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This study presents a stochastic dynamic programming-based cooling controller for the battery thermal management system in electric vehicles. Addressing the complex interplay between battery performance and safety, our approach optimizes temperature regulation while minimizing power consumption. Notable contributions include minimum transitions between refrigeration and radiator modes, integration of an artificial neural network for computing efficiency, and an infinity-horizon expected cost formulation considering future heat disturbances. Comparative analyses demonstrate superior performance, showcasing the proposed controller's efficiency in achieving smaller battery temperature variation and consistently lower energy consumption across diverse ambient conditions. The proposed controller shows 56% lower energy consumption on average compared to rule-based controller.
Park et al. (Thu,) studied this question.