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Optimal charging/discharging management strategy for electric vehicles

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  • Algafri, Mohammed
  • Baroudi, Uthman

Abstract

Electric vehicles (EVs) are experiencing substantial investment and widespread acceptance. However, successful penetration of the global market is contingent upon the development of a strategic plan for the efficient allocation of EVs to optimal charging stations (CSs). This study combines several optimization models to systematically assign EVs to the optimal charging stations, with the goal of maximizing trading energy while simultaneously minimizing total response time. Factors taken into consideration include traveling distance, charging (V2G), and discharging (G2V) energy trading, total response time, and energy prices. The efficacy of the combined models is validated using GAMS and BARON solvers, with a focus on EV satisfaction factor, updated energy and response time, number of served EVs, and alleviation of range anxiety. The proposed models demonstrate 85% satisfaction factor for the majority of charging requests, reaching almost 99% for discharging requests. These results surpass those of contemporary models, underscoring the heightened effectiveness of the proposed approach.

Suggested Citation

  • Algafri, Mohammed & Baroudi, Uthman, 2024. "Optimal charging/discharging management strategy for electric vehicles," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005701
    DOI: 10.1016/j.apenergy.2024.123187
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    References listed on IDEAS

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    1. Trinko, David & Horesh, Noah & Porter, Emily & Dunckley, Jamie & Miller, Erika & Bradley, Thomas, 2023. "Transportation and electricity systems integration via electric vehicle charging-as-a-service: A review of techno-economic and societal benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
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    Cited by:

    1. Qian, Tao & Liang, Zeyu & Shao, Chengcheng & Guo, Zishan & Hu, Qinran & Wu, Zaijun, 2025. "Unsupervised learning for efficiently distributing EVs charging loads and traffic flows in coupled power and transportation systems," Applied Energy, Elsevier, vol. 377(PB).
    2. Chen, Sheng & Cheng, Hao & Zhang, Hongcai & Lv, Si & Wei, Zhinong & Jin, Yuyang, 2025. "Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands," Applied Energy, Elsevier, vol. 377(PA).

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