桥间分配四驱混合动力电动汽车能耗优化控制策略研究
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摘要
混合动力汽车整车能量管理控制策略，是混合动力汽车核心技术之一，长期以来作为混合动力技术研究的热点而受到广泛重视。本文基于桥间分配型四轮驱动混合动力电动汽车(A4WDHEV)，以降低能耗、提高燃油经济性为目标，对等效油耗最小控制策略(ECMS)进行了深入研究，从优化计算和行驶工况识别等方面对其进行了改进。
首先，分析了A4WDHEV的结构特点和车辆工作模式转换控制；应用理论建模与实验建模相结合的方法，建立了动力总成关键部件和整车系统仿真模型；设计了基于动态规划(DP)优化算法的车辆能量管理控制策略(DPCS)，通过仿真分析了车辆燃油经济性提高的效果，确定了理论上的车辆燃油经济性极限，为等效燃油消耗最小策略提供了改进的依据。
其次，介绍了等效油耗最小能量管理策略的基本理论；系统分析了该策略中等效系数的理论意义，以及基于电池荷电状态水平的等效油耗修正方法；设计了应用于A4WDHEV的ECMS策略；应用模式搜索和并行计算加快优化计算速度；多工况下的仿真结果表明，运用等效油耗最小策略的A4WDHEV相对仅用发动机的原型纯发动机车辆平均燃油节省率可达20%以上。
再次，分析了等效系数和维持电池荷电状态(SOC)平衡的修正方法对等效油耗最小策略控制效果的影响。以动态规划计算出的桥间分配四驱混合动力汽车最佳控制结果为基础，提出利用DIRECT算法优化求解具体工况下最优等效系数的方法。为使等效油耗最小策略在保持SOC平衡的同时兼顾燃油经济性最优，研究了基于SOC的修正函数的优化方法。在此基础上，提出了一种全局优化型等效油耗最小策略(GECMS)。多工况下的仿真结果表明，GECMS比原有ECMS的燃油经济性平均提升了6.8%。
然后，针对GECMS工况适应性不强的问题，提出了一种行驶工况自调整的等效油耗最小策略。以实车采集的广州典型行驶工况数据为例设计学习向量量化(LVQ)神经网络工况识别器，该识别器经过训练后实时工况识别准确率接近100%。将工况识别器引入GECMS后提出一种实际行驶路况自调整全局优化型等效油耗最小策略(AGECMS)。开发了一套用于横向对比DPCS、ECMS、GECMS和AGECMS的A4WDHEV能耗优化控制策略评估仿真软件，运用该软件进行的多工况仿真结果表明，AGECMS的平均燃油经济性在GECMS基础上进一步提高了2.76%，对比原有ECMS提高了9.5%。
最后，结合具体A4WDHEV乘用车(原车)实际开发项目，对GECMS策略控制效果进行了硬件在环仿真试验研究。根据GECMS方法原理对原车整车控制器(HCU)核心应用软件模块进行了修改实现。完成了硬件在环平台的调试。运用该平台进行了GECMS和原车策略的仿真对比试验。试验结果表明，在优化效果受到了原车HCU软件架构限制的情况下，GECMS燃油经济性比原车提高了3.83%。
Hybrid Electric Vehicle (HEV) energy management control strategy grabs muchattention for a long time as a key technology of HEV. This dissertation focues on energyconsumption minimization using an Axle-split Four Wheel Drive Hybrid Electric Vehicle(A4WDHEV) as a case study. Equivalent (fuel) Consumption Minimization Strategy (ECMS)has been improved with respect to optimization method and driving condition recognition.
Firstly, through A4WDHEV structure and vehicle mode shift control analysis, the HEVand its key components model has been built on theoretical and experiment modelling. ADynamic Programming based Control Strategy (DPCS) for A4WDHEV is designed tooptimize vehicle energy consumption. Simulation results indicate the theoretical fuel savelimit while providing database for other control strategy improvement.
After a brief introduction of the basic theory of Equivalent fule ConsumptionMinimization Strategy (ECMS), the equivalent factors implication and equivalent fuelconsumption penaty method are discussed systematically. The ECMS is applied toA4WDHEV for energy save optimization with numerical computation acceleration methodsuch as pattern search and parallel computation. The simulation results under multiple drivecycles show that ECMS can achive above20%fule saving compare to engine-onlyconventional vehicle.
The influence to ECMS caused by equivalent factors variation and the correction forSOC maintenance is analysed. A new optimal method for equivalent factors calculationunder certain drive cycle is proposed. This method refers to DPCS optimal control outputand uses DIRECT algorithm to solve the optimization problem. A SOC based corcttionfunction optimization method is provided to keep the advantage of fule save while correctingequivalent consumption to maintain SOC. Then, present a new Global optimization basedECMS (GECMS) according to these two methods sutdied above. Simulation results showthat the fuel consumption using GECMS can be decreased by about6.8%under multipledrive cycles compared to ECMS.
An Adaptive GECMS (AGECMS) is proposed to deal with the fact that ECMS has poordrive cycle adaptability.Four typical Guangzhou drive cycles are formed through real roadtest data acquisition as the example data for LVQ neural network drive condition identifierdesign. After training, the LVQ identifier can achive about100%identify accuracy. Add theroad condition identifier to GECMS, an Adjustable Global optimization based Equivalent Consumption Minimization Strategy (AGECMS) is put forward. Simulation software isdeveloped for the comparison study of DPCS, ECMS, GECMS and AGECMS forA4WDHEV energy consumption optimization. Mutiple drive cycle simulation resultsindicate that AGECMS is2.76%above to GECMS with respect to fule saving.The overallfuel saving is9.5%compared with original ECMS in a drive-cycle-average sense.
Finally, related to an industrial A4WDHEV sedan development project, the GECMSHardware In Loop (HIL) simulation is conducted for evaluation of the control effectiveness.Some core parts of the original HEV HCU software are modified to realize the GECMS.After the construction of HIL platform, a comparison test is carried out for GECMS andoriginal HEV strategy. Under circumstances that the optimization effect is restricted to theHCU software architecture, the test results show that GECMS increase the fuel economy by3.83%compared with the original HEV strategy.
首先，分析了A4WDHEV的结构特点和车辆工作模式转换控制；应用理论建模与实验建模相结合的方法，建立了动力总成关键部件和整车系统仿真模型；设计了基于动态规划(DP)优化算法的车辆能量管理控制策略(DPCS)，通过仿真分析了车辆燃油经济性提高的效果，确定了理论上的车辆燃油经济性极限，为等效燃油消耗最小策略提供了改进的依据。
其次，介绍了等效油耗最小能量管理策略的基本理论；系统分析了该策略中等效系数的理论意义，以及基于电池荷电状态水平的等效油耗修正方法；设计了应用于A4WDHEV的ECMS策略；应用模式搜索和并行计算加快优化计算速度；多工况下的仿真结果表明，运用等效油耗最小策略的A4WDHEV相对仅用发动机的原型纯发动机车辆平均燃油节省率可达20%以上。
再次，分析了等效系数和维持电池荷电状态(SOC)平衡的修正方法对等效油耗最小策略控制效果的影响。以动态规划计算出的桥间分配四驱混合动力汽车最佳控制结果为基础，提出利用DIRECT算法优化求解具体工况下最优等效系数的方法。为使等效油耗最小策略在保持SOC平衡的同时兼顾燃油经济性最优，研究了基于SOC的修正函数的优化方法。在此基础上，提出了一种全局优化型等效油耗最小策略(GECMS)。多工况下的仿真结果表明，GECMS比原有ECMS的燃油经济性平均提升了6.8%。
然后，针对GECMS工况适应性不强的问题，提出了一种行驶工况自调整的等效油耗最小策略。以实车采集的广州典型行驶工况数据为例设计学习向量量化(LVQ)神经网络工况识别器，该识别器经过训练后实时工况识别准确率接近100%。将工况识别器引入GECMS后提出一种实际行驶路况自调整全局优化型等效油耗最小策略(AGECMS)。开发了一套用于横向对比DPCS、ECMS、GECMS和AGECMS的A4WDHEV能耗优化控制策略评估仿真软件，运用该软件进行的多工况仿真结果表明，AGECMS的平均燃油经济性在GECMS基础上进一步提高了2.76%，对比原有ECMS提高了9.5%。
最后，结合具体A4WDHEV乘用车(原车)实际开发项目，对GECMS策略控制效果进行了硬件在环仿真试验研究。根据GECMS方法原理对原车整车控制器(HCU)核心应用软件模块进行了修改实现。完成了硬件在环平台的调试。运用该平台进行了GECMS和原车策略的仿真对比试验。试验结果表明，在优化效果受到了原车HCU软件架构限制的情况下，GECMS燃油经济性比原车提高了3.83%。
Hybrid Electric Vehicle (HEV) energy management control strategy grabs muchattention for a long time as a key technology of HEV. This dissertation focues on energyconsumption minimization using an Axle-split Four Wheel Drive Hybrid Electric Vehicle(A4WDHEV) as a case study. Equivalent (fuel) Consumption Minimization Strategy (ECMS)has been improved with respect to optimization method and driving condition recognition.
Firstly, through A4WDHEV structure and vehicle mode shift control analysis, the HEVand its key components model has been built on theoretical and experiment modelling. ADynamic Programming based Control Strategy (DPCS) for A4WDHEV is designed tooptimize vehicle energy consumption. Simulation results indicate the theoretical fuel savelimit while providing database for other control strategy improvement.
After a brief introduction of the basic theory of Equivalent fule ConsumptionMinimization Strategy (ECMS), the equivalent factors implication and equivalent fuelconsumption penaty method are discussed systematically. The ECMS is applied toA4WDHEV for energy save optimization with numerical computation acceleration methodsuch as pattern search and parallel computation. The simulation results under multiple drivecycles show that ECMS can achive above20%fule saving compare to engine-onlyconventional vehicle.
The influence to ECMS caused by equivalent factors variation and the correction forSOC maintenance is analysed. A new optimal method for equivalent factors calculationunder certain drive cycle is proposed. This method refers to DPCS optimal control outputand uses DIRECT algorithm to solve the optimization problem. A SOC based corcttionfunction optimization method is provided to keep the advantage of fule save while correctingequivalent consumption to maintain SOC. Then, present a new Global optimization basedECMS (GECMS) according to these two methods sutdied above. Simulation results showthat the fuel consumption using GECMS can be decreased by about6.8%under multipledrive cycles compared to ECMS.
An Adaptive GECMS (AGECMS) is proposed to deal with the fact that ECMS has poordrive cycle adaptability.Four typical Guangzhou drive cycles are formed through real roadtest data acquisition as the example data for LVQ neural network drive condition identifierdesign. After training, the LVQ identifier can achive about100%identify accuracy. Add theroad condition identifier to GECMS, an Adjustable Global optimization based Equivalent Consumption Minimization Strategy (AGECMS) is put forward. Simulation software isdeveloped for the comparison study of DPCS, ECMS, GECMS and AGECMS forA4WDHEV energy consumption optimization. Mutiple drive cycle simulation resultsindicate that AGECMS is2.76%above to GECMS with respect to fule saving.The overallfuel saving is9.5%compared with original ECMS in a drive-cycle-average sense.
Finally, related to an industrial A4WDHEV sedan development project, the GECMSHardware In Loop (HIL) simulation is conducted for evaluation of the control effectiveness.Some core parts of the original HEV HCU software are modified to realize the GECMS.After the construction of HIL platform, a comparison test is carried out for GECMS andoriginal HEV strategy. Under circumstances that the optimization effect is restricted to theHCU software architecture, the test results show that GECMS increase the fuel economy by3.83%compared with the original HEV strategy.
引文
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