Predictive Battery Preconditioning Strategy Considering Charging Time, Battery Degradation and Energy Consumption

Electric vehicles (EVs) play a key role in reducing greenhouse gas emissions, yet their widespread adoption remains limited due to long charging times and concerns about battery degradation. To address these challenges, this paper presents a predictive battery preconditioning strategy to optimally prepare the battery before fast charging, with the goal of minimizing either charging time, battery degradation, or energy consumption. The proposed approach employs route-based velocity prediction together with a longitudinal vehicle dynamics model to predict the battery load, ambient temperature, and arrival time at the charging station. Based on this predictive information, the optimal battery temperature trajectory is determined using nonlinear programming with precomputed maps derived from a high-fidelity vehicle model and an electrochemical battery model including physics-based degradation mechanisms. The optimized temperature trajectory is then realized through a nonlinear model predictive controller (NMPC) for the thermal management system. The control-oriented models used for optimization and control, as well as the high-fidelity vehicle model, are parameterized and validated using measurement data. Simulation results demonstrate that the predictive preconditioning strategy enables a reduction in charging time of up to 8.9% or a reduction in battery degradation of up to 6.2% compared to no preconditioning, while outperforming a rule-based preconditioning strategy. Furthermore, the results show that energy consumption cannot be reduced through active preconditioning. Overall, the findings highlight the potential of predictive battery preconditioning to improve charging performance and battery longevity in electric vehicles.