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

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

Applied Energy, 2025, vol. 377, issue PC, No S0306261924020099

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.

Keywords: Energy consumption; Vehicle electrification; Driving cycle database; Plateau city; Machine learning; Markov chain (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124626

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