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Charged up data: a case for open charge point usage data

Much has been said and written about the number of chargepoints, or the perceived lack thereof, in the UK[1]. Yes, it is certainly true that, as electric vehicles (EVs) become the norm, well need sufficient infrastructure both public and private to support these vehicles. Less has been written and said about the appropriateness of charge points and their locations. Less still has been said about how current charge points are utilised. Data, particularly open data, can be a powerful tool in understanding how we use transport. So, what can we find out about how charge points are being used, and what can we learn from it?

Some of the best charge point usage data comes from Transport Scotland which, since 2010, has developed its ChargePlace Scotland scheme to grow Scotlands charge point network. At the RAC Foundation I've produced a number of short reports analysing electric charge point usage in the ChargePlace Scotland network with the data being supplied by Transport Scotland. Our analysis examines charge point usage for each August from 2013 to 2017 and the results are very interesting.

For those less familiar with the different speeds of chargepoints they broadly fall into three categories and it is easiest to thinkabout them in terms of a typical EV such as a Nissan Leaf:

  • Slow chargers (up to 3kW) would charge this vehicle in about 6-8 hours
  • Fast chargers (7 to 22kW) in about 3-4 hours
  • Rapid chargers (43, 50 or 120kW) would take between 20-30 minutes

The interactive map below (Figure 1) shows the location and usage of the different types of chargepoints for Scotland in August 2017

Figure 1 (click to go to Tableau)

How charge points were used in Scotland in August 2017

At the end of August 2017, there were 1,133 charge points (2,089 connectors) serving approximately 5,500 EVs registered at that time in Scotland. Of these charge points 16 were rapid chargers.

In that month, there were a total of 37,433 charging sessions, an increase of 43 from August 2016 and an increase of 5,947 from the number of sessions which took place in August 2013 (619) over the same period (Aug 13 to Aug 17) the number of EVs grew by 1,425 (from 362). Half of all charging sessions in the ChargePlace Scotland network took place at rapid charge points with 31 and 18 of all sessions taking place at 7kW and 22kW charge points respectively.

The average electricity used also tells us something else rather interesting. A typical battery electric vehicle has a battery size of 30-40kWh. The median electricity consumed for all types of charge points was 6.9kWh per charging event which suggests, taking into consideration plug-in hybrids that have smaller batteries, that EV users are topping-up on the public charge point network rather than refuelling from a low charge.

The median electricity consumed was higher for rapid chargers at 8kWh potentially suggesting these charge points are used more when batteries are at a lower capacity.

Figure 2 below illustrates this in terms of the number of miles a median charging session would add to a first generation Nissan Leaf.

Figure 2: The number of miles a typical Nissan Leaf would be capable of from the median electricity usedin a session by charge point type.

So we should only install rapid chargers, right? Well, not quite.

Plugging into policy

An important consideration for the appropriate charge point type at a location is the dwell time of EV users. The dwell time refers to the time spent at the location for the purpose of charging however the time spent at a location is also dependant on other factors such as what services or shops are available at that location.

For example, at a retail centre car park people are likely to stay for as long as it takes to shop rather than their duration being dictated by their EV charging time. At locations where the vehicle is likely to be parked for longer periods, it is more appropriate to install fast chargepoints rather than rapids; the slow rate of charge is better for the battery, rapids are far more costly to install and require a greater consideration for local capacity and grid.

It is more effective to install rapid charge points in places where people will not need to stay for longer periods of time such as along trunk roads or in city centres where EV users want to quickly add more range to their vehicles. The data suggests that the rapid charge points are being used in an appropriate way the average duration of stay at a rapid charge point was just 32 minutes in August 2017. Whereas, Scottish EV users typically spent almost 5.5 hours at slower 22kW chargers and over 9 hours on average at 7kW charge points and typically use less electricity despite being plugged in for longer.

Open data?

Clearly there are interesting insights that can be drawn from charge point usage data that can be used to inform policy regarding appropriate charge point types at different locations. More data would allow for greater analysis of trends - both local and national - in how public charging infrastructure is used. Transport Scotland, funders of the ChargePlace Scotland network, have maintained ownership of the charge point usage data - making them a central hub for data and policy relating to charge point installation. In other parts of the UK, the charge point network ownership and operation is fragmented regionally and between private and public organisations making it more difficult to request charge point usage data.

There needs to be more clarity regarding the extent, and by whom, charge point usage data is held ideally with the view that some of the data should be made available for analysis. The recent Department for Transport publication of electric charge point analysis of rapid chargers for 26 local authorities in England is a step in the right direction but more work needs to be done on providing reliable and accurate evidence that can inform policy decisions to ensure appropriate public infrastructure inappropriate locations.

You can read the latest report on charge point usage in Scotland. The analysis and report were both generated using R.

[1] See for example