Battery Life Prediction Using Physics-Based Modeling and Coati Optimization

Accurate remaining useful life (RUL) prediction is essential for ensuring the reliability and efficiency of Lithium-ion (Li-ion) batteries. This paper presents an approach using the Coati Optimization Algorithm (COA) to optimize the physics-based model for RUL prediction of Li-ion batteries. This method combines COA to optimize the physics-based degradation model to improve battery aging predictions, considering factors like cycle time, rest time, temperature, state of charge (SOC), and load conditions. The model can more accurately simulate real-world battery usage patterns and degradation mechanisms by incorporating these variables. Simulation results show that COA enhances the accuracy of the model's calendar and cycle aging prediction, and reduces RMSE and MAE values for RUL prediction. Furthermore, the robustness of the proposed method is demonstrated through extensive testing under various operational scenarios, highlighting its potential for application in battery management systems to extend battery life and improve performance.