The objective of this research is to design and implement an optimization model that integrates the UK national road network with charging station infrastructure to provide intelligent charging path planning for heavy-duty electric trucks. The model is established to overcome the challenges of range limitations, charging costs, operational efficiency and robustness against extreme events, like power outrage, traffic accident and flood in heavy-duty electric truck transport.

• A simplified UK motorway – charging station road network is constructed.
• A parameterized routing algorithm is developed to determine optimal route and schedule.
• Restricted areas are supported so that affected regions can be excluded and alternative routes can be automatically re-selected.

• Contributes a new integrated modelling framework for heavy-duty EV routing, combining large-scale GIS data, optimization, and AI techniques.
• Offers logistics companies a practical tool for minimizing costs and downtime, supporting the large-scale adoption of electric trucks.
• Provides insights for policymakers on charging infrastructure deployment, road freight electrification strategies, and investment planning.
• Supports decarbonization of transport by enabling efficient utilization of charging infrastructure, reducing operational emissions, and promoting energy-efficient logistics.

This project develops an intelligent path planning system for heavy-duty electric trucks, leveraging a national-scale roadmap & charging station optimization model. It progresses to scalable path planning algorithms, and culminates in advanced features such as multi-objective optimization, AI technology, and fleet-level coordination. The outcomes will serve academia, industry, and policy alike, accelerating the electrification of freight transport and contributing to the Net-Zero goals.

A Multi-agent Simulation Model for Fleet Electrification. Ameer Mustafa, Carolina Veiga, Maurizio Albano, Liana Cipcigan, Omer Rana, Ashish Sharma, Rigel Gjomemo. Transportation Research Symposium. May 2025, Rotterdam, Netherlands.

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