Accelerated Prediction and Analysis of Lithium-Ion Battery Lifetime Using Efficient Electrochemical Models

Abstract

Lithium-ion batteries degrade over their lifetime due to several parasitic reactions that occur in the cell. These reactions lead to loss of lithium inventory, loss of active material, mechanical and thermal damage to the cell’s component. Predicting cell aging and capacity fade is crucial for battery management systems (BMS) to monitor and control the battery's state of health. Physics-based models are valuable in battery management systems (BMS) to monitor the battery’s state of health and predict battery failure. BMS algorithms require fast codes that can predict and estimate battery parameters in real-time and control the battery’s performance under different loads.1 Common reduced-order models, such as the SPM (Single Particle Model) consider approximations that may not be valid as the cell ages. The Tanks-in-Series model is a model developed by Subramaniam et al., where the governing equations from the full-order ‘pseudo-2-D’ (p2D) model are volume-averaged over each cell region.2 This ensures that the physics captured by the mass and charge conservation equations are maintained without loss of accuracy while providing simulation solutions at a few milliseconds. The Tanks-in-Series model is ideal for predicting cell aging due to its reduced computation speed and predictability beyond other reduced-order models.This work combines the Tanks-in-Series model with governing equations for several degradation mechanisms, including the growth of the SEI layer, lithium plating, particle cracking, etc. A comprehensive analysis of capacity fade predictions is shown, with comparisons to predictions from the P2D model. References V. Ramadesigan et al., J. Electrochem. Soc., 159, R31–R45 (2012).A. Subramaniam et al., J. Electrochem. Soc., 167, 013534 (2020).