电池2023，Vol.53Issue(6)：634-638,5.DOI:10.19535/j.1001-1579.2023.06.010
基于TVFRLS和SVD-UKF的锂离子电池SOC估算
SOC estimation for Li-ion battery based on TVFRLS and SVD-UKF
摘要
Abstract
The accuracy of traditional off-line parameter identification methods under the influence of complex vehicle operating conditions was low,the unscented Kalman filter(UKF)algorithm was vulnerable to encounters issues that the non-positive-definite covariance matrix during the estimation of state of charge(SOC),resulting in SOC estimation failures.The joint online SOC estimation was carried out using the time-variable forgetting factor recursive least squares method(TVFRLS)and singular value decomposition unscented Kalman filter(SVD-UKF)to improve the accuracy and robustness of the algorithm under complex conditions.The algorithm was verified by urban dynamometer driving schedule(UDDS).The absolute estimation error(AEE)of the combined algorithm of TVFRLS and SVD-UKF was 1.31%,the mean absolute error(MEA)was 0.56%and the root mean square error(RMSE)was 0.75%.Compared with the traditional UKF algorithm,its MEA and RMSE were reduced by 60.0%and 51.9%.关键词
锂离子电池/荷电状态(SOC)/时变遗忘因子最小二乘法(TVFRLS)/无迹卡尔曼滤波(UKF)/电动汽车/参数辨识Key words
Li-ion battery/state of charge(SOC)/time-variable forgetting factor recursive least squares method(TVFRLS)/unscented Kalman filter(UKF)/electric vehicle/parameter identification分类
信息技术与安全科学引用本文复制引用
林正廉,卢玉斌,陈亮,柯彦舜..基于TVFRLS和SVD-UKF的锂离子电池SOC估算[J].电池,2023,53(6):634-638,5.基金项目
国家自然科学基金青年项目(42202302),福建省自然科学基金项目(2021J05104) （42202302）
