电源学报

基于ISSA-ELM算法的锂电池SOC估计

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寇发荣 ¹^,² , 杨天祥 ¹ , 罗希 ¹ , 门浩 ¹

电源学报 | 电池与储能 2025,23(8): 246-253

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电源学报 |电池与储能 2025 , 23 (8) : 246 -253

基于ISSA-ELM算法的锂电池SOC估计

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寇发荣¹^,², 杨天祥¹, 罗希¹, 门浩¹

作者信息

^1.西安科技大学机械工程学院，西安　710054

^2.西安科技大学电气与控制工程学院，西安　710054

通讯作者:

寇发荣（1973—　），男，通信作者，博士，教授。研究方向：新能源汽车技术。E-mail：koufarong@xust.edu.cn。

作者简介:

杨天祥（1999—　），男，硕士研究生。研究方向：锂离子电池管理系统。E-mail：22205224053@stu.xust.edu.cn。

罗希（1994—　），男，硕士研究生。研究方向：锂离子电池管理系统。E-mail：21205224132@stu.xust.edu.cn。

门浩（1998—　），男，硕士研究生。研究方向：锂离子电池管理系统。E-mail：20205224104@stu.xust.edu.cn。

SOC Estimation of Lithium-ion Battery Based on ISSA-ELM Algorithm

Farong KOU¹^,², Tianxiang YANG¹, Xi LUO¹, Hao MEN¹

Affiliations

^1.School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

^2.School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

出版时间: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.246

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摘要

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为提高锂离子电池荷电状态SOC（state-of-charge）估计精度，提出1种基于改进麻雀搜索算法优化极限学习机的SOC估计方法。针对人为给定的极限学习机隐含层神经元数量不当导致估计精度不理想的问题，选用麻雀搜索算法对极限学习机隐含层神经元数量进行寻优；为避免麻雀搜索算法随机生成种群多样性差和后期易陷入局部最优的问题，引入Tent混沌映射和模拟退火算法中的Metropolis准则对麻雀搜索算法改进，以提高麻雀搜索算法的全局搜索能力。通过常温下美国联邦城市运行FUDS（federal urban driving schedule）工况仿真试验，验证了所提算法较传统极限学习机和长短期记忆网络在精度和效率上的优势。最后利用搭建的dSPACE硬件在环试验平台在0 ℃和25 ℃环境城市道路循环UDDS（urban dynamometer driving schedule）工况下进行在线验证，结果表明：所提算法在2种环境温度下SOC估计误差均保持在1.3%以内，均方根误差较传统极限学习机分别降低了46.7%和51.8%，验证了该算法的可行性与优越性。

关键词

荷电状态 / 极限学习机 / 麻雀搜索算法 / Tent混沌映 / Metropolis准则

Abstract

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To improve the accuracy of state-of-charge (SOC) estimation for lithium-ion batteries, an SOC estimation method based on an improved sparrow search algorithm optimized extreme learning machine is proposed. In response to the problem of unsatisfactory estimation accuracy caused by the improper number of neurons in the hidden layer of the extreme learning machine, the sparrow search algorithm is selected to optimize the number of neurons in the hidden layer of the extreme learning machine. To avoid the problems of poor diversity of randomly generated populations and the tendency to fall into local optima at the later stage of the sparrow search algorithm, Tent chaotic mapping and the Metropolis criterion in a simulated annealing algorithm are introduced to improve the sparrow search algorithm’s global search capability. The advantages of the proposed algorithm in terms of accuracy and efficiency compared with the traditional extreme learning machine and long short-term memory networks were verified through simulation experiments under FUDS operating conditions at room temperatures. Finally, a dSPACE hardware-in-the-loop experimental platform was established and further used for online verification under UDDS conditions at 0 ℃ and 25 ℃. Results showed that the proposed algorithm maintained SOC estimation errors within 1.3% at both the environmental temperatures, and the root mean square error decreased by 46.7% and 51.8% compared with the traditional extreme learning machine, respectively, thereby verifying its feasibility and superiority.

Key words

State-of-charge (SOC) / extreme learning machine / sparrow search algorithm / Tent chaotic map / Metropolis criterion

引用本文

寇发荣, 杨天祥, 罗希, 门浩. 基于ISSA-ELM算法的锂电池SOC估计. 电源学报, 2025 , 23 (8) : 246 -253 . DOI: 10.13234/j.issn.2095-2805.2025.8.246

Farong KOU, Tianxiang YANG, Xi LUO, Hao MEN. SOC Estimation of Lithium-ion Battery Based on ISSA-ELM Algorithm[J]. Journal of Power Supply, 2025 , 23 (8) : 246 -253 . DOI: 10.13234/j.issn.2095-2805.2025.8.246

基金

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国家自然科学基金资助项目(51775426)

参考文献

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2025年第23卷第8期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.8.246

接收时间：2023-09-04
首发时间：2026-10-26
出版时间：2025-12-30

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出版历史

收稿日期：2023-09-04
修回日期：2023-10-20
录用日期：2023-11-06

基金

National Natural Science Foundation of China(51775426)

国家自然科学基金资助项目(51775426)

作者信息

^1.西安科技大学机械工程学院，西安　710054

^2.西安科技大学电气与控制工程学院，西安　710054

通讯作者:

寇发荣（1973—　），男，通信作者，博士，教授。研究方向：新能源汽车技术。E-mail：koufarong@xust.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.8.246

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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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