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

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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.
    8. 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).
    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. 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).
    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.
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