Comparative Sensitivity Analyses of Energy Consumption in Response to Average Speed Between Electric Vehicles and Conventional Vehicles: Case Study in Beijing, China

Understanding the sensitivity of vehicle energy consumption to average speed variations is critical for accurately assessing the environmental impacts of urban transportation systems. While the energy consumption patterns of conventional vehicles (CVs) have been extensively studied, the response characteristics of electric vehicles (EVs) and their fundamental differences from CVs remain insufficiently explored. This knowledge gap may lead to misguided policy interventions—for instance, implementing congestion mitigation strategies that may paradoxically increase EV energy demand. To address this research gap, we developed an energy consumption model based on vehicle-specific power (VSP) distribution analysis, calibrated with over 25 million second-by-second driving records from Beijing. The proposed comparative framework systematically evaluates the sensitivity of EV and CV energy consumption across different speed regimes. The results indicated that EV energy use exhibits a distinctive parabolic trend, with high energy use at both low and high speeds, and a notable increase beyond approximately 70 km/h. A case study indicates that, during the pandemic lockdown, which led to a significant increase in average speed, CV energy use generally decreased, whereas EV energy consumption increased. This discrepancy is primarily attributed to differences in energy consumption rates rather than variations in driving behavior, as reflected in VSP distributions.

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Publication Details

Journal
Energies
Published
2025-04-29
DOI
https://doi.org/10.3390/en18092268
Citations
3
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
2.19

Funders

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article

Comparative Sensitivity Analyses of Energy Consumption in Response to Average Speed Between Electric Vehicles and Conventional Vehicles: Case Study in Beijing, China

Lei Xue, Pengfei Fan, Yizheng Wu, Hongyu Lu et al.
3 citations
Energies
Electric Vehicles and Infrastructure
2.19
article

Comparative Sensitivity Analyses of Energy Consumption in Response to Average Speed Between Electric Vehicles and Conventional Vehicles: Case Study in Beijing, China

Lei Xue, Pengfei Fan, Yizheng Wu, Hongyu Lu, Rui Liu, Songsong Li, Guohua Song
article en
3 citations

Abstract

Understanding the sensitivity of vehicle energy consumption to average speed variations is critical for accurately assessing the environmental impacts of urban transportation systems. While the energy consumption patterns of conventional vehicles (CVs) have been extensively studied, the response characteristics of electric vehicles (EVs) and their fundamental differences from CVs remain insufficiently explored. This knowledge gap may lead to misguided policy interventions—for instance, implementing congestion mitigation strategies that may paradoxically increase EV energy demand. To address this research gap, we developed an energy consumption model based on vehicle-specific power (VSP) distribution analysis, calibrated with over 25 million second-by-second driving records from Beijing. The proposed comparative framework systematically evaluates the sensitivity of EV and CV energy consumption across different speed regimes. The results indicated that EV energy use exhibits a distinctive parabolic trend, with high energy use at both low and high speeds, and a notable increase beyond approximately 70 km/h. A case study indicates that, during the pandemic lockdown, which led to a significant increase in average speed, CV energy use generally decreased, whereas EV energy consumption increased. This discrepancy is primarily attributed to differences in energy consumption rates rather than variations in driving behavior, as reflected in VSP distributions.

EnergiesVol. 18(9)
Georgia Institute of Technology (US), Beijing Jiaotong University (CN), Ministry of Transport (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Electric Vehicles and Infrastructure
2.19
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