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An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City

Author

Listed:
  • Pannee Suanpang

    (Faculty of Science & Technology, Suan Dusit University, Bangkok 10300, Thailand)

  • Pitchaya Jamjuntr

    (Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand)

  • Phuripoj Kaewyong

    (Faculty of Science & Technology, Suan Dusit University, Bangkok 10300, Thailand)

  • Chawalin Niamsorn

    (Faculty of Management Sciences, Suan Dusit University, Bangkok 10300, Thailand)

  • Kittisak Jermsittiparsert

    (Faculty of Education, University of City Island, Famagusta 9945, Cyprus
    Faculty of Social and Political Sciences, Universitas Muhammadiyah Sinjai, Kabupaten Sinjai 92615, Sulawesi Selatan, Indonesia
    Faculty of Social and Political Sciences, Universitas Muhammadiyah Makassar, Kota Makassar 90221, Sulawesi Selatan, Indonesia
    Publication Research Institute and Community Service, Universitas Muhammadiyah Sidenreng Rappang, Sidenreng Rappang Regency 91651, Sulawesi Selatan, Indonesia)

Abstract

The world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in today’s transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time ( CWT ), average charging price ( CP ), and charging failure rate ( CFR ) of EVs.

Suggested Citation

  • Pannee Suanpang & Pitchaya Jamjuntr & Phuripoj Kaewyong & Chawalin Niamsorn & Kittisak Jermsittiparsert, 2022. "An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:455-:d:1016843
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    Citations

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    Cited by:

    1. Pannee Suanpang & Pitchaya Jamjuntr, 2024. "Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities," Sustainability, MDPI, vol. 16(14), pages 1-29, July.

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