Centre of Applied Research Technology

A validated agent-based model for stress testing charging infrastructure utilization


Deployment and management of environmental infrastructures, such as charging infrastructure for Electric Vehicles (EV), is a challenging task. For policy makers, it is particularly difficult to estimate the capacity of current deployed public charging infrastructure for a given EV user population.

While data analysis of charging data has shown added value for monitoring EV systems, it is not valid to linearly extrapolate charging infrastructure performance when increasing population size. We developed a data-driven agent-based model that can explore future scenarios to identify non-trivial dynamics that may be caused by EV user interaction, such as competition or collaboration, and that may affect performance metrics. We validated the model by comparing EV user activity patterns in time and space. We performed stress tests on the 4 largest cities the Netherlands to explore the capacity of the existing charging network. Our results demonstrate that (i) a non-linear relation exists between system utilization and inconvenience even at the base case; (ii) from 2.5x current population, the occupancy of non-habitual charging increases at the expense of habitual users, leading to an expected decline of occupancy for habitual users; and (iii) from a ratio of 0.6 non-habitual users to habitual users competition effects intensify. For the infrastructure to which the stress test is applied, a ratio of approximately 0.6 may indicate a maximum allowed ratio that balances performance with inconvenience. For policy makers, this implies that when they see diminishing marginal performance of KPIs in their monitoring reports, they should be aware of potential exponential increase of inconvenience for EV users.

Reference Helmus, J. R., Lees, M. H., & van den Hoed, R. (2022). A validated agent-based model for stress testing charging infrastructure utilization. Transportation Research Part A: Policy and Practice, 159, 237-262. https://doi.org/10.1016/j.tra.2022.03.028
Published by  Centre for Applied Research Technology 1 May 2022

Publication date

May 2022


Michael H. Lees
Robert van den Hoed


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