佛山科学技术学院学报（自然科学版）2025，Vol.43Issue(1)：19-26,8.
基于高阶扩展卡尔曼滤波的锂电池剩余使用寿命自适应预测
Adaptive prediction of remaining useful life for lithium battery based on high-order extended Kalman filter
摘要
Abstract
Remaining Useful Life(RUL)prediction of lithium batteries is crucial for preventive maintenance of battery health management systems,and degradation models based on stochastic processes play a key role in this field,especially the use of two-implied-state nonlinear Wiener process,which can describe the degradation trend of lithium batteries more flexibly and specifically.In the lithium batteries RUL adaptive prediction,the degradation model parameters are often updated online using the Extended Kalman Filter(EKF)method,which is a nonlinear Gaussian filter with an approximate accuracy of only one order,and the filtering accuracy is low for strongly nonlinear systems.This paper is based on the nonlinear Wiener degradation model of lithium batteries with double hidden states.It designs a High-order Extended Kalman Filter(HEKF)to reduce truncation error by utilizing information from high-order terms and obtains optimal online parameter estimates.It further derives the probability density function of RUL and achieves adaptive prediction of RUL for lithium batteries.Finally,the example validation by NASA's lithium battery degradation data shows that the accuracy of this paper's method is improved by 83.9%and 53.3%,respectively,compared with the prediction results of the other two methods.关键词
锂电池/剩余使用寿命/高阶扩展卡尔曼滤波/非线性维纳过程/双隐含状态Key words
lithium battery/remaining useful life/higher-order extended Kalman filter/nonlinear wiener process/double hidden states分类
交通工程引用本文复制引用
余伟,周云刚,朱文博,黎海兵,张忠波..基于高阶扩展卡尔曼滤波的锂电池剩余使用寿命自适应预测[J].佛山科学技术学院学报（自然科学版）,2025,43(1):19-26,8.基金项目
国家自然科学基金资助项目(61803086,62106048) （61803086,62106048）
广东省基础与应用基础研究基金资助项目(2022A1515110187,2022A1515140042) （2022A1515110187,2022A1515140042）
