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Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

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  • Jia, Zhenyu
  • Yin, Jiawei
  • Cao, Zeping
  • Wu, Lin
  • Wei, Ning
  • Zhang, Yanjie
  • Jiang, Zhiwen
  • Guo, Dongping
  • Zhang, Qijun
  • Mao, Hongjun

Abstract

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

Suggested Citation

  • Jia, Zhenyu & Yin, Jiawei & Cao, Zeping & Wu, Lin & Wei, Ning & Zhang, Yanjie & Jiang, Zhiwen & Guo, Dongping & Zhang, Qijun & Mao, Hongjun, 2025. "Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China," Applied Energy, Elsevier, vol. 377(PC).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924020099
    DOI: 10.1016/j.apenergy.2024.124626
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    as
    1. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    2. Ashley Nunes & Lucas Woodley & Philip Rossetti, 2022. "Re-thinking procurement incentives for electric vehicles to achieve net-zero emissions," Nature Sustainability, Nature, vol. 5(6), pages 527-532, June.
    3. 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.
    4. Mahmud, Khizir & Town, Graham E., 2016. "A review of computer tools for modeling electric vehicle energy requirements and their impact on power distribution networks," Applied Energy, Elsevier, vol. 172(C), pages 337-359.
    5. Zhou, Xiaochuan & Wu, Gang & Wang, Chunyan & Zhang, Ruijun & Shi, Shuaipeng & Zhao, Wanzhong, 2024. "Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions," Energy, Elsevier, vol. 301(C).
    6. Wang, Yanji & Li, Hangyu & Xu, Jianchun & Liu, Shuyang & Tan, Qizhi & Wang, Xiaopu, 2023. "Machine learning assisted two-phase upscaling for large-scale oil-water system," Applied Energy, Elsevier, vol. 337(C).
    7. Fernandes, P. & Tomás, R. & Ferreira, E. & Bahmankhah, B. & Coelho, M.C., 2021. "Driving aggressiveness in hybrid electric vehicles: Assessing the impact of driving volatility on emission rates," Applied Energy, Elsevier, vol. 284(C).
    8. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    9. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    10. Hiermann, Gerhard & Hartl, Richard F. & Puchinger, Jakob & Vidal, Thibaut, 2019. "Routing a mix of conventional, plug-in hybrid, and electric vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 235-248.
    11. Wager, Guido & Whale, Jonathan & Braunl, Thomas, 2016. "Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 158-165.
    12. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    13. Tao, Siyou & Ding, Ke & Li, Zhuoyun & Zhang, Hui, 2022. "Development of a representative driving cycle for evaluating exhaust emission and fuel consumption for Chinese switcher locomotives," Applied Energy, Elsevier, vol. 322(C).
    14. 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.
    15. Küng, Lukas & Bütler, Thomas & Georges, Gil & Boulouchos, Konstantinos, 2019. "How much energy does a car need on the road?," Applied Energy, Elsevier, vol. 256(C).
    16. 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).
    17. 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.
    18. 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).
    19. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    20. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
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