电源学报

基于脉冲电压和CNN-GRU的梯次利用锂电池健康状态估计

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杨传凯 ¹ , 刘皓璐 ¹ , 杨毅琛 ¹ , 樊玉 ² , 张慧颖中国电源学会学生会员 ³ , 邵建伟中国电源学会高级会员 ³

电源学报 | 新能源发电与储能 2026,24(4): 178-187

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电源学报 |新能源发电与储能 2026 , 24 (4) : 178 -187

基于脉冲电压和CNN-GRU的梯次利用锂电池健康状态估计

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杨传凯¹, 刘皓璐¹, 杨毅琛¹, 樊玉², 张慧颖中国电源学会学生会员³, 邵建伟中国电源学会高级会员³

作者信息

^1.国网陕西省电力有限公司电力科学研究院，西安　710100

^2.陕西大秦电能集团有限公司，西安　710004

^3.武汉大学电气与自动化学院，武汉　430072

通讯作者:

邵建伟（1992—　），男，通信作者，博士后。研究方向：综合能源与高效储能。E-mail：hitwhshao@whu.edu.cn。

作者简介:

杨传凯（1986—　），男，博士，高级工程师。研究方向：储能技术研究。E-mail：yangchuankai@dky.sn.sgcc.com.cn。

刘皓璐（1997—　），女，硕士，助理工程师。研究方向：储能电池状态评估。E-mail：liuhaolu@dky.sn.sgcc.com.cn。

杨毅琛（1997—　），男，硕士，助理工程师。研究方向：储能系统管理。E-mail：yangyichen@dky.sn.sgcc.com.cn。

樊玉（1984—　），男，硕士，高级工程师。研究方向：储能电池状态评估。E-mail：fanyu@sn.sgcc.com.cn。

张慧颖（2000—　），女，中国电源学会学生会员，硕士研究生。研究方向：储能电池状态估计及预测。E-mail：huiyingzhang@whu.edu.cn。

Estimation of State-of-health for Cascaded Utilized Lithium Batteries Based on Pulse Voltage and CNN-GRU

Chuankai YANG¹, Haolu LIU¹, Yichen YANG¹, Yu FAN², Huiying ZHANG Student Member, CPSS³, Jianwei SHAO Senior Member, CPSS³

Affiliations

^1.Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710100, China

^2.Shaanxi Daqin Electric Energy Group Co., Ltd., Xi’an 710004, China

^3.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

出版时间: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.178

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摘要

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针对梯次利用锂电池健康状态SOH（state-of-health）在线估算困难、估算精度不足的问题，提出1种基于脉冲电压与混合神经网络的估算方法，该方法将电池脉冲电压响应曲线提取与电池SOH退化相关的端电压斜率和校正标准差作为健康特征，结合卷积神经网络CNN（convolutional neural networks）与门控循环单元GRU（gated recurrent unit），实现高精度和高稳定性的梯次利用锂电池SOH在线估算。搭建电池测试系统平台，对所提梯次利用锂电池SOH估算策略进行检验。结果表明，所提方法在实时精确估算锂电池SOH方面表现出色，平均绝对误差和均方根误差均在0.01内，同时具有良好的泛化能力和鲁棒性。

关键词

梯次利用锂电池 / 健康状态 / 脉冲电压 / 卷积神经网络-门控循环单元

Abstract

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Aimed at the difficulties and lack of precision in the online estimation of state-of-health (SOH) for cascaded utilized lithium batteries, an estimation method based on pulse voltage and hybrid neural network is proposed. Based on the battery pulse voltage response curve, this method extracts the slope of terminal voltage and the corrected standard deviation which are related to battery SOH degradation as health features. By integrating convolutional neural networks (CNN) and gated recurrent unit (GRU), the online estimation of SOH for cascaded utilized lithium batteries with high accuracy and high stability is realized. A platform for a battery testing system was established to validate the proposed SOH estimation strategy for cascaded utilized lithium batteries. Results indicate that the proposed method excels in real-time accurate estimation of lithium battery SOH, and both the average absolute error and root mean square error are controlled within 0.01, demonstrating excellent generalization capability and robustness.

Key words

Cascaded utilized lithium battery / state-of-health (SOH) / pulse voltage / convolutional neural networks-gated recurrent unit (CNN-GRU)

引用本文

杨传凯, 刘皓璐, 杨毅琛, 樊玉, 张慧颖中国电源学会学生会员, 邵建伟中国电源学会高级会员. 基于脉冲电压和CNN-GRU的梯次利用锂电池健康状态估计. 电源学报, 2026 , 24 (4) : 178 -187 . DOI: 10.13234/j.issn.2095-2805.2026.4.178

Chuankai YANG, Haolu LIU, Yichen YANG, Yu FAN, Huiying ZHANG, Jianwei SHAO. Estimation of State-of-health for Cascaded Utilized Lithium Batteries Based on Pulse Voltage and CNN-GRU[J]. Journal of Power Supply, 2026 , 24 (4) : 178 -187 . DOI: 10.13234/j.issn.2095-2805.2026.4.178

基金

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国家重点研发计划资助项目(2017YFB1201003)
国网陕西省电力有限公司科技资助项目(5226KY23000P)

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.2095-2805.2026.4.178

接收时间：2024-01-18
首发时间：2026-10-29
出版时间：2026-04-30

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出版历史

收稿日期：2024-01-18
修回日期：2024-03-25
录用日期：2024-04-07

基金

State Key Research and Development Program(2017YFB1201003)

国家重点研发计划资助项目(2017YFB1201003)

Science and Technology Program of State Grid Shaanxi Electric Power Co., Ltd.(5226KY23000P)

国网陕西省电力有限公司科技资助项目(5226KY23000P)

作者信息

^1.国网陕西省电力有限公司电力科学研究院，西安　710100

^2.陕西大秦电能集团有限公司，西安　710004

^3.武汉大学电气与自动化学院，武汉　430072

通讯作者:

邵建伟（1992—　），男，通信作者，博士后。研究方向：综合能源与高效储能。E-mail：hitwhshao@whu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.4.178

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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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