The implementation of a sustainable and efficient electric transportation network requires addressing multiple concerns such as: limited driving range and battery charging/discharging time. Nowadays, electric buses can travel up to 200KM on a full charge and the charging time varies depending on the technology from a couple of minutes (with fast-charging stations rated at 450 kWh) to hours (with slow-charging stations rated at 30 kWh). In order to address these limitations, we plan to use multiple AI technologies to optimize critical components for the electrification of the public bus transportation system.
In this context, SMARTeBuses plans to design and implement new algorithms to optimise critical components for the electrification of the public bus transportation system. First, we will use Big Data and Machine Learning to analyse the current operations of a selected set of bus routes in Ireland. Second, we will identify suitable locations for slow and fast charging stations satisfying a predefined set of power and space capacity constraints. Finally, we will use optimization technology (e.g., constraint programming, mixed integer linear programming, and meta-heuristic search) to provide robust and stable schedules to charge the electric fleet without overloading the national grid and maximizing the use of wind power.