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Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database

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  • Maksymilian Mądziel

    (Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland)

  • Tiziana Campisi

    (Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy)

Abstract

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

Suggested Citation

  • Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1437-:d:1053825
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    Cited by:

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    2. Khalil Bachiri & Ali Yahyaouy & Hamid Gualous & Maria Malek & Younes Bennani & Philippe Makany & Nicoleta Rogovschi, 2023. "Multi-Agent DDPG Based Electric Vehicles Charging Station Recommendation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    3. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    4. Ruoxi Pan & Yiping Liang & Yifei Li & Kai Zhou & Jiarui Miao, 2023. "Environmental and Health Benefits of Promoting New Energy Vehicles: A Case Study Based on Chongqing City," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    5. 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).
    6. Maksymilian Mądziel, 2024. "Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses," Energies, MDPI, vol. 17(5), pages 1-22, February.
    7. Steffen Limmer & Johannes Varga & Günther Robert Raidl, 2023. "Large Neighborhood Search for Electric Vehicle Fleet Scheduling," Energies, MDPI, vol. 16(12), pages 1-14, June.
    8. Martin Weiss & Trey Winbush & Alexandra Newman & Eckard Helmers, 2024. "Energy Consumption of Electric Vehicles in Europe," Sustainability, MDPI, vol. 16(17), pages 1-26, August.

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