吉林大学学报（信息科学版）2018，Vol.36Issue(2)：173-177,5.
基于改进粒子滤波的锂离子电池RUL预测
Improved Particle Filter Algorithm for RUL Prediction of Lithium-Ion Batteries
摘要
Abstract
In the process of predicting, the remaining useful life of Lithium-ion batteries is based on particle filter algorithm. The fundamental particle filter algorithm has the problem of particle degeneration and it is difficult to ensure the accuracy of the remaining useful life prediction, so an improved unscented particle filter algorithm based on MCMC (Monte Carlo Markov Chain) is proposed. This algorithm overcomes the problem of particle degeneration by selecting the appropriate importance density function and resampling strategy, and improves the accuracy of the remaining useful life prediction. The simulation experiment shows that the improved particle filter algorithm can track the decline trend of battery capacity better and achieve higher precision than the fundamental particle filter algorithm, which can provide a new idea for predicting the remaining useful life of Lithium-ion batteries.关键词
锂离子电池/剩余使用寿命/粒子滤波/粒子退化/Monte Carlo Markov链/无迹粒子滤波Key words
lithium-ion batteries/the remaining useful prediction/particle filter/particles degeneracy/Monte Carlo Markov Chain ( MCMC)/unscented particle filter分类
信息技术与安全科学引用本文复制引用
刘亚姣,刘振泽,宋晨辉..基于改进粒子滤波的锂离子电池RUL预测[J].吉林大学学报（信息科学版）,2018,36(2):173-177,5.基金项目
吉林省科技发展计划基金资助项目(20100184) （20100184）
