摘要
运用粒子滤波算法,进行了锂离子电池剩余寿命(RUL)的预测,提出了一种基于模型法和数据驱动法相融合的简单有效的RUL预测方法。该方法通过模型法和数据驱动法的融合,将双指数经验退化模型进行变形,以减少模型参数,降低参数训练难度,利用粒子滤波算法跟踪电池容量衰退的过程;为提高预测精确度,引入自回归(AR)时间序列模型修正状态空间方程的观测值。实验证实,该方法可以有效地预估出锂电池的剩余寿命。
The ies, and particle filtering is used to stud a simple and effective algorithm y the p fusing rediction of the remaining useful life （RUL） lithium-ion batterthe model method and the data-driven method for RUL predicting is proposed. The algorithm uses the fusion of the model method and the data-driven method to modify the double exponential empirical degradation model to reduce the model parameters and the parameter training difficulty, uses the particle filter algorithm to track the modify the observation value of the results show that the proposed algori battery capacity degradation process, and uses the auto regression model to state space equation to improve the prediction accuracy. The experimental thm can effectively predict the remaining useful life of lithium batteries.
作者
张凝
徐皑冬
王锴
韩晓佳
Seung Ho Hong
Zhang Ning;Xu Aidong;Wang Kai;Han Xiaojia;Seung Ho Hong(Industrial Control Networks and Systems Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016;University of Chinese Academy of Sciences, Beijing 100049;Department of Electronic Systems Engineering, Hanyang University, Ansan 15588, Korea)
出处
《高技术通讯》
北大核心
2017年第8期699-707,共9页
Chinese High Technology Letters
基金
国家自然科学基金(71651147005)资助项目
关键词
锂离子电池
剩余寿命(RUL)
粒子滤波
双指数经验模型
lithium-ion battery, remaining useful life （RUL）, particle filter, double exponential empirical model
