Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions

Dawei Song, Shiqian Wang, Li Di, Weijian Zhang, Qian Wang and Jing V. Wang ()
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Dawei Song: State Grid Henan Electric Power Economic and Technological Research Institute, Zhengzhou 450052, China
Shiqian Wang: State Grid Henan Electric Power Economic and Technological Research Institute, Zhengzhou 450052, China
Li Di: Internet Department, State Grid Henan Electric Power Company, Zhengzhou 450052, China
Weijian Zhang: Internet Department, State Grid Henan Electric Power Company, Zhengzhou 450052, China
Qian Wang: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Jing V. Wang: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Energies, 2023, vol. 16, issue 2, 1-13

Abstract: Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree.

Keywords: thermal gradient; capacity degradation; life prediction; extreme learning machine; sparrow search algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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