A Shift Schedule to Optimize Pure Electric Vehicles Based on RL Using Q-Learning and Opt LHD

Abstract

Range anxiety is a problem that restricts the development of pure electric vehicles. For this reason, much research starts from a shift schedule and strives to improve mileage. However, the proposed shift schedules have poor adaptive ability and are not suitable for dynamic conditions. In this paper, a shift schedule based on reinforcement learning (RL) is proposed, which uses Q-learning for optimization. However, the massive state variables and huge Q table in the state space put forward higher requirements on the computing power and storage space of the controller. Traditionally, the application of RL algorithms needs to rely on expensive GPU devices. To reduce high costs, we use an innovative treatment method, the optimal Latin hypercube design (Opt LHD), which is used for sampling, and state reduction is performed on the state space. Based on the above, the mileage is effectively improved by applying the shift schedule based on RL.