摘要
针对电池健康状态局部波动增加预测难度,采用粒子滤波和自回归整合移动平均模型分别预测由经验模态分解提取的健康状态趋势项和细节项,实现锂离子电池剩余寿命预测。提出的PF-ARIMA方法相对误差均值约4.0%,表明该方法能够较为准确地预测锂离子电池剩余寿命。
To improve the prediction accuracy,particle filter and autoregressive integrated moving average model are employed to predict degradation trend and fluctuation details extracted from state of health series by empirical model decomposition,respectively.The proposed PF-ARIMA method has an average relative error of 4.0%,indicating PF-ARIMA method can accurately predict the remaining useful life of lithium-ion battery.
作者
周亚鹏
郭彪
王一纯
ZHOU Yapeng;Guo Biao;Wang Yichun(China Merchants Testing Certification Vehicle Technology Research Institute Co.,Ltd.,Department of Power Test and Research,Chongqing,401329,China;Chongqing Key Laboratory of Industrial and Information Technology of Electric Vehicle Safety Evaluation,Chongqing,401329,China;Information Engineering Institute,Chongqing Vocational and technical university of mechatronics,Chongqing,402760,China;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing,400044,China)
出处
《电池工业》
CAS
2022年第1期19-22,共4页
Chinese Battery Industry
基金
重庆市场监督管理局科技计划项目,CQSJKJ2019024,CQSJKJ2019025
招商局检测车辆技术研究院有限公司自研项目,20AKC3。
关键词
锂离子电池
粒子滤波
自回归整合移动平均模型
剩余寿命
预测
lithium-ion battery
particle filter
autoregressive integrated moving average model
remaining useful life
prediction
