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SOC Prediction of Li-ion Battery Pack Based on ISSA-KELM
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Yingda ZHANG1, Hongyan MA1, 2, 3, Jiaming DOU1, Shuai WANG1, Shengyan LI1, Lujin HU4
Journal of Power Supply | 2024, 22(6) : 217 - 224
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Journal of Power Supply | 2024, 22(6): 217-224
• Battery and Energy Storage •
SOC Prediction of Li-ion Battery Pack Based on ISSA-KELM
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Yingda ZHANG1, Hongyan MA1, 2, 3, Jiaming DOU1, Shuai WANG1, Shengyan LI1, Lujin HU4
Affiliations
  • 1 School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China
  • 2 Institute of Distributed Energy Storage Safety Big Data Beijing 100044 China
  • 3 Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044 China
  • 4 School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing 100044 China
Published: 2024-11-30 doi: 10.13234/j.issn.2095-2805.2024.6.217
Outline
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To address the difficulty in predicting the state-of-charge (SOC) of a Li-ion battery pack, an SOC prediction model based on kernel extreme learning machine (KELM) optimized by the improved sparrow search algorithm (ISSA) is proposed. First, Logistic chaotic mapping is introduced to improve the standard SSA and acquire the best population individuals. Second, the improved algorithm is used to optimize the kernel function parameter S and penalty coefficient C of KELM to create an ISSA-KELM prediction model. The simulation is carried out utilizing the historical data from an energystorage device, and the results predicted by ELM, KELM and ISSA-ISSA-KELM models were compared and analyzed. In addition, the robustness of the model was verified using data under other working conditions. Results show that the root mean square error and mean absolute error of predicted SOC decreased to 2.06% and 1.54%, respectively. The proposed model improved the prediction accuracy, and its convergence, generalization and robustness were also satisfying.

Li-ion battery pack  /  state-of-charge (SOC)  /  kernel extreme learning machine (KELM)  /  algorithm optimization
Yingda ZHANG, Hongyan MA, Jiaming DOU, Shuai WANG, Shengyan LI, Lujin HU. SOC Prediction of Li-ion Battery Pack Based on ISSA-KELM[J]. Journal of Power Supply, 2024 , 22 (6) : 217 -224 . DOI: 10.13234/j.issn.2095-2805.2024.6.217
Funding
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  • Doctoral Scientific Research Foundation of Beijing University of Civil Engineering and Architecture(ZF15054)
  • The Beijing University of Civil Engineering and Architecture 2021 Post Graduate Innovation Project(PG2021056)
Appendix
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Year 2024 volume 22 Issue 6
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Article Info
doi: 10.13234/j.issn.2095-2805.2024.6.217
  • Receive Date：2021-08-18
  • Online Date：2025-07-19
  • Published：2024-11-30
Article Data
Affiliations
History
  • Received：2021-08-18
  • Revised：2021-10-20
  • Accepted：2021-11-01
Funding
Doctoral Scientific Research Foundation of Beijing University of Civil Engineering and Architecture(ZF15054)
The Beijing University of Civil Engineering and Architecture 2021 Post Graduate Innovation Project(PG2021056)
Affiliations
    1 School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China
    2 Institute of Distributed Energy Storage Safety Big Data Beijing 100044 China
    3 Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044 China
    4 School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing 100044 China
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