纯电动汽车车载电源性能在环测试平台研究  

Hardware-in-the-loop test bench research of hybrid energy storage systems in electric vehicles

作　　者：李勇[1] Mehrdad Kazerani 马飞[1] 

机构地区：[1]北京科技大学机械工程学院,北京100083 [2]滑铁卢大学电气与计算机系

出　　处：《北京科技大学学报》2014年第10期1369-1377,共9页Journal of University of Science and Technology Beijing

基　　金：国家高技术研究发展计划资助项目(2011AA060403);北京市科技计划资助项目(Z121100003012016)

摘　　要：为研究纯电动汽车车载电源性能,提出并搭建了由异步电动机和直流电动机组成的在环测试平台.异步电动机用来模拟纯电动汽车的牵引电动机,直流电动机用来模拟汽车行驶时的阻力和惯量,对异步电动机和直流电动机分别实施转速控制和转矩控制.分析了电动汽车行驶工况,给出了简单循环工况下参考转速、转距和功率.设计了异步电动机调速系统转速控制器和电流控制器,建立了异步电动机调速系统的数学模型,提出了基于自适应模糊神经网络控制的异步电动机调速系统.仿真和实验结果表明,基于自适应模糊神经网络控制的调速系统明显优于PID控制的交流调速系统,在环测试平台能够较好跟踪参考转速和参考转距的变化.Hybrid energy storage systems （HESS） play an important role in electric vehicles. This paper mainly focuses on a hardware-in-the-loop （HIL） test bench for testing the performance of HESS. The scenario of an induction motor and a DC motor was proposed. The induction motor was used as a traction motor while the DC motor worked as the load and moment of inertia of the vehicle. Speed control was implemented on the induction motor while torque control was applied to the DC motor. The speed, torque and power of the traction motor were obtained from a simple drive cycle based on real parameters. The motor speed was given as a reference of the induction motor while the load torque was used as a reference of the DC motor. The speed control system of the induction motor and the torque control of the DC motor were analyzed and designed. Meanwhile, the speed control system of the induction motor was modeled. Adaptive fuzzy neural-network control was proposed to achieve high accuracy due to the low accuracy of PID control. Simulation and experimental results agreed with the proposal. The test bench follows the reference speed and reference torque well.

关 键 词：电动汽车 复合电源 在环测试 自适应 模糊控制 神经网络 

分 类 号：U469.72[机械工程—车辆工程]