A novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries. (May 2023)

• Record Type: Journal Article
• Authors: Shao, Junya; Li, Junfu; Yuan, Weizhe; Dai, Changsong; Wang, Zhenbo; Zhao, Ming; Pecht, Michael
• Is Part Of: Journal of energy storage. Volume 61(2023)
• Publication Date: 2023-05
• Subjects: Lithium-ion batteries -- Simplified electrochemical model -- Solid electrolyte interphase layer growth -- Particle volume expansion -- Aging mechanisms -- Capacity prediction; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.106788
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: 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 %. Graphical abstract: Unlabelled Image Highlights: Developed a simplified electrochemical coupled aging mechanism model. Accurate and rapid capacity prediction for early, middle and late stage of aging process. The developed method is verified based on the aging test data at different discharge rates. PSO algorithm is applied to estimate aging parameters based on degradation modeling.