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A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus

Author

Listed:
  • Hongli Liu

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Weiguo Yun

    (Zhejiang Geely Farizon New Energy Commercial Vehicles Group Co., Ltd., Hangzhou 311243, China)

  • Bin Li

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Mengling Dai

    (Guizhou Xingqian Talent Resources Co., Ltd., Guiyang 550003, China)

  • Yangyuhang Wang

    (School of Automobile, Chang’an University, Xi’an 710064, China)

Abstract

In order to help improve the economy, energy savings and emission reductions of pure electric buses, based on the driving data, a new driving cycle construction method is proposed. Through the dividing of short trips and the calculation of characteristic parameter values, two typical driving conditions (weekday driving condition and weekend driving condition) are constructed via principal components analysis and the k-means clustering method, and both have a high degree of compatibility with the actual conditions. Based on the two typical driving conditions, the CRITIC (Criteria Importance Through Intercriteria Correlation) method and the quantitative analysis are used to establish a quantitative evaluation model to score the economy of the driver’s driving behavior. The result shows that the weekend working condition with the better traffic environment promotes the generation of aggressive driving behavior and increases the random fluctuation seen in the driver’s driving process: for the weekend driving condition, the proportion of low economic efficiency is about 4.5 times bigger than the proportion on weekdays, and the former’s fluctuation range for the driving behavior score is 37% higher than that of the latter, meaning that the overall economy of the pure electric bus is much worse on weekends.

Suggested Citation

  • Hongli Liu & Weiguo Yun & Bin Li & Mengling Dai & Yangyuhang Wang, 2023. "A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8033-:d:1147284
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    References listed on IDEAS

    as
    1. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng & Li, Xiaoyu & Qu, Changhui, 2019. "Driving cycles construction for electric vehicles considering road environment: A case study in Beijing," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    3. Yiwen Zhou & Fengxiang Guo & Simin Wu & Wenyao He & Xuefei Xiong & Zheng Chen & Dingan Ni, 2022. "Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    4. Kibok Kim & Jinil Park & Jonghwa Lee, 2021. "Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning," Energies, MDPI, vol. 14(15), pages 1-13, July.
    5. Ran Tu & Junshi Xu & Tiezhu Li & Haibo Chen, 2022. "Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
    6. Ali Ashtari & Eric Bibeau & Soheil Shahidinejad, 2014. "Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle," Transportation Science, INFORMS, vol. 48(2), pages 170-183, May.
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