Applying data analytics and artificial intelligence techniques for optimal design and operation for electrification of the public bus transportation system

Project Insights

  • €202,250

    Total Project Costs
  • 3 yr

    Project Duration
  • 2019

    Year Funded

Project Description

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, this research team plan to use multiple AI technologies to optimise critical components for the electrification of the public bus transportation system. In this context, they plan to design and implement new algorithms to optimise critical components for the electrification of the public bus transportation system. First, by applying Big Data to analyse the current operations of a selected set of routes. Second, by identifying suitable locations for slow and fast charging stations satisfying a pre-defined set of power and space capacity constraints. And finally, using optimisation technology (e.g., constraint programming, mixed integer linear programming, and metaheuristic search) to provide robust and stable schedules to charge the electric fleet without overloading the national grid and maximising the use of wind power.

Project Details

Total Project Cost: €202,250

Funding Agency: SEAI

Year Funded: 2019

Lead Organisation: University College Cork (UCC)

Alejandro Arbelaez | Lead Researcher(s)