电源学报

基于Wasserstein距离的电池SOH估算方法

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赵靖杰中国电源学会会员 ¹^,² , 胡聪慧 ¹

电源学报 | 新能源发电与储能 2026,24(8): 140-150

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电源学报 |新能源发电与储能 2026 , 24 (8) : 140 -150

基于Wasserstein距离的电池SOH估算方法

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赵靖杰中国电源学会会员¹^,², 胡聪慧¹

作者信息

^1.河南理工大学鹤壁工程技术学院，鹤壁　458030

^2.合肥工业大学机械工程学院，合肥　230009

通讯作者:

赵靖杰（1996—　），男，中国电源学会会员，通信作者，硕士，助教。研究方向：锂离子电池健康管理。E-mail：zhaojingjie@mail.hfut.edu.cn。

作者简介:

胡聪慧（1998—　），女，硕士，助教。研究方向：新能源发电与智能微电网。E-mail：2991086978@qq.com。

Estimation Method for SOH of Batteries Based on Wasserstein Distance

Jingjie ZHAO Member, CPSS¹^,², Conghui HU¹

Affiliations

^1.Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi 458030, China

^2.School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China

出版时间: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.140

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摘要

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电池健康状态SOH（state of health）评估在确保储能系统安全稳定运行方面具有重要意义。然而，使用固定电压段作为模型输入的方法在实际应用中受到用户使用习惯变化的限制，且多数利用容量增量IC（incremental capacity）分析估计SOH的研究仍存在着平滑参数选择较为困难的问题。提出的滑动窗口平滑方法在获得平滑IC曲线的同时，简化了实际应用中参数的选择过程。该方法使用了2种互补的Wasserstein距离计算方法评估目标IC曲线和参考曲线的差异，并将该差异作为健康特征HF（health feature）与对应电压段起始电压结合，作为模型输入，用于训练高斯过程回归模型。测试结果显示，任意区间的电压段，即使是未包含IC曲线峰值的部分充电数据，均可作为模型输入来获得低于2%均方根误差RMSE（root mean square error）的SOH估算结果。此外，分析了不同平滑参数、采样频率及估计模型对估算结果的影响。分析结果表明，所提取的HFs具有较强的鲁棒性，模型具有较高的估计精度。

关键词

容量增量IC / Wasserstein距离 / 部分充电数据 / 健康状态SOH估算

Abstract

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The state-of-health (SOH) estimation of batteries is crucial for ensuring the safe and stable operation of energy storage systems. However, the methods which use fixed voltage segments as model inputs face limitations in practical applications due to varying user habits, and most of the studies based on incremental capacity (IC) analysis still face challenges in selecting the smoothing parameters. A sliding window smoothing method is proposed to obtain smooth IC curves while simplifying the parameter selection process in real-world applications. This method uses two complementary Wasserstein distance calculation methods to evaluate the differences between the target IC curves and reference curves, which serve as health feature (HF) and are combined with the corresponding starting voltages of voltage segments as model inputs. These inputs are further used to train a Gaussian process regression model. Test results indicate that the voltage segments at any position, even those that do not include the IC curve peak, can be used as model inputs to achieve SOH estimation results with a root mean square error (RMSE) below 2%. In addition, the effects of different smoothing parameters, sampling frequencies and estimation models on the estimation results are also analyzed. The analysis results indicate that the extracted HFs exhibit strong robustness, and the model achieves high estimation accuracy.

Key words

Incremental capacity (IC) / Wasserstein distance / partial charging data / state-of-health (SOH) estimation

引用本文

赵靖杰中国电源学会会员, 胡聪慧. 基于Wasserstein距离的电池SOH估算方法. 电源学报, 2026 , 24 (8) : 140 -150 . DOI: 10.13234/j.issn.2095-2805.2026.8.140

Jingjie ZHAO, Conghui HU. Estimation Method for SOH of Batteries Based on Wasserstein Distance[J]. Journal of Power Supply, 2026 , 24 (8) : 140 -150 . DOI: 10.13234/j.issn.2095-2805.2026.8.140

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2026年第24卷第8期

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doi: 10.13234/j.issn.2095-2805.2026.8.140

接收时间：2024-10-31
首发时间：2026-10-29
出版时间：2026-08-30

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收稿日期：2024-10-31
修回日期：2024-12-30
录用日期：2025-02-05

基金

作者信息

^1.河南理工大学鹤壁工程技术学院，鹤壁　458030

^2.合肥工业大学机械工程学院，合肥　230009

通讯作者:

赵靖杰（1996—　），男，中国电源学会会员，通信作者，硕士，助教。研究方向：锂离子电池健康管理。E-mail：zhaojingjie@mail.hfut.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.8.140

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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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