RLC: A Reinforcement Learning Based Charging Scheme for Battery Swap Stations

Over the past decade, the Electric Vehicle (EV) market has witnessed remarkable expansion. Nevertheless, apprehensions regarding prolonged charging durations frequently contribute to range anxiety. Battery Swap Station (BSS) is a promising solution to the range anxiety problem. Typically, when an EV arrives at a BSS, the depleted battery in the EV is replaced with a fully charged one, then the depleted battery is charged at the maximum speed. In this paper, we propose a novel battery swapping/charging scheme for BSS, Reinforcement Learning based Charging (RLC), to serve as many EVs as possible and minimize the total electricity cost. Specifically, with RLC, for the EVs that do not need full batteries, partially charged ones will be provided. To reduce the electricity cost whenever possible, RLC tries to shift the charging time of a battery to a low-electricity-price period. Technically, RLC uses Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, to optimize the charging strategy for the batteries in BSS. Our experimental results indicate that RLC outperforms the existing charging/swapping schemes in terms of battery service rate and total electricity cost.