IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v289y2024ics0360544223033716.html
   My bibliography  Save this article

Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data

Author

Listed:
  • Choi, Mingi
  • Cha, Junepyo
  • Song, Jingeun

Abstract

The reduction in driving range during winter is one of the major disadvantages of battery electric vehicles and hybrid electric vehicles. On-road driving tests showed that the fuel efficiency was 23.1 km/L at an ambient temperature of 20 °C, but it dropped to 20 km/L at 0 °C under the same driving conditions. Therefore, this study analyzed the effect of road load and battery internal resistance on fuel economy reduction during winter. The road load and battery internal resistance for various temperatures were extracted from on-road driving data using intuitive and simple methods. The results showed that the road load was the major factor that reduced the fuel economy of hybrid electric vehicles in winter, whereas the effect of battery internal resistance on fuel economy reduction was negligible. As the ambient temperature decreased from 20 °C to 0 °C, the energy loss due to road load increased by 2.1 kWh, while the energy loss due to battery internal resistance only increased by 0.0345 kWh. Although this study focused on hybrid vehicles, the methodology used can also be applied to battery electric vehicles.

Suggested Citation

  • Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033716
    DOI: 10.1016/j.energy.2023.129977
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223033716
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129977?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. Dian Wang & Yun Bao & Jianjun Shi, 2017. "Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-11, August.
    2. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. 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).
    4. Jingeun Song & Junepyo Cha, 2021. "Analysis of Driving Dynamics Considering Driving Resistances in On-Road Driving," Energies, MDPI, vol. 14(12), pages 1-16, June.
    5. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    6. Zou, Yuan & Wei, Shouyang & Sun, Fengchun & Hu, Xiaosong & Shiao, Yaojung, 2016. "Large-scale deployment of electric taxis in Beijing: A real-world analysis," Energy, Elsevier, vol. 100(C), pages 25-39.
    7. Wang, An & Xu, Junshi & Zhang, Mingqian & Zhai, Zhiqiang & Song, Guohua & Hatzopoulou, Marianne, 2022. "Emissions and fuel consumption of a hybrid electric vehicle in real-world metropolitan traffic conditions," Applied Energy, Elsevier, vol. 306(PB).
    8. Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    9. Anselma, Pier Giuseppe & Biswas, Atriya & Belingardi, Giovanni & Emadi, Ali, 2020. "Rapid assessment of the fuel economy capability of parallel and series-parallel hybrid electric vehicles," Applied Energy, Elsevier, vol. 275(C).
    10. Han, Lijin & Yang, Ke & Ma, Tian & Yang, Ningkang & Liu, Hui & Guo, Lingxiong, 2022. "Battery life constrained real-time energy management strategy for hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 259(C).
    11. Li, Da & Zhang, Lei & Zhang, Zhaosheng & Liu, Peng & Deng, Junjun & Wang, Qiushi & Wang, Zhenpo, 2023. "Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality," Energy, Elsevier, vol. 284(C).
    12. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    13. Cao, Jianfei & He, Hongwen & Wei, Dong, 2021. "Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario," Applied Energy, Elsevier, vol. 281(C).
    14. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    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. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    2. Xian Zhao & Siqi Wang & Xiaoyue Wang, 2018. "Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science," Sustainability, MDPI, vol. 10(10), pages 1-25, October.
    3. Foad H. Gandoman & Emad M. Ahmed & Ziad M. Ali & Maitane Berecibar & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "Reliability Evaluation of Lithium-Ion Batteries for E-Mobility Applications from Practical and Technical Perspectives: A Case Study," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    4. Chen, Shuang & Hu, Minghui & Guo, Shanqi, 2023. "Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles," Energy, Elsevier, vol. 273(C).
    5. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    6. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    7. Panchal, S. & Dincer, I. & Agelin-Chaab, M. & Fraser, R. & Fowler, M., 2016. "Experimental and simulated temperature variations in a LiFePO4-20Ah battery during discharge process," Applied Energy, Elsevier, vol. 180(C), pages 504-515.
    8. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    9. Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    10. Yashraj Tripathy & Andrew McGordon & Chee Tong John Low, 2018. "A New Consideration for Validating Battery Performance at Low Ambient Temperatures," Energies, MDPI, vol. 11(9), pages 1-16, September.
    11. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    12. Zhao, Yinan & Wen, Yifan & Wang, Fang & Tu, Wei & Zhang, Shaojun & Wu, Ye & Hao, Jiming, 2023. "Feasibility, economic and carbon reduction benefits of ride-hailing vehicle electrification by coupling travel trajectory and charging infrastructure data," Applied Energy, Elsevier, vol. 342(C).
    13. Anselma, Pier Giuseppe, 2022. "Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios," Applied Energy, Elsevier, vol. 314(C).
    14. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    15. Zhou, Min & Long, Piao & Kong, Nan & Zhao, Lindu & Jia, Fu & Campy, Kathryn S., 2021. "Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 134-152.
    16. Asadullah Khalid & Alexander Stevenson & Arif I. Sarwat, 2021. "Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed," Energies, MDPI, vol. 14(23), pages 1-14, December.
    17. Sandra Castano-Solis & Daniel Serrano-Jimenez & Lucia Gauchia & Javier Sanz, 2017. "The Influence of BMSs on the Characterization and Modeling of Series and Parallel Li-Ion Packs," Energies, MDPI, vol. 10(3), pages 1-13, February.
    18. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    19. Yun Bao & Yuansheng Chen, 2021. "Lithium-Ion Battery Real-Time Diagnosis with Direct Current Impedance Spectroscopy," Energies, MDPI, vol. 14(15), pages 1-16, July.
    20. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

    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:energy:v:289:y:2024:i:c:s0360544223033716. 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.journals.elsevier.com/energy .

    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.