电源学报

一种改进的CNN-Seq2Seq电池荷电与健康状态联合估计方法

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张宇 , 周天宇 , 张永康 , 吴铁洲

电源学报 | 新能源发电与储能 2026,24(1): 217-224

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电源学报 |新能源发电与储能 2026 , 24 (1) : 217 -224

一种改进的CNN-Seq2Seq电池荷电与健康状态联合估计方法

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张永康（1999—　），男，硕士研究生。研究方向：光伏发电技术。E-mail：2279970471@qq.com。

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张永康（1999—　），男，硕士研究生。研究方向：光伏发电技术。E-mail：2279970471@qq.com。

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吴铁洲（1966—　），男，博士，教授。研究方向：光伏发电技术。E-mail：wtz315@163.com。

张宇, 周天宇, 张永康, 吴铁洲

作者信息

湖北工业大学电气与电子工程学院，武汉　430068

通讯作者:

周天宇（1998—　），男，通信作者，硕士研究生。研究方向：电池管理系统。E-mail：1137388934@qq.com。

作者简介:

张宇（1972—　），女，硕士，教授。研究方向：电池管理系统。E-mail：zhangyu.3383@163.com。

张永康（1999—　），男，硕士研究生。研究方向：光伏发电技术。E-mail：2279970471@qq.com。

吴铁洲（1966—　），男，博士，教授。研究方向：光伏发电技术。E-mail：wtz315@163.com。

Improved CNN-Seq2Seq Joint Estimation Method for SOC and SOH of Batteries

Yu ZHANG, Tianyu ZHOU, Yongkang ZHANG, Tiezhou WU

Affiliations

College of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China

出版时间: 2026-01-30 doi: 10.13234/j.issn.2095-2805.2026.1.217

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摘要

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为保证电动汽车长期安全稳定运行，降低锂电池故障率，针对电动汽车电池管理系统能否精准有效地检测电池荷电状态SOC（state-of-charge）与电池健康状态SOH（state-of-health）这2个重要参数的问题，提出了1种基于卷积神经网络-长短期记忆CNN-LSTM（convolutional neural networks-long short-term memory）神经网络改进的卷积神经网络-序列到序列CNN-Seq2Seq（CNN-sequence-to-sequence）神经网络的锂电池SOC与SOH联合估计方法。在公共数据集上的对比实验表明，该方法提高了锂电池SOC与SOH估计结果的稳定性与准确性。

关键词

荷电状态 / 健康状态 / 卷积神经网络 / 序列到序列 / 锂电池 / 深度学习

Abstract

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To ensure the long-term safe and stable operation of electric vehicles and reduce the failure rate of lithium-ion batteries, aimed at the issue of whether the battery management system of electric vehicles can accurately and effectively detect the two important parameters of batteries, i.e., state-of-charge (SOC) and state-of-health (SOH), an improved convolutional neural networks-sequence-to-sequence (CNN-Seq2Seq) neural networks joint estimation method for the SOC and SOH of lithium-ion batteries based on CNN-long short-term memory (CNN-LSTM) neural networks was proposed. The results of comparative experiments on the public data set show that the proposed method improves the stability and accuracy of SOC and SOH estimation results for lithium-ion batteries.

Key words

State-of-charge (SOC) / state-of-health (SOH) / convolutional neural networks (CNN) / sequence-to-sequence (Seq2Seq) / lithium-ion battery / deep learning

引用本文

张宇, 周天宇, 张永康, 吴铁洲. 一种改进的CNN-Seq2Seq电池荷电与健康状态联合估计方法. 电源学报, 2026 , 24 (1) : 217 -224 . DOI: 10.13234/j.issn.2095-2805.2026.1.217

Yu ZHANG, Tianyu ZHOU, Yongkang ZHANG, Tiezhou WU. Improved CNN-Seq2Seq Joint Estimation Method for SOC and SOH of Batteries[J]. Journal of Power Supply, 2026 , 24 (1) : 217 -224 . DOI: 10.13234/j.issn.2095-2805.2026.1.217

基金

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国家自然科学基金资助项目(51677058)

参考文献

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2026年第24卷第1期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.1.217

接收时间：2023-03-10
首发时间：2026-10-26
出版时间：2026-01-30

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出版历史

收稿日期：2023-03-10
修回日期：2023-06-08
录用日期：2023-07-17

基金

National Natural Science Foundation of China(51677058)

国家自然科学基金资助项目(51677058)

作者信息

湖北工业大学电气与电子工程学院，武汉　430068

通讯作者:

周天宇（1998—　），男，通信作者，硕士研究生。研究方向：电池管理系统。E-mail：1137388934@qq.com。

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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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