Optimization of Electric Truck Charging Infrastructure Planning Considering GHG Emissions

Compared with widely adopted passenger Electric Vehicles (EVs), electric trucks (e-trucks) have high requirement for charging infrastructure and low operational efficiency (such as long charging time and frequent charging stops), which hinders market adoption. A reliable and easily accessible charging infrastructure network can facilitate this transformation from traditional truck fleets to electric alternatives. This paper proposes a charging infrastructure location optimization strategy to develop such charging stations network for e-trucks. Candidate charging station locations of e-trucks are identified using ArcGIS and evaluated based on various factors such as accessibility, grid network, power supply, and corridors priority. Besides, the source of electricity used to refill e-trucks' batteries will impact the overall GHG emissions during the operation stage of e-trucks. To ensure the lowest overall GHG emissions of e-trucks, energy mix of power supplier is incorporated to evaluate the overall environmental impact. A mixed integer programming model is formulated to minimize the total economic investment and GHG emissions. The model is then applied to investigate operation of commercial e-truck fleet in Arizona. Other managerial insights are further discussed.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/naps61145.2024.10741622
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.21
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article

Optimization of Electric Truck Charging Infrastructure Planning Considering GHG Emissions

Neng Fan, Hongyue Jin, Weiliang Deng
1 citations
Electric Vehicles and Infrastructure
0.21
article

Optimization of Electric Truck Charging Infrastructure Planning Considering GHG Emissions

Neng Fan, Hongyue Jin, Weiliang Deng
article en
1 citations

Abstract

Compared with widely adopted passenger Electric Vehicles (EVs), electric trucks (e-trucks) have high requirement for charging infrastructure and low operational efficiency (such as long charging time and frequent charging stops), which hinders market adoption. A reliable and easily accessible charging infrastructure network can facilitate this transformation from traditional truck fleets to electric alternatives. This paper proposes a charging infrastructure location optimization strategy to develop such charging stations network for e-trucks. Candidate charging station locations of e-trucks are identified using ArcGIS and evaluated based on various factors such as accessibility, grid network, power supply, and corridors priority. Besides, the source of electricity used to refill e-trucks' batteries will impact the overall GHG emissions during the operation stage of e-trucks. To ensure the lowest overall GHG emissions of e-trucks, energy mix of power supplier is incorporated to evaluate the overall environmental impact. A mixed integer programming model is formulated to minimize the total economic investment and GHG emissions. The model is then applied to investigate operation of commercial e-truck fleet in Arizona. Other managerial insights are further discussed.

University of Arizona (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Electric Vehicles and Infrastructure
0.21
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Optimization of Electric Truck Charging Infrastructure Planning Considering GHG Emissions — Neng Fan, Hongyue Jin, et al. · (2024) | TGRS Research Map | TGRS