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SOC Estimation of Lithium-ion Battery Based on ISSA-ELM Algorithm
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Farong KOU1, 2, Tianxiang YANG1, Xi LUO1, Hao MEN1
Journal of Power Supply | 2025, 23(8) : 246 - 253
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Journal of Power Supply | 2025, 23(8): 246-253
• Battery and Energy Storage •
SOC Estimation of Lithium-ion Battery Based on ISSA-ELM Algorithm
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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
Published: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.246
Outline
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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.

State-of-charge (SOC)  /  extreme learning machine  /  sparrow search algorithm  /  Tent chaotic map  /  Metropolis criterion
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
  • National Natural Science Foundation of China(51775426)
Year 2025 volume 23 Issue 8
42
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.8.246
  • Receive Date:2023-09-04
  • Online Date:2026-10-26
  • Published:2025-12-30
Article Data
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History
  • Received:2023-09-04
  • Revised:2023-10-20
  • Accepted:2023-11-06
Funding
National Natural Science Foundation of China(51775426)
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
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表12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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