基于信息熵与PSO-LSTM的锂电池组健康状态估计方法

doi:10.3901/JME.2022.10.180

机械工程学报 ›› 2022, Vol. 58 ›› Issue (10): 180-190.doi: 10.3901/JME.2022.10.180

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基于信息熵与PSO-LSTM的锂电池组健康状态估计方法

张朝龙^1,2, 赵筛筛¹, 何怡刚²

1. 安庆师范大学电子工程与智能制造学院安庆 246011;
2. 武汉大学电气与自动化学院武汉 430072

收稿日期:2021-08-02 修回日期:2022-03-20 出版日期:2022-05-20 发布日期:2022-07-07
通讯作者: 何怡刚(通信作者)，男，1966年出生，教授，博士研究生导师。主要研究方向为模拟和混合集成电路设计、测试与故障诊断、智能电网技术、射频识别技术、虚拟仪器和智能信号处理。E-mail：18655136887@163.com
作者简介:张朝龙，男，1982年出生，博士，教授。主要研究方向为动力电池测试技术，故障诊断和预测。E-mail：zhangchaolong@126.com;赵筛筛，男，1997年出生。主要研究方向为动力电池管理技术。E-mail：zhaoshaishai@126.com
基金资助:
国家自然科学基金(51637004,51607004)、国家重点研发计划(2016YFF0102200)、安徽高校协同创新(GXXT-2019-002)、安徽高校自然科学研究重点(KJ2020A0509)和安庆师范大学研究生学术创新(2021yjsXSCX009)资助项目。

State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM

ZHANG Chaolong^1,2, ZHAO Shaishai¹, HE Yigang²

1. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011;
2. School of Electrical and Automation, Wuhan University, Wuhan 430072

Received:2021-08-02 Revised:2022-03-20 Online:2022-05-20 Published:2022-07-07

摘要/Abstract

摘要： 针对目前锂电池组健康状态估计方法的不足,提出一种基于信息熵与粒子群算法(Particle swarm optimization, PSO)优化长短时记忆神经网络(Long short-term memory neural network, LSTM)的锂电池组健康状态估计方法。基于锂电池组恒流-恒压充电阶段锂电池组内各单体端电压的信息熵和平均温度信息,应用PSO-LSTM方法提取锂电池组电压熵、平均温度和锂电池组健康状态之间的映射关系,从而建立锂电池组健康状态估计模型。应用试验室测量的锂电池组老化数据对提出的方法进行测试。测试结果表明,该方法能够准确估计锂电池组的健康状态,平均估计误差在1%以内。同时,为验证提出的方法可推广至锂电池单体,利用美国航天航空局测得的锂电池加速老化数据再次测试,平均估计误差在0.7%以内。并针对锂电池组与锂电池单体设计对比试验,进一步验证提出的方法具有良好的估计性能。

关键词: 锂电池组, 健康状态, 信息熵, 粒子群算法, 长短时记忆神经网络

Abstract: In order to address the shortcoming of the existing lithium-ion battery pack state of health(SOH) estimation methods, a SOH estimation approach for lithium-ion battery pack using information entropy and particle swarm optimization(PSO) to optimize the long short-term memory(LSTM) neural network is proposed. The data of the information entropy and the average temperature of each cell terminal voltage in the lithium-ion battery pack during the constant current-constant voltage charging stage are utilized to extract the mapping relationship between the voltage entropy, average temperature, and SOH of the lithium-ion battery pack using PSO-LSTM, and then establish the lithium-ion battery pack SOH estimation model. The measured aging data of lithium-ion battery pack in the laboratory are employed to verify the validity of the presented method. The results show that the developed approach can accurately estimate the SOH of the lithium-ion battery pack with the average estimation error within 1%. Meanwhile, in order to verify the proposed method can be extended to lithium-ion batteries, the accelerated aging data of lithium-ion batteries measured by National Aeronautics and Space Administration(NASA) to test again with the average estimation error within 0.7%. The compared experiment is designed for the battery pack and cells, which further demonstrates that the suggested method offers a favorable estimation performance.

Key words: lithium-ion battery pack, state of health, information entropy, particle swarm optimization, long short-term memory neural network

中图分类号:

TM910

张朝龙, 赵筛筛, 何怡刚. 基于信息熵与PSO-LSTM的锂电池组健康状态估计方法[J]. 机械工程学报, 2022, 58(10): 180-190.

ZHANG Chaolong, ZHAO Shaishai, HE Yigang. State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM[J]. Journal of Mechanical Engineering, 2022, 58(10): 180-190.

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基于信息熵与PSO-LSTM的锂电池组健康状态估计方法

State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM

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