Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Singh, Harprinderjot; Kavianipour, Mohammadreza; Soltanpour, Amirali; Fakhrmoosavi, Fatemeh; Ghamami, Mehrnaz; Zockaie, Ali; Jackson, Robert

doi:10.1177/03611981221093625

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Journal Article · 11 June 2022 · Transportation Research Record: Journal of the Transportation Research Board

DOI:https://doi.org/10.1177/03611981221093625· OSTI ID:2417736

^[1]; ^[2]; Soltanpour, Amirali ^[2]; ^[2]; ^[2]; Zockaie, Ali ^[2]; ^[3]

Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI; OSTI
Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI
Michigan Department of Environment, Great Lakes, and Energy, Lansing, MI

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

Research Organization:: Michigan Department of Environment, Great Lakes, and Energy (EGLE), Lansing, MI (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: EE0008653

OSTI ID:: 2417736

Journal Information:: Transportation Research Record: Journal of the Transportation Research Board, Journal Name: Transportation Research Record: Journal of the Transportation Research Board Journal Issue: 11 Vol. 2676; ISSN 0361-1981

Publisher:: SAGE

Country of Publication:: United States

Language:: English

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