Computationally Efficient Stochastic Model Predictive Controller for Battery Thermal Management of Electric Vehicle

Abstract

The performance and safety of batteries of electric vehicles deteriorate when the battery temperature is too low or too high. The thermal management system regulating the battery temperature consumes considerable electric energy, particularly, for cooling the battery. To maximize the vehicle driving range, the means of controlling the battery temperature should minimize the energy consumption. In this paper, stochastic model predictive control is applied to the battery-cooling controller. Effective model predictive control requires a good but simple system model with proper estimation of near-future disturbances. The components of the battery cooling system are modeled to represent the energy-consumption and heat exchange mechanism with some assumptions for simplicity. The future information is estimated using historical driving data in a stochastic sense. For real-time implementation, an unequally spaced probability distribution is introduced when designing a stochastic model of future heat generation. The proposed control method shows significantly lower energy consumption while maintaining an acceptable temperature regulation performance compared to other temperature controllers, such as a thermostat-type controller and a model predictive controller with simply assumed future information. The proposed predictive controller shows robust performance compared to typical controllers by utilizing the stochastic estimation of future information.