Machine Learning-Based Surrogate Model Development for the Estimation of State-of-Charge and Minimization of Charging Time for Batteries of Lithium-Ion in Electric Vehicles

Abstract

Lithium-ion batteries (LIBs) are the main energy source for electric vehicles (EVs), but they require sophisticated Battery Management Systems (BMS) for optimal functionality. In response to this need, the Python Battery Mathematical Model (PyBaMM) was used to apply the Doyle–Fuller–Newman (DFN) electrochemical model, which provided detailed battery data. This research utilizes the electrochemical DFN model to develop a surrogate model based on machine learning for precise state-of-charge (SoC) with predicted values of 15% to 90%, which is the recommended value of SoC in electric vehicle technology. The surrogate model showed impressive accuracy, achieving a 99.6% R-score and a mean squared error (MSE) of 2.6%. Additionally, the study implemented a machine learning strategy integrated with particle swarm optimization (PSO) to determine optimal charging parameters that reduce charging time while preserving battery health and safety. These optimized parameters decreased the projected charging time to 130 s, although actual charging is expected to take around 225 s.