Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase

Hai-chao Huang, Hong-di He and Zhong-ren Peng

Energy, 2024, vol. 293, issue C

Abstract: Ride-hailing Electric Vehicles (EVs) offer a dual benefit by mitigating carbon emissions and providing convenient transportation solutions. Nevertheless, current emissions estimation models for urban-scale lack consideration of the real-world travel behaviors during operational phase. This study extracted the characteristics of ride-hailing EVs in Shanghai and Shenzhen based on 14.77 million real-world driving records, and established an emission estimation model. The emission estimation model delineates the relationship between speed State of Charge (SOC) and operational efficient of ride-hailing EVs in cities. Compared to traditional methods relying on manufacturer-claimed energy consumption rates, the developed model demonstrates a 38.4% increase in accuracy. It reveals that ride-hailing EVs deviate from the optimal speed in approximately 90% of their trips under the current urban transportation system. A 5 km/h increase in ride-hailing EV speed can improve emission reduction potential, cutting annual emissions by 0.89 ktCO2 in Shanghai and 4.74 ktCO2 in Shenzhen. Additionally, when a ride-hailing EV operates with SOC between 10 and 30%, emissions are 21.4%–36.0% higher compared to SOC between 70 and 90%. At last, the model derives emission factors for ride-hailing EVs during operational phase in Shanghai and Shenzhen are 0.2635 and 0.1535 10−5MtCO2kWh−1km−1

Keywords: Ride-hailing electric vehicles; Carbon emissions; Data-driven; Urban scale (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224004377

DOI: 10.1016/j.energy.2024.130665

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