电源学报

基于弛豫电压和BO-DNN的锂离子电池健康状态估计

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许培德 ¹ , 刘康 ² , 康龙云 ²^,³ , 王怀远 ⁴

电源学报 | 新能源发电与储能 2026,24(7): 163-172

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电源学报 |新能源发电与储能 2026 , 24 (7) : 163 -172

基于弛豫电压和BO-DNN的锂离子电池健康状态估计

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许培德¹, 刘康², 康龙云²^,³, 王怀远⁴

作者信息

^1.福建水利电力职业技术学院电力工程学院，永安　366000

^2.华南理工大学电力学院，广州　510640

^3.陇东学院新能源学院，庆阳　745000

^4.福州大学电气工程与自动化学院，福州　350116

通讯作者:

刘康（1999—　），男，通信作者，硕士。研究方向：电池状态估计与健康诊断技术。E-mail：epkangliu@mail.scut.edu.cn。

作者简介:

许培德（1984—　），男，硕士，副教授。研究方向：电力电子与电力传动，设备健康评估技术。E-mail：15859875552@163.com。

康龙云（1961—　），男，博士，教授。研究方向：电池储能技术和电网逆变技术，电池管理系统技术。E-mail：lykang@scut.edu.cn。

王怀远（1987—　），男，博士，副教授。研究方向：电力系统运行与控制，人工智能。E-mail：79749544@qq.com。

State of Health Estimation of Lithium-ion Batteries Based on Relaxation Voltage and BO-DNN

Peide XU¹, Kang LIU², Longyun KANG²^,³, Huaiyuan WANG⁴

Affiliations

^1.School of Power Engineering, Fujian Polytechnic of Water Conservancy and Electric Power, Yongan 366000, China

^2.School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

^3.College of New Energy, Longdong University, Qingyang 745000, China

^4.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China

出版时间: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.163

文章导航

摘要

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准确的健康状态SOH（state of health）估计对锂离子电池安全运行至关重要。然而，锂离子电池复杂的循环条件和随机的初始充电状态使得多数估计方法的输入难以始终得到满足。针对该问题，提出一种基于弛豫电压的端到端SOH估计方法。通过贝叶斯优化辅助的深度前馈神经网络BO-DNN（Bayesian optimization-assisted deep feedforward neural network）直接建立起弛豫电压与电池SOH之间的非线性关系，省去了烦琐的特征提取过程。使用3组不同的电池老化数据集对估计方法的性能进行验证。结果表明，在用于模型构建的数据集上，该方法实现了均方根误差RMSE（root mean square error）为0.011 0的SOH估计。通过微调基础模型建立的迁移学习模型仅依靠低于3.5%的数据训练，便在用于模型验证的数据集上取得了小于0.016 3的RMSE。

关键词

健康状态估计 / 锂离子电池 / 弛豫电压 / 贝叶斯优化 / 深度前馈神经网络

Abstract

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Accurate state of health (SOH) estimation is crucial for the safe operation of lithium-ion batteries. However, the complex cycling conditions and random initial charging state of lithium-ion batteries make the inputs of most existing methods unable to be satisfied. To address this problem, we propose an end-to-end SOH estimation method based on relaxation voltage. This method directly establishes the nonlinear relationship between relaxation voltage and SOH using a Bayesian optimization-assisted deep feedforward neural network (BO-DNN), eliminating the laborious feature extraction process. The performance of the estimation method was verified on three different battery aging datasets. The results show that the model achieves a root mean square error (RMSE) of 0.011 0 on the dataset used for model building. A transfer learning model developed by fine-tuning the basic model only relies on less than 3.5% training data, and it achieves an RMSE of less than 0.016 3 on the datasets used for model validation.

Key words

State of health estimation / lithium-ion battery / relaxation voltage / Bayesian optimization / deep feedforward neural network

引用本文

许培德, 刘康, 康龙云, 王怀远. 基于弛豫电压和BO-DNN的锂离子电池健康状态估计. 电源学报, 2026 , 24 (7) : 163 -172 . DOI: 10.13234/j.issn.2095-2805.2026.7.163

Peide XU, Kang LIU, Longyun KANG, Huaiyuan WANG. State of Health Estimation of Lithium-ion Batteries Based on Relaxation Voltage and BO-DNN[J]. Journal of Power Supply, 2026 , 24 (7) : 163 -172 . DOI: 10.13234/j.issn.2095-2805.2026.7.163

基金

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广东省基础与应用基础研究基金区域联合基金资助项目(2022A1515140009)
福建省自然科学基金资助项目(2022J01113)

参考文献

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2026年第24卷第7期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.7.163

接收时间：2024-07-30
首发时间：2026-10-29
出版时间：2026-07-30

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出版历史

收稿日期：2024-07-30
修回日期：2024-09-25
录用日期：2024-10-26

基金

Guangdong Basic and Applied Basic Research Foundation(2022A1515140009)

广东省基础与应用基础研究基金区域联合基金资助项目(2022A1515140009)

Natural Science Foundation of Fujian Province(2022J01113)

福建省自然科学基金资助项目(2022J01113)

作者信息

^1.福建水利电力职业技术学院电力工程学院，永安　366000

^2.华南理工大学电力学院，广州　510640

^3.陇东学院新能源学院，庆阳　745000

^4.福州大学电气工程与自动化学院，福州　350116

通讯作者:

刘康（1999—　），男，通信作者，硕士。研究方向：电池状态估计与健康诊断技术。E-mail：epkangliu@mail.scut.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.7.163

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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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