摘要
为有效预测锂离子电池剩余寿命,引入了粒子滤波算法。对粒子滤波的基本概念和算法实现步骤进行介绍,在给出锂离子电池寿命统计数据的基础上,应用粒子滤波算法计算其剩余寿命,解决了锂离子电池剩余寿命预测的问题。对相同的锂离子电池统计数据,利用扩展卡尔曼滤波方法计算进行对比实验。分析结果表明:粒子滤波算法比扩展卡尔曼滤波算法可靠,能较好地预测出锂离子电池的剩余寿命,误差小于5%。
A particle filtering algorithm for predicting the remaining useful life of the lithium-ion battery is presented. First, the concepts and steps of the proposed method are introduced. Then, the particle filtering based method is used to predict the remaining useful life of the lithium-ion battery with experimental data. Comparison study with the extended Kalman filtering based prediction technique is conducted to evaluate the performance of the particle filtering algorithm. The results show that the particle filtering algorithm is more accurate and can predict the actual failure time with an error less than 5 %.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第8期47-52,60,共7页
Journal of Chongqing University
基金
国家自然科学基金资助项目(51275554
50905028)
教育部新世纪优秀人才支持计划资助项目(NCET-0063)
关键词
锂离子电池
粒子滤波
剩余寿命
扩展卡尔曼滤波
lithium-ion battery
particle filtering
remaining life
extended Kalman filtering
