轮毂电机电动汽车(in-wheel motor electric vehicle,IWM-EV)的电机激励与车辆系统的耦合特性严重的恶化车辆的动力学性能以及电机的工作稳定性,针对这种振动负效应问题,建立了考虑机电耦合的车辆动力学耦合模型,并设计了工况识别的主...轮毂电机电动汽车(in-wheel motor electric vehicle,IWM-EV)的电机激励与车辆系统的耦合特性严重的恶化车辆的动力学性能以及电机的工作稳定性,针对这种振动负效应问题,建立了考虑机电耦合的车辆动力学耦合模型,并设计了工况识别的主动悬架多目标粒子群(multi-objective particle swarm optimization,MOPSO)模糊滑模控制器。基于傅里叶级数法建立了轮毂电机的垂向不平衡激励与电机转矩的电机模型;将电机模型与车辆动力学模型结合建立了电机与悬架联合的垂向-驱动非线性动力学耦合模型。基于耦合模型分析了车辆的机电耦合振动负效应特性,针对模型强非线性的特点,设计了耦合模型的非线性控制器。仿真结果表明,控制器能既能有效的减小电机的相对偏心率,抑制电机不平衡电磁力,又能提升车辆动力学性能,有效的抑制了轮毂电机电动汽车的振动负效应。展开更多
Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the pa...Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.展开更多
文摘轮毂电机电动汽车(in-wheel motor electric vehicle,IWM-EV)的电机激励与车辆系统的耦合特性严重的恶化车辆的动力学性能以及电机的工作稳定性,针对这种振动负效应问题,建立了考虑机电耦合的车辆动力学耦合模型,并设计了工况识别的主动悬架多目标粒子群(multi-objective particle swarm optimization,MOPSO)模糊滑模控制器。基于傅里叶级数法建立了轮毂电机的垂向不平衡激励与电机转矩的电机模型;将电机模型与车辆动力学模型结合建立了电机与悬架联合的垂向-驱动非线性动力学耦合模型。基于耦合模型分析了车辆的机电耦合振动负效应特性,针对模型强非线性的特点,设计了耦合模型的非线性控制器。仿真结果表明,控制器能既能有效的减小电机的相对偏心率,抑制电机不平衡电磁力,又能提升车辆动力学性能,有效的抑制了轮毂电机电动汽车的振动负效应。
基金This study was supported by Young Scientists Fund(Grant No.51805028)Postdoctoral Research Foundation of China(Grant No.BX201600017).
文摘Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.
