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Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning

Author

Listed:
  • Hyukjoon Lee

    (KAIST (Korea Advanced Institute of Science and Technology), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Dongjin Ji

    (KAIST (Korea Advanced Institute of Science and Technology), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Dong-Ho Cho

    (KAIST (Korea Advanced Institute of Science and Technology), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

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).

Suggested Citation

  • Hyukjoon Lee & Dongjin Ji & Dong-Ho Cho, 2019. "Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning," Energies, MDPI, vol. 12(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1229-:d:218604
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    References listed on IDEAS

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    Cited by:

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    5. Wang, Yun & Zhou, Yu & Yan, Xuedong, 2024. "Reliable dynamic wireless charging infrastructure deployment problem for public transport services," European Journal of Operational Research, Elsevier, vol. 313(2), pages 747-766.

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