IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v267y2020ics0306261920305936.html
   My bibliography  Save this article

Microsimulation of electric vehicle energy consumption and driving range

Author

Listed:
  • Xie, Yunkun
  • Li, Yangyang
  • Zhao, Zhichao
  • Dong, Hao
  • Wang, Shuqian
  • Liu, Jingping
  • Guan, Jinhuan
  • Duan, Xiongbo

Abstract

In order to predict and study the effects of different parameters on performance characteristics of electric vehicles. A vehicle simulation model of pure battery electric vehicles equipped with single pedal control system is established and calibrated by the experimental data based on vehicle energy flow and driving range analysis, the simulation doesn’t include thermal aspect of the battery/vehicle. Next, the effects of different environmental and control parameters on energy consumption and driving range of pure electric vehicles are analyzed. The main findings are: (1) for the single driving cycle, the relative error of battery power and current is below 5%, and the absolute error of battery voltage is below 2.5 V. For the whole driving range, the absolute error of driving range is only about 5.75 km. (2) The main factors influencing energy consumption and driving range are average vehicle speed, running time and the frequency distribution of braking process, besides, the energy consumption of congested traffic with/without regenerative brake control system are 46.07 kW·h/100 km and 47.19 kW·h/100 km, respectively, meanwhile, vehicle with regenerative braking saves 2.43% energy under congested traffic. (3) The threshold of quitting the working condition of energy recovery for the motor can be set in a certain value based on the safety of driver in the emergencies and energy conversion. Further, the model and data in the paper can be applied to evaluate and optimize the energy consumption and driving range by changing different technologies or strategies in the future.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920305936
    DOI: 10.1016/j.apenergy.2020.115081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920305936
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115081?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. Carbot-Rojas, D.A. & Escobar-Jiménez, R.F. & Gómez-Aguilar, J.F. & Téllez-Anguiano, A.C., 2017. "A survey on modeling, biofuels, control and supervision systems applied in internal combustion engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1070-1085.
    2. Chen, Zheng & Zhang, Fan & Xu, Boya & Zhang, Quanchang & Liu, Jingping, 2017. "Influence of methane content on a LNG heavy-duty engine with high compression ratio," Energy, Elsevier, vol. 128(C), pages 329-336.
    3. Hsu, Chia-Yu & Yang, Chin-Sheng & Yu, Liang-Chih & Lin, Chi-Fang & Yao, Hsiu-Hsen & Chen, Duan-Yu & Robert Lai, K. & Chang, Pei-Chann, 2015. "Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system," International Journal of Production Economics, Elsevier, vol. 164(C), pages 454-461.
    4. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    5. 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.
    6. 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.
    7. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    8. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    9. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    10. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
    11. Ballinger, Benjamin & Stringer, Martin & Schmeda-Lopez, Diego R. & Kefford, Benjamin & Parkinson, Brett & Greig, Chris & Smart, Simon, 2019. "The vulnerability of electric vehicle deployment to critical mineral supply," Applied Energy, Elsevier, vol. 255(C).
    12. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    13. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    14. Khan, Muhammad Imran & Yasmin, Tabassum & Shakoor, Abdul, 2015. "Technical overview of compressed natural gas (CNG) as a transportation fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 785-797.
    15. Li, Lifu & Liu, Qin, 2019. "Acceleration curve optimization for electric vehicle based on energy consumption and battery life," Energy, Elsevier, vol. 169(C), pages 1039-1053.
    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. 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.
    2. Zhang, Jian & Tang, Tie-Qiao & Yan, Yadan & Qu, Xiaobo, 2021. "Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging," Applied Energy, Elsevier, vol. 282(PA).
    3. Duan, Xiongbo & Liu, Jingping & Yao, Jun & Chen, Zheng & Wu, Cheng & Chen, Ceyuan & Dong, Hao, 2018. "Performance, combustion and knock assessment of a high compression ratio and lean-burn heavy-duty spark-ignition engine fuelled with n-butane and liquefied methane gas blend," Energy, Elsevier, vol. 158(C), pages 256-268.
    4. 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.
    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. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    7. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    8. Al-Bahrani, Loau Tawfak & Horan, Ben & Seyedmahmoudian, Mehdi & Stojcevski, Alex, 2020. "Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment," Energy, Elsevier, vol. 195(C).
    9. Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    10. 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).
    11. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(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. 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).
    14. 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).
    15. 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.
    16. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).
    17. Mauler, Lukas & Duffner, Fabian & Leker, Jens, 2021. "Economies of scale in battery cell manufacturing: The impact of material and process innovations," Applied Energy, Elsevier, vol. 286(C).
    18. Elshkaki, Ayman, 2020. "Long-term analysis of critical materials in future vehicles electrification in China and their national and global implications," Energy, Elsevier, vol. 202(C).
    19. 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.
    20. 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).

    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:appene:v:267:y:2020:i:c:s0306261920305936. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.