Remaining useful life prediction of lithium battery based on capacity regeneration point detection

作     者：Ma, Qiuhui Zheng, Ying Yang, Weidong Zhang, Yong Zhang, Hong 

作者机构：Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China 

出 版 物：《ENERGY》 (能)

年 卷 期：2021年第234卷

页      面：121233-121233页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Natural Science Foundation of China [61873102, 61873197] Key Natural Science Foundation of Hubei [2019CFA047] MOE Key Laboratory of Image Processing and Intelligence Control [IPIC2018-10] 

主　　题：Capacity regeneration RUL Lithium battery Particle filter Mann-Whitney U test Autoregressive model 

摘      要：Lithium batteries have been widely used in various electronic devices, and the accurate prediction of its remaining useful life (RUL) can prevent the occurrence of sudden equipment failure. Battery capacity is a commonly used indicator to represent the health status of lithium batteries. However, the capacity regeneration is usually unavoidable due to the impact of battery rest time between two cycles, which leads to inaccurate prediction of the RUL. To solve this problem, this paper combines the particle filter (PF) and Mann-Whitney U test (PF-U) to detect the capacity regeneration point (CRP). In this light, the autoregressive (AR) model and PF algorithm are adopted for RUL prediction. The predicted capacity through AR model is used to update the degradation model parameters of PF algorithm, and the validation of our approach is verified through the lithium battery dataset of NASA. In comparison, our proposed method exhibits the highest precision and provides a platform to detect the points with capacity regeneration, and further reduce the RUL prediction error. (c) 2021 Elsevier Ltd. All rights reserved.