Stochastic model predictive control for electric vehicles thermal management

Abstract

A stochastic Model Predictive Control approach is developed to efficiently optimize the thermal management of electric vehicles and accommodate scenarios with multiple routes. To account for the uncertainties, the cost function is constructed to minimize the expected cost across all possible routes over the prediction horizon. Thermal constraints are treated as soft constraints using slack variables. This approach allows for flexibility in satisfying the constraints while optimizing the performance. Through simulations, the performance of the proposed method is evaluated using a fleet of vehicles. In this way, the proposed method achieves a good trade-off between multiple competing performance metrics. Furthermore, an adaptation strategy is introduced, which dynamically adjusts the penalty weight value. This adaptive approach eliminates the need for offline calibration and further enhances performance. The results indicate that the time-varying penalty weight significantly reduces the total constraint violations by up to 20% without impacting the performance on energy consumption.