Methodology

1. Profiling HGV journeys in Scotland to assess the representativeness of the sample data.
2. Obtaining new telematics and scheduling data of existing diesel HGV routes.
3. Creating synthetic data for sectors where telematics data is missing.
4. Analysing fleet routes using Agent-Based Modelling (ABM).
   1.  Create a core network of BEV charging locations based on existing and potential infrastructure.

   2. Map existing diesel truck routes onto the core network. A route “starts” when the truck leaves a depot and “finishes” upon arriving at a depot run by the same company.

   3. Create a simulation to investigate how BEVs would complete each route. Each vehicle selects the optimal en-route location from the core charger network.

   4. Primary truck routes are further analysed for other potentially high-value charge sites to begin creating an enhanced charger network.

   5. Charging locations are analysed to determine utilisation and electricity delivered.
5. Mapping impacts of charging on the electricity grid.
6. Reporting the findings.

Agent-Based Modelling (ABM)

Agent-Based Modelling (ABM) is a computer-based approach for simulating the behaviour of autonomous agents and their interactions. In this study, the agents are individual Battery Electric Vehicles (BEVs). Different rules can be applied to govern agent behaviour. For example, when a BEV agent requires charging, the rule is that it seeks locations that minimise both diversion time and distance from its original planned route. As these simulated agents interact according to these rules, collective phenomena such as charger usage, vehicle charge status, journey times, and other key information emerge. This approach is state of the art in complex systems problems like transport decarbonisation.