Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

Zhu, Yanchen; Zou, Honghui; Liu, Chufan; Luo, Yuyu; Wu, Yuankai; Liang, Yuxuan

doi:10.24963/ijcai.2025/1113

Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

Yanchen Zhu, Honghui Zou, Chufan Liu, Yuyu Luo, Yuankai Wu, Yuxuan Liang

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 10017-10025.

https://doi.org/10.24963/ijcai.2025/1113

Keywords

Planning and Scheduling: General
Machine Learning: General

Abstract

efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability—achieving up to 244.4% increase in coverage—and reduces user inconvenience with up to 79.8% shorter waiting times, compared to existing solutions.

Yanchen Zhu, Honghui Zou, Chufan Liu, Yuyu Luo, Yuankai Wu, and Yuxuan Liang. 2025. Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10017–10025. https://doi.org/10.24963/ijcai.2025/1113

@inproceedings{ijcai2025p1113,
  title     = {Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers},
  author    = {Zhu, Yanchen and Zou, Honghui and Liu, Chufan and Luo, Yuyu and Wu, Yuankai and Liang, Yuxuan},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {10017--10025},
  year      = {2025},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2025/1113},
  url       = {https://doi.org/10.24963/ijcai.2025/1113},
}

TY  - CPAPER
TI  - Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
AU  - Zhu, Yanchen
AU  - Zou, Honghui
AU  - Liu, Chufan
AU  - Luo, Yuyu
AU  - Wu, Yuankai
AU  - Liang, Yuxuan
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 10017
EP  - 10025
DO  - 10.24963/ijcai.2025/1113
UR  - https://doi.org/10.24963/ijcai.2025/1113
ER  -