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锂离子电池的多状态模型剩余寿命预测方法
引用本文:陈万,蔡艳平,苏延召,姜柯,黄华. 锂离子电池的多状态模型剩余寿命预测方法[J]. 科学技术与工程, 2021, 21(10): 4078-4083. DOI: 10.3969/j.issn.1671-1815.2021.10.031
作者姓名:陈万  蔡艳平  苏延召  姜柯  黄华
作者单位:火箭军工程大学305教研室, 西安710025
摘    要:针对锂离子电池的容量恢复现象导致的剩余寿命预测精度不高的问题,提出了一种锂离子电池的多状态模型剩余寿命预测方法.首先通过分析锂电池的衰退数据将锂离子电池的退化过程分为正常退化、容量恢复和加速退化三种状态,然后分别对三种状态的退化过程进行建模并验证了模型的有效性,将3种状态的模型组合得到锂离子电池多状态容量衰退模型.然后...

关 键 词:锂离子电池  剩余寿命预测  多状态模型  容量恢复  粒子群优化粒子滤波算法
收稿时间:2020-06-13
修稿时间:2021-04-16

Method for predicting remaining useful life of lithium-ion battery based on multi-state model
Chen Wan,Cai Yanping,Su Yanzhao,Jiang Ke,Huang Hua. Method for predicting remaining useful life of lithium-ion battery based on multi-state model[J]. Science Technology and Engineering, 2021, 21(10): 4078-4083. DOI: 10.3969/j.issn.1671-1815.2021.10.031
Authors:Chen Wan  Cai Yanping  Su Yanzhao  Jiang Ke  Huang Hua
Affiliation:The Rocket Force Engineering University,,The Rocket Force Engineering University,The Rocket Force Engineering University,The Rocket Force Engineering University
Abstract:Aiming at the problem of low accuracy of remaining useful life prediction caused by the phenomenon of lithium-ion battery capacity recovery, a method for predicting the remaining useful life of the lithium-ion battery based on the multi-state model is proposed. First, by analyzing lithium batteries'' degradation data, the degradation process of lithium-ion batteries is divided into three states: normal degradation, capacity recovery, and accelerated degradation. Then, the three states'' degradation processes are modeled, and the validity of the model is verified. The lithium-ion battery multi-state capacity decline model is obtained by combining the three-state models. Based on the established model, the particle swarm optimization-particle filter algorithm is proposed, which is used for parameter identification and state update of the multi-state capacity decay model. Finally, the remaining useful life prediction of the lithium-ion battery and the uncertainty expression of the prediction result is realized. Compared with other methods, the experimental results show that the proposed method has higher accuracy and stronger robustness.
Keywords:Lithium-ion battery   remaining useful life prediction   Multi-state model   capacity recovery   particle swarm optimization particle filter algorithm
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