Optimising Electric Bus Fleet Charging Using a Simulation-based Energy Consumption Model

The increasing dissemination of electric buses brings both benefits and challenges. Limited driving range, charging requirements, and uncertain energy consumption are significant obstacles that must be overcome. To address them, decision-makers use optimisation techniques and data-driven models to better plan and operate the fleets. However, obtaining the necessary data for these analyses can be difficult and timeconsuming. Simulation models can be used to have the necessary data reliably to overcome this limitation. In this paper, we propose an agent-based approach that combines a traffic simulation environment with an optimisation model to enhance the management of electric bus systems. The locally calibrated simulation model offers the speed profile of electric buses in Coimbra, Portugal. The resulting energy consumption data estimated by the simulation output are then integrated into the optimisation model to improve fleet operation, addressing data limitations in public transportation planning models. The study results indicate that the optimal charging plan can reduce the charging costs by 29% with respect to the cost of a 'business as usual' charging scenario. In addition, the energy consumption model based on the agent- based driving profile leads to a 16% higher energy consumption compared to the model using a constant speed.