Geographical Modeling of Charging Infrastructure Requirements for Heavy-Duty Electric Autonomous Truck Operations

Feyijimi Adegbohun, Annette von Jouanne (), Emmanuel Agamloh and Alex Yokochi
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Feyijimi Adegbohun: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
Annette von Jouanne: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
Emmanuel Agamloh: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
Alex Yokochi: Department of Mechanical Engineering, Baylor University, Waco, TX 76798, USA

Energies, 2023, vol. 16, issue 10, 1-17

Abstract: This study presents an analysis of the charging infrastructure requirements for autonomous electric trucks (AETs) in a specified geographical region, focusing on the state of Texas as a case study. A discrete-time, agent-based model is used to simulate the AET fleet and consider various model parameters such as trip distance/duration, the number of trips, and charging speeds. The framework incorporates unique properties of the Texas road network to assess the sensitivity of charging infrastructure needs. By synergizing electrification and automation, AETs offer benefits such as reduced carbon emissions, enhanced transportation safety, decreased congestion, and improved operational costs for fleets. By simulating daily trips and energy consumption patterns, an analysis of the charging infrastructure needs for cities along the Texas highway triangle formed by I-35, I-45 and I-10 revealed that the total charging energy and average charging power for these major cities ranges between 443~533 MWh/day and 18.5~22 MW, with costs in the range of USD $7.74~$15.93 million for each city, depending on charging infrastructure design and exclusive of any enhancements to the distribution grid infrastructure needed to support the charging infrastructure. This data-driven approach may be replicated for other regions by adapting the simulation parameters to allow policymakers and stakeholders to assess the charging infrastructure requirements and related investments needed to support the transition to electric and autonomous heavy-duty trucking.

Keywords: autonomous electric truck; charging infrastructure; grid requirements; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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