基于CARMA模型的动力电池荷电状态估计  

Charge State Estimation of Power Battery Based on CARMA Model

作　　者：黄玉莎 陈玉珊 秦琳琳[1] 石春[1] 吴刚[1] HUANG Yusha;CHEN Yushan;QIN Linlin;SHI Chun;WU Gang(School of Information Science and Technology,University of Science and Technology of China,Hefei,Anhui 230026,China)

机构地区：[1]中国科学技术大学信息科学技术学院,安徽合肥230026

出　　处：《控制与信息技术》2020年第2期36-40,59,共6页CONTROL AND INFORMATION TECHNOLOGY

摘　　要：为完善电动汽车电池管理系统的主要功能,实现对电池准确建模及荷电状态(state of charge,SOC)的准确估计,文章基于二阶RC等效电路建立了一种受控自回归滑动平均模型(controlled auto-regressive moving average,CARMA),推导得到电池开路电压(open circuit voltage,OCV)的最优估计,并结合分段建立的电池OCVSOC模型实现电池SOC估计,从而实现了电池模型参数在线实时辨识以及SOC实时估计,解决了因初值设定不合理而影响SOC估计准确度的问题。仿真结果表明:在美国联邦城市运行工况下,SOC估计误差的绝对值不超过2.39%,实现了较为准确的SOC估计。In order to perfect main functions of electric vehicle battery management system,this paper aims to realize accurate battery modeling and state of charge(SOC)estimation.In this paper,based on the second-order RC equivalent circuit model,a controlled autoregressive moving average(CARMA)of the battery was established.The optimal estimation of open circuit voltage(OCV)is derived from the CARMA model,and battery SOC estimation is realised by OCV-SOC segmentation model.The method realizes online real-time identification of battery model parameters and real-time SOC estimation,which solves the problem of unreasonable initial value setting that affects the accuracy of SOC estimation.Simulation results show that under the operating conditions of the federal city in the United States,the absolute value of the SOC estimation error does not exceed 2.39%,and a more accurate SOC estimation is achieved.

关 键 词：荷电状态 受控自回归滑动平均模型 动力电池 SOC估计 

分 类 号：TM911[电气工程—电力电子与电力传动] U46[机械工程—车辆工程]