Data-driven EV charging station location analysis and network planning

The rapid growth of the adoption of electric vehicles (EV) brings tremendous business opportunities for the expansion of the EV charging infrastructure. And it also works the other way around: an adequate charging network helps to further expedite the transition from combustion cars to electric vehicles.

This is why both the number and size of Charge Point Operators (CPOs) has increased significantly over the years. Some CPOs (including Blink Charging, ChargePoint, EVgo and soon Allego) went public to raise money to accelerate the growth of their business and charging network, as there are considerable investments involved. The costs associated with setting up EV charging stations are very high, so land, supply equipment, manpower, and electricity connection need to be funded. The average payback period for a charging station is over five years.

It is of vital importance that new charging units are installed at the most suitable locations, and, to achieve maximum utilisation and shorten the time for return on investment. Increased utilisation is proven to directly increase the margin on revenue from the charging station.

Strategic network planning is also of interest to governments, as they aim to promote adoption of electric driving and try to provide the best conditions to facilitate this. But developing an effective model for location analysis and charging network planning can be challenging due to the high amount of influential factors. Common questions and challenges are:

  • Where are the chargers located now?
  • What type of chargers are there? What is the available information on the dimensioning of the locations, the number of charging stations, fast/standard charging, etc.?
  • How many EV drivers can be expected in a certain district or region? And what are their driving patterns?
  • What is their charging behaviour?
  • Are they charging mainly at home, at work, on the street or at fast chargers along the highway?
  • What is the historic development of the charging infrastructure in the relevant region?
  • Which points of interest are nearby, to make a charging session more desirable?
  • What is the business model of the present operators in the region, and how do they price for charging?

By answering these questions, the CPO or governmental institution will be able to develop a solid understanding on which locations are best fit for maximising capacity utilisation of the charging stations. They will gain valuable insights into how many charging stations are needed and the type, for example, DC fast chargers or AC normal chargers.

Answering these questions and assessing the most suitable locations for installing new chargers is inherently a data-driven practice. Usually, the necessary data is available, but dispersed, and the data is very difficult to obtain. Fortunately, with Eco-Movement’s renewed reporting solution, consultancies, suppliers and governments can easily retrieve and leverage this data to support a successful location assessment.

With the Eco-Movement reporting solution, insights on the exact location and characteristics of all the charging stations in North America, wider Europe, and Oceania are easily derived. The determination of the density of charging stations in a specific region is a few mouse clicks away; analysts can even filter quickly on connector types (plugs), charger type (e.g. DC/AC), and the maximum power of the charger. Besides current information, the tool also provides historical insights on the trends and developments of the charging infrastructure in the region of interest.

The EV charging station sites should be located in areas which are convenient for drivers and near areas of high charging demands. That is why the Eco-Movement reporting solution not only focuses on the charging stations themselves, but also provides insights into the surrounding amenities; this gives an understanding of different points of interest around the existing charging stations and the reasons why people are actually charging there.

The tool also provides valuable insights in EV drivers’ charging behaviour by the use of Machine Learning models: are they charging mainly at home during the night, or at work during office hours, sporadically on public AC stations, or are they always on the road and need a quick recharge along the motorway?

Last but not least, price information is often of essential importance for analysts to get their predictions right. For this reason, Eco-Movement’s reporting tool provides a full overview of both, public ad hoc prices and electric Mobility Service Provider (eMSP) prices per charging plug. This also includes the historic data.

If you would like to know more about our data and how it can support your network planning efforts, please reach out to or via the contact form on our website.