电源学报

基于融合EKF与改进DELM的锂电池SOC实时估计

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陈晓飞 ¹ , 蒋淑霞 ¹ , 崔祥波 ¹ , 赵文卓中国电源学会学生会员 ² , 王思思 ¹

电源学报 | 电池与储能 2025,23(8): 273-283

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电源学报 |电池与储能 2025 , 23 (8) : 273 -283

基于融合EKF与改进DELM的锂电池SOC实时估计

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陈晓飞¹, 蒋淑霞¹, 崔祥波¹, 赵文卓中国电源学会学生会员², 王思思¹

作者信息

^1.中南林业科技大学机械与智能制造学院，长沙　410004

^2.电子科技大学机械与电气工程学院，成都　611731

通讯作者:

蒋淑霞（1977—　），女，通信作者，博士，副教授。研究方向：汽车工程和数据挖掘与处理。E-mail：jiangshuxia_2004@126.com。

作者简介:

陈晓飞（1999—　），男，硕士研究生。研究方向：电动汽车电池管理系统。E-mail：1005581953@qq.com。

崔祥波（1989—　），男，博士，讲师。研究方向：电池状态估计及剩余寿命预测。E-mail：xiangb.c@csu.edu.cn。

赵文卓（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：双向储能DC-DC变换器的优化调制策略。E-mail：2717301046@qq.com。

王思思（2000—　），女，硕士研究生。研究方向：轴承故障诊断及剩余寿命预测。E-mail：w2632643960@126.com。

Real-time SOC Estimation of Lithium-ion Batteries Based on Fusion of EKF and Improved DELM

Xiaofei CHEN¹, Shuxia JIANG¹, Xiangbo CUI¹, Wenzhuo ZHAO Student Member, CPSS², Sisi WANG¹

Affiliations

^1.School of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology, Changsha 410004, China

^2.School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

出版时间: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.273

文章导航

摘要

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针对单一算法在估计锂电池荷电状态SOC（state-of-charge）时估计性能低的问题，提出1种结合天鹰算法改进的深度极限学习机AO-DELM（aquila optimizer-deep extreme learning machine）与扩展卡尔曼滤波EKF（extended Kalman filter）算法优势的混合算法，用于锂电池SOC估计。首先，采用带遗忘因子的递推最小二乘法辨识电池模型参数；然后，基于高加速循环（US06）工况数据，将电流、电压与EKF算法提取到的多种电池状态特征作为输入、实际SOC作为输出训练AO-DELM；最后，将训练完成的AO-DELM模型与EKF算法组成闭环的SOC实时估计系统。结果表明，在4种动态工况下，所提的AO-DELM-EKF算法较EKF算法、DELM-EKF算法显著提升了SOC估计精度，均方根误差不超过0.49%。

关键词

锂电池 / 荷电状态估计 / 扩展卡尔曼滤波算法 / 天鹰算法改进的深度极限学习机 / 混合算法

Abstract

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Aimed at the poor estimation performance of single algorithms in estimating the state-of-charge (SOC) of lithium-ion batteries, a hybrid algorithm which combines the advantages of deep extreme learning machine optimized by the aquila optimizer (AO-DELM) and the extended Kalman filter (EKF) algorithm is proposed for the SOC estimation of lithium-ion batteries. First, the recursive least squares method with forgetting factor is used to identify the parameters of the battery model. Then, based on the data under a high acceleration cycle (US06) condition, the AO-DELM is trained using the current, voltage and various battery state features extracted by the EKF algorithm as input and the actual SOC as output. Finally, the trained AO-DELM model and the EKF algorithm are used to form a closedloop real-time SOC estimation system. Results show that compared with the EKF and DELM-EKF algorithms, the proposed AO-DELM-EKF algorithm significantly improves the SOC estimation accuracy under four dynamic conditions, with the root mean square error not more than 0.49%.

Key words

Lithium-ion battery / state-of-charge (SOC) estimation / extended Kalman filter (EKF) algorithm / aquila optimizer-deep extreme learning machine (AO-DELM) / hybrid algorithm

引用本文

陈晓飞, 蒋淑霞, 崔祥波, 赵文卓中国电源学会学生会员, 王思思. 基于融合EKF与改进DELM的锂电池SOC实时估计. 电源学报, 2025 , 23 (8) : 273 -283 . DOI: 10.13234/j.issn.2095-2805.2025.8.273

Xiaofei CHEN, Shuxia JIANG, Xiangbo CUI, Wenzhuo ZHAO, Sisi WANG. Real-time SOC Estimation of Lithium-ion Batteries Based on Fusion of EKF and Improved DELM[J]. Journal of Power Supply, 2025 , 23 (8) : 273 -283 . DOI: 10.13234/j.issn.2095-2805.2025.8.273

基金

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湖南省自然科学基金资助项目(2019JJ60076)
教育部产学合作协同育人项目(230826311807159)
中南林业科技大学研究生科技创新基金项目(2023CX02077)

参考文献

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参考文献引证文献

排序方式：

[1]

Pei Zhongwen, Liu Kaimin, Zhang Song, et al. Optimized EKF algorithm using TSO-BP neural network for lithium battery state of charge estimation[J]. Journal of Energy Storage, 2023, 73: 108882.

[2]

刘旖琦, 雷万钧, 刘茜, 等. 基于双自适应扩展粒子滤波器的锂离子电池状态联合估计[J]. 电工技术学报, 2024, 39(2): 607-616. Liu Yiqi, Lei Wanjun, Liu Qian, etal. Joint state estimation of lithium-ion battery based on dual adaptive extended particle filter[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 607-616. (in Chinese)

[3]

Zhou Wenlu, Zheng Yanping, Pan Zhengjun, et al. Review on the battery model and SOC estimation method[J]. Processes, 2021, 9(9): 1685.

[4]

刘兴涛, 李坤, 武骥, 等. 基于EKF-SVM算法的动力电池SOC估计[J]. 汽车工程, 2020, 42(11): 1522-1528, 1544. Liu Xingtao, Li Kun, Wu Ji, et al. State of charge estimation for traction battery based on EKF-SVM algorithm[J]. Automotive Engineering, 2020, 42(11): 1522-1528, 1544. (in Chinese)

[5]

Zhang Xin, Hou Jiawei, Wang Zekun, et al. Study of SOC estimation by the ampere-hour integral method with capacity correction based on LSTM[J]. Batteries, 2022, 8(10): 170.

[6]

高峰, 贾建芳, 元淑芳, 等. 基于GRU-UKF的锂离子电池SOC估计方法研究[J]. 电子测量与仪器学报, 2022, 36(11): 160-169. Gao Feng, Jia Jianfang, Yuan Shufang, et al. Research on SOC estimation method of lithium-ion battery based on GRU-UKF[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(11): 160-169. (in Chinese)

[7]

Ren Yitong, Ding Jie, Hao Tianyun. GA-ELM-based adaptive Kalman estimator for SOC of lithium-ion batteries[J]. Ionics, 2022, 28(12): 5377-5388.

[8]

Tian Yong, Lai Rucong, Li Xiaoyu, et al. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter[J]. Applied Energy, 2020, 265: 114789.

[9]

Feng Fei, Lu Rengui, Zhu Chunbo. A combined state of charge estimation method for lithium-ion batteries used in a wide ambient temperature range[J]. Energies, 2014, 7(5): 3004-3032.

[10]

Zheng Yongliang, He Feng, Wang Wenliang. A method to identify lithium battery parameters and estimate SOC based on different temperatures and driving conditions[J]. Electronics, 2019, 8(12): 1391.

[11]

Li Ming, Zhang Yingjie, Hu Zuolei, et al. A battery SOC estimation method based on AFFRLS-EKF[J]. Sensors, 2021, 21(17): 5698.

[12]

贠祥, 张鑫, 王超, 等. 基于联合参数辨识的粒子群优化扩展粒子滤波的锂电池荷电状态估计[J]. 电工技术学报, 2024, 39(2): 595-606. Yun Xiang, Zhang Xin, Wang Chao, etal. State of charge estimation of Li-ion battery using particle swarm optimization extended Kalman particle filter based on joint parameter identification[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 595-606. (in Chinese)

[13]

Cui Zhenhua, Dai Jiyong, Sun Jianrui, et al. Hybrid methods using neural network and Kalman filter for the state of charge estimation of lithium-ion battery[J]. Mathematical Problems in Engineering, 2022, 2022(1): 9616124.

[14]

How D N T, Hannan M A, Hossain Lipu M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review[J]. IEEE Access, 2019, 7: 136116-136136.

[15]

张英达, 马鸿雁, 窦嘉铭, 等. 基于ISSA-KELM的锂离子电池组SOC预测[J]. 电源学报, 2024, 22(6): 217-224. Zhang Yingda, Ma Hongyan, Dou Jiaming, et al. SOC prediction of Li-ion battery pack based on ISSA-KELM[J]. Journal of Power Supply, 2024, 22(6): 217-224. (in Chinese)

[16]

Gu Tianyu, Sheng Jie, Fan Qiuhua, et al. The modified multi-innovation adaptive EKF algorithm for identifying battery SOC[J]. Ionics, 2022, 28(8): 3877-3891.

[17]

傅军栋, 陈浩杰, 孙翔, 等. 基于EKF-Markov的UPS荷电状态预测与健康管理系统[J]. 电源学报, 2024, 22(6): 225-233. Fu Jundong, Chen Haojie, Sun Xiang, et al. SOC prediction and health management system of UPS based on EKF-Markov[J]. Journal of Power Supply, 2024, 22(6): 225-233. (in Chinese)

[18]

汪首坤, 鲁帅, 陈志华, 等. 基于FFRLS-AEKF的6轮足机器人电池SOC估计[J]. 北京理工大学学报, 2022, 42(3): 271-278. Wang Shoukun, Lu Shuai, Chen Zhihua, et al. SOC estimation of six-wheeled-legged robot battery based on FFRLS-AEKF[J]. Transactions of Beijing Institute of Technology, 2022, 42(3): 271-278. (in Chinese)

[19]

Abualigah L, Yousri D, Abd Elaziz M, et al. Aquila Optimizer: A novel meta-heuristic optimization algorithm[J]. Computers & Industrial Engineering, 2021, 157: 107250.

[20]

Liu Xiaoyue, Zhang Zeming, Meng Fanwei, et al. Fault diagnosis of wind turbine bearings based on CNN and SSA-ELM[J]. Journal of Vibration Engineering & Technologies, 2023, 11(8): 3929-3945.

[21]

Dai Ye, Pang Jian, Rui Xukun, et al. Thermal error prediction model of high-speed motorized spindle based on DELM network optimized by weighted mean of vectors algorithm[J]. Case Studies in Thermal Engineering, 2023, 47: 103054.

[22]

AlRassas A M, Al-qaness M A A, Ewees A A, et al. Optimized ANFIS model using Aquila optimizer for oil production forecasting[J]. Processes, 2021, 9(7): 1194.

[23]

高文凯, 郑岳久, 许霜霜, 等. 基于增量误差的卡尔曼滤波算法全区间荷电状态估计[J]. 电源学报, 2019, 17(5): 162-169. Gao Wenkai, Zheng Yuejiu, Xu Shuangshuang, et al. Entire range state-of-charge estimation based on incremental error using Kalman filtering algorithm[J]. Journal of Power Supply, 2019, 17(5): 162-169. (in Chinese)

[24]

Zhang Guanyong, Xia Bizhong, Wang Jiamin, et al. Intelligent state of charge estimation of battery pack based on particle swarm optimization algorithm improved radical basis function neural network[J]. Journal of Energy Storage, 2022, 50: 104211.

[25]

王萍, 弓清瑞, 程泽, 等. 基于AUKF的锂离子电池SOC估计方法[J]. 汽车工程, 2022, 44(7): 1080-1087. Wang Ping, Gong Qingrui, Cheng Ze, et al. An AUKF-based SOC estimation method for lithium-ion battery[J]. Automotive Engineering, 2022, 44(7): 1080-1087. (in Chinese)

2025年第23卷第8期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.8.273

接收时间：2024-04-29
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：2024-04-29
修回日期：2024-06-30
录用日期：2024-08-04

基金

Natural Science Foundation of Hunan Province(2019JJ60076)

湖南省自然科学基金资助项目(2019JJ60076)

Industry-University Cooperation Collaborative Parenting Project of Ministry of Education(230826311807159)

教育部产学合作协同育人项目(230826311807159)

Graduate Student Science and Technology Innovation Fund Project of Central South University of Forestry and Technology(2023CX02077)

中南林业科技大学研究生科技创新基金项目(2023CX02077)

作者信息

^1.中南林业科技大学机械与智能制造学院，长沙　410004

^2.电子科技大学机械与电气工程学院，成都　611731

通讯作者:

蒋淑霞（1977—　），女，通信作者，博士，副教授。研究方向：汽车工程和数据挖掘与处理。E-mail：jiangshuxia_2004@126.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.8.273

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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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