This paper presents the development and validation of a high-fidelity thermo-electric model for lithium-ion batteries at both the cell and module levels, using MATLAB/Simulink and the Simscape Battery toolbox. Lithium-ion batteries exhibit highly non-linear electrical and thermal behaviours, making accurate modelling essential for effective design, simulation, and control in real-world applications. An equivalent circuit modelling (ECM) technique is adopted to develop a detailed cell-level model that captures both electrical dynamics and heat generation characteristics under various load conditions. This model is then scaled up to a module-level representation, maintaining electrical accuracy while incorporating the effects of thermal inhomogeneity across cells within the module.

A major contribution of this work is the creation of a computationally efficient thermal modelling method that predicts internal temperature distribution within the module using a limited number of temperature sensors, eliminating the need for complex finite element method (FEM) simulations. This enables faster design iterations and facilitates the development of thermal management strategies in a virtual environment. Validation experiments involving charge/discharge cycles were designed to evaluate the model’s accuracy in capturing internal thermal behaviour. Results show that the electrical model exceeds 99% accuracy at the cell level and 98% at the module level, while the thermal model maintains a temperature error of less than 1°C at the hottest point in the module during testing.

The ability to accurately simulate both electrical and thermal behaviour makes this modelling framework a valuable tool for rapid virtual prototyping of battery systems. It reduces reliance on extensive experimental testing and enables quick evaluation of performance under different electrical loads and thermal conditions. This approach supports early-stage system development, control algorithm design, and optimised thermal management. Future work will focus on extending the model to include battery ageing effects, degradation mechanisms, and integration with advanced battery management systems (BMS), paving the way for long-term predictive modelling and improved reliability in real-world applications.