Personalised Electric Vehicle Routing Using Online Estimators

In this paper, we develop a novel approach to help drivers of electric vehicles (EVs) plan charging stops on long journeys. A key challenge here is eliciting the highly heterogeneous preferences of drivers. Here we develop an intelligent personal agent that learns preferences through multiple interactions. To minimise the cognitive burden on the driver, we propose a novel technique which applies a small-scale discrete choice experiment to interact with the driver. Specifically, the agent provides drivers with several routes with possible combinations of charging stops based on their latest beliefs about the driver’s preferences. Then, through subsequent iterations, the personal agent learns and refines its beliefs about the driver’s preferences. It suggests better routes closer to the driver’s preferences. We evaluated our novel algorithm with real preference data from EV drivers, showing that our approach converges quickly to the optimal routes after only a small number of queries.