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Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives

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  • Huang, Haichao
  • Li, Bowen
  • Wang, Yizhou
  • Zhang, Zhe
  • He, Hongdi

Abstract

Analyzing the influence of various factors on the energy consumption of Electric Vehicles (EVs) yields a positive impact on mitigating range anxiety. Nevertheless, prevailing research predominantly relies on statistical or predictive methodologies to assess the influence of factors on energy consumption, offering limited causal insights. This study addresses this gap by utilizing the double/debiased machine learning (DML) combined with bootstrap-of-little-bags (BIB) inference approach to make causal inferences concerning the influence of factors on energy consumption. Furthermore, the study introduces a comprehensive framework that integrates statistical, predictive, and causal perspectives to examine the consistency and discrepancies in the influence of twelve factors on energy consumption. Notably, it unveils variations in the influence of these factors on energy consumption concerning different trip categories. Field datasets are utilized for framework validation, covering 20,385 valid trips from eight cities and four vehicle models. The findings demonstrate that DML can yield interpretable causal inferences, and the framework allows these three perspectives to complement each other, unveiling insights that remain concealed when using a single method. Furthermore, even for the same factor, there are variations in the influence on energy consumption across different trip categories. The proposed framework opens avenues for a comprehensive understanding of influencing factors on energy consumption of EVs, crucial for eco-driving and energy consumption predictions.

Suggested Citation

  • Huang, Haichao & Li, Bowen & Wang, Yizhou & Zhang, Zhe & He, Hongdi, 2024. "Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives," Applied Energy, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924014934
    DOI: 10.1016/j.apenergy.2024.124110
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    References listed on IDEAS

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