Research On Electric Vehicle Battery Switching Strategy Based On Reinforcement Learning

With the gradual improvement of living standards,people focus more on the environment.With the implementation of related policies by the country,electric cars are widely spread.Consequently,the related problems such as high cost,insufficient endurance mileage and poor charging experience need to be solved urgently.As an innovative commercial solution,the electric vehicle switch mode is re-mentioned after several years of silence.Meanwhile,circumstances such as the shortage of battery switch infrastructure and uneven utilization rate need be resolved,which provides a good stage for electric switch mode with the advantage time efficiency.With the help of relevant knowledge,the existing problems and the factors that restrict the waiting time for electric vehicles during the electric vehicle battery switch,and used the realtime communication between vehicles and roadside units,roadside units and the substation in the intelligent transportation system are analyzed.The paper focuses on solving the problem of electric vehicle power transmission scheduling during traveling,improving of the utilization rate of the power station and reducing the waiting time for electric vehicle power conversion,with a scheduling scheme based on reinforcement learning proposed to determine the best strategy for this problem.Based on this scheme,a double-layer partitionable,intelligentoriented battery switch scheduling strategy is proposed.The research in this paper mainly includes the following two aspects:(1)A Reinforcement Learning Based Electric Vehicle Battery Switch Strategy(RLBS)is proposed: a system environment model for the urban intelligent transportation system is established,which fully considers the utilization rate of the power station and the waiting time for batteries switch.Based on the characteristics of the electric vehicle’s electric vehicle power dispatching in progress,a state transition scheme,reward strategy,and a value function update method that can review the maximum value are designed.(2)On the basis of RLBS,Dynamic Area Division Battery Switch Strategy based on Reinforcement Learning(DABS)is further proposed: in order to solve the high concurrency problem when an electric vehicle chooses a power station during traveling,and further improve the utilization rate of the substation in urban scenarios,the competition and cooperation between the substations are evaluated.A strategy for the RLBS battery switch area division is proposed to further balance the utilization rate of the substation and the waiting time for batteries switch.Finally,the effects of different parameters of DABS on the relevant indicators of the strategy system are discussed experimentally on the ONE simulation platform.The DABS is evaluated by comparing with the existing classic strategy schemes.The experimental results have shown that the DABS scheduling strategy proposed in this paper can effectively improve the utilization rate of the substation and reduce the average waiting time of battery switch for electric vehicles.