Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Wendimagegnehu, Yared Tadesse, Ayalew, Beshah, Ivanco, Andrej, Hailemichael, Habtamu
Format: Preprint
Published: 2026
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866916070710837248
author Wendimagegnehu, Yared Tadesse
Ayalew, Beshah
Ivanco, Andrej
Hailemichael, Habtamu
author_facet Wendimagegnehu, Yared Tadesse
Ayalew, Beshah
Ivanco, Andrej
Hailemichael, Habtamu
contents Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast charging at the depot. Findings indicate that at typical energy density of current battery technology, there is an optimal battery pack capacity that results from the contradicting effects of increasing pack capacity on cost, life span, weight and energy consumption. It is also observed that with currently improving trends in battery pack costs and availability of reduced electricity prices at the depot, such as with renewable microgrids, fleet electrification can become viable even at low levels of penetration.
format Preprint
id arxiv_https___arxiv_org_abs_2606_00792
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses
Wendimagegnehu, Yared Tadesse
Ayalew, Beshah
Ivanco, Andrej
Hailemichael, Habtamu
Optimization and Control
Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast charging at the depot. Findings indicate that at typical energy density of current battery technology, there is an optimal battery pack capacity that results from the contradicting effects of increasing pack capacity on cost, life span, weight and energy consumption. It is also observed that with currently improving trends in battery pack costs and availability of reduced electricity prices at the depot, such as with renewable microgrids, fleet electrification can become viable even at low levels of penetration.
title Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses
topic Optimization and Control
url https://arxiv.org/abs/2606.00792