混合动力客车控制策略优化
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摘要
本文结合“十一五”国家“863”计划重大项目(2008AA11A140)“一汽解放牌混合动力客车新型整车技术开发”和吉林省、一汽集团、吉林大学联合行动项目“一汽第二代混合动力客车关键技术预研”，对一汽解放牌混合动力客车的整车控制策略进行了优化。全文包括五个方面的研究内容：
1.提出了驾驶意图模糊识别方法。对驾驶意图进行了分类并且确定了驾驶意图识别参数。阐述了驾驶意图识别的理论基础，并且建立了驾驶意图模糊识别的隶属函数及模糊推理规则。搭建了基于MATLAB/SIMULINK的驾驶意图模糊识别模型。为了验证驾驶意图识别方法识别驾驶意图的准确性，对驾驶意图识别方法进行了实验验证。根据验证结果，运用模糊神经网络对驾驶意图识别模块的部分参数的隶属函数进行了优化，进一步提高了驾驶意图识别模块对驾驶员驾驶意图识别的准确性。
2.根据驾驶员驾驶意图识别结果，提出了整车需求扭矩模糊计算方法，使整车需求扭矩的计算结果能更符合驾驶员的驾驶需求。针对不同风格的驾驶员制定了不同类型的整车控制策略。整车控制策略可以根据不同风格的驾驶员进行实时调整，使整车控制策略具有自适应性。对整车控制策略进行了仿真实验，结果表明经济型控制策略在动力性没有下降的前提下，经济性比原始控制策略更好。动力型控制策略在经济性没有下降的前提下，动力性比原始控制策略更出色。基于驾驶风格识别的自适应控制策略相比原始控制策略，动力性和经济性均有提高。
3.提出了基于马尔科夫链的驾驶意图预测方法。通过大量重复实验和对实验数据的观察分析，对车辆运行过程中驾驶员的驾驶意图进行数理统计，得出驾驶意图一步转移概率的统计规律性，得到了驾驶意图马尔科夫链的一步转移概率矩阵。提出了基于驾驶意图预测的随机动态规划混合动力汽车控制算法。根据驾驶意图之间的一步转移概率矩阵对动态规划代价函数“取平均”，对代价函数采取加权平均的方法，使每个驾驶意图都能按其跳转概率对下一阶段的代价函数产生影响。本算法可以不依赖于每一时刻具体的驾驶意图，把驾驶意图的跳转看作是一个随机过程，在已知驾驶意图之间的一步转移概率矩阵的条件下，便可计算出近似最优的一组控制量。通过仿真实验将基于驾驶意图预测的整车控制策略与其它几种控制策略进行了对比。从燃油经济性方面可以看出，基于驾驶意图预测的整车控制策略虽然不能达到最优控制，但可以达到近似最优，使动态规划算法不再需要已知每一时刻的驾驶意图，为动态规划算法能在混合动力汽车整车控制中得到实时应用打下了基础。
4.对混合动力客车的制动特性进行了分析，得到了混合动力客车制动力分配系数的下限值及在ECE制动法规限制下电机再生制动力的上限值。根据驾驶员制动意图的识别结果优化了期望制动强度的计算方法。提出了基于驾驶员制动意图识别的再生制动控制策略。此控制策略根据驾驶员制动意图的紧急程度，在制动的时候充分利用电机制动，提高了能量回收的比率。
5.对实验台架进行了功能性的调试，验证了整车控制策略可以使试验台架实现混合动力汽车常用的功能和运行模式。进行了实车实验，在一汽技术中心的重型车辆底盘转毂试验台对实验车进行了动力性和经济性的实验。验证了整车控制策略可以在实车上实现设计的功能和运行模式。在车辆经济性和动力性方面，实车实验结果略逊于仿真实验结果，但要优于一汽第一代混合动力客车。
The control strategy optimization of JieFang Hybrid Electric Bus is studied in thispaper, which is sponsored by the state “863” high-tech program (No.2008AA11A140)“Development of New Vehicle Technologies for the JieFang Hybrid Electric Bus ofFAW”and the jointed action project among Jilin province, FAW, and Jilin university“Pre-research on the Key Technologies for the SecondGeneration Hybrid Electric BUS ofFAW”. There are five parts of contents were proposed in this paper.
1. A driving intention fuzzy identification method was proposed.Driving intentionswere classified and driving intention identification parameters were selected.Thetheoretical basis of driving intention identification was elaborated. The membershipfunctions, fuzzy inference rules and driving intention fuzzy identification model based onSIMULINK were built.To test and verify the driving intention identification method, theexperimental verification was done. According to identification result, someparameters’membership functions were optimized by fuzzy-neural network, whichincreases the identification accuracy of driving intention fuzzy identification model.
2. A demand torque calculation method of hybrid electric bus was proposed based ondriving intention identification result, which makes demand torque calculation resultaccord with drivers’ demand perfectly.Aiming at drivers of different driving styles,different control strategies of hybrid electric bus were designed which lets control strategyadjust at real time according with the driver’s driving style and makes control strategyhave adaptability.Control strategy simulation was done, which indicates that economycontrol strategy makes the hybrid electric bus’s economy performance better with nodynamic performance decline and dynamic control strategy makes the hybrid electricbus’s dynamic performance better with no economy performance. Comparing withoriginal control strategy, the economy and dynamic performance are better because ofadaptive control strategy based on driving style identification.
3. Driving intention prediction method based on Markov chain is proposed. Byanalysis of massive experimental data, driving intention has been analyzed statistically inthe process of driving and one-step transition probability matrix of driving intentionMarkov chain has been calculated. A hybrid vehicle control algorithm based on drivingintention prediction stochastic dynamic programming is proposed. Take the average ofdynamic programming cost function, according to driving intention one-step transition probability matrix and adopt weighted average method to calculate the cost function,which make every driving intention has an effect on next stage cost function based ondriving intention one-step transition probability. This algorithm is independent on drivingintentions at every moment and takes driving intention transition as a stochastic process.On the condition that driving intention one-step transition probability matrix is known, agroup of optimal control variables can be calculated. The control strategy based ondriving intention prediction and other control strategies have been contrasted bysimulation. From the fuel economy aspect, control strategy based on driving intentionprediction cannot obtain optimal control, but it is an approximate optimal control strategy.This control strategy makes dynamic programming algorithm don’t need knowing drivingintention at every moment and lay a solid foundation for the real-time using of dynamicprogramming algorithm in hybrid vehicle control strategy.
4. Braking characteristics of hybrid electric bus were analyzed. The minimal value ofbraking distribution coefficient and the maximal value of motor regenerative brakingforce were calculated.Computing method of braking demand severity was optimizedbased on braking intention identification result. A regenerative braking control strategybased on braking intention identification was proposed. Motor brake is mainly used in thebrake process according to the brake emergency degree, so energy recovery ratio isincreased.
5. Bench test was done to prove that the bench has functions and operation modes ofhybrid electric vehicles under the control strategy. Dynamic and economic performancetests of prototype vehicle were done on the heavy-duty vehicle chassis dynamometer ofFAW R&D center to prove that prototype vehicle has functions and operation modes thatare preconceived.At dynamic and economic performance aspect, prototype vehicle testresult is not better than simulation result, but is better than FAW first generation hybridelectric bus.
1.提出了驾驶意图模糊识别方法。对驾驶意图进行了分类并且确定了驾驶意图识别参数。阐述了驾驶意图识别的理论基础，并且建立了驾驶意图模糊识别的隶属函数及模糊推理规则。搭建了基于MATLAB/SIMULINK的驾驶意图模糊识别模型。为了验证驾驶意图识别方法识别驾驶意图的准确性，对驾驶意图识别方法进行了实验验证。根据验证结果，运用模糊神经网络对驾驶意图识别模块的部分参数的隶属函数进行了优化，进一步提高了驾驶意图识别模块对驾驶员驾驶意图识别的准确性。
2.根据驾驶员驾驶意图识别结果，提出了整车需求扭矩模糊计算方法，使整车需求扭矩的计算结果能更符合驾驶员的驾驶需求。针对不同风格的驾驶员制定了不同类型的整车控制策略。整车控制策略可以根据不同风格的驾驶员进行实时调整，使整车控制策略具有自适应性。对整车控制策略进行了仿真实验，结果表明经济型控制策略在动力性没有下降的前提下，经济性比原始控制策略更好。动力型控制策略在经济性没有下降的前提下，动力性比原始控制策略更出色。基于驾驶风格识别的自适应控制策略相比原始控制策略，动力性和经济性均有提高。
3.提出了基于马尔科夫链的驾驶意图预测方法。通过大量重复实验和对实验数据的观察分析，对车辆运行过程中驾驶员的驾驶意图进行数理统计，得出驾驶意图一步转移概率的统计规律性，得到了驾驶意图马尔科夫链的一步转移概率矩阵。提出了基于驾驶意图预测的随机动态规划混合动力汽车控制算法。根据驾驶意图之间的一步转移概率矩阵对动态规划代价函数“取平均”，对代价函数采取加权平均的方法，使每个驾驶意图都能按其跳转概率对下一阶段的代价函数产生影响。本算法可以不依赖于每一时刻具体的驾驶意图，把驾驶意图的跳转看作是一个随机过程，在已知驾驶意图之间的一步转移概率矩阵的条件下，便可计算出近似最优的一组控制量。通过仿真实验将基于驾驶意图预测的整车控制策略与其它几种控制策略进行了对比。从燃油经济性方面可以看出，基于驾驶意图预测的整车控制策略虽然不能达到最优控制，但可以达到近似最优，使动态规划算法不再需要已知每一时刻的驾驶意图，为动态规划算法能在混合动力汽车整车控制中得到实时应用打下了基础。
4.对混合动力客车的制动特性进行了分析，得到了混合动力客车制动力分配系数的下限值及在ECE制动法规限制下电机再生制动力的上限值。根据驾驶员制动意图的识别结果优化了期望制动强度的计算方法。提出了基于驾驶员制动意图识别的再生制动控制策略。此控制策略根据驾驶员制动意图的紧急程度，在制动的时候充分利用电机制动，提高了能量回收的比率。
5.对实验台架进行了功能性的调试，验证了整车控制策略可以使试验台架实现混合动力汽车常用的功能和运行模式。进行了实车实验，在一汽技术中心的重型车辆底盘转毂试验台对实验车进行了动力性和经济性的实验。验证了整车控制策略可以在实车上实现设计的功能和运行模式。在车辆经济性和动力性方面，实车实验结果略逊于仿真实验结果，但要优于一汽第一代混合动力客车。
The control strategy optimization of JieFang Hybrid Electric Bus is studied in thispaper, which is sponsored by the state “863” high-tech program (No.2008AA11A140)“Development of New Vehicle Technologies for the JieFang Hybrid Electric Bus ofFAW”and the jointed action project among Jilin province, FAW, and Jilin university“Pre-research on the Key Technologies for the SecondGeneration Hybrid Electric BUS ofFAW”. There are five parts of contents were proposed in this paper.
1. A driving intention fuzzy identification method was proposed.Driving intentionswere classified and driving intention identification parameters were selected.Thetheoretical basis of driving intention identification was elaborated. The membershipfunctions, fuzzy inference rules and driving intention fuzzy identification model based onSIMULINK were built.To test and verify the driving intention identification method, theexperimental verification was done. According to identification result, someparameters’membership functions were optimized by fuzzy-neural network, whichincreases the identification accuracy of driving intention fuzzy identification model.
2. A demand torque calculation method of hybrid electric bus was proposed based ondriving intention identification result, which makes demand torque calculation resultaccord with drivers’ demand perfectly.Aiming at drivers of different driving styles,different control strategies of hybrid electric bus were designed which lets control strategyadjust at real time according with the driver’s driving style and makes control strategyhave adaptability.Control strategy simulation was done, which indicates that economycontrol strategy makes the hybrid electric bus’s economy performance better with nodynamic performance decline and dynamic control strategy makes the hybrid electricbus’s dynamic performance better with no economy performance. Comparing withoriginal control strategy, the economy and dynamic performance are better because ofadaptive control strategy based on driving style identification.
3. Driving intention prediction method based on Markov chain is proposed. Byanalysis of massive experimental data, driving intention has been analyzed statistically inthe process of driving and one-step transition probability matrix of driving intentionMarkov chain has been calculated. A hybrid vehicle control algorithm based on drivingintention prediction stochastic dynamic programming is proposed. Take the average ofdynamic programming cost function, according to driving intention one-step transition probability matrix and adopt weighted average method to calculate the cost function,which make every driving intention has an effect on next stage cost function based ondriving intention one-step transition probability. This algorithm is independent on drivingintentions at every moment and takes driving intention transition as a stochastic process.On the condition that driving intention one-step transition probability matrix is known, agroup of optimal control variables can be calculated. The control strategy based ondriving intention prediction and other control strategies have been contrasted bysimulation. From the fuel economy aspect, control strategy based on driving intentionprediction cannot obtain optimal control, but it is an approximate optimal control strategy.This control strategy makes dynamic programming algorithm don’t need knowing drivingintention at every moment and lay a solid foundation for the real-time using of dynamicprogramming algorithm in hybrid vehicle control strategy.
4. Braking characteristics of hybrid electric bus were analyzed. The minimal value ofbraking distribution coefficient and the maximal value of motor regenerative brakingforce were calculated.Computing method of braking demand severity was optimizedbased on braking intention identification result. A regenerative braking control strategybased on braking intention identification was proposed. Motor brake is mainly used in thebrake process according to the brake emergency degree, so energy recovery ratio isincreased.
5. Bench test was done to prove that the bench has functions and operation modes ofhybrid electric vehicles under the control strategy. Dynamic and economic performancetests of prototype vehicle were done on the heavy-duty vehicle chassis dynamometer ofFAW R&D center to prove that prototype vehicle has functions and operation modes thatare preconceived.At dynamic and economic performance aspect, prototype vehicle testresult is not better than simulation result, but is better than FAW first generation hybridelectric bus.
引文
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