Energy management method for battery swapping station combining reinforcement learning and mathematical optimization

Abstract

Abstract The rapid proliferation of electric vehicles has spurred the expansion of scalable Battery Swapping Station (BSS) to cater to the demand for swift charging. However, the current energy management faces challenges in coping with the fluctuation of charging batteries in scalable BSS, the unpredictability of electricity prices and battery demand, as well as the intricacy of demand response. So, this paper presents a two-layer optimization framework for energy management in scalable BSS. The framework decomposes the power scheduling problem in BSS into two subproblems and solves them with deep reinforcement learning and mathematical optimization. The upper layer uses deep reinforcement learning to schedule the BSS’s total power, while the lower layer uses mathematical optimization to allocate the power to each charging pile. The two layers cooperate to achieve an efficient solution. The experiments demonstrate that the proposed method can reduce the operating cost of the BSS, satisfy the safety and user’s demand, and facilitate grid demand response, in BSSs of different sizes. The method is an effective solution for power scheduling of scalable BSS.