电动汽车动力电池荷电状态估计  

Estimation of Electric Vehicle Power Battery State of Charge

作　　者：邱志鹏 QIU Zhipeng(School of Automobile,Chang'an University,Xi'an 710061,China)

机构地区：[1]长安大学汽车学院,陕西西安710061

出　　处：《汽车实用技术》2023年第23期26-30,共5页Automobile Applied Technology

摘　　要：精准可靠的荷电状态估计对电动汽车整车性能有举足轻重的影响,从而成为国内外学者研究的重要方向。文章选取三元锂离子电池作为研究对象,然后介绍了几种荷电状态的估计方法,选择使用模型基础法进行荷电状态的估计,构造一阶Thevenin模型,然后利用充放电实验所得数据和MATLAB里的多项式拟合工具构造得出开路电压-荷电状态(OCV-SOC)的函数对应关系。通过使用最小二乘法来辨识对模型中的参数值可以进行离线辨识,再使用自适应扩展卡尔曼滤波(AEKF)算法来估计锂离子电池的荷电状态。随后进行了动态应力测试(DST)实验,将扩展卡尔曼滤波(EKF)算法与AEKF算法进行荷电状态估计的对比及误差分析,从而对AEKF算法的精确度进行检验。最终通过实验数据可以得出结论,文章介绍的AEKF算法对DST实验工况的荷电状态估计可以保证较满意的精确度,并且此算法不是很复杂,操作性强。Accurate and reliable estimate of the state of charge has a pivotal impact on the performance of electric vehicles,thus becoming an important research direction for scholars at home and abroad.This paper chooses ternary lithium-ion battery as the research object,and then introduces several estimation methods of charge state.Model based method is chosen to estimate charge state,and first-order Thevenin model is constructed.Then the function correspondence of open circuit voltage-state of charge(OCV-SOC)is constructed by using the data of charge and discharge experiment and polynomial fitting tool in MATLAB.The parameter values in the model can be identified offline by using the least square method,and then the adaptive extended Kalman filter(AEKF)algorithm is used to estimate the state of charge of lithium-ion batteries.Then the dynamic stress test(DST)experiment is carried out,and the extended Kalman filter(EKF)algorithm and AEKF algorithm are compared and the error is analyzed,so as to check the accuracy of AEKF algorithm.Finally,through the experimental data,it can be concluded that the AEKF algorithm introduced in this paper can ensure a satisfactory accuracy in the estimation of the DST experimental conditions,and this algorithm is not very complicated and has strong operability.

关 键 词：三元锂离子电池 SOC估计 一阶Thevenin模型 AEKF 

分 类 号：U469.7[机械工程—车辆工程]