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Real-time SOC Estimation of Lithium-ion Batteries Based on Fusion of EKF and Improved DELM
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Xiaofei CHEN1, Shuxia JIANG1, Xiangbo CUI1, Wenzhuo ZHAO  Student Member, CPSS2, Sisi WANG1
Journal of Power Supply | 2025, 23(8) : 273 - 283
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Journal of Power Supply | 2025, 23(8): 273-283
• Battery and Energy Storage •
Real-time SOC Estimation of Lithium-ion Batteries Based on Fusion of EKF and Improved DELM
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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
Published: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.273
Outline
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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%.

Lithium-ion battery  /  state-of-charge (SOC) estimation  /  extended Kalman filter (EKF) algorithm  /  aquila optimizer-deep extreme learning machine (AO-DELM)  /  hybrid algorithm
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
Funding
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  • Natural Science Foundation of Hunan Province(2019JJ60076)
  • Industry-University Cooperation Collaborative Parenting Project of Ministry of Education(230826311807159)
  • Graduate Student Science and Technology Innovation Fund Project of Central South University of Forestry and Technology(2023CX02077)
Appendix
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Year 2025 volume 23 Issue 8
53
8
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.8.273
  • Receive Date：2024-04-29
  • Online Date：2026-10-26
  • Published：2025-12-30
Article Data
Affiliations
History
  • Received：2024-04-29
  • Revised：2024-06-30
  • Accepted：2024-08-04
Funding
Natural Science Foundation of Hunan Province(2019JJ60076)
Industry-University Cooperation Collaborative Parenting Project of Ministry of Education(230826311807159)
Graduate Student Science and Technology Innovation Fund Project of Central South University of Forestry and Technology(2023CX02077)
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
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
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Number of
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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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