IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3466-d811775.html
   My bibliography  Save this article

Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles

Author

Listed:
  • David Watling

    (Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK)

  • Patrícia Baptista

    (Centre for Innovation, Technology and Policy Research (IN+), Associação para o Desenvolvimento do Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais, 1049-001 Lisboa, Portugal)

  • Gonçalo Duarte

    (Centre for Innovation, Technology and Policy Research (IN+), Associação para o Desenvolvimento do Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais, 1049-001 Lisboa, Portugal
    Mechanical Engineering Department, Instituto Superior de Engenharia de Lisboa (ISEL), Rua Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal)

  • Jianbing Gao

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Haibo Chen

    (Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK)

Abstract

Increasingly, demanding environmental standards reflect the need for improved energy efficiency and reduced externalities in the transportation sector. Reference driving cycles provide standard speed profiles against which future developments and innovations may be tested. In the paper, we develop such profiles for a class of electric L-category vehicles, which are anticipated to play an increasing future role in urban areas. While such driving cycles exist for regular L-category vehicles, these may not be suitable in the case of electric vehicles, due to their power output limitations. We present a methodology for deriving these new driving cycles, developed from empirically deduced power relationships, before demonstrating their application under different assumptions on the terrain and vehicle characteristics. The applications demonstrate the feasibility of the method in developing appropriate driving patterns for alternative real-world contexts. On flat terrain, the adjustments made to cope with the power limitations of L-EV do not introduce significant differences in energy consumption, suggesting that the certification does not require extensive modification. However, when considering road slope, differences of up to 5% in energy use and up to 10% in regenerated energy were observed, showing the importance of the developed method for assessing vehicle performance in real-world driving.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3466-:d:811775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3466/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3466/
    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. Amelie Ewert & Mascha Brost & Christine Eisenmann & Sylvia Stieler, 2020. "Small and Light Electric Vehicles: An Analysis of Feasible Transport Impacts and Opportunities for Improved Urban Land Use," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    3. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    4. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2021. "Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving," Applied Energy, Elsevier, vol. 297(C).
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    6. Schücking, Maximilian & Jochem, Patrick, 2021. "Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets," Applied Energy, Elsevier, vol. 293(C).
    7. Welzel, Fynn & Klinck, Carl-Friedrich & Pohlmann, Yannick & Bednarczyk, Mats, 2021. "Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model," Applied Energy, Elsevier, vol. 290(C).
    8. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2019. "Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting," Energies, MDPI, vol. 12(24), pages 1-23, December.
    9. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    10. Pu Shi & Wenxian Yang & Meiping Sheng & Minqing Wang, 2017. "An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals," Energies, MDPI, vol. 10(7), pages 1-13, July.
    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. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    2. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(C).
    3. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    4. Sun, Xilei & Fu, Jianqin, 2024. "Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle," Energy, Elsevier, vol. 300(C).
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    6. 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).
    7. 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.
    8. Zhichao Zhao & Lu Li & Yang Ou & Yi Wang & Shaoyang Wang & Jing Yu & Renhua Feng, 2023. "A Comparative Study on the Energy Flow of Electric Vehicle Batteries among Different Environmental Temperatures," Energies, MDPI, vol. 16(14), pages 1-15, July.
    9. Polychronis Spanoudakis & Gerasimos Moschopoulos & Theodoros Stefanoulis & Nikolaos Sarantinoudis & Eftichios Papadokokolakis & Ioannis Ioannou & Savvas Piperidis & Lefteris Doitsidis & Nikolaos C. Ts, 2020. "Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption," Sustainability, MDPI, vol. 12(21), pages 1-19, November.
    10. Maksymilian Mądziel, 2024. "Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses," Energies, MDPI, vol. 17(5), pages 1-22, February.
    11. 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).
    12. 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).
    13. Ibrahim, Amier & Jiang, Fangming, 2021. "The electric vehicle energy management: An overview of the energy system and related modeling and simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    14. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    15. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Zhang, Lei & Dorrell, David G. & Sun, Fengchun, 2022. "Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation," Applied Energy, Elsevier, vol. 327(C).
    16. 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).
    17. Atiquzzaman Khan Ankur & Stefan Kraus & Thomas Grube & Rui Castro & Detlef Stolten, 2022. "A Versatile Model for Estimating the Fuel Consumption of a Wide Range of Transport Modes," Energies, MDPI, vol. 15(6), pages 1-24, March.
    18. Zhou, Xiaochuan & Wu, Gang & Wang, Chunyan & Zhang, Ruijun & Shi, Shuaipeng & Zhao, Wanzhong, 2024. "Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions," Energy, Elsevier, vol. 301(C).
    19. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    20. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.

    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:jeners:v:15:y:2022:i:9:p:3466-:d:811775. 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.