State of Charge Prediction for Li-Ion Batteries in EVs for Traffic Microsimulation

This study presents a novel, minimalist framework for real-time State of Charge (SOC) prediction in electric vehicles, using only four inputs—vehicle speed, acceleration, road gradient, and ambient temperature—readily available from vehicle sensors or standard microsimulation outputs. An XGBoost model was trained and validated on 87,000 observations collected from real-world vehicle tests spanning a temperature range of –1 °C to 35 °C, achieving an R2 = 0.86, RMSE = 7.21% SOC, MAE = 4.07% SOC, and SMAPE = 3.60%. The trained model was then applied to Vissim and SUMO traffic simulations to generate spatial SOC distributions and evaluate energy-saving interventions. By eliminating the need for expensive current and voltage sensors, this approach enables scalable SOC estimation for both real-world and simulated datasets, supporting energy-aware traffic management and charging infrastructure planning.