Validating an Electric Vehicle Infrastructure Location Model with Empirical Charging Data from an Existing Network

Validating an Electric Vehicle Infrastructure Location Model with Empirical Charging Data from an Existing Network

Nominated for The Planning for Sustainable Land Use and Transport Award


Laurence Chittock, Mott MacDonald, Dani Strickland, Aston University, Tom Van Vuren, Mott MacDonald


This paper describes the development and validation of a rapid charging location model. Knowledge is advanced by analysing empirical charging data and evaluating the strategic value of each location. Practical guidance for future progress discussed.


As the market for electric vehicles (EVs) grows so too does the need for a supporting charging infrastructure. With the provision of standard chargers at people’s homes and workplaces, studies throughout Europe have found that the range of an EV is sufficient for the majority of daily needs (Cocron et al., 2011; Technology Strategy Board UK, 2011). Despite this, EVs are struggling to reach the mainstream, in part, due to an insufficient public charging network and the inability to travel beyond their standard range (Huebner et al., 2013). As a means to address this and maximize the efficacy of investment, it is necessary to sufficiently plan the layout of a charging infrastructure so that locations are chosen which allow more EVs to expand their journey capability.

This paper demonstrates the development and validation of an infrastructure location model to support the charging needs of electric vehicles. Building on previous location models of this type (Kuby and Lim, 2005), this model is specifically tailored to meet the needs of electric vehicles and recommend locations for rapid chargers, which can provide range-extension options for an EV driver. Based on habitual journey patterns, the model is formed from an Origin-Destination (OD) network to reflect where there is greatest need for range-extension. By overlaying the demand from all routes in the network a hotspot map is generated to indicate potential strategic locations for the charging infrastructure.

As a means to evaluate this approach, empirical charging data from a set of existing rapid chargers in the UK is used as a comparison to the model outputs. Current charging locations are loaded into the model and expected demand is calculated by ‘capturing’ the OD flow in the network. For each route the most suitable charger is chosen and removed from the model to avoid cannibalization of demand assignment. The results are compared to observed usage at 25 rapid charging points throughout the Midlands, UK to help determine the suitability of the modelling approach. A correlation between the two datasets is calculated, with this suggesting the location model is fit-for-purpose. Furthermore, locations which do not fit the correlation well are analyzed and in some cases anomalies in the observed data are identified (such as the repeat use of a charger by a single driver, and instances of missing/misreported data). With these factors controlled for, the correlation improves to a statistically significant level. This work provides novel evidence on the usage of current charging infrastructure and suggests that the developed location model is suitable for practical use and can be used to guide the planning of future strategic charging networks throughout Europe. With a sufficient charging infrastructure in place the barrier of EV range can be mitigated, making electric vehicles a viable option and providing a path towards a more sustainable transport future.

The full paper will report on the work carried out, providing further explanation of the methodology and analysis of the results. Implications of the research will be presented with recommendations for implementation given to help guide practitioners in this area.


Association for European Transport