基于HIL仿真平台的电动汽车SOE在线估计研究

Research on the Online Estimation of SOE for Electric Vehicles Based on a Hardware-in-the-Loop (HIL) Simulation Platform

本文基于硬件在环(HIL)仿真对电动汽车SOE在线估计进行研究。首先基于dSPACE平台搭建了以锂电池为硬件的HIL仿真平台。然后分析了电动汽车动力电池剩余能量(SOE)的计算方法,并为此在Simulink环境下建立了2阶Thevenin等效电路电池模型和包含汽车动力学模型、驾驶员模型、电机模型三部分的整车模型。接下来借助RTW和RTI软件平台完成编译和代码的转换,将上述Simulink仿真模型作为HIL平台的控制逻辑上传到dSPACE实验系统中。综合以上工作,实现了由HIL平台控制真实锂电池在电动汽车NEDC和WLTP混合工况下作为电池组电芯进行充放电,并实时采集和监控真实电池的电流电压数据。最后基于该HIL仿真平台的在线运行,以均方根误差(RMSE)为评价标准实时地调试优化动态参数模型、算法,完成了对电池SOE在线估计的研究。

This paper conducts research on the online estimation of state of energy (SOE) for electric vehicles using hardware-in-the-loop (HIL) simulation. First, a HIL simulation platform with a lithium battery as the hardware was established on the dSPACE platform. Then, the calculation method for the re-maining energy of the electric vehicle power battery (SOE) was analyzed, and a two-order Thevenin equivalent circuit battery model and a vehicle model consisting of three components— vehicle dy-namics, driver model, and motor model—were created in Simulink. Subsequently, the Simulink simulation model was compiled and converted into code using the RTW and RTI software platforms, and uploaded to the dSPACE experimental system as the control logic for the HIL platform. With these steps, the real lithium battery can be controlled by the HIL platform to charge and discharge as battery cells in electric vehicles under the mixed operating conditions of NEDC and WLTP, while real-time current and voltage data are collected and monitored. Finally, based on the real-time op-eration of the HIL simulation platform, the research on online estimation of battery SOE was com-pleted by dynamically optimizing the parameter model and algorithm in real time using root mean square error (RMSE) as the evaluation standard.