电源学报

基于CNN-LSTM的退役动力电池寿命评估

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李海军中国电源学会学生会员 , 吴子尧中国电源学会会员 , 王浩

电源学报 | 新能源发电与储能 2026,24(9): 164-174

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电源学报 |新能源发电与储能 2026 , 24 (9) : 164 -174

基于CNN-LSTM的退役动力电池寿命评估

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李海军（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：退役车用动力电池寿命评估及梯次利用。E-mail：1278676376@qq.com。

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王浩（1999—　），男，硕士研究生。研究方向：锂离子电池状态估计。E-mail：1264295639@qq.com。

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王浩（1999—　），男，硕士研究生。研究方向：锂离子电池状态估计。E-mail：1264295639@qq.com。

李海军中国电源学会学生会员, 吴子尧中国电源学会会员, 王浩

作者信息

沈阳工业大学机械工程学院，沈阳　110870

通讯作者:

吴子尧（1986—　），男，中国电源学会会员，通信作者，博士，讲师。研究方向：氢燃料电池检测，车用动力电池及储能电池检测，水电解制氢。E-mail：wzy-1986520@163.com。

作者简介:

李海军（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：退役车用动力电池寿命评估及梯次利用。E-mail：1278676376@qq.com。

王浩（1999—　），男，硕士研究生。研究方向：锂离子电池状态估计。E-mail：1264295639@qq.com。

Research on the Life Assessment Method of Retired Power Battery Based on CNN-LSTM

Haijun LI Student Member, CPSS, Ziyao WU Member, CPSS, Hao WANG

Affiliations

School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China

出版时间: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.164

文章导航

摘要

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针对退役动力电池寿命评估模型特征参数较难提取及在模型改进方面存在精度和评估效率低的问题，在不增加计算负担的前提下，通过增加易于提取的输入特征参数，对卷积神经网络CNN（convolutional neural network）和长短期记忆LSTM（long short-term memory）网络相结合的寿命评估模型进行改进。选取了退役电池在放电过程中的等压降放电时间、等温升放电时间和在循环过程中的充电时间作为CNN-LSTM寿命评估模型的输入特征参数。所得新模型与现有CNN-LSTM寿命评估模型相比，不仅评估准确性和稳定性均有所提高，且更高效。此外，为提高模型在评估退役电池寿命时的效率，探索了缩减训练资源占比对评估结果的影响。研究发现，仅选择电池循环总数的50%作为模型训练集时，模型评估的准确率和稳定性依然可以保持在99.00%以上。

关键词

退役动力电池 / 卷积神经网络 / 长短期记忆网络 / 寿命评估

Abstract

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Aiming at the problems of difficulty in extracting feature parameters of the life assessment model of retired power batteries and low accuracy and low assessment efficiency in model improvement, the life assessment model combining convolutional neural network (CNN) and long short-term memory (LSTM) network is improved by adding input feature parameters that are easy to extract, without increasing the computational burden. The equal voltage drop discharge time, equal temperature rise discharge time, and charging time during cycling of retired batteries are selected as the input feature parameters of the CNN-LSTM life assessment model, and the proposed model has improved accuracy and stability and is more efficient compared with the existing CNN-LSTM life assessment model. In addition, in order to improve the efficiency of the model in evaluating the life of retired batteries, the effect of reducing the proportion of training resources on the assessment results is explored, and it is found that when only 50% of the total number of battery cycles is selected as the training set of the model, the accuracy and stability of the model assessment can still be maintained at more than 99.00%.

Key words

Retired power battery / convolutional neural network (CNN) / long short-term memory (LSTM) network / life assessment

引用本文

李海军中国电源学会学生会员, 吴子尧中国电源学会会员, 王浩. 基于CNN-LSTM的退役动力电池寿命评估. 电源学报, 2026 , 24 (9) : 164 -174 . DOI: 10.13234/j.issn.2095-2805.2026.9.164

Haijun LI, Ziyao WU, Hao WANG. Research on the Life Assessment Method of Retired Power Battery Based on CNN-LSTM[J]. Journal of Power Supply, 2026 , 24 (9) : 164 -174 . DOI: 10.13234/j.issn.2095-2805.2026.9.164

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2026年第24卷第9期

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doi: 10.13234/j.issn.2095-2805.2026.9.164

接收时间：2024-09-25
首发时间：2026-10-29
出版时间：2026-09-30

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收稿日期：2024-09-25
修回日期：2025-03-17
录用日期：2025-03-25

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沈阳工业大学机械工程学院，沈阳　110870

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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