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Electric vehicle charging load forecasting considering weather impact

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  • Wang, Wenhao
  • Tang, Aihong
  • Wei, Feng
  • Yang, Huiyuan
  • Xinran, Li
  • Peng, Jiao

Abstract

Electric vehicles require continuous charging, and the energy demand to ensure timely charging is enormous and constantly growing. The driving range of electric vehicles is one of the decisive factors determining users' total charging demand, while changes in users' travel modes and environmental temperatures influence the frequency of charging and battery capacity. For instance, hot and rainy summers, as well as cold and snowy winters, can directly or indirectly impact the charging load. Therefore, it is necessary to establish models that capture the relationship between user travel behavior and weather changes. In this study, we investigate the correlation between travel modes and different weather conditions through large-scale user travel data analysis. A multi-logistic model is employed to quantitatively analyze the influence mechanism of rainy and snowy weather on user travel behavior. Subsequently, travel chain models of varying complexity are constructed to simulate user travel characteristics, and a road traffic model is established. Furthermore, considering users' habitual constraints on mode choices under different weather conditions, a user travel and electric vehicle charging model is developed based on user travel demand, road impedance function relationships, and regional functional attributes. Finally, the Monte Carlo method is adopted to solve the model, obtaining the spatiotemporal distribution patterns of electric vehicle charging load predictions and charging demand. The results validate the effectiveness of the proposed model and method.

Suggested Citation

  • Wang, Wenhao & Tang, Aihong & Wei, Feng & Yang, Huiyuan & Xinran, Li & Peng, Jiao, 2025. "Electric vehicle charging load forecasting considering weather impact," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925000674
    DOI: 10.1016/j.apenergy.2025.125337
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    References listed on IDEAS

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    Cited by:

    1. Yangqing Dan & Ke Sun & Jun Wang & Yanan Fei & Le Yu & Licheng Sun, 2025. "Novel Distributed Power Flow Controller Topology and Its Coordinated Output Optimization in Distribution Networks," Energies, MDPI, vol. 18(9), pages 1-21, April.
    2. Xunxun Chen & Xiaohong Zhang & Qingyuan Yan & Yanxue Li, 2025. "Spatio-Temporal Adaptive Voltage Coordination Control Strategy for Distribution Networks with High Photovoltaic Penetration," Energies, MDPI, vol. 18(8), pages 1-35, April.
    3. Maksymilian Mądziel, 2025. "Impact of Weather Conditions on Energy Consumption Modeling for Electric Vehicles," Energies, MDPI, vol. 18(8), pages 1-21, April.

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