IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8033-d1147284.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8033/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8033/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schücking, Maximilian & Jochem, Patrick, 2021. "Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets," Applied Energy, Elsevier, vol. 293(C).
    2. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    3. Sascha Krysmon & Frank Dorscheidt & Johannes Claßen & Marc Düzgün & Stefan Pischinger, 2021. "Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms," Energies, MDPI, vol. 14(16), pages 1-27, August.
    4. José I. Huertas & Michael Giraldo & Luis F. Quirama & Jenny Díaz, 2018. "Driving Cycles Based on Fuel Consumption," Energies, MDPI, vol. 11(11), pages 1-13, November.
    5. Weiqi Zhou & Nanchi Wu & Qingchao Liu & Chaofeng Pan & Long Chen, 2023. "Research on Ecological Driving Following Strategy Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
    6. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    7. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    8. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    9. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.
    10. David Watling & Patrícia Baptista & Gonçalo Duarte & Jianbing Gao & Haibo Chen, 2022. "Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles," Energies, MDPI, vol. 15(9), pages 1-28, May.
    11. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    12. Triluck Kusalaphirom & Thaned Satiennam & Wichuda Satiennam & Atthapol Seedam, 2022. "Development of a Real-World Eco-Driving Cycle for Motorcycles," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    13. Basso, Franco & Feijoo, Felipe & Pezoa, Raúl & Varas, Mauricio & Vidal, Brian, 2024. "The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile," Energy, Elsevier, vol. 286(C).
    14. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    15. S. M. Ashrafur Rahman & I. M. Rizwanul Fattah & Hwai Chyuan Ong & Fajle Rabbi Ashik & Mohammad Mahmudul Hassan & Md Tausif Murshed & Md Ashraful Imran & Md Hamidur Rahman & Md Akibur Rahman & Mohammad, 2021. "State-of-the-Art of Establishing Test Procedures for Real Driving Gaseous Emissions from Light- and Heavy-Duty Vehicles," Energies, MDPI, vol. 14(14), pages 1-32, July.
    16. Avenali, Alessandro & Catalano, Giuseppe & Giagnorio, Mirko & Matteucci, Giorgio, 2024. "Factors influencing the adoption of zero-emission buses: A review-based framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8033-:d:1147284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.