Skip to main content
SpringerLink
Log in

Starting Behavior Analysis of Pure Electric Bus Based on K-means Clustering

  • Conference paper
  • First Online:
Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

  • 1567 Accesses

Abstract

Due to the relatively fixed route and obvious difference between different routes, how to reduce the energy consumption level of pure electric buses is a research hotspot at home and abroad. Based on the actual operation of pure electric buses in a city as the research object and through the analysis of its starting data, the K-means clustering method is used to study the influencing factors and differences of energy consumption under different starting modes of electric buses. Then, the evaluation system of drivers’ starting behavior is built. The results show that the energy consumption of pure electric buses varies greatly under different starting modes. The energy consumption of radical starting buses is much higher than that of safe starting buses. The evaluation system can objectively and effectively evaluate drivers’ starting behaviors, provide a sufficient basis for the supervision of operating enterprises, and have certain reference value.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Grunditz, E.A., Thiringer, T.: Characterizing BEV powertrain energy consumption, efficiency, and range during official and drive cycles from Gothenburg, Sweden. IEEE Trans. Veh. Technol. 65(6), 3964–3980 (2016)

    Article  Google Scholar 

  2. Yi, Z., Bauer, P.H.: Adaptive multi-resolution energy consumption prediction for electric vehicles. IEEE Trans. Veh. Technol. 66(11), 10515–10525 (2017)

    Article  Google Scholar 

  3. Ying, Y., Jihui, Z., Hui, X.: Effects of driving behavior differences on energy consumption of pure electric bus drivers. J. Tianjin Univ. (Nat. Sci. Eng. Technol. Ed.) 47(03), 231–236 (2014)

    Google Scholar 

  4. Ying, Y.: Research on auxiliary optimization strategy for driving characteristics of accelerator pedal of pure electric bus. Tianjin University (2012)

    Google Scholar 

  5. Huibao, Y., Qingxiang, Z.: Analysis of the influence of driving behavior on energy consumption of pure electric vehicles. Electromecha. Technol. 02: 82–85 + 88 (2019)

    Google Scholar 

  6. Zhang, R., Yao, E.: Electric vehicles’energy consumption estimation with real driving condition data. Transp. Res. Part D Transp. Environ. 41, 177–187 (2015)

    Article  Google Scholar 

  7. Song, Y., Xinliang, W., Meng, J.: Control strategy optimization of pure electric bus based on driving characteristics. Veh. Power Technol. 04, 23–27 (2017)

    Google Scholar 

  8. Junhui, S.: Research on Optimization of Drive Control Strategy of pure electric City Bus. Beijing Institute of Technology, Beijing (2016)

    Google Scholar 

  9. Yuanyuan, S.: Research on Energy Consumption Modeling and driving range estimation of pure electric vehicle based on driving cycle. Beijing Jiaotong University (2014)

    Google Scholar 

  10. Zou, Y., Wei, S., Sun, F., et al.: Large-scale deployment of electric taxis in Beijing: a real-world analysis. Energy 100, 25–39 (2016)

    Article  Google Scholar 

  11. Wu, X., Freese, D., Cabrera, A., et al.: Electric vehicles’ energy consumption measurement and estimation. Transp. Res. Part D 34(34), 52–67 (2015)

    Article  Google Scholar 

  12. Ningan, Z.: Energy consumption prediction and mileage estimation of pure electric vehicles. Dalian University of Technology (2016)

    Google Scholar 

Download references

Acknowledgment

This work was supported in part by Projects of the National Science Foundation of China (41971340),Fujian science and technology innovation platform project in 2020 (2020D002), Provincial candidate of Fujian Provincial talent project (GY-Z19113), Fuzhou Science and Technology Bureau Municipal Science and technology plan project (2019-G-40), General project of Natural Science Foundation of Fujian Science and Technology Department (2019I0019).

Author information

Authors and Affiliations

  1. Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Tianjie Wang, Fumin Zou, Yifan Li & Kuo-Chi Chang

  2. Fujian Provincial Big Data Research Institute of Intelligent Transportation, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Tianjie Wang, Fumin Zou, Yifan Li & Kuo-Chi Chang

Authors
  1. Tianjie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fumin Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yifan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kuo-Chi Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fumin Zou .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, T., Zou, F., Li, Y., Chang, KC. (2021). Starting Behavior Analysis of Pure Electric Bus Based on K-means Clustering. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_33

Download citation

Publish with us

Policies and ethics

Search

Navigation