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.