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Modelling the energy consumption of electric vehicles under uncertain and small data conditions

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  • Liu, Yang
  • Zhang, Qi
  • Lyu, Cheng
  • Liu, Zhiyuan

Abstract

This study models the energy consumption of electric vehicles (EVs) under uncertain and small data conditions by combining the machine learning method and the idea of controlled experiments. We propose a Machine Learning-Control Variable model, termed the MLCV model, to estimate the trip energy consumption of EVs. Different data augmentation methods, ensemble methods, sampling factors are adopted as the parameters of the proposed method. Through parameter search, the accuracy of the base learner can be further improved. Our method utilizes real driving behaviours that are generated by real drivers and collected in a complex urban environment, making the approach generalizable. The experimental results demonstrate that the proposed MLCV model is superior to existing machine learning models in terms of estimation accuracy.

Suggested Citation

  • Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    • Handle: RePEc:eee:transa:v:154:y:2021:i:c:p:313-328
    DOI: 10.1016/j.tra.2021.10.009
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    References listed on IDEAS

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    Cited by:

    1. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    2. 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.
    3. Piotr Szeląg & Sebastian Dudzik & Anna Podsiedlik, 2023. "Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy," Energies, MDPI, vol. 16(3), pages 1-30, January.
    4. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    5. Liu, Yang & Wu, Fanyou & Lyu, Cheng & Li, Shen & Ye, Jieping & Qu, Xiaobo, 2022. "Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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