IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4689-d541451.html
   My bibliography  Save this article

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

Author

Listed:
  • Wei Qin

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Linhong Wang

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Yuhan Liu

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Cheng Xu

    (Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou 310053, China)

Abstract

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Suggested Citation

  • Wei Qin & Linhong Wang & Yuhan Liu & Cheng Xu, 2021. "Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4689-:d:541451
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4689/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/9/4689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gao, Kun & Yang, Ying & Li, Aoyong & Li, Junhong & Yu, Bo, 2021. "Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 89-103.
    2. Saadon Al-Ogaili, Ali & Ramasamy, Agileswari & Juhana Tengku Hashim, Tengku & Al-Masri, Ahmed N. & Hoon, Yap & Neamah Jebur, Mustafa & Verayiah, Renuga & Marsadek, Marayati, 2020. "Estimation of the energy consumption of battery driven electric buses by integrating digital elevation and longitudinal dynamic models: Malaysia as a case study," Applied Energy, Elsevier, vol. 280(C).
    3. Liang, Shidong & He, Shengxue & Zhang, Hu & Ma, Minghui, 2021. "Optimal holding time calculation algorithm to improve the reliability of high frequency bus route considering the bus capacity constraint," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Wang, Yusheng & Huang, Yongxi & Xu, Jiuping & Barclay, Nicole, 2017. "Optimal recharging scheduling for urban electric buses: A case study in Davis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 115-132.
    5. Rogge, Matthias & van der Hurk, Evelien & Larsen, Allan & Sauer, Dirk Uwe, 2018. "Electric bus fleet size and mix problem with optimization of charging infrastructure," Applied Energy, Elsevier, vol. 211(C), pages 282-295.
    6. Antti Ritari & Jari Vepsäläinen & Klaus Kivekäs & Kari Tammi & Heikki Laitinen, 2020. "Energy Consumption and Lifecycle Cost Analysis of Electric City Buses with Multispeed Gearboxes," Energies, MDPI, vol. 13(8), pages 1-21, April.
    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. 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.
    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. Mena ElMenshawy & Ahmed Massoud, 2022. "Medium-Voltage DC-DC Converter Topologies for Electric Bus Fast Charging Stations: State-of-the-Art Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
    2. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    3. Araby Mahdy & Abdullah Shaheen & Ragab El-Sehiemy & Ahmed Ginidi & Saad F. Al-Gahtani, 2023. "Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor," Energies, MDPI, vol. 16(5), pages 1-27, March.
    4. 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).
    5. 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).

    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. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2023. "A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris," Energy, Elsevier, vol. 285(C).
    2. 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.
    3. Yiming Bie & Mingjie Hao & Mengzhu Guo, 2021. "Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    4. 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).
    5. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
    6. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
    7. 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.
    8. Sofia Dahlgren & Jonas Ammenberg, 2021. "Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus Technologies," Sustainability, MDPI, vol. 13(3), pages 1-30, January.
    9. Shaohua Cui & Hui Zhao & Cuiping Zhang, 2018. "Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-22, November.
    10. He, Yi & Liu, Zhaocai & Song, Ziqi, 2020. "Optimal charging scheduling and management for a fast-charging battery electric bus system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    11. Cui, Shaohua & Gao, Kun & Yu, Bin & Ma, Zhenliang & Najafi, Arsalan, 2023. "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    12. Foda, Ahmed & Abdelaty, Hatem & Mohamed, Moataz & El-Saadany, Ehab, 2023. "A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling," Energy, Elsevier, vol. 277(C).
    13. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    14. 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).
    15. Zhou, Yu & Wang, Hua & Wang, Yun & Yu, Bin & Tang, Tianpei, 2024. "Charging facility planning and scheduling problems for battery electric bus systems: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    16. 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).
    17. Hatem Abdelaty & Ahmed Foda & Moataz Mohamed, 2023. "The Robustness of Battery Electric Bus Transit Networks under Charging Infrastructure Disruptions," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    18. Say, Kelvin & Brown, Felix Gabriel & Csereklyei, Zsuzsanna, 2024. "The economics of public transport electrification: When does infrastructure investment matter?," Applied Energy, Elsevier, vol. 360(C).
    19. Bálint Csonka, 2021. "Optimization of Static and Dynamic Charging Infrastructure for Electric Buses," Energies, MDPI, vol. 14(12), pages 1-18, June.
    20. Alvo, Matías & Angulo, Gustavo & Klapp, Mathias A., 2021. "An exact solution approach for an electric bus dispatch problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(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:13:y:2021:i:9:p:4689-:d:541451. 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.