Aging modeling of lithium-ion cells by integrating active material loss, electrolyte degradation, solid electrolyte interphase growth, and lithium plating

Multiple interrelated degradation mechanisms, heavily dependent on temperature, current rate, and rest periods, govern the performance and lifetime of Lithium-ion batteries (LIBs). This study extends a previously established modeling framework, initially focused on loss of Lithium inventory (LLI) from solid electrolyte interphase (SEI) growth and Lithium plating, to include a more comprehensive suite of degradation pathways. Specifically, the solvent-diffusion-limited SEI growth (SD) and combined calendar and cyclic aging (CCC) models are enhanced to incorporate loss of active material (LAM) driven by diffusion-induced mechanical stress. Additionally, the framework now accounts for electrolyte depletion and the loss of electrical connectivity. Electrolyte consumption from SEI thickening and Lithium plating is dynamically modeled via a depletion factor, capturing the cascading effects on transport properties and accelerated aging. The proposed models are validated against experimental data from 43 Ah automotive-grade Li-ion pouch cells. These cells were cycled at 10 °C using optimized charging profiles ranging from 50% to 150% of the experimentally determined Lithium plating current limit. To assess predictive accuracy, results are benchmarked against both experimental trends and the original SD and CCC modeling frameworks. Simulations demonstrate how degradation pathways shift under varying charging intensities. While lower current rates mitigate Lithium plating risks, degradation continues through SEI thickening and progressive LAM. Conversely, at higher current rates, Lithium plating accelerates electrolyte depletion. This triggers a self-reinforcing feedback loop that amplifies LAM. Temperature variations further modulate these trends by modifying intercalation kinetics and mechanical stress evolution. Root Mean Square Error (RMSE) analysis of the capacity fade (SoH C ) confirms that the enhanced models achieve errors within 5% across all charging strategies. This accuracy demonstrates the framework’s robustness in capturing long-term degradation dynamics. By highlighting the critical interplay between Lithium plating, SEI growth, LAM, and electrolyte depletion, this study provides a mechanistic foundation for developing optimized battery management and fast-charging strategies.