility Assessment and Calibration: Stochastic DynamicProgramming approach，Proceedings of the American Control Conference2002.8~10,
[54]. Chan-Chiao Lin, Huei Peng. Power Management Strategy for a Parallel HybridElectric Truck,IEEE Transactionson Control Systems Technology,2003,11(6):25~31
[55]. E.D. Tate,S. P. Boyd.Finding ultimate limits of performance for hybrid electricvehicles.SAE paper00FTT-50,1998
[56]. S. Delpar, J. Lauber, T.M. Guerra, J. Rimaux.Control of a parallel hybridpowertrain: optimal control, IEEE Trans.Vehicular Tech.,2004,53(3):872~881
[57]. H. Peng, J.W. Grizzle. Power Management strategy for a parallel hybrid electrictruck.,IEEE Trans. Cont. Syst. Tech,2003,11(6):839~848
[58]. Lin, H. Peng, J.W. Grizzle.A Stochastic Control Strategy for Hybrid ElectricVehicles, American Control Conference,2004,5:4710~4715
[59]. A. Piccolo, L. Ippolito, V. Galdi, A. Vaccaro.Optimization of enery flowmanagement in hybrid electric vehicles via genetic algorithms, Proc.IEEE/ASME Int. Conf. on Adv. Intelligent Mechanical,2001,1:434~439
[60]. Morteza Montazeri-Gh,Amir Poursamad,Babak Ghalichi.Application of geneticalgorithm for optimization of control strategy in parallel hybrid electricvehicles,Journal of the Franklin Institute,2006,343:420~435
[61]. P O. Barbarisi, E.R. Westervelt, F. Vasca, and G. Rizzoni.Power managementdecoupling control for a hybrid electric vehicle,44th IEEE Conf. Decision andControl,2005.2012~2017
[62]. P. Pisu, G. Rizzoni.H∞Control for Hybrid Electric Vehicles,43rd IEEEConference:Decision and Control,2004,4:3497~3502
[63]. S.Delprat,T.M.Guerra,G.Pagamelli.Control Strategy Optimization for an HybridParallel Powertrain,American Conference,2001,2:1315~1320
[64]. Patrik Thuring.Model Predictive Control Based Energy Management Algorithmfor a Hybrid Excavator,Sweden:Lund University,2008
[65]. Steve Yurkovich, Balaji Sampathnarayanan.Model Predictive Control as anEnergy Management Strategy for Hybrid Electric Vehicles. Proceedings of theASME2009Dynamic Systems and Control Conference,2009.249~256
[66]. M.J. West, C. M. Bingham N. Schofield. Predictive control for energymanagement in all/more electric vehicles with multiple energy storage units.Electric Machines and Drives Conference,2003,1:222~228
[67]. Patrik Thuring. Model Predictive Control Based Energy Management Algorithmfor a Hybrid Excavator,Department of Automatic Control,Lund University,2008
[68]. H. Ali Borhan Ardalan Vahidi. Model Predictive Control of a Power-split HybridElectric Vehicle with Combined Battery and Ultracapacitor Energy Storage.American Control Conference,2001.5031~5036
[69]. A. Phillips M. Kuang H. Borhan, A. Vahidi and I. Kolmanovsky.Predictiveenergy management of a power-split hybrid electric vehicle,American ControlConference,2009.3970~3976
[70]. R. Beck，S. Saenger. Model Predictive Control of a Parallel Hybrid VehicleDrivetrain,Proceedings of the44th IEEE Conference on Decision and Control,and the European Control Conference,2005.2670~2675
[71]. A. Vasebi，M. Partovibakhsh. Predicting State of Charge of Lead-acid Batteriesfor Hybrid Electric Vehicles by Extended Kalman filter,Energy Conversion andManagement,2008,49:75~82
[72]. Jiaxin Chen.Modeling and Performance Analysis of Energy regenerationsystemin electric vehicle with permanent magnet DC motor drivingsystem,Proceeding of International Conference on Electrical Machines andSystems,2007
[73]. Li Junwei.The Stability Control of Electric Vehicle Based on Optimal PredictiveControl method,2nd International Conference on Computer Engineering andTechnology,2010,4:500~504
[74]. Woonki Na, Taesik Park, Taehyung Kim.Light Fuel-Cell Hybrid ElectricVehicles Based on Predictive Controllers. IEEE Transaction on VehicleTechnology,2011,60(1):89~97
[75]. Fazal Urrahman Syed.Modeling and Control Methods for Improving Drivability,Power Management and Fuel Economy in a Hybrid ElectricVehicle,[Ph.D.dissertation],Wayne State University;Detroit,Michigan,2008
[76].罗玉涛，胡红斐，沈继军，混合动力电动汽车行驶工况分析与识别，华南理工大学学报，2007,24（6）：59~63
[77]. Joel Prieto, Jos′e A. Riveros, Blas Bogado.Electric Propulsion Technology Basedin Predictive Direct Torque Control and Asymmetrical Dual Three–Phase Drives.13th International IEEE Annual Conference on Intelligent TransportationSystems,2010,1:26~34
[78]. Sciarretta A, Guzzella L. Control of Hybrid Electric Vehicles. IEEE Transactionson Control Systems Technology,2007,27(2):60~70
[79]. Johannesson L.Assessing the Potential of Predictive Control for Hybrid VehiclePowertrains using Stochastic Dynamic Programming,Proceedings of the8thInternational IEEE Conference on Intelligent Transportation System,2005.366~371
[80]. Chen Zhang.Predictive Energy Management in Connected Vehicles utilizingroute information preview for energy saving:[Ph.D.Dissertation],ClemsonUniversity,2010
[81].夏超英，申彩英，基于改进型动态规划算法的串联混合动力汽车控制策略，控制理论与应用，2011，28（3）：428~431
[82].李升波，王建强，李克强，MPC实用化问题处理及在车辆ACC中的应用，清华大学学报，2010，50（5）：646~650
[83]. Carlos Bordons, Miguel A. Ridao.Model Predictive Control for PowerManagement in Hybrid Fuel Cell vehicles. Vehicle Power and PropulsionConference,2010
[84].舒红，蒋勇，中度混合动力汽车模型预测控制策略，重庆大学学报，2010，33（1）：36~41
[85]. H. Ali Borhan,Ardalan Vahidi. Predictive Energy Management of a Power-SplitHybrid Electric Vehicle,American Control Conference Hyatt Regency Riverfront,2009.3970~3976
[86]. Seneca Schepmann,Ardalan Vahidi. Heavy Vehicle Fuel Economy Improvementusing Ultracapacitor Power Assist and Preview-based MPC EnergyManagement,American Control Conference, San Francisco,2011.2707~2712
[87]. Michael Back,Matthias Simons.Predictive Control of Drivetrains,15th TriennialWorld Congress,2002,15(1):1235~1240
[88]. M. Bichi, G. Ripaccioli, S. Di Cairano.Stochastic Model Predictive Control withDriver Behavior Learning for improved powertrain control,49th IEEE Conferenceon Decision and Control,2010.6077~6082
[89]. G. Ripaccioli, D. Bernardini.A Stochastic Model Predictive Control Approach forSeries Hybrid electric vehicle. American Control Conference,2010.5844~5849
[90]. Partha Chakroborty,Saurabh Agrawal,Kyatham Vasishtha.Microscopic Modelingof Driver Behavior in Uninterrupted Traffic Flow,Journal of TransportationEngineering,2004,130(4):89~95
[91].肖献强，王其东，基于驾驶行为的汽车主动安全技术研究，中国机械工程，2010，29（19）:2390~2393
[92]. David Shinar. A Context-sensitive Model of Driving Behaviour and itsImplications for In-vehicle Safety Systems. Cogn Tech Work,2011,13(4):82~87
[93]. Joel C.McCall,Mohan M.Trivedi.Driver Behavior and Situation Aware BrakeAssistance for Intelligent Vehicles.2007,95(2):1278~1285
[94]. Guoqing XU, Li Liu,Zhangjun Song.Driver behavior analysis based on Bayesiannetwork and multiple classifiers. Intelligent Computing and Intelligent Systems,2010.663~668
[95]. F Wang, N Ma,H hooka. A Driver Assistant System for Improvement ofPassenger Ride Comfort through Modification of Driving Behavior. InternationalConference on Advanced Driver Assistance Systems,2001.483~495
[96]. Yun-Jie Hsu, Jung-Ho Cheng. Effect of Vehicle Control Unit Parameters onElectric Vehicle Driving Operation Responses,The25th International Battery,Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010
[97]. Yi Lu Murphey, Robert Milton.Driver's style classification using Jerkanalysis,Computational Intelligence in Vehicles and Vehicular Systems,2009
[98]. C. Cheng, A. McGordon. A Model to Investigate the Effects of Driver Behaviouron Hybrid Vehicle Control,The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010
[99]. Matthew G. Shirk, Benjamin M. Geller. Factors Affecting the Fuel Consumptionof Plug-In Hybrid Electric Vehicles,The25th International Battery, Hybrid andFuel Cell Electric Vehicle Symposium&Exposition,2010
[100]. Yun-Jie Hsu, Jung-Ho Cheng, Yi-Hsiang Yang. Effect of Vehicle Control UnitParameters on Electric Vehicle Driving Operation Responses,The25thInternational Battery, Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010
[101]. A R Chaudhari,R H Thring. Energy Economy Analysis of the G Wiz:a Two YearCase Study based on Two Vehicles,Journal of AutomobileEngineering,2011,225(7):1505~1519
[102]. A McGordon1, J E W Poxon, C.Cheng. Development of a Driver Model to Studythe Effects of Real-world Driver Behaviour on the Fuel Consumption,Journal ofAutomobile Engineering,2011,225(7):1520~1534
[103].朱道伟，基于道路工况远程自学习的混合动力城市客车实时优化控制策略的研究：[博士学位论文]，天津；天津大学，2010
[104].宋振磊，基于道路工况的串联式混合动力公交车控制策略开发研究：[硕士学位论文]，天津；天津大学，2011
[105]. Daowei Zhu, Xie Hui. A Study of Driving Cycles Synthesis Methodology basedon the Wireless Remote Data Collection,Mechanic Automation and ControlEngineering （MACE）,2011.2846~2849
[106]. Daowei Zhu, Xie Hui. Control Strategy Dynamic Optimization of the HybridElectric Bus Based on Driving Cycle Self-learning, Journal of MechanicalEngineering,2010,46(06):33~38
[107]. Zhu Daowei, Xie Hui. Control Strategy Optimization of the Hybrid Electric Busbased on Remote Self-learning Driving Cycles,Vehicle Power and PropulsionConference,2008.1285~1291
[108]. Zhuang Jihui,Xie Hui. Remote Self-learning of Driving Cycle for Hybrid ElectricVehicle,Electrical and Control Engineering InternationalConference,2011.4029~4032
[109]. Zhuang Jihui,Xie Hui. Research and Development of Electric Vehicle DataCollection and Calibration Platform based on GPRS and INTERNET. IEEEVehicle Power and Propulsion Conference,2008.986~991
[110]. Zhuang Jihui,Xie Hui. Remote Self-learning of Driving Cycle for ElectricVehicle Demonstrating Area,IEEE Vehicle Power and Propulsion Conference,2008.4102~4106
[111]. Zhuang Jihui,Xie Hui. GPRS based Driving Cycle Self-learning for ElectricVehicle,Journal of Tianjin University Science and Technology,2010,43(4):283~286
[112].张津涛，电动汽车城市道路行驶工况自学习方法的研究：[硕士学位论文]，天津；天津大学，2008
[113]. Chiyomi Miyajima. Driver Modeling Based on Driving Behavior and ItsEvaluation in Driver Identification, IEEE Intelligent Vehicles Symposium2007,95(2):1456~1463
[114]. Pongtep Angkititrakul. Modeling and Adaptation of Stochastic Driver-behaviorModel with Application to Car Following,IEEE Intelligent Vehicles Symposium（IV）,2011.814~819
[115]. A McGordon. Development of a Driver Model to Study the Effects of Real-worldDriver Behaviour on the Fuel Consumption,Proceedings of the Institution ofMechanical Engineers, Part D: Journal of Automobile Engineering,2011.1518~1523
[116]. Junyan Zhao, Qi Li. A Method for Modeling Drivers' Behavior Rules inAgent-based Traffic Simulation,Geoinformatics,18th International Conference,2010.4502~4508
[117]. Hari Thiruvengada. Affordance-based Computational Model of Driver Behavioron Highway Systems: A Colored Petri Net Approach,Systems, Man andCybernetics,2007.888~893
[118]. Partha Chakroborty, Saurabh Agrawal, Kyatham Vasishtha.MicroscopicModeling of Driver Behavior in Uninterrupted Traffic Flow,Journal ofTransportation Engineering,2004.438~451
[119].袁曾任，人工神经元网络及应用，北京：清华大学出版社，1999.36~42
[120]. Hui Xie,Ying Yan.Model Predictive Control for Series-parallel Plug-in HybridElectrical Vehicle using GPS System,Electrical and Control Engineering,2011.2334~2337
[2].王海艳，中国能源警世通言:六成石油将依赖进口，南方都市报，2008
[3].魏曙光，去年我国石油对外依存度升至51.3%，证券时报，2009
[4].欧阳明高，发展节能环保汽车实现能源转型与产业振兴，全国政协十一届二次会议，2009
[5]. Green Car congress. http://www.greencarcongress.com/electric battery/index.html.
[6]. Energy Information Administration, Official Engergy Statistics from the U.S.Government.http://www.eia.doe.gov/oil_gas/petroleum/info_glance/petroleum.html.
[7].陈清泉，孙逢春，现代电动车技术，北京：北京理工大学出版社，2002.96~97
[8]. Michael H. The Electric Car,The Institution of Electrical Engineers,2001
[9]. Baldasano, J. Soriano, C. Boada. Emission Inventory for Greenhouse Gases in theCity of Barcelona. Atmospheric Environment,1999,33(23):59~65
[10].天相投资顾问有限公司，汽车行业2009年研究报告,2009
[11].万沛霖，电动汽车的关键技术，北京：北京理工大学出版社，1998
[12]. Mehrdad Ehsani,Yimin Gao.Modern Electric hybrid Electric and Fuel CellVehiucles:Fundamentals,Theory, and Design, CRC Press,2010.124~125
[13]. Joel C. McCall, Mohan M. Trivedi Driver Behavior and Situation Aware BrakeAssistance for Intelligent Vehicles. Proceedings of the IEEE.2007,95(2):374~387
[14]. Arif Mehmood. Application of System Dynamics in Car-Following Models.Journal of Transportation Engineering.2003,129(6):625~634
[15]. Mingyuan Bian. Road Condition Estimation for Automotive Anti-Skid ControlSystem Based on BP Neural Network. Proceedings of the IEEE InternationalConference on Mechatronics&Automation.2005:1017~1022
[16].周东华，叶银忠，现代故障诊断与容错控制，北京：清华大学出版社，2000
[17].李俊松，基于神经网络的混合动力汽车故障诊断研究：[硕士学位论文]，武汉；武汉理工大学，2003
[18]. Mohamed El Hachemi Benbouzid. Advanced Fault-Tolerant Control ofInduction-Motor Drives for EV/HEV Traction Applications: From Conventionalto Modern and Intelligent Control Techniques. IEEE Transactions on VehicleTechnology,2007,56(2):519~528
[19]. J. A. Autonino, J. Rusek, M. Riera. Case Histories in large motor: Diagnosis ofElectromechanical Faults through Extraction of Characteristic ComponentsDuringthe Startup. IEEE International Symposium on Semped Cracow,2007:161~166
[20]. Bellini, Alberto, Filippetti, Fiorenzo. On-field Experience with Online Diagnosisof Large Induction Motors Cage Failures Using MCSA. IEEE Trans. on IndustryApplications.2002,38(4):1045~1053
[21]. R. Ong, J. H. Dymond, R. D. Findlay. Bearing Damage Analysis in a LargeOil-ring-lubricated Induction Machine. IEEE Transaction on IndustrialElectronics.2000,47(5):1085~1089
[22].朱建新，郑荣良，电动汽车高压电安全诊断与控制策略的研究，汽车工程，2007，29（4）：308~312
[23].刘晓俊，电池故障智能诊断系统的研究与实现：[硕士学位论文]，北京；北京邮电大学，2010
[24].张又伟，基于学习算法的公交电动汽车故障诊断模型及应用研究：[硕士学位论文]，北京；北京交通大学，2007
[25]. X. Zhang, P. N. Ross. Diagnostic Characterization of High Power Lithium-IonBatteries for Use in Hybrid Electric Vehicles. Journal of The ElectrochemicalSociety,2001,148(5):463~470
[26]. Chris Bingham, Power Energy Storage Technologies and EnergyManagement.3th Advanced Engine Control Symposium,TianJin,2010
[27]. Phatiphat Thounthong, Control Strategy of Fuel Cell/Supercapacitors HybridPower Sources for Electric Vehicle. Journal of Power Sources,2006(158):806~814
[28]. Ke Li,Chenghui Zhang,Naxin Cui.Am Improved Energy Optimization ControlStrategy for Electric Vehicle Drive System.Chinese Control and DecisionConferenee,Chinese.2008:2244~2249
[29].白志峰，张传伟，电动汽车驱动与再生制动的鲁棒控制，西安交通大学学报，2005，39（3）：256~260
[30]. Chia-Cheng Wueng, Yi-Hsiang Yang. An Improved Regenerative BrakingControl Strategy and System for Dual Motor Electric Vehicle. The25thInternational Battery, Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010.138~142
[31]. Y. Gao, L. Chen, and M. Ehsani, Investigation of the effectiveness ofregenerative braking for EV and HEV,SAE Journal, SP-1466,1999.1999-01-2901
[32]. Y. Gao and M. Ehsani, Electronic Braking System of EV and HEV-integrationof Regenerative Braking, automatic braking force control and ABS, inProceedings of the SAE2001Future Transportation Technology Conference,2001.Paper No.2001-01-2478,
[33]. Kim, D., Oh, K., Yeo, H. Operation and Brake Force Distribution Algorithm for a4WD HEV.20th Electric Vehicle Symposium,2003.1214~1219
[34]. Jeongmin Kim, Sangmok Lee. Analysis of Regenerative Braking Patterns forHybrid Electric Vehicles. The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010.3458~3462
[35]. F. Wang, and B. Zhuo, Regenerative Braking Strategy for Hybrid ElectricVehicles based on Regenerative Torque Optimization Control, Proc. IMechE,Part D: J. Automobile Engineering,2008,222(12):499~513,
[36]. H. Yeo, H. Kim, Hardware-in-the-loop Simulation of Regenerative Braking for aHybrid Electric Vehicle, Proc. IMechE, Part D: J. Automobile Engineering,2002,216(15):855~864
[37]. C. Albrichsfeld, and J. Karner, Brake System for Hybrid and Electric Vehicles,SAE,2009.2009-01-1217,
[38]. Jeongmin Kim, Sangmok Lee. Analysis of regenerative braking patterns forhybrid electric vehicles. The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010.2489~2493
[39]. J. Fox, R. Roberts, C. Baier-Welt, L. M. Ho, L. Lacraru, and B. Gombert,Modeling and Control of a Single Motor Electronic Wedge Brake, SAE paper2007-01-0866,2007
[40]. B. Cho, J. Oh, and W. Lee, Modeling of Pulse Width Modulation PressureControl System for Automatic Transmission, SAE paper2002-01-1257,2002.
[41]. Yimin Gao, Liang Chu and M. Ehsani, Design and control principles of hybridbraking system for EV, HEV and FCV, Vehicle Power and PropulsionConference,2007.384~391
[42]. S. R. Cikanek and K. E. Bailey, Electric Vehicle Braking Systems, InternationalElectric Vehicle Symposium,1997.1458~1463
[43]. N. Mutoh, Y. Hayano, H. Yahagi and K. Takita, Electric braking control methodsfor electric vehicles with independently driven front and rear wheels,IEEE Trans.Industrial Electronics,2007,54(15):1168~1176
[44]. H. Yeo and H. Kim, Regenerative Braking Algorithm for a Hybrid ElectricVehicle with CVT Ratio Control,Proc. IMechE, Part D:1AutomobileEngineering,2006,220(4):1589~1600
[45]. L. Hellgren, E. Jonasson.Maximisation of Brake Energy Regeneration in aHybrid Electric Parallel Car,Electric and Hybrid Vehicles,2007,5:95~121
[46]. Zhong Z.M, Sun Z.C. A Fuzzy Logic based Regenerative Braking Regulation fora Fuel Cell Bus. In Proceedings of the IEEE International Conference onVehicular Electronics and Safety,2006.22-25
[47]. Zolot, Markel, Pesaran, A. Analysis of Fuel Cell Vehicle Hybridization andImplications for Energy Storage Devices. In Proceedings of the4th AdvancedAutomotive Battery Conference,2004.121~124
[48]. Zhang, C.W, Bai, Z.F. Study on Regenerative Braking of Electric Vehicle.InProceedings of the4th International Power Electronics and Motion ControlConference,2004,2.836~839
[49]. Binggang Cao,Zhifeng Bai. Research on Control for Regenerative Braking ofElectric Vehicle.IEEE Conference on Vehicluar Elecltronics andSafety,2005.92-97
[50]. S.DELPRAT. Optimal Control of a Parallerl Powertrain: from GlobalOptimization to Real Control Strategy, IEEE,2002.1345~1350
[51]. Kyoungcheol Oh, Hyunsoo Kim,Optimal Power Distribution Control for ParallelHybrid Electric Vehicles, Vehicular Electronics and Safety, InternationalConference,2005.79~85
[52]. Delprat Sebastien.Control Strategy Optimization for an Hybrid ParallelPowertrain，Proceedings of the American Control Conference Arlington,2001.25~27
[53]. Ilya Kolmanovskyt, Irina Siverguina, Bob Lygoe. Optimization of PowertrainOperating Policy for Feasibility Assessment and Calibration: Stochastic DynamicProgramming approach，Proceedings of the American Control Conference2002.8~10,
[54]. Chan-Chiao Lin, Huei Peng. Power Management Strategy for a Parallel HybridElectric Truck,IEEE Transactionson Control Systems Technology,2003,11(6):25~31
[55]. E.D. Tate,S. P. Boyd.Finding ultimate limits of performance for hybrid electricvehicles.SAE paper00FTT-50,1998
[56]. S. Delpar, J. Lauber, T.M. Guerra, J. Rimaux.Control of a parallel hybridpowertrain: optimal control, IEEE Trans.Vehicular Tech.,2004,53(3):872~881
[57]. H. Peng, J.W. Grizzle. Power Management strategy for a parallel hybrid electrictruck.,IEEE Trans. Cont. Syst. Tech,2003,11(6):839~848
[58]. Lin, H. Peng, J.W. Grizzle.A Stochastic Control Strategy for Hybrid ElectricVehicles, American Control Conference,2004,5:4710~4715
[59]. A. Piccolo, L. Ippolito, V. Galdi, A. Vaccaro.Optimization of enery flowmanagement in hybrid electric vehicles via genetic algorithms, Proc.IEEE/ASME Int. Conf. on Adv. Intelligent Mechanical,2001,1:434~439
[60]. Morteza Montazeri-Gh,Amir Poursamad,Babak Ghalichi.Application of geneticalgorithm for optimization of control strategy in parallel hybrid electricvehicles,Journal of the Franklin Institute,2006,343:420~435
[61]. P O. Barbarisi, E.R. Westervelt, F. Vasca, and G. Rizzoni.Power managementdecoupling control for a hybrid electric vehicle,44th IEEE Conf. Decision andControl,2005.2012~2017
[62]. P. Pisu, G. Rizzoni.H∞Control for Hybrid Electric Vehicles,43rd IEEEConference:Decision and Control,2004,4:3497~3502
[63]. S.Delprat,T.M.Guerra,G.Pagamelli.Control Strategy Optimization for an HybridParallel Powertrain,American Conference,2001,2:1315~1320
[64]. Patrik Thuring.Model Predictive Control Based Energy Management Algorithmfor a Hybrid Excavator,Sweden:Lund University,2008
[65]. Steve Yurkovich, Balaji Sampathnarayanan.Model Predictive Control as anEnergy Management Strategy for Hybrid Electric Vehicles. Proceedings of theASME2009Dynamic Systems and Control Conference,2009.249~256
[66]. M.J. West, C. M. Bingham N. Schofield. Predictive control for energymanagement in all/more electric vehicles with multiple energy storage units.Electric Machines and Drives Conference,2003,1:222~228
[67]. Patrik Thuring. Model Predictive Control Based Energy Management Algorithmfor a Hybrid Excavator,Department of Automatic Control,Lund University,2008
[68]. H. Ali Borhan Ardalan Vahidi. Model Predictive Control of a Power-split HybridElectric Vehicle with Combined Battery and Ultracapacitor Energy Storage.American Control Conference,2001.5031~5036
[69]. A. Phillips M. Kuang H. Borhan, A. Vahidi and I. Kolmanovsky.Predictiveenergy management of a power-split hybrid electric vehicle,American ControlConference,2009.3970~3976
[70]. R. Beck，S. Saenger. Model Predictive Control of a Parallel Hybrid VehicleDrivetrain,Proceedings of the44th IEEE Conference on Decision and Control,and the European Control Conference,2005.2670~2675
[71]. A. Vasebi，M. Partovibakhsh. Predicting State of Charge of Lead-acid Batteriesfor Hybrid Electric Vehicles by Extended Kalman filter,Energy Conversion andManagement,2008,49:75~82
[72]. Jiaxin Chen.Modeling and Performance Analysis of Energy regenerationsystemin electric vehicle with permanent magnet DC motor drivingsystem,Proceeding of International Conference on Electrical Machines andSystems,2007
[73]. Li Junwei.The Stability Control of Electric Vehicle Based on Optimal PredictiveControl method,2nd International Conference on Computer Engineering andTechnology,2010,4:500~504
[74]. Woonki Na, Taesik Park, Taehyung Kim.Light Fuel-Cell Hybrid ElectricVehicles Based on Predictive Controllers. IEEE Transaction on VehicleTechnology,2011,60(1):89~97
[75]. Fazal Urrahman Syed.Modeling and Control Methods for Improving Drivability,Power Management and Fuel Economy in a Hybrid ElectricVehicle,[Ph.D.dissertation],Wayne State University;Detroit,Michigan,2008
[76].罗玉涛，胡红斐，沈继军，混合动力电动汽车行驶工况分析与识别，华南理工大学学报，2007,24（6）：59~63
[77]. Joel Prieto, Jos′e A. Riveros, Blas Bogado.Electric Propulsion Technology Basedin Predictive Direct Torque Control and Asymmetrical Dual Three–Phase Drives.13th International IEEE Annual Conference on Intelligent TransportationSystems,2010,1:26~34
[78]. Sciarretta A, Guzzella L. Control of Hybrid Electric Vehicles. IEEE Transactionson Control Systems Technology,2007,27(2):60~70
[79]. Johannesson L.Assessing the Potential of Predictive Control for Hybrid VehiclePowertrains using Stochastic Dynamic Programming,Proceedings of the8thInternational IEEE Conference on Intelligent Transportation System,2005.366~371
[80]. Chen Zhang.Predictive Energy Management in Connected Vehicles utilizingroute information preview for energy saving:[Ph.D.Dissertation],ClemsonUniversity,2010
[81].夏超英，申彩英，基于改进型动态规划算法的串联混合动力汽车控制策略，控制理论与应用，2011，28（3）：428~431
[82].李升波，王建强，李克强，MPC实用化问题处理及在车辆ACC中的应用，清华大学学报，2010，50（5）：646~650
[83]. Carlos Bordons, Miguel A. Ridao.Model Predictive Control for PowerManagement in Hybrid Fuel Cell vehicles. Vehicle Power and PropulsionConference,2010
[84].舒红，蒋勇，中度混合动力汽车模型预测控制策略，重庆大学学报，2010，33（1）：36~41
[85]. H. Ali Borhan,Ardalan Vahidi. Predictive Energy Management of a Power-SplitHybrid Electric Vehicle,American Control Conference Hyatt Regency Riverfront,2009.3970~3976
[86]. Seneca Schepmann,Ardalan Vahidi. Heavy Vehicle Fuel Economy Improvementusing Ultracapacitor Power Assist and Preview-based MPC EnergyManagement,American Control Conference, San Francisco,2011.2707~2712
[87]. Michael Back,Matthias Simons.Predictive Control of Drivetrains,15th TriennialWorld Congress,2002,15(1):1235~1240
[88]. M. Bichi, G. Ripaccioli, S. Di Cairano.Stochastic Model Predictive Control withDriver Behavior Learning for improved powertrain control,49th IEEE Conferenceon Decision and Control,2010.6077~6082
[89]. G. Ripaccioli, D. Bernardini.A Stochastic Model Predictive Control Approach forSeries Hybrid electric vehicle. American Control Conference,2010.5844~5849
[90]. Partha Chakroborty,Saurabh Agrawal,Kyatham Vasishtha.Microscopic Modelingof Driver Behavior in Uninterrupted Traffic Flow,Journal of TransportationEngineering,2004,130(4):89~95
[91].肖献强，王其东，基于驾驶行为的汽车主动安全技术研究，中国机械工程，2010，29（19）:2390~2393
[92]. David Shinar. A Context-sensitive Model of Driving Behaviour and itsImplications for In-vehicle Safety Systems. Cogn Tech Work,2011,13(4):82~87
[93]. Joel C.McCall,Mohan M.Trivedi.Driver Behavior and Situation Aware BrakeAssistance for Intelligent Vehicles.2007,95(2):1278~1285
[94]. Guoqing XU, Li Liu,Zhangjun Song.Driver behavior analysis based on Bayesiannetwork and multiple classifiers. Intelligent Computing and Intelligent Systems,2010.663~668
[95]. F Wang, N Ma,H hooka. A Driver Assistant System for Improvement ofPassenger Ride Comfort through Modification of Driving Behavior. InternationalConference on Advanced Driver Assistance Systems,2001.483~495
[96]. Yun-Jie Hsu, Jung-Ho Cheng. Effect of Vehicle Control Unit Parameters onElectric Vehicle Driving Operation Responses,The25th International Battery,Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010
[97]. Yi Lu Murphey, Robert Milton.Driver's style classification using Jerkanalysis,Computational Intelligence in Vehicles and Vehicular Systems,2009
[98]. C. Cheng, A. McGordon. A Model to Investigate the Effects of Driver Behaviouron Hybrid Vehicle Control,The25th International Battery, Hybrid and Fuel CellElectric Vehicle Symposium&Exposition,2010
[99]. Matthew G. Shirk, Benjamin M. Geller. Factors Affecting the Fuel Consumptionof Plug-In Hybrid Electric Vehicles,The25th International Battery, Hybrid andFuel Cell Electric Vehicle Symposium&Exposition,2010
[100]. Yun-Jie Hsu, Jung-Ho Cheng, Yi-Hsiang Yang. Effect of Vehicle Control UnitParameters on Electric Vehicle Driving Operation Responses,The25thInternational Battery, Hybrid and Fuel Cell Electric Vehicle Symposium&Exposition,2010
[101]. A R Chaudhari,R H Thring. Energy Economy Analysis of the G Wiz:a Two YearCase Study based on Two Vehicles,Journal of AutomobileEngineering,2011,225(7):1505~1519
[102]. A McGordon1, J E W Poxon, C.Cheng. Development of a Driver Model to Studythe Effects of Real-world Driver Behaviour on the Fuel Consumption,Journal ofAutomobile Engineering,2011,225(7):1520~1534
[103].朱道伟，基于道路工况远程自学习的混合动力城市客车实时优化控制策略的研究：[博士学位论文]，天津；天津大学，2010
[104].宋振磊，基于道路工况的串联式混合动力公交车控制策略开发研究：[硕士学位论文]，天津；天津大学，2011
[105]. Daowei Zhu, Xie Hui. A Study of Driving Cycles Synthesis Methodology basedon the Wireless Remote Data Collection,Mechanic Automation and ControlEngineering （MACE）,2011.2846~2849
[106]. Daowei Zhu, Xie Hui. Control Strategy Dynamic Optimization of the HybridElectric Bus Based on Driving Cycle Self-learning, Journal of MechanicalEngineering,2010,46(06):33~38
[107]. Zhu Daowei, Xie Hui. Control Strategy Optimization of the Hybrid Electric Busbased on Remote Self-learning Driving Cycles,Vehicle Power and PropulsionConference,2008.1285~1291
[108]. Zhuang Jihui,Xie Hui. Remote Self-learning of Driving Cycle for Hybrid ElectricVehicle,Electrical and Control Engineering InternationalConference,2011.4029~4032
[109]. Zhuang Jihui,Xie Hui. Research and Development of Electric Vehicle DataCollection and Calibration Platform based on GPRS and INTERNET. IEEEVehicle Power and Propulsion Conference,2008.986~991
[110]. Zhuang Jihui,Xie Hui. Remote Self-learning of Driving Cycle for ElectricVehicle Demonstrating Area,IEEE Vehicle Power and Propulsion Conference,2008.4102~4106
[111]. Zhuang Jihui,Xie Hui. GPRS based Driving Cycle Self-learning for ElectricVehicle,Journal of Tianjin University Science and Technology,2010,43(4):283~286
[112].张津涛，电动汽车城市道路行驶工况自学习方法的研究：[硕士学位论文]，天津；天津大学，2008
[113]. Chiyomi Miyajima. Driver Modeling Based on Driving Behavior and ItsEvaluation in Driver Identification, IEEE Intelligent Vehicles Symposium2007,95(2):1456~1463
[114]. Pongtep Angkititrakul. Modeling and Adaptation of Stochastic Driver-behaviorModel with Application to Car Following,IEEE Intelligent Vehicles Symposium（IV）,2011.814~819
[115]. A McGordon. Development of a Driver Model to Study the Effects of Real-worldDriver Behaviour on the Fuel Consumption,Proceedings of the Institution ofMechanical Engineers, Part D: Journal of Automobile Engineering,2011.1518~1523
[116]. Junyan Zhao, Qi Li. A Method for Modeling Drivers' Behavior Rules inAgent-based Traffic Simulation,Geoinformatics,18th International Conference,2010.4502~4508
[117]. Hari Thiruvengada. Affordance-based Computational Model of Driver Behavioron Highway Systems: A Colored Petri Net Approach,Systems, Man andCybernetics,2007.888~893
[118]. Partha Chakroborty, Saurabh Agrawal, Kyatham Vasishtha.MicroscopicModeling of Driver Behavior in Uninterrupted Traffic Flow,Journal ofTransportation Engineering,2004.438~451
[119].袁曾任，人工神经元网络及应用，北京：清华大学出版社，1999.36~42
[120]. Hui Xie,Ying Yan.Model Predictive Control for Series-parallel Plug-in HybridElectrical Vehicle using GPS System,Electrical and Control Engineering,2011.2334~2337