IDEAS home Printed from https://ideas.repec.org/a/gam/jcltec/v5y2022i1p2-37d1012577.html
   My bibliography  Save this article

A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation

Author

Listed:
  • Quynh T. Tran

    (Hawaii Natural Energy Institute, University of Hawai’i at Mānoa, Honolulu, HI 96822, USA
    Institute of Energy Science, Vietnam Academy of Science and Technology, Hanoi 10000-04, Vietnam)

  • Leon Roose

    (Hawaii Natural Energy Institute, University of Hawai’i at Mānoa, Honolulu, HI 96822, USA)

  • Chayaphol Vichitpunt

    (Digital Strategy Department, Provincial Electricity Authority, Chatuchak, Bangkok 10900, Thailand)

  • Kumpanat Thongmai

    (Power Economic Policy Department, Provincial Electricity Authority, Chatucha, Bangkok 10900, Thailand)

  • Krittanat Noisopa

    (Provincial Electricity Authority KhokKham Branch, Samut Sakhon 74000, Thailand)

Abstract

EV development is being prioritized in order to attain the target of net zero emissions by 2050. Electric vehicles have the potential to decrease greenhouse gas (GHG) emissions, which contribute to global warming. The driving range of electric vehicles is a significant limitation that prevents people from using them generally. This paper proposes a comprehensive model for calculating the amount of energy needed to charge EVs for a scheduled trip. The model contains anticipated consumption energy for the whole trip as well as contingency energy to account for unpredictable conditions. The model is simple to apply to various types of electric vehicles and produces results with sufficient precision. A number of driving tests with different road characteristics and weather conditions were implemented to evaluate the success of the proposed method. The findings could help the users feel more confidence when driving EVs, promote the usage of EVs, and advocate for the increased use of green and renewable energy sources.

Suggested Citation

  • Quynh T. Tran & Leon Roose & Chayaphol Vichitpunt & Kumpanat Thongmai & Krittanat Noisopa, 2022. "A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation," Clean Technol., MDPI, vol. 5(1), pages 1-13, December.
    • Handle: RePEc:gam:jcltec:v:5:y:2022:i:1:p:2-37:d:1012577
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8797/5/1/2/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-8797/5/1/2/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Enjian Yao & Zhiqiang Yang & Yuanyuan Song & Ting Zuo, 2013. "Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-7, December.
    2. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli, 2021. "Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions," Energies, MDPI, vol. 14(4), pages 1-15, February.
    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. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    2. 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.
    3. Triluck Kusalaphirom & Thaned Satiennam & Wichuda Satiennam, 2023. "Factors Influencing the Real-World Electricity Consumption of Electric Motorcycles," Energies, MDPI, vol. 16(17), pages 1-11, September.
    4. 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.
    5. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    6. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    7. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).
    8. Ioannou, Petros & Giuliano, Genevieve & Dessouky, Maged & Chen, Pengfei & Dexter, Sue, 2020. "Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes," Institute of Transportation Studies, Working Paper Series qt3ns4b894, Institute of Transportation Studies, UC Davis.
    9. Scarinci, Riccardo & Zanarini, Alessandro & Bierlaire, Michel, 2019. "Electrification of urban mobility: The case of catenary-free buses," Transport Policy, Elsevier, vol. 80(C), pages 39-48.
    10. Graba, M. & Mamala, J. & Bieniek, A. & Sroka, Z., 2021. "Impact of the acceleration intensity of a passenger car in a road test on energy consumption," Energy, Elsevier, vol. 226(C).
    11. Wang, Zihao & Ge, Hongxia & Cheng, Rongjun, 2020. "An extended macro model accounting for the driver’s timid and aggressive attributions and bounded rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    12. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    13. Ioannou, Petros & Chen, Pengfei, 2023. "Centrally Coordinated Schedules and Routes of Airport Shuttles with LAX Terminals as Application Area," Institute of Transportation Studies, Working Paper Series qt6gg7r6c5, Institute of Transportation Studies, UC Davis.
    14. Jiao, Yulei & Ge, Hongxia & Cheng, Rongjun, 2019. "Nonlinear analysis for a modified continuum model considering electronic throttle (ET) and backward looking effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    15. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.
    16. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    17. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    18. Dwivedi, Pankaj Prasad & Sharma, Dilip Kumar, 2023. "Evaluation and ranking of battery electric vehicles by Shannon’s entropy and TOPSIS methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 457-474.
    19. 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.
    20. Jarosław Mamala & Michał Śmieja & Krzysztof Prażnowski, 2021. "Analysis of the Total Unit Energy Consumption of a Car with a Hybrid Drive System in Real Operating Conditions," Energies, MDPI, vol. 14(13), pages 1-16, July.

    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:jcltec:v:5:y:2022:i:1:p:2-37:d:1012577. 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.