This paper presents a simulation approach to assess the impact of changes to the charge point infrastructure and policies on Electric Vehicle (EV) user satisfaction, combining both market drivers with the practicalities of EV usage. An agent-based model (ABM) approach is developed where a large number of EVs, that represent the user population, drive within a region of interest. By simulating the driver’s response to their charging experience, the model allows large scale trends to emerge from the population to guide infrastructure policies as the number of EVs increases beyond the initial early adopter market.
The model incorporates a Monte Carlo approach to generate EV and driver agent instances with distinct characteristics, including battery size, vehicle type, driving style, sensitivity to range. The driver model is constructed to respond to events that may increase range anxiety, e.g. increasing the likelihood of charging as the driver becomes more anxious.
A charge point infrastructure and EV population scenario is simulated, including a queuing system for charge stations. The impact on EV driver satisfaction of new policies, including the number of charge points, power rating of charge points, pricing models, green energy providers and numbers of EVs is simulated. The driver satisfaction is assessed by combining a number of metrics, e.g. range anxiety metric, time spent in queues through to access to the desired brand or green energy.
As the number of EVs increase, the policies need to focus on the efficient use of existing charge points to maintain customer satisfaction. The study uses the results to consider the balance between the minimum requirements and value enhancing requirements for customer satisfaction.