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https://doi.org/10.26434/chemr...
Article . 2023 . Peer-reviewed
License: CC BY
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Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis

Authors: Kariana Moreno Sader; Sayandeep Biswas; Rob Jones; Marian Mennig; Reza Rezaei; William H. Green;

Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis

Abstract

This work presents a costing and emissions analysis of long-haul battery electric trucks (BETs) with overnight charging for the U.S. market. First, we compute the energy requirements of a long-haul truck for a 600-mile real-world driving range and perform battery sizing. The battery sizes are used along with a fleet-replacement model and the U.S. payload distribution to compute payload losses for two different chemistries, Nickel-Manganese-Cobalt (NMC) and Lithium-Iron-Phosphate (LFP). Given present battery energy densities, BET fleets will have 1.06 and 1.27 times the trucks of a diesel fleet to provide the same cargo capacity. Next, we perform electricity pricing analysis for direct current fast charging (DCFC) applications. Our baseline scenario estimates a price of 0.32 USD/kWh, and it only decreases to 0.15 USD/kWh for the optimistic scenario. Currently, we compute the total cost to society for BETs to be more than twice (>2x) that of diesel trucks, however, the price premium is projected to decrease significantly to 1.2x in the long term. BETs could become economically competitive with diesel if the delivered cost of electricity for DCFC applications drops below 0.08 USD/kWh, and we realize the battery energy density and cost improvements projected for the coming decades. Our emissions analysis shows negligible present-day greenhouse gas (GHG) benefits from switching to BETs, primarily due to grid and battery manufacturing emissions. However, we project a 62% reduction in GHG emissions for BETs in a few decades from now. Today, BETs are not well-suited for the long-haul trucking business. However, they may be attractive for short-haul applications or with new business models such as battery swapping, and we encourage further investigation. We provide our analysis framework as a Google Colab Notebook that can be modified to assist these needed future studies.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
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