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Mobile charging stations for EV charging management in urban areas: A case study in Chattanooga

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  • Afshar, Shahab
  • Pecenak, Zachary K.
  • Barati, Masoud
  • Disfani, Vahid

Abstract

The advancement of fixed charging stations (FCS) has facilitated electric vehicle (EV) charging and increased EV adoption. FCSs are especially important in dense urban areas, where home charging is not an option. However, FCSs’ construction is limited by budget, fragile power grid infrastructure, and site constraints, among others. Therefore, suitable locations for building FCSs are limited. Even in the presence of FCSs, EV drivers face challenges such as long charging times and inconvenient charging locations. Mobile charging stations (MCS), which allow for scheduling and convenient charging, can be a supplementary charging technology to address these shortcomings and allow rapid expansion of charging infrastructure, assuming the MCS can be scheduled efficiently. This paper develops an optimization framework for the optimal charging management of EVs considering a multi-charger system integrating different types of FCSs and MCSs. Considering the EV driver’s travel trajectory and time value, the proposed framework finds the optimal charging technology and location to minimize the EV user’s overall charging cost and time. The model is implemented in Chattanooga, TN, USA, where the evaluation results demonstrated that MCSs are the optimal charging method for many EV users. Moreover, the results indicated that MCS mitigates the power grid stress caused by EV charging during peak hours.

Suggested Citation

  • Afshar, Shahab & Pecenak, Zachary K. & Barati, Masoud & Disfani, Vahid, 2022. "Mobile charging stations for EV charging management in urban areas: A case study in Chattanooga," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s030626192201162x
    DOI: 10.1016/j.apenergy.2022.119901
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    References listed on IDEAS

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    1. Hedayat Saboori & Shahram Jadid & Mehdi Savaghebi, 2021. "Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption," Energies, MDPI, vol. 14(3), pages 1-19, January.
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    5. Duan, Sudong & Zhang, Zhonghui & Wang, Zhaojun & Xiong, Xiaoyue & Chen, Xinhan & Que, Xiaoyu, 2025. "A study on mobile charging station combined with integrated energy system: Emphasis on energy dispatch strategy and multi-scenario analysis," Renewable Energy, Elsevier, vol. 239(C).
    6. Golsefidi, Atefeh Hemmati & Hipolito, F. & Pereira, Francisco Câmara & Samaranayake, Samitha, 2025. "Incremental expansion of large scale fixed and mobile charging infrastructure in stochastic environments: A novel graph-based Benders decomposition approach," Applied Energy, Elsevier, vol. 380(C).
    7. 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).
    8. An, Sihai & Qiu, Jing & Lin, Jiafeng & Yao, Zongyu & Liang, Qijun & Lu, Xin, 2025. "Planning of a multi-agent mobile robot-based adaptive charging network for enhancing power system resilience under extreme conditions," Applied Energy, Elsevier, vol. 395(C).
    9. Cui, Jingshi & Jiang, Wenqian & Wu, Chenye, 2025. "Pricing mechanism design for future EV charging station with hybrid fixed and mobile charging modes," Applied Energy, Elsevier, vol. 380(C).
    10. Rafael C. Neto & Camila M. Bandeira & Gustavo M. S. Azevedo & Leonardo R. Limongi & Márcio R. S. de Carvalho & José F. C. Castro & Pedro A. C. Rosas & Augusto C. Venerando & Newmar Spader & Emilio Bue, 2024. "Mobile Charging Stations: A Comprehensive Review of Converter Topologies and Market Solutions," Energies, MDPI, vol. 17(23), pages 1-38, November.
    11. Robert Serrano & Arifa Sultana & Declan Kavanaugh & Hongjie Wang, 2025. "Increasing Electric Vehicle Charger Availability with a Mobile, Self-Contained Charging Station," Sustainability, MDPI, vol. 17(6), pages 1-16, March.
    12. Atefeh Alirezazadeh & Vahid Disfani, 2025. "Deep Reinforcement Learning-Based Optimization of Mobile Charging Station and Battery Recharging Under Grid Constraints," Energies, MDPI, vol. 18(20), pages 1-21, October.

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