IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v378y2025ipbs0306261924022062.html
   My bibliography  Save this article

Optimizing autonomous electric taxi operations with integrated mobile charging services: An approximate dynamic programming approach

Author

Listed:
  • Hu, Qinru
  • Hu, Simon
  • Shen, Shiyu
  • Ouyang, Yanfeng
  • Chen, Xiqun (Michael)

Abstract

This paper focuses on optimizing the routing and charging schedules of an autonomous electric taxi (AET) system integrated with mobile charging services. In this system, a fleet of AETs provides on-demand ride services for customers, while mobile charging vehicles (MCVs) are deployed as a flexible complement to fixed charging stations, offering fast charging options for AETs. A dynamic programming model is developed to optimize the joint operations of AETs and MCVs, considering stochastics in customer demand, AET energy consumption, and charging station resources. The objective is to maximize the operator’s overall profit over the entire planning horizon, including revenues from serving customer requests, travel costs, charging costs, and penalties associated with both fleets. To address the stochastic and dynamic nature of the problem, an approximate dynamic programming (ADP) approach, incorporating customized pruning strategies to reduce the state and decision space, is proposed. This approach balances immediate operational gains with future potential profits. A series of numerical experiments have been conducted to evaluate the effectiveness of the proposed model and algorithm. Results show that the ADP-based policy significantly improves system performance compared to classical myopic benchmarks.

Suggested Citation

  • Hu, Qinru & Hu, Simon & Shen, Shiyu & Ouyang, Yanfeng & Chen, Xiqun (Michael), 2025. "Optimizing autonomous electric taxi operations with integrated mobile charging services: An approximate dynamic programming approach," Applied Energy, Elsevier, vol. 378(PB).
    • Handle: RePEc:eee:appene:v:378:y:2025:i:pb:s0306261924022062
    DOI: 10.1016/j.apenergy.2024.124823
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924022062
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124823?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    2. Al-Kanj, Lina & Nascimento, Juliana & Powell, Warren B., 2020. "Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1088-1106.
    3. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan, 2021. "Electric vehicle routing problem with machine learning for energy prediction," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 24-55.
    4. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    5. Bruno Canizes & João Soares & Zita Vale & Juan M. Corchado, 2019. "Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City," Energies, MDPI, vol. 12(4), pages 1-40, February.
    6. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    7. Julian Hof & Michael Schneider, 2021. "Intraroute Resource Replenishment with Mobile Depots," Transportation Science, INFORMS, vol. 55(3), pages 660-686, May.
    8. Cui, Shaohua & Ma, Xiaolei & Zhang, Mingheng & Yu, Bin & Yao, Baozhen, 2022. "The parallel mobile charging service for free-floating shared electric vehicle clusters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    9. Cui, Shaohua & Yao, Baozhen & Chen, Gang & Zhu, Chao & Yu, Bin, 2020. "The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution," Energy, Elsevier, vol. 198(C).
    10. Wang, Jiawei & Guo, Qinglai & Sun, Hongbin & Chen, Min, 2023. "Collaborative optimization of logistics and electricity for the mobile charging service system," Applied Energy, Elsevier, vol. 336(C).
    11. Sujie Shao & Shaoyong Guo & Xuesong Qiu, 2017. "A Mobile Battery Swapping Service for Electric Vehicles Based on a Battery Swapping Van," Energies, MDPI, vol. 10(10), pages 1-21, October.
    12. Lei, Chao & Jiang, Zhoutong & Ouyang, Yanfeng, 2020. "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 60-75.
    13. Marlin W. Ulmer & Justin C. Goodson & Dirk C. Mattfeld & Marco Hennig, 2019. "Offline–Online Approximate Dynamic Programming for Dynamic Vehicle Routing with Stochastic Requests," Service Science, INFORMS, vol. 53(1), pages 185-202, February.
    14. Li, Li & Pantelidis, Theodoros & Chow, Joseph Y.J. & Jabari, Saif Eddin, 2021. "A real-time dispatching strategy for shared automated electric vehicles with performance guarantees," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    2. Zhang, Li & Chen, Tingting & Yao, Baozhen & Yu, Bin & Wang, Yunpeng, 2025. "Routing and charging scheduling for the electric carsharing system with mobile charging vehicles," Omega, Elsevier, vol. 131(C).
    3. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    4. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    5. Cui, Shaohua & Gao, Kun & Yu, Bin & Ma, Zhenliang & Najafi, Arsalan, 2023. "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    6. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    7. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    8. 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).
    9. Bosse, Alexander & Ulmer, Marlin W. & Manni, Emanuele & Mattfeld, Dirk C., 2023. "Dynamic priority rules for combining on-demand passenger transportation and transportation of goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 399-408.
    10. Wang, Jianxin & Lim, Ming K. & Liu, Weihua, 2024. "Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    11. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    12. Tan, Zhen & Liu, Fan & Chan, Hing Kai & Gao, H. Oliver, 2022. "Transportation systems management considering dynamic wireless charging electric vehicles: Review and prospects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    13. Meijian Yang & Enjun Xia, 2021. "A Systematic Literature Review on Pricing Strategies in the Sharing Economy," Sustainability, MDPI, vol. 13(17), pages 1-28, August.
    14. Mousavi, Kianoush & Bodur, Merve & Cevik, Mucahit & Roorda, Matthew J., 2024. "Approximate dynamic programming for pickup and delivery problem with crowd-shipping," Transportation Research Part B: Methodological, Elsevier, vol. 187(C).
    15. Nikola Mardešić & Tomislav Erdelić & Tonči Carić & Marko Đurasević, 2023. "Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment," Mathematics, MDPI, vol. 12(1), pages 1-44, December.
    16. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    17. Marianne Guillet & Maximilian Schiffer, 2023. "Coordinated charging station search in stochastic environments: A multiagent approach," Production and Operations Management, Production and Operations Management Society, vol. 32(8), pages 2596-2618, August.
    18. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    19. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    20. Men, Jinkun & Zhao, Chunmeng, 2024. "A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location," Energy, Elsevier, vol. 293(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:378:y:2025:i:pb:s0306261924022062. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.