电源学报

基于随机充电行为的锂离子电池健康状态评估方法比较

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颜泽 ¹ , 赵虎 ¹ , 张庆超 ² , 李兵强 ¹

电源学报 | 新能源发电与储能 2026,24(3): 142-152

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电源学报 |新能源发电与储能 2026 , 24 (3) : 142 -152

基于随机充电行为的锂离子电池健康状态评估方法比较

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颜泽¹, 赵虎¹, 张庆超², 李兵强¹

作者信息

^1.西北工业大学自动化学院，西安　710129

^2.中国人民解放军火箭军工程大学，西安　710025

通讯作者:

赵虎（1986—　），男，通信作者，博士，副教授。研究方向：智能电网与智能电器装备及关键技术，新能源用电器设备及关键技术。E-mail：hzhao@nwpu.edu.cn。

作者简介:

颜泽（1999—　），男，硕士研究生。研究方向：电池管理系统。E-mail：yippeezeal@mail.nwpu.edu.cn。

张庆超（1990—　），男，博士，讲师。研究方向：伺服控制与故障诊断技术。E-mail：qczhangls@163.com。

李兵强（1982—　），男，博士。教授。研究方向：伺服控制/运动控制技术，现代电机控制技术，运动传感技术，检测技术与自动化装置。E-mail：libingqiang@nwpu.edu.cn。

Comparison Among State-of-health Estimation Methods for Lithium-ion Batteries Based on Random Charging Behavior

Ze YAN¹, Hu ZHAO¹, Qingchao ZHANG², Bingqiang LI¹

Affiliations

^1.School of Automation, Northwestern Polytechnical University, Xi’an 710129, China

^2.Rocket Force University of Engineering, Xi’an 710025, China

出版时间: 2026-03-30 doi: 10.13234/j.issn.2095-2805.2026.3.142

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摘要

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作为储能系统中最常用的锂离子电池，其健康状态SOH（state-of-health）是电池管理系统中所监测的重要电池参数，除了预测准确性，模拟用户的随机充电行为，实现任意时刻下的实时评估也十分重要。基于用户的随机充电行为，从获取数据完整性的角度出发，将电池健康状态预测分为基于完整充电周期数据、基于片段充电数据、基于片段充电数据还原完整数据3种方法。使用NASA电池老化实验数据充电过程中电池容量变化的均值与方差作为不同预测模型的特征输入，比较分析3种方法的预测结果。结果表明，完整的充电数据特征具有较高的预测准确度，片段数据提取特征的质量与片段采集时长有关，经过还原网络可在较短时间获得完整数据特征，但预测准确度受还原网络的影响。此外，实际的预测需要对充电行为的随机性与预测结果的实时性进行考虑。

关键词

锂离子电池 / 健康状态 / 随机充电行为 / 数据完整性 / 机器学习

Abstract

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Lithium-ion batteries are the most commonly used batteries in an energy storage system, and their state-of-health (SOH) is an important battery parameter monitored in a battery management system. In addition to the accuracy of prediction, the simulation of a user’s random charging behavior for realizingreal-time evaluation at any time is also very important. Based on the user’s random charging behavior and from the perspective of obtaining the data integrity, the SOH prediction methods are divided into three types, which are based on complete charging cycle data, segment of charging data and segment of charging data restored complete data, respectively. The mean and variance of battery capacity changes during the charging process of NASA battery aging experimental data are used as feature inputs of different prediction models, and the prediction results obtained using three methods are compared and analyzed. Results show that the complete charging data features have high prediction accuracy, and the quality of features extracted from segment data is related to the segment collection time. The complete data features can be obtained in a short time through the restoration network, but the prediction accuracy is affected by the restoration network. In addition, the actual prediction needs to take into account the randomness of charging behavior and the real-time performance of prediction results.

Key words

Lithium-ion battery / state-of-health (SOH) / random charging behavior / data integrity / machine learning

引用本文

颜泽, 赵虎, 张庆超, 李兵强. 基于随机充电行为的锂离子电池健康状态评估方法比较. 电源学报, 2026 , 24 (3) : 142 -152 . DOI: 10.13234/j.issn.2095-2805.2026.3.142

Ze YAN, Hu ZHAO, Qingchao ZHANG, Bingqiang LI. Comparison Among State-of-health Estimation Methods for Lithium-ion Batteries Based on Random Charging Behavior[J]. Journal of Power Supply, 2026 , 24 (3) : 142 -152 . DOI: 10.13234/j.issn.2095-2805.2026.3.142

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2026年第24卷第3期

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doi: 10.13234/j.issn.2095-2805.2026.3.142

接收时间：2023-11-24
首发时间：2026-10-29
出版时间：2026-03-30

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收稿日期：2023-11-24
修回日期：2024-01-11
录用日期：2024-01-20

基金

作者信息

^1.西北工业大学自动化学院，西安　710129

^2.中国人民解放军火箭军工程大学，西安　710025

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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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