电源学报

基于多健康因子LSTM的电池容量估计方法

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莫易敏 ¹ , 陈佳浩 ¹ , 叶鹏 ¹ , 范文健 ² , 韦军 ² , 孙静宇 ¹

电源学报 | 新能源发电与储能 2026,24(1): 207-216

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电源学报 |新能源发电与储能 2026 , 24 (1) : 207 -216

基于多健康因子LSTM的电池容量估计方法

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莫易敏¹, 陈佳浩¹, 叶鹏¹, 范文健², 韦军², 孙静宇¹

作者信息

^1.武汉理工大学机电工程学院，武汉　430070

^2.上汽通用五菱股份有限公司，柳州　545007

通讯作者:

莫易敏（1960—　），男，通信作者，博士，教授。研究方向：摩擦学，表面工程，机电一体化，先进管理与先进制造技术。E-mail：moyimin@whut.edu.cn。

作者简介:

陈佳浩（1999—　），男，硕士研究生。研究方向：电动汽车能源管理，机电一体化，摩擦学。E-mail：rollyu@whut.edu.cn。

叶鹏（1991—　），男，博士。研究方向：机电一体化，摩擦学，先进制造技术。E-mail：yepeng@whut.edu.cn。

范文健（1974—　），男，硕士，高级工程师。研究方向：智能制造，生产运营管理，摩擦与润滑。E-mail：Wenjian.Fan@sgmw.com.cn。

韦军（1984—　），男，本科，工程师。研究方向：先进总装工艺。E-mail：Jun3.Wei@sgmw.com.cn。

孙静宇（1998—　），男，硕士研究生。研究方向：机电一体化，摩擦学，电动汽车能源管理。Email：271321@whut.edu.cn。

Estimation Method for Battery Capacity Based on Multi-health Factor LSTM

Yimin MO¹, Jiahao CHEN¹, Peng YE¹, Wenjian FAN², Jun WEI², Jingyu SUN¹

Affiliations

^1.School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

^2.SAIC-GM-Wuling Automobile Co., Ltd., Liuzhou 545007, China

出版时间: 2026-01-30 doi: 10.13234/j.issn.2095-2805.2026.1.207

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摘要

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为了准确判断电池可用容量，采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线，计算其与电池容量之间的皮尔逊相关系数，选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用美国先进寿命周期工程中心CALCE（Center for Advanced Life Cycle Engineering）电池数据集进行模型训练并估算电池容量，估计模型的平均百分误差为1.19%。分析估算误差产生的原因，通过电池初始容量参数修正和电池老化参数修正进行模型优化。优化结果表明，使用电池电压、内阻、恒流充电时间和4.0~3.4 V等压降时间构建模型估计误差在0.55%左右。

关键词

容量估计 / 电池健康状态 / 长短期记忆神经网络 / 锂离子电池

Abstract

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To accurately determine the battery capacity, long short-term memory (LSTM) neural network was used to estimate the battery capacity. First, the life-cycle curves for different battery parameters were analyzed. The Pearson correlation coefficients between the parameters and battery capacity were calculated, and the battery voltage, internal resistance, equal voltage drop time and other parameters were selected as health factors to construct a battery capacity estimation model. Then, the Center for Advanced Life Cycle Engineering (CALCE) battery data set was used for model training and capacity estimation, and the average percentage error of the estimation model was 1.19%. The causes of estimation errors were analyzed, and the model was optimized by revising the battery’s initial capacity and aging parameters. The optimization results show that the estimation error of the model constructed using battery voltage, internal resistance, constant-current charging time and 4.0–3.4 V equal voltage drop time was about 0.55%.

Key words

Capacity estimation / battery state-of-health / long short-term memory (LSTM) neural network / lithium-ion battery

引用本文

莫易敏, 陈佳浩, 叶鹏, 范文健, 韦军, 孙静宇. 基于多健康因子LSTM的电池容量估计方法. 电源学报, 2026 , 24 (1) : 207 -216 . DOI: 10.13234/j.issn.2095-2805.2026.1.207

Yimin MO, Jiahao CHEN, Peng YE, Wenjian FAN, Jun WEI, Jingyu SUN. Estimation Method for Battery Capacity Based on Multi-health Factor LSTM[J]. Journal of Power Supply, 2026 , 24 (1) : 207 -216 . DOI: 10.13234/j.issn.2095-2805.2026.1.207

参考文献

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2026年第24卷第1期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.1.207

接收时间：2023-03-20
首发时间：2026-10-26
出版时间：2026-01-30

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收稿日期：2023-03-20
修回日期：2023-05-12
录用日期：2023-05-20

基金

作者信息

^1.武汉理工大学机电工程学院，武汉　430070

^2.上汽通用五菱股份有限公司，柳州　545007

通讯作者:

莫易敏（1960—　），男，通信作者，博士，教授。研究方向：摩擦学，表面工程，机电一体化，先进管理与先进制造技术。E-mail：moyimin@whut.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.1.207

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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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