电源学报

基于ISSA-GPR的锂离子电池健康状态估计

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张梦迪 ¹ , 刘洋 ¹ , 陈健 ² , 吉金鹏 ¹ , 姚智伟 ¹ , 公衍勇 ¹

电源学报 | 人工智能与电力电子 2026,24(2): 233-243

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电源学报 |人工智能与电力电子 2026 , 24 (2) : 233 -243

基于ISSA-GPR的锂离子电池健康状态估计

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张梦迪¹, 刘洋¹, 陈健², 吉金鹏¹, 姚智伟¹, 公衍勇¹

作者信息

^1.山东理工大学电气与电子工程学院，淄博　255049

^2.淄博市计量技术研究院，淄博　255025

通讯作者:

刘洋（1977—　），男，通信作者，博士，副教授。研究方向：智能配电网优化运行与保护技术，信号与数据处理在配电网中的应用。E-mail：bqxyl@sdut.edu.cn。

作者简介:

张梦迪（1997—　），男，硕士研究生。研究方向：储能设备状态监测。E-mail：zl160420@163.com。

陈健（1976—　），女，本科，高级工程师。研究方向：装置计量与质检技术。E-mail：bqxyly@gmail.com。

吉金鹏（1997—　），男，硕士研究生。研究方向：智能配电网自动化技术及应用。E-mail：jjinpeng@163.com。

姚智伟（1998—　），男，硕士研究生。研究方向：智能配电网保护与自动化。E-mail：17852031759@163.com。

公衍勇（2001—　），男，硕士研究生。研究方向：智能配电网保护与自动化。E-mail：18363711318@163.com。

Estimation of State-of-health of Lithium-ion Batteries Based on ISSA-GPR

Mengdi ZHANG¹, Yang LIU¹, Jian CHEN², Jinpeng JI¹, Zhiwei YAO¹, Yanyong GONG¹

Affiliations

^1.School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China

^2.Zibo Institute of Measurement Technology, Zibo 255025, China

出版时间: 2026-02-28 doi: 10.13234/j.issn.2095-2805.2026.2.233

文章导航

摘要

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由于电池健康状态SOH（state-of-health）难以被直接测量，因此对SOH的准确估计对保证电池安全运行至关重要。基于此，提出了1种改进麻雀搜索算法优化高斯过程回归ISSA-GPR（improved sparrow search algorithm-Gaussian process regression）锂离子电池健康状态估计方法。首先采用改进麻雀搜索算法优化高斯过程回归模型参数，构建基于改进麻雀搜索算法的高斯过程回归模型；然后分析容量增量曲线，提取表征电池容量退化的健康因子作为模型的输入，并通过改进麻雀搜索算法确定以峰值为中心峰面积的最佳电压区间长度，进而得到电池健康状态估计模型；最后利用公开的实验数据集进行验证。结果表明，所提ISSA-GPR方法能够对电池健康状态进行准确估计。

关键词

锂离子电池 / 健康状态 / 容量增量曲线 / 健康因子 / 麻雀搜索算法 / 高斯过程回归

Abstract

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The state-of-health (SOH) of a battery is difficult to be directly measured, so an accurate estimation of SOH is critical for ensuring its safe operation. Aimed at the problem of SOH estimation of lithium-ion batteries, an SOH estimation method based on improved sparrow search algorithm-Gaussian process regression (ISSA-GPR) is proposed. First, ISSA is used to optimize the parameters of a GPR model, and the GPR model based on ISSA is constructed. Then, the capacity increment curve is analyzed to extract the health factors that characterize the battery capacity degradation as the input of the model, the optimal voltage interval length with the peak as the center area is determined by ISSA, and the SOH of batteries is obtained to perform estimation. Finally, the public experimental data sets are applied to verify the proposed method. Experimental results show that the proposed ISSA-GPR method can accurately estimate the SOH of batteries.

Key words

Lithium-ion battery / state-of-health (SOH) / capacity increment curve / health factor / sparrow search algorithm / Gaussian process regression (GPR)

引用本文

张梦迪, 刘洋, 陈健, 吉金鹏, 姚智伟, 公衍勇. 基于ISSA-GPR的锂离子电池健康状态估计. 电源学报, 2026 , 24 (2) : 233 -243 . DOI: 10.13234/j.issn.2095-2805.2026.2.233

Mengdi ZHANG, Yang LIU, Jian CHEN, Jinpeng JI, Zhiwei YAO, Yanyong GONG. Estimation of State-of-health of Lithium-ion Batteries Based on ISSA-GPR[J]. Journal of Power Supply, 2026 , 24 (2) : 233 -243 . DOI: 10.13234/j.issn.2095-2805.2026.2.233

基金

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国家重点研发计划项目(2017YFB0902800)
山东省研究生教育教学改革研究项目(SDYJG19103)
国家电网有限公司科技项目(52094017003D)

参考文献

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2026年第24卷第2期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.2.233

接收时间：2023-07-11
首发时间：2026-10-29
出版时间：2026-02-28

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出版历史

收稿日期：2023-07-11
修回日期：2023-09-12
录用日期：2023-10-13

基金

National Key R&D Program of China(2017YFB0902800)

国家重点研发计划项目(2017YFB0902800)

Shandong Province Postgraduate Education and Teaching Reform Research Project(SDYJG19103)

山东省研究生教育教学改革研究项目(SDYJG19103)

Science and Technology Project of State Grid Corporation of China(52094017003D)

国家电网有限公司科技项目(52094017003D)

作者信息

^1.山东理工大学电气与电子工程学院，淄博　255049

^2.淄博市计量技术研究院，淄博　255025

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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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