Abstract
The design of conventional electric vehicles (EVs) is affected by numerous limitations, such as a short travel distance and long charging time. As one of the first wireless charging systems, the Online Electric Vehicle (OLEV) was developed to overcome the limitations of the current generation of EVs. Using wireless charging, an electric vehicle can be charged by power cables embedded in the road. In this paper, a model and algorithm for the optimal design of a wireless charging electric bus system is proposed. The model is built using a Markov decision process and is used to verify the optimal number of power cables, as well as optimal pickup capacity and battery capacity. Using reinforcement learning, the optimization problem of a wireless charging electric bus system in a diverse traffic environment is then solved. The numerical results show that the proposed algorithm maximizes average reward and minimizes total cost. We show the effectiveness of the proposed algorithm compared with obtaining the exact solution via mixed integer programming (MIP).
Highlights
Electric vehicles are soon likely to replace those powered by an internal combustion engine due to their high efficiency and low pollution
We propose a precise model of a wireless charging electric bus system based on a Markov decision process (MDP), which is composed of environment, state, action, reward, and policy
The first term of the reward is expressed as max (C t) indicates the cost of the wireless charging electric bus system, including the battery and pickup module, while the second term represents the total cost of the power cables installed along the route
Summary
Electric vehicles are soon likely to replace those powered by an internal combustion engine due to their high efficiency and low pollution. In order to efficiently optimize the wireless charging electric bus system in a dynamic traffic environment, the reinforcement-learning algorithm approach is an attractive alternative compared to the MIP-based exact solution. When the MIP-based exact solution needs to be modified after receiving new traffic information, the proposed algorithm can adapt to diverse traffic environments This is possible because it does not require any prior knowledge of traffic changes in the environment, which makes it suitable for a system based on real-time data without any future information. The suboptimal design of a wireless charging electric bus system based on reinforcement learning was modeled to find the optimal values of battery capacity, pickup capacity, and the number of power-cable installations. A simulation of the proposed model was conducted for both static and dynamic traffic environments
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