Junya Shao1, Junfu Li1,2,3, Weizhe Yuan4, Changsong Dai2 , Zhenbo Wang2,5, Ming Zhao3,5 and Michael Pecht6
1School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, Shandong, China
2School of Chemical Engineering and Chemistry, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
3Guangdong Guanghua Sci-Tech Co., Ltd., Shantou 515000, Guangdong, China
4School of New energy, Harbin Institute of Technology, Weihai 264209, Shandong, China
5Zhuhai Zhongli New Energy Sci-Tech Co., Ltd., Zhuhai 519000, Guangdong, China
6Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
For more information about this article and related research, please contact Prof. Michael Pecht
Obtaining the State of Health of lithium-ion batteries and mastering its degradation laws are crucial for the utilization of Electric Vehicles. However, the prediction of discharge capacity of lithium-ion batteries requires high accuracy, which is subject to the variation of cells and the uncertainty of operating conditions. In this work, a discharge capacity prognostics method for lithium-ion batteries is developed based on a simplified electrochemical coupled aging mechanism model. Firstly, the solid-phase diffusion process is analyzed by using a simplified electrochemical model, and the particle rupture stress at different C rates is obtained. Then, based on the aging mechanisms in terms of Solid Electrolyte Interphase (SEI) layer growth model and particle volume expansion model, the SEI growth rate and correlated aging kinetics parameters are optimized by using particle swarm optimization algorithm. Finally, combined with the further analysis of aging mechanisms and variation of model parameters at early, middle, and late stage of degradation, the developed discharge capacity prediction method is verified at separate stages for batteries at 1C, 2C and 3C respectively, with the average relative error of full life cycle no more than 4%.
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