有机/无机复合多功能微球的合成、表征及其在光化学传感和生物成像方面的应用
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摘要
制动能量再生是混合动力汽车节油最有效的途径之一。对再生制动中的关键技术进行研究具有重要的现实意义和应用价值。
为了在保证汽车的制动性的同时又能让电机尽量多地回收制动能量，就要制定适当的再生制动控制策略，合理地分配电制动和机械制动的强度，使整车在保证制动性的同时，最大限度地回收制动能量。目前的控制策略的研究主要集中在电机和机械制动的制动力分配上，对驾驶员制动意图的识别还只是靠制动踏板开度这一单一参数。只根据制动踏板开度来识别制动意图所得到的结果还不够精确。目前的控制策略虽然采用了许多先进的控制理论，但控制效果都差强人意。
因此，单一的制动踏板开度已不能满足再生制动的控制需要，多参数精确识别制动意图对再生制动的控制具有重要意义，一方面可以根据驾驶员的真实制动意图更加合理地分配电机制动与机械制动，既能保证汽车的制动性又能使电机回收更多制动能量；另一方面可以保证汽车的滑行距离，在提高滑行时的驾驶性能的前提下回收滑行制动的能量。
本课题组在863混合动力汽车开发项目中与一汽技术研发中心共同承担“混合动力汽车整车控制策略研究”项目，课题来源正是上述项目中的关键技术——辅助控制模块中的驾驶员意图识别模块。意图识别分为驱动意图识别和制动意图识别，本论文研究内容是制动意图识别及控制算法。论文进行了如下工作：
第一，分析和研究大量实车工况数据，对制动意图进行了分类和表征。
第二，用MATLAB建立了模糊辨识器模型，识别常规制动意图。
第三，通过实车道路试验对模糊辨识器的识别效果进行在线验证，并用神经网络优化了模糊辨识的隶属函数，提高了识别准确率。
第四，针对滑行制动意图的识别，通过对传统车空档滑行的动力学分析，对驾驶员的分析以及对大量实车滑行数据的统计，建立了滑行制动的V-t数学模型，并推导出滑行制动时电机的负荷率控制公式。
最后，在制动意图识别的基础上，优化了再生制动的控制策略。建立了CRUISE—SIMULINK正向仿真平台，对控制策略进行了仿真对比验证。证明了基于制动意图识别的控制策略的优化，对混合动力汽车的驾驶性能，以及整车的燃油经济性都起到了积极的作用。
Braking energy regeneration is one of the most effective ways of fuel-efficient forhybrid cars. The key technology of regenerative braking research has important realisticsignificance and application value.
In order to ensure that the car brake well, at the same time, let the motor recycle as faras possible many braking energy, we should make the proper regenerative braking controlstrategy and make the distribution of electric braking and mechanical braking intensityreasonable. Make the vehicle to ensure the brake at the same time to maximize the recoveryof braking energy. Current control strategies research mainly concentrates in the motor andmechanical brake braking force distribution. The driver's braking intention identification isonly on the brake pedal opening this single parameter. The obtained results according to thedegree of brake pedal drive to identify braking intention are not accurate. Current controlstrategies while using a number of advanced control theory, but the control effect is justpassable.
So, single brake pedal opening has been unable to meet the needs of regenerativebraking control. Multi parameter precise identification of braking intention on regenerativebraking control is of great significance. On one hand, according to the actual driver brakingintention to distribute the motor braking and the mechanical braking more reasonable. Notonly can ensure the vehicle braking and can make the motor recovery more braking energy.On the other hand, can guarantee the automobile coasting distance. Improve the drivingperformance under the premise of coasting brake energy recovery.
This task group shared" hybrid vehicle control strategy research" project with FAW R &D center in863hybrid vehicle development projects. Origin of the subject is the driverintention recognition module of the key technology in the project--auxiliary control module.Intention recognition is divided into driver intention recognition and braking intentionrecognition. The content of this paper is the braking intention recognition and controlalgorithm. The paper carried out the following works:
First, analysis and study of lots of vehicle condition data, and make the classificationand characterization of braking intention.
In second, MATLAB is used to establish the fuzzy identification model to identify theconventional braking intention.
Third, verify fuzzy identifier recognition effect online through the real vehicle road test.Use neural network to optimize the fuzzy membership function. Improved the recognitionaccuracy rate.
In fourth, established a coasting brake V-t mathematical model and deduced thecoasting brake motor load rate control formula by analyzing the traditional car free slidingkinetic analysis, the driver's analysis and a large number of real vehicle sliding data statisticsfor The coasting braking intention recognition.
Finally, optimized regenerative braking control strategy based on the braking intentionidentification. Then established a CRUISE-SIMULINK forward simulation platform, andthe control strategy was validated by simulation. Proofed of the optimization of controlstrategy for hybrid electric vehicle based on braking intention identification has played apositive role to driving performance and vehicle fuel economy.
为了在保证汽车的制动性的同时又能让电机尽量多地回收制动能量，就要制定适当的再生制动控制策略，合理地分配电制动和机械制动的强度，使整车在保证制动性的同时，最大限度地回收制动能量。目前的控制策略的研究主要集中在电机和机械制动的制动力分配上，对驾驶员制动意图的识别还只是靠制动踏板开度这一单一参数。只根据制动踏板开度来识别制动意图所得到的结果还不够精确。目前的控制策略虽然采用了许多先进的控制理论，但控制效果都差强人意。
因此，单一的制动踏板开度已不能满足再生制动的控制需要，多参数精确识别制动意图对再生制动的控制具有重要意义，一方面可以根据驾驶员的真实制动意图更加合理地分配电机制动与机械制动，既能保证汽车的制动性又能使电机回收更多制动能量；另一方面可以保证汽车的滑行距离，在提高滑行时的驾驶性能的前提下回收滑行制动的能量。
本课题组在863混合动力汽车开发项目中与一汽技术研发中心共同承担“混合动力汽车整车控制策略研究”项目，课题来源正是上述项目中的关键技术——辅助控制模块中的驾驶员意图识别模块。意图识别分为驱动意图识别和制动意图识别，本论文研究内容是制动意图识别及控制算法。论文进行了如下工作：
第一，分析和研究大量实车工况数据，对制动意图进行了分类和表征。
第二，用MATLAB建立了模糊辨识器模型，识别常规制动意图。
第三，通过实车道路试验对模糊辨识器的识别效果进行在线验证，并用神经网络优化了模糊辨识的隶属函数，提高了识别准确率。
第四，针对滑行制动意图的识别，通过对传统车空档滑行的动力学分析，对驾驶员的分析以及对大量实车滑行数据的统计，建立了滑行制动的V-t数学模型，并推导出滑行制动时电机的负荷率控制公式。
最后，在制动意图识别的基础上，优化了再生制动的控制策略。建立了CRUISE—SIMULINK正向仿真平台，对控制策略进行了仿真对比验证。证明了基于制动意图识别的控制策略的优化，对混合动力汽车的驾驶性能，以及整车的燃油经济性都起到了积极的作用。
Braking energy regeneration is one of the most effective ways of fuel-efficient forhybrid cars. The key technology of regenerative braking research has important realisticsignificance and application value.
In order to ensure that the car brake well, at the same time, let the motor recycle as faras possible many braking energy, we should make the proper regenerative braking controlstrategy and make the distribution of electric braking and mechanical braking intensityreasonable. Make the vehicle to ensure the brake at the same time to maximize the recoveryof braking energy. Current control strategies research mainly concentrates in the motor andmechanical brake braking force distribution. The driver's braking intention identification isonly on the brake pedal opening this single parameter. The obtained results according to thedegree of brake pedal drive to identify braking intention are not accurate. Current controlstrategies while using a number of advanced control theory, but the control effect is justpassable.
So, single brake pedal opening has been unable to meet the needs of regenerativebraking control. Multi parameter precise identification of braking intention on regenerativebraking control is of great significance. On one hand, according to the actual driver brakingintention to distribute the motor braking and the mechanical braking more reasonable. Notonly can ensure the vehicle braking and can make the motor recovery more braking energy.On the other hand, can guarantee the automobile coasting distance. Improve the drivingperformance under the premise of coasting brake energy recovery.
This task group shared" hybrid vehicle control strategy research" project with FAW R &D center in863hybrid vehicle development projects. Origin of the subject is the driverintention recognition module of the key technology in the project--auxiliary control module.Intention recognition is divided into driver intention recognition and braking intentionrecognition. The content of this paper is the braking intention recognition and controlalgorithm. The paper carried out the following works:
First, analysis and study of lots of vehicle condition data, and make the classificationand characterization of braking intention.
In second, MATLAB is used to establish the fuzzy identification model to identify theconventional braking intention.
Third, verify fuzzy identifier recognition effect online through the real vehicle road test.Use neural network to optimize the fuzzy membership function. Improved the recognitionaccuracy rate.
In fourth, established a coasting brake V-t mathematical model and deduced thecoasting brake motor load rate control formula by analyzing the traditional car free slidingkinetic analysis, the driver's analysis and a large number of real vehicle sliding data statisticsfor The coasting braking intention recognition.
Finally, optimized regenerative braking control strategy based on the braking intentionidentification. Then established a CRUISE-SIMULINK forward simulation platform, andthe control strategy was validated by simulation. Proofed of the optimization of controlstrategy for hybrid electric vehicle based on braking intention identification has played apositive role to driving performance and vehicle fuel economy.
引文
[1]曾小华.混合动力客车节能机理与参数设计方法研究[D].长春:吉林大学，2006.
[2]胡安平.基于AMESim-Simulink联合仿真的再生制动系统研究[D].长春:吉林大学，2008.
[3]Motomu Hakiai,Toshio Taichi,Masahiko Shoda,Takashi Koizumi,TadashiAshikaga,Hiroshi Shimizu.Brake system of"Eco-Vehicle".The14th International ElectricVehicle Symposium and Exposition,Orlando,USA,1997.
[4]周杰敏，童毅，欧阳明高.轻度混合动力系统典型瞬态过程方针分析[J].汽车工程，2003，25（1）；65-69.
[5]蒋励.基于理想制动力分配曲线的复合制动设计.汽车科技，2006年7月第四期.
[6]Toshifumi Takaoka,Osamu Harada.Highly efficient and super low emission hybrid vehicle.
[7]Tatsuo,Teratani,Kohjiro,Kuramochi.Development of Toyota Mild-hybrid system(THS-M).Toyota Technical Review,2002,Vol.52No.1Sep.2002:46~51.
[8]Sumiko Sekiguchi, Koichi Kondo,etc.Development of Electrical4WD System for HybridVehicles. Toyota Technical Review.2002,Vol.51No.2Mar,2002:86~89.
[9]Y.Sasaki, A.Otomo, F.Kawahata, A.Matui. Toyota Braking System for Hybrid Vehicle withRegenerative System. EVS-14,proceeding CD,1997.
[10]Masami Ogura, and Yasushi Aoki and Steve Mathison. The Honda EV Plus RegenerativeBraking System. EVS-14, proceeding CD.1997.
[11]Koichi Fukuo, Akira Fujimura. Development of the ultra-low-fuel consumption hybridcar-INSIGHT. JSAE Review. JSAE20014016,2001:95~103.
[12]Isaya Matsuo, Takeshi Miyamoto. The Nissan hybrid vehicle. SAE2000-01-1568,2000:1~9.
[13]Hidehiro Oba,Akihiro Yamanaka,etc. Development of a Hybrid Powertrain System UsingCVT in a Minivan. Toyota Technical Review.2002,Vol.51No.2Mar.74~79,2002:52~57.
[14]张元才，余卓平，徐乐，熊璐.基于制动意图的电动汽车复合制动系统制动力分配策略研究.汽车工程，2009050.
[15]王会义.汽车防抱死制动系统模糊控制方法的应用及其仿真试验台的构建.博士后工作报告，长春:吉林工业大学，1998年.
[16]丁海涛.制动防抱死系统模糊控制的初步研究.硕士学位论文[D].长春:吉林工业大学，1999年.
[17]刘胜铁.并联混合动力客车模糊控制策略的研究.吉林大学硕士学位论文，2006年10月.
[18]孙以泽，王其明.车辆AMT中道路条件及驾驶意图的模糊识别.汽车工程，2001104.
[19]何忠波，白鸿柏，孔庆春，张培林.基于驾驶员意图的AMT车辆控制研究.军械工程学院学报，2005年6月.
[20]王玉海，宋健，李兴坤.基于模糊推理的驾驶员意图识别研究.公路交通科技，2005年12月.
[21]王玉海，宋健，李兴坤.驾驶员意图与行驶环境的统一识别及实时算法.机械工程学报，2006年4月.
[22]Reza Langari and Jong-Seob Won.Texas A&M University.A.Driving SituationAwareness-Based Energy ManagementStrategy for Parallel HybridVehicles.SAE2003-01-2311.
[23]Steven Boyd and Douglas J. Nelson Virginia Tech,Blacksburg, Virginia. Hybrid ElectricVehicle Control Strategy Based on Power Loss Calculations. SAE2008-01-0084.
[24]Yuhai Wang, Jian Song.Inference of Drivers Intention and its Realization on Fuzzy Logic
[C]1JSAE NUM,20024621.
[25]Sakaguchi S, Sakai I, Haga T1. Application of Fuzzy Logic to Shift Scheduling Methodfor Automatic Transmission [C].12nd IEEE Conference on Fuzzy Systems,1993.
[26]李士勇.模糊控制·神经控制和智能控制论[M].哈尔滨工业大学出版社，2006年4月.
[27]dSPACE/ControlDesk References2000.
[28]余志生.汽车理论[M].机械工业出版社，2004年1月.
[29]王望予.汽车设计[M].机械工业出版社，3003年9月.
[30]方泳龙.汽车制动理论与设计[M].国防工业出版社，2005年1月.
[31]秦大同，邓涛，杨阳，林志煌.基于前向建模的ISG型CVT混合动力系统再生制动仿真研究[J].中国机械工程师，2008，19(5):618-624.
[32]王益全.电动机原理与实用技术[M].北京:科学出版社，2005.
[33]王庆年,于永涛，曾小华，于远彬.基于CRUISE软件的混合动力汽车正向仿真平台的开发[J].吉林大学学报(工学版)，2009，39(6)：1413-1419.
[34]彭栋.AVL CRUISE V3.0整车性能仿真平台.AVL李斯特技术中心.
[35]AVL CRUISE高级培训教程.北京恒润科技有限公司，2005年9月.
[36]一汽技术研发中心，吉林大学混合动力课题组.第二代城市混合动力客车方案论证参数匹配及性能分析研究.
[37]一汽技术研发中心，吉林大学混合动力课题组.第二代混合动力汽车控制策略研究报告.
[38]杨林，卓斌，冒晓建，王好战.混合动力电动汽车动力控制系统方案分析.
[39]刘博，杜继宏，等.电动汽车制动能量回收控制策略的研究[J].电子技术应用，2004，（1）：34~36.
[40]过学迅，张靖.混合动力电动汽车再生制动系统的建模与仿真[J].武汉理工大电动汽车再生制动稳定性研究学学报(信息与管理工程版)，2005，27(1):116~120.
[41]罗玉涛，俞明，陈炳坤等.电动车制动能量再生反馈控制研究[J].机床与液压2002年第5期.
[42]徐海东.电动汽车再生制动能量高效回收控制策略研究[D].山东大学硕士学位论文2007年.
[43]王鹏宇.混合动力汽车复式制动系统的设计与性能仿真[D].吉林大学硕士学位论文.2005年.
[44]Yimin Gao and Mehrdad Ehsani.Electronic Braking System of EV and HEV-Integrationof Regenerative Braking，Automatic Braking Force Control andABS．SAE2001-01-2478．
[45]Michael Panagiotidis，George Delagrammatikas，etc. Development and use of aregenerative braking model for a parallel hybrid electric vehicle. SAE2000-01-0995．
[46]S.R Cikanek．Electric Vehicle Braking System[C]. Proceedings of the14th InternationalElectric Vehicle Symposium,Orlando，Florida,USA,1997．
[47]Hongwei Gao, Yimin Gao and Mehrdad Ehsani. A Neural Network Based SRM DriveControl Strategy for Regenerative Braking in EV and HEV [C]. Electric Machines andDrives Conference,2001.
[48]Zeng Xiao-hua,Ba Te,Qu Xiao-dong. A kind of accurately optimized braking energydistribution strategy applied to switched series-parallel hybrid electric bus [C]20112ndInternational Conference on Artificial Intelligence, Management Science and ElectronicCommerce.
[49]詹讯，秦大同，杨阳，等.轻度混合动力汽车再生制动控制策略与仿真研究[J].中国机械工程，2006，17(3):321~324.
[50]邹广才，罗禹贡，边明远，等.并联式HEV制动能量回收控制策略的仿真研究[J].汽车技术，2003，7:14~16.
[2]胡安平.基于AMESim-Simulink联合仿真的再生制动系统研究[D].长春:吉林大学，2008.
[3]Motomu Hakiai,Toshio Taichi,Masahiko Shoda,Takashi Koizumi,TadashiAshikaga,Hiroshi Shimizu.Brake system of"Eco-Vehicle".The14th International ElectricVehicle Symposium and Exposition,Orlando,USA,1997.
[4]周杰敏，童毅，欧阳明高.轻度混合动力系统典型瞬态过程方针分析[J].汽车工程，2003，25（1）；65-69.
[5]蒋励.基于理想制动力分配曲线的复合制动设计.汽车科技，2006年7月第四期.
[6]Toshifumi Takaoka,Osamu Harada.Highly efficient and super low emission hybrid vehicle.
[7]Tatsuo,Teratani,Kohjiro,Kuramochi.Development of Toyota Mild-hybrid system(THS-M).Toyota Technical Review,2002,Vol.52No.1Sep.2002:46~51.
[8]Sumiko Sekiguchi, Koichi Kondo,etc.Development of Electrical4WD System for HybridVehicles. Toyota Technical Review.2002,Vol.51No.2Mar,2002:86~89.
[9]Y.Sasaki, A.Otomo, F.Kawahata, A.Matui. Toyota Braking System for Hybrid Vehicle withRegenerative System. EVS-14,proceeding CD,1997.
[10]Masami Ogura, and Yasushi Aoki and Steve Mathison. The Honda EV Plus RegenerativeBraking System. EVS-14, proceeding CD.1997.
[11]Koichi Fukuo, Akira Fujimura. Development of the ultra-low-fuel consumption hybridcar-INSIGHT. JSAE Review. JSAE20014016,2001:95~103.
[12]Isaya Matsuo, Takeshi Miyamoto. The Nissan hybrid vehicle. SAE2000-01-1568,2000:1~9.
[13]Hidehiro Oba,Akihiro Yamanaka,etc. Development of a Hybrid Powertrain System UsingCVT in a Minivan. Toyota Technical Review.2002,Vol.51No.2Mar.74~79,2002:52~57.
[14]张元才，余卓平，徐乐，熊璐.基于制动意图的电动汽车复合制动系统制动力分配策略研究.汽车工程，2009050.
[15]王会义.汽车防抱死制动系统模糊控制方法的应用及其仿真试验台的构建.博士后工作报告，长春:吉林工业大学，1998年.
[16]丁海涛.制动防抱死系统模糊控制的初步研究.硕士学位论文[D].长春:吉林工业大学，1999年.
[17]刘胜铁.并联混合动力客车模糊控制策略的研究.吉林大学硕士学位论文，2006年10月.
[18]孙以泽，王其明.车辆AMT中道路条件及驾驶意图的模糊识别.汽车工程，2001104.
[19]何忠波，白鸿柏，孔庆春，张培林.基于驾驶员意图的AMT车辆控制研究.军械工程学院学报，2005年6月.
[20]王玉海，宋健，李兴坤.基于模糊推理的驾驶员意图识别研究.公路交通科技，2005年12月.
[21]王玉海，宋健，李兴坤.驾驶员意图与行驶环境的统一识别及实时算法.机械工程学报，2006年4月.
[22]Reza Langari and Jong-Seob Won.Texas A&M University.A.Driving SituationAwareness-Based Energy ManagementStrategy for Parallel HybridVehicles.SAE2003-01-2311.
[23]Steven Boyd and Douglas J. Nelson Virginia Tech,Blacksburg, Virginia. Hybrid ElectricVehicle Control Strategy Based on Power Loss Calculations. SAE2008-01-0084.
[24]Yuhai Wang, Jian Song.Inference of Drivers Intention and its Realization on Fuzzy Logic
[C]1JSAE NUM,20024621.
[25]Sakaguchi S, Sakai I, Haga T1. Application of Fuzzy Logic to Shift Scheduling Methodfor Automatic Transmission [C].12nd IEEE Conference on Fuzzy Systems,1993.
[26]李士勇.模糊控制·神经控制和智能控制论[M].哈尔滨工业大学出版社，2006年4月.
[27]dSPACE/ControlDesk References2000.
[28]余志生.汽车理论[M].机械工业出版社，2004年1月.
[29]王望予.汽车设计[M].机械工业出版社，3003年9月.
[30]方泳龙.汽车制动理论与设计[M].国防工业出版社，2005年1月.
[31]秦大同，邓涛，杨阳，林志煌.基于前向建模的ISG型CVT混合动力系统再生制动仿真研究[J].中国机械工程师，2008，19(5):618-624.
[32]王益全.电动机原理与实用技术[M].北京:科学出版社，2005.
[33]王庆年,于永涛，曾小华，于远彬.基于CRUISE软件的混合动力汽车正向仿真平台的开发[J].吉林大学学报(工学版)，2009，39(6)：1413-1419.
[34]彭栋.AVL CRUISE V3.0整车性能仿真平台.AVL李斯特技术中心.
[35]AVL CRUISE高级培训教程.北京恒润科技有限公司，2005年9月.
[36]一汽技术研发中心，吉林大学混合动力课题组.第二代城市混合动力客车方案论证参数匹配及性能分析研究.
[37]一汽技术研发中心，吉林大学混合动力课题组.第二代混合动力汽车控制策略研究报告.
[38]杨林，卓斌，冒晓建，王好战.混合动力电动汽车动力控制系统方案分析.
[39]刘博，杜继宏，等.电动汽车制动能量回收控制策略的研究[J].电子技术应用，2004，（1）：34~36.
[40]过学迅，张靖.混合动力电动汽车再生制动系统的建模与仿真[J].武汉理工大电动汽车再生制动稳定性研究学学报(信息与管理工程版)，2005，27(1):116~120.
[41]罗玉涛，俞明，陈炳坤等.电动车制动能量再生反馈控制研究[J].机床与液压2002年第5期.
[42]徐海东.电动汽车再生制动能量高效回收控制策略研究[D].山东大学硕士学位论文2007年.
[43]王鹏宇.混合动力汽车复式制动系统的设计与性能仿真[D].吉林大学硕士学位论文.2005年.
[44]Yimin Gao and Mehrdad Ehsani.Electronic Braking System of EV and HEV-Integrationof Regenerative Braking，Automatic Braking Force Control andABS．SAE2001-01-2478．
[45]Michael Panagiotidis，George Delagrammatikas，etc. Development and use of aregenerative braking model for a parallel hybrid electric vehicle. SAE2000-01-0995．
[46]S.R Cikanek．Electric Vehicle Braking System[C]. Proceedings of the14th InternationalElectric Vehicle Symposium,Orlando，Florida,USA,1997．
[47]Hongwei Gao, Yimin Gao and Mehrdad Ehsani. A Neural Network Based SRM DriveControl Strategy for Regenerative Braking in EV and HEV [C]. Electric Machines andDrives Conference,2001.
[48]Zeng Xiao-hua,Ba Te,Qu Xiao-dong. A kind of accurately optimized braking energydistribution strategy applied to switched series-parallel hybrid electric bus [C]20112ndInternational Conference on Artificial Intelligence, Management Science and ElectronicCommerce.
[49]詹讯，秦大同，杨阳，等.轻度混合动力汽车再生制动控制策略与仿真研究[J].中国机械工程，2006，17(3):321~324.
[50]邹广才，罗禹贡，边明远，等.并联式HEV制动能量回收控制策略的仿真研究[J].汽车技术，2003，7:14~16.
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