Implementation of megawatt charging system for battery electric heavy good vehicles using an agent-based model

Le Grand, Tangui, Dedduwa Pathiranage, Chandima, Alharbi, Fahd, Khan, Khalid and Cipcigan, Liana ORCID: https://orcid.org/0000-0002-5015-3334 2025. Implementation of megawatt charging system for battery electric heavy good vehicles using an agent-based model. Presented at: IET Powering Net Zero 2024, Birmingham, UK, 03-06 December 2024. IET Conference Proceedings. IET Conference Proceedings. , vol.2024 (32) Institution of Engineering and Technology (IET), pp. 282-287. 10.1049/icp.2024.4591

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Abstract

The UK’s commercial transport electrification necessitates the modernization of its charging infrastructure, particularly for electric heavy goods vehicles (HGVs). This study explores the implementation of megawatt fast-charging technology as a viable solution for long-haul battery-electric HGVs. Employing an agent-based model developed in AnyLogic, the research simulates the movement and battery dynamics of various electric truck models, including BYD 8 Day Cab, FreightLiner eCascadia, Scania R-Series, Volvo FH Electric, and Tesla Semi, with a focused analysis on the Scania R-Series at different speeds. Key findings reveal that the Tesla Semi, with its 900kWh battery, required only 58 minutes of enroute charging, significantly outperforming other models. Economic analysis indicated substantial cost savings, with electric HGVs operating at 22% to 27.5% of diesel fuel costs. Most vehicles adhered to the British government’s recommended stop pattern, pausing every 4.5 hours for 45 minutes. Speed significantly impacted energy consumption and charging needs, with a 34.87% increase in charging costs observed when driving at 65mph compared to 50mph. The study concludes that megawatt fast-charging can effectively support long-haul electric HGVs, especially those with large battery capacities. However, higher vehicle speeds exacerbate grid demand and elevate charging expenses, underscoring the need for optimized vehicle speed management in future infrastructure planning.

Item Type:	Conference or Workshop Item (Other)
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Institution of Engineering and Technology (IET)
Funders:	UKRI
Last Modified:	28 Mar 2025 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/176959

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