电源学报

基于改进高斯过程回归的锂离子电池健康状态估计

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邓鸿枥 ¹^,² , 吴松荣 ¹^,² , 刘齐 ¹^,² , 张翰文 ¹^,² , 柳博 ¹^,² , 付聪 ¹^,²

电源学报 | 新能源发电与储能 2026,24(4): 205-214

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电源学报 |新能源发电与储能 2026 , 24 (4) : 205 -214

基于改进高斯过程回归的锂离子电池健康状态估计

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邓鸿枥¹^,², 吴松荣¹^,², 刘齐¹^,², 张翰文¹^,², 柳博¹^,², 付聪¹^,²

作者信息

^1.磁浮技术与磁浮列车教育部重点实验室，成都　610031

^2.西南交通大学电气工程学院，成都　611756

通讯作者:

吴松荣（1977—　），男，通信作者，博士，副教授。研究方向：电力电子技术和机车/动车组牵引传动。E-mail：srwu88@163.com。

作者简介:

邓鸿枥（1998—　），男，硕士。研究方向：锂离子电池健康状态估计。E-mail：dhongli123@163.com。

刘齐（1999—　），男，硕士。研究方向：电池SOC估计。E-mail：1203954416@qq.com。

张翰文（1999—　），男，硕士。研究方向：图像识别。E-mail：925513303@qq.com。

柳博（2000—　），男，硕士。研究方向：电力电子技术。E-mail：691637020@qq.com。

付聪（2000—　），男，硕士。研究方向：电力电子技术。E-mail：2606880283@qq.com。

State-of-health Estimation of Lithium-ion Batteries Based on Improved Gaussian Process Regression

Hongli DENG¹^,², Songrong WU¹^,², Qi LIU¹^,², Hanwen ZHANG¹^,², Bo LIU¹^,², Cong FU¹^,²

Affiliations

^1.Key Laboratory of Magnetic Suspension Technique and Maglev Train of Ministry of Education, Chengdu 610031, China

^2.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China

出版时间: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.205

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摘要

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准确估计锂离子电池的健康状态SOH（state-of-health）是保障电池系统安全、稳定和高效运行的基础。基于此，提出1种基于健康因子提取和改进高斯过程回归GPR（Gaussian progress regression）的锂离子电池SOH估计方法。首先，从充放电电流电压曲线中提取健康因子，使用Pearson相关分析法筛选出与容量相关性高的健康因子并通过自编码器深度学习算法对健康因子降维；其次，利用GPR建立SOH估计模型并通过组合核函数方式来构造GPR的核函数；然后，使用非洲秃鹫优化算法对组合核函数中的超参数进行寻优；最后，采用NASA锂离子电池数据集进行模型验证，实验结果表明本文所提方法具有较高的准确性。

关键词

锂离子电池 / 健康状态 / 高斯过程回归 / 非洲秃鹫优化算法

Abstract

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The accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a basis for ensuring the safe, stable and efficient operation of battery systems. On this basis, a method for estimating the SOH of lithium-ion batteries was proposed based on health indicator extraction and improved Gaussian process regression (GPR). First, the health indicators were extracted from the charge/discharge current and voltage curves, and those with a high correlation with capacity were screened out by Pearson correlation analysis, which were further reduced by an autoencoder deep learning algorithm. Second, GPR was used to establish an SOH estimation model, and the kernel function of GPR was constructed by combinatorial kernel functions. Third, the African vultures optimization algorithm was used to optimize the hyperparameters in the combinatorial kernel function. Finally, the NASA lithium-ion battery dataset was used for model verification, and experimental results show that the proposed method has high accuracy.

Key words

Lithium-ion battery / state-of-health (SOH) / Gaussian process regression (GPR) / African vultures optimization algorithm

引用本文

邓鸿枥, 吴松荣, 刘齐, 张翰文, 柳博, 付聪. 基于改进高斯过程回归的锂离子电池健康状态估计. 电源学报, 2026 , 24 (4) : 205 -214 . DOI: 10.13234/j.issn.2095-2805.2026.4.205

Hongli DENG, Songrong WU, Qi LIU, Hanwen ZHANG, Bo LIU, Cong FU. State-of-health Estimation of Lithium-ion Batteries Based on Improved Gaussian Process Regression[J]. Journal of Power Supply, 2026 , 24 (4) : 205 -214 . DOI: 10.13234/j.issn.2095-2805.2026.4.205

参考文献

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2026年第24卷第4期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.4.205

接收时间：2023-11-28
首发时间：2026-10-29
出版时间：2026-04-30

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收稿日期：2023-11-28
修回日期：2024-01-27
录用日期：2024-02-19

基金

作者信息

^1.磁浮技术与磁浮列车教育部重点实验室，成都　610031

^2.西南交通大学电气工程学院，成都　611756

通讯作者:

吴松荣（1977—　），男，通信作者，博士，副教授。研究方向：电力电子技术和机车/动车组牵引传动。E-mail：srwu88@163.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.4.205

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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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