Influence of charging behaviour given charging infrastructure specification: A case study of Singapore

Abstract

Electric vehicles (EVs) are set to play a crucial role in making transportation systems more sustainable. However, charging infrastructure needs to be built up before EV adoption can increase. A crucial factor that is ignored in most existing studies of optimal charging station (CS) deployment applying agent-based nanoscopic traffic simulation is the role played by the charging behaviour of drivers. In this study, through an agent-based traffic simulation, we analyse the impact of different driver charging behaviour under the assumption that CSs are placed at existing petrol stations and residential car park locations in Singapore. Three models are implemented: a simple model with a charging threshold and two more sophisticated models where the driver takes the current trip distance and existing CS locations into account. We analyse the effect of these three charging behaviour models on the performance of the charging infrastructure with respect to a number of different measures. Results suggest that charging behaviours do indeed have a significant impact on the simulation outcome. We also discover that the sensitivity of model parameters in each charging behaviour and initialisation parameters of the agents are an important factor to consider. Variations in model and initialisation parameters can lead to significant different results. In addition, we investigate into a different charging infrastructure distribution using a grid-based approach for Singapore. Results propose that a more evenly distributed charging infrastructure with the grid-based approach is less effective than the one with charging station placement at existing petrol stations and residential car park locations.