IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3178-d803086.html
   My bibliography  Save this article

Effects of High Ambient Temperature on Electric Vehicle Efficiency and Range: Case Study of Kuwait

Author

Listed:
  • Hidab Hamwi

    (Kuwait Institute for Scientific Research (KISR), Safat 13109, Kuwait)

  • Tom Rushby

    (Energy & Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO16 7QF, UK)

  • Mostafa Mahdy

    (Energy & Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO16 7QF, UK)

  • AbuBakr S. Bahaj

    (Energy & Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO16 7QF, UK)

Abstract

The use of electric vehicles (EVs) provides a pathway to sustainable transport, reducing emissions and contributing to net-zero carbon aspirations. However, consumer acceptance has been limited by travel range anxiety and a lack of knowledge about EV technology and its infrastructure. This is especially the case in hot and oil-rich areas such as Kuwait, where transport is predominantly fossil fuel-driven. Studying the effects of high ambient temperature on EV efficiency and range is essential to improve EV performance, increase the user base and promote early adoption to secure more environmental benefits. The ability to determine the energy consumption of electric vehicles (EVs) is not only vital to reduce travel range anxiety but also forms an important foundation for the spatial siting, operation and management of EV charging points in cities and towns. This research presents an analysis of data gathered from more than 3000 journeys of an EV in Kuwait representing typical vehicle usage. The average energy intensity and consumption of the car/kilometre travelled were calculated for each journey, along with ambient temperature measured by the vehicle. The analysis indicates that energy intensity reaches a minimum at a starting temperature between 22 °C and 23 °C. Energy intensity rises with decreasing temperature below this point and with increasing temperature above this point. The results show that many vehicle journeys started with high temperatures, with about half of journeys starting at 30 °C or above and approximately a quarter at 40 °C or above. Fitting a model to the empirical data for trip starting temperature and energy intensity, average efficiency is impacted at high car temperatures, with energy intensity modelled at 30 °C and 40 °C to be higher by 6% and 22%, respectively. These findings have implications for vehicle range, EV charging infrastructure and car storage and parking provision.

Suggested Citation

  • Hidab Hamwi & Tom Rushby & Mostafa Mahdy & AbuBakr S. Bahaj, 2022. "Effects of High Ambient Temperature on Electric Vehicle Efficiency and Range: Case Study of Kuwait," Energies, MDPI, vol. 15(9), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3178-:d:803086
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3178/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3178/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Alshawaf, Mohammad & Poudineh, Rahmatallah & Alhajeri, Nawaf S., 2020. "Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. Mostafa Mahdy & AbuBakr S. Bahaj & Philip Turner & Naomi Wise & Abdulsalam S. Alghamdi & Hidab Hamwi, 2022. "Multi Criteria Decision Analysis to Optimise Siting of Electric Vehicle Charging Points—Case Study Winchester District, UK," Energies, MDPI, vol. 15(7), pages 1-16, March.
    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. Daniel Rasbash & Kevin Joseph Dillman & Jukka Heinonen & Eyjólfur Ingi Ásgeirsson, 2023. "A National and Regional Greenhouse Gas Breakeven Assessment of EVs across North America," Sustainability, MDPI, vol. 15(3), pages 1-26, January.
    2. Andri Ottesen & Sumayya Banna & Basil Alzougool, 2023. "Women Will Drive the Demand for EVs in the Middle East over the Next 10 Years—Lessons from Today’s Kuwait and 1960s USA," Energies, MDPI, vol. 16(9), pages 1-24, April.

    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. 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.
    2. 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).
    3. Srivastava, Raj Shekhar & Kumar, Anuruddh & Thakur, Harishchandra & Vaish, Rahul, 2022. "Solar assisted thermoelectric cooling/heating system for vehicle cabin during parking: A numerical study," Renewable Energy, Elsevier, vol. 181(C), pages 384-403.
    4. Hong Gao & Kai Liu & Xinchao Peng & Cheng Li, 2020. "Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands," Energies, MDPI, vol. 13(8), pages 1-16, April.
    5. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    6. 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).
    7. Panagiotis Skaloumpakas & Evangelos Spiliotis & Elissaios Sarmas & Alexios Lekidis & George Stravodimos & Dimitris Sarigiannis & Ioanna Makarouni & Vangelis Marinakis & John Psarras, 2022. "A Multi-Criteria Approach for Optimizing the Placement of Electric Vehicle Charging Stations in Highways," Energies, MDPI, vol. 15(24), pages 1-13, December.
    8. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    9. Irfan Ullah & Muhammad Safdar & Jianfeng Zheng & Alessandro Severino & Arshad Jamal, 2023. "Employing Bibliometric Analysis to Identify the Current State of the Art and Future Prospects of Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-24, February.
    10. Irfan Ullah & Kai Liu & Tran Vanduy, 2019. "Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan," Sustainability, MDPI, vol. 11(3), pages 1-16, February.
    11. Amor Hamied & Adel Mellit & Mohamed Benghanem & Sahbi Boubaker, 2023. "IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region," Energies, MDPI, vol. 16(9), pages 1-21, April.
    12. 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.
    13. Wang, An & Tu, Ran & Gai, Yijun & Pereira, Lucas G. & Vaughan, J. & Posen, I. Daniel & Miller, Eric J. & Hatzopoulou, Marianne, 2020. "Capturing uncertainty in emission estimates related to vehicle electrification and implications for metropolitan greenhouse gas emission inventories," Applied Energy, Elsevier, vol. 265(C).
    14. Malladi, Satya S. & Christensen, Jonas M. & Ramírez, David & Larsen, Allan & Pacino, Dario, 2022. "Stochastic fleet mix optimization: Evaluating electromobility in urban logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    15. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    16. 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.
    17. 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.
    18. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    19. 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).
    20. 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.

    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:3178-:d:803086. 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.