Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles

Abstract

This paper presents an integrated computational framework that couples electro-thermal and degradation dynamics for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature, and degradation parameters. Results show the strong interaction between thermal behavior and battery aging. Model validation against experimental data is not performed in this study and remains an important direction for future research. The current work focuses on the derivation and parametric analysis of the modelling framework. The proposed framework provides a clear, low-cost, and scalable computational approach for EV battery analysis, design studies, and engineering education.