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Data-driven on-demand energy supplement planning for electric vehicles considering multi-charging/swapping services

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  • Tao, Yuechuan
  • Qiu, Jing
  • Lai, Shuying
  • Sun, Xianzhuo
  • Zhao, Junhua
  • Zhou, Baorong
  • Cheng, Lanfen

Abstract

Electric vehicles (EVs) have been experiencing steady growth in many countries in recent years. Given the increasing transportation electrification, it is urgent to establish an efficient on-demand energy supplement system for EVs. In this paper, we present a data-driven two-stage charging/swapping service scheme, where the EV owners can select multi-services, including fast charging at the fast-charging station (FCS), slow charging at the charging post (CP), and battery swapping at the battery-swapping station (BSS). In the first stage, a service recommendation is provided according to the proposed hybrid recommendation algorithm based on the collaborative filtering (CF) algorithm. In the second stage, the on-demand energy supplement orders are dispatched to the swapping/charging infrastructure. To ensure the long-term revenue of the energy supplement system, we formulate the Markov Decision Processes (MDPs) of different types of charging/swapping infrastructures. Then, deep reinforcement learning (DRL) and mixed-integer linear programming (MILP) are jointly used to solve the large-scale sequential decision-making problem. The proposed methodologies are numerically verified in case studies. According to the simulation results, compared with the state-of-art, our methods can better relieve the burden of the power sectors and shows better performance in daily revenue, answer rate, and queue length at FCS.

Suggested Citation

  • Tao, Yuechuan & Qiu, Jing & Lai, Shuying & Sun, Xianzhuo & Zhao, Junhua & Zhou, Baorong & Cheng, Lanfen, 2022. "Data-driven on-demand energy supplement planning for electric vehicles considering multi-charging/swapping services," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922001039
    DOI: 10.1016/j.apenergy.2022.118632
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    References listed on IDEAS

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    1. Cao, Jianing & Han, Yuhang & Pan, Nan & Zhang, Jingcheng & Yang, Junwei, 2024. "A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city," Energy, Elsevier, vol. 300(C).
    2. Adu-Gyamfi, Gibbson & Song, Huaming & Nketiah, Emmanuel & Obuobi, Bright & Wu, Qin & Cudjoe, Dan, 2024. "Refueling convenience and range satisfaction in electric mobility: Investigating consumer willingness to use battery swap services for electric vehicles," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Wang, Zhichao & Guo, Zhaomiao & Liu, Kai & Lin, Zhenhong & Li, Shunxi, 2024. "Multi-stage layout strategies of intercity charging and swapping infrastructure for electric logistics vehicles: random, radical, or conservative?," Transport Policy, Elsevier, vol. 153(C), pages 12-23.
    5. Tian, Zhaoming & Cao, Xiaoyu & Zeng, Bo & Guan, Xiaohong, 2025. "Adaptive infinite-horizon control of hybrid EV/FCEV charging hubs: A large-model based deep reinforcement learning approach," Applied Energy, Elsevier, vol. 390(C).
    6. Raghu Raman & Sangeetha Gunasekar & Deepa Kaliyaperumal & Prema Nedungadi, 2024. "Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals," Sustainability, MDPI, vol. 16(21), pages 1-25, October.
    7. Lu, Yanming & Yang, Nan & Liu, Zhigang & Zhang, Qiao & Lu, Bing & Liang, Pengcheng & Dai, Zhou, 2025. "Capacity configuration approach for battery swapping station: A staged programming model via double-end non-segmented-driven strategy," Renewable Energy, Elsevier, vol. 244(C).

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