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Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

Author

Listed:
  • Witsarut Achariyaviriya

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Wongkot Wongsapai

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
    Multidisciplinary Research Institute, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Kittitat Janpoom

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Tossapon Katongtung

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Yuttana Mona

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Nakorn Tippayawong

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Pana Suttakul

    (Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification.

Suggested Citation

  • Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6351-:d:1231261
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    References listed on IDEAS

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    1. Zuzanna Kłos-Adamkiewicz & Elżbieta Szaruga & Agnieszka Gozdek & Magdalena Kogut-Jaworska, 2023. "Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland," Energies, MDPI, vol. 16(9), pages 1-25, April.
    2. H. Christopher Frey & Xiaohui Zheng & Jiangchuan Hu, 2020. "Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 13(5), pages 1-23, March.
    3. Elżbieta Szaruga & Elżbieta Załoga, 2022. "Qualitative–Quantitative Warning Modeling of Energy Consumption Processes in Inland Waterway Freight Transport on River Sections for Environmental Management," Energies, MDPI, vol. 15(13), pages 1-21, June.
    4. Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    5. Zhang, Cheng & Yang, Fan & Ke, Xinyou & Liu, Zhifeng & Yuan, Chris, 2019. "Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations," Applied Energy, Elsevier, vol. 254(C).
    6. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan, 2021. "Electric vehicle routing problem with machine learning for energy prediction," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 24-55.
    7. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2021. "Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving," Applied Energy, Elsevier, vol. 297(C).
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    1. 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).

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