IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v218y2021ics0360544220325445.html
   My bibliography  Save this article

The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus

Author

Listed:
  • Li, Pengshun
  • Zhang, Yi
  • Zhang, Yi
  • Zhang, Kai
  • Jiang, Mengyan

Abstract

As prediction of trip-based electricity consumption has become an prerequisite for the deployment of large-scale EB fleets, this study has established random forest-based models to systematically investigate the impacts of environmental conditions, route characteristics, and dynamic traffic conditions. The models have been performed on real-world data collected from 1024 EBs over five consecutive months in Shenzhen, China. The results show that considering all the influencing variables can significantly enhance the prediction performance, but comparatively speaking, the route characteristics contribute the most among the three categories and involving more variables demonstrates greater advantages within the trip length under 20 km. It is also found that the trip length, the number of bus stops and the number of the traffic lights passed rank the top three most influencing factors, while the wet-dry condition is the least one. In addition, the variations under five operation scenarios show similar trend. The trip length and average travel speed are inversely proportional to the specific electricity consumption, while the number of bus stops visited, traffic lights passed, and ambient temperature exhibit a gentle proportional relationship. Moreover, it is suggested to plan the new bus line over 10 km in terms of reducing electricity consumption per kilometre.

Suggested Citation

  • Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325445
    DOI: 10.1016/j.energy.2020.119437
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220325445
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119437?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tatsuya Sakurahara & Seyed Reihani & Ernie Kee & Zahra Mohaghegh, 2020. "Global importance measure methodology for integrated probabilistic risk assessment," Journal of Risk and Reliability, , vol. 234(2), pages 377-396, April.
    2. Kropiwnicki, Jacek, 2019. "A unified approach to the analysis of electric energy and fuel consumption of cars in city traffic," Energy, Elsevier, vol. 182(C), pages 1045-1057.
    3. Yajing Gao & Shixiao Guo & Jiafeng Ren & Zheng Zhao & Ali Ehsan & Yanan Zheng, 2018. "An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors," Energies, MDPI, vol. 11(8), pages 1-17, August.
    4. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
    5. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    6. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    7. Lajunen, Antti & Lipman, Timothy, 2016. "Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses," Energy, Elsevier, vol. 106(C), pages 329-342.
    8. 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).
    9. Zhou, Boya & Wu, Ye & Zhou, Bin & Wang, Renjie & Ke, Wenwei & Zhang, Shaojun & Hao, Jiming, 2016. "Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions," Energy, Elsevier, vol. 96(C), pages 603-613.
    10. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    11. Gallet, Marc & Massier, Tobias & Hamacher, Thomas, 2018. "Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks," Applied Energy, Elsevier, vol. 230(C), pages 344-356.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    2. Torkey, Alaa & Abdelgawad, Hossam, 2022. "Framework for planning of EV charging infrastructure: Where should cities start?," Transport Policy, Elsevier, vol. 128(C), pages 193-208.
    3. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
    4. Zhang, Kaixuan & Ruan, Jiageng & Li, Tongyang & Cui, Hanghang & Wu, Changcheng, 2023. "The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus," Energy, Elsevier, vol. 269(C).
    5. Wang, Yanxia & Gan, Shaojun & Li, Kang & Chen, Yanyan, 2022. "Planning for low-carbon energy-transportation system at metropolitan scale: A case study of Beijing, China," Energy, Elsevier, vol. 246(C).
    6. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    7. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    8. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    9. 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).
    10. Viana-Fons, Joan Dídac & Payá, Jorge, 2024. "HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities," Energy, Elsevier, vol. 296(C).
    11. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    12. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).

    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. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    2. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    3. Hatem Abdelaty & Moataz Mohamed, 2021. "A Prediction Model for Battery Electric Bus Energy Consumption in Transit," Energies, MDPI, vol. 14(10), pages 1-26, May.
    4. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    5. Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
    6. Brinkel, Nico & Zijlstra, Marle & van Bezu, Ronald & van Twuijver, Tim & Lampropoulos, Ioannis & van Sark, Wilfried, 2023. "A comparative analysis of charging strategies for battery electric buses in wholesale electricity and ancillary services markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    7. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2020. "A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies," Applied Energy, Elsevier, vol. 261(C).
    8. Ma, Xiaolei & Miao, Ran & Wu, Xinkai & Liu, Xianglong, 2021. "Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing," Energy, Elsevier, vol. 216(C).
    9. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    10. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
    11. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    12. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2022. "Energy consumption and battery sizing for different types of electric bus service," Energy, Elsevier, vol. 239(PE).
    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. Mustafa Hamurcu & Tamer Eren, 2020. "Electric Bus Selection with Multicriteria Decision Analysis for Green Transportation," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    15. Lin, Boqiang & Tan, Ruipeng, 2017. "Are people willing to pay more for new energy bus fares?," Energy, Elsevier, vol. 130(C), pages 365-372.
    16. Krzysztof KRAWIEC, 2021. "Vehicle Cycle Hierarchization Model To Determine The Order Of Battery Electric Bus Deployment In Public Transport," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(1), pages 99-112, March.
    17. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2018. "Assessing life cycle impacts and the risk and uncertainty of alternative bus technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 569-579.
    18. Say, Kelvin & Csereklyei, Zsuzsanna & Brown, Felix Gabriel & Wang, Changlong, 2023. "The economics of public transport electrification: A case study from Victoria, Australia," Energy Economics, Elsevier, vol. 120(C).
    19. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    20. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(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:eee:energy:v:218:y:2021:i:c:s0360544220325445. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.