We are entering a new era of mass market EV. In 2019, sales of electric cars reached 2.1 million globally and the global stock of electric cars registered a 40% year-on-year increase. There are now more than 7.2 million electric cars in the world – a far cry from the 17,000 driving around in 2010.
This growth has been boosted by expanding charging infrastructure and government subsidy schemes – particularly in Europe and China. However, the evolution of EV technology itself has had a big part to play in the burgeoning popularity of these vehicles.
Early technophile adopters might have put up with squeezing themselves into a tiny two-seater with minimal drive range – but not mass market consumers. These drivers expect a choice of models, a comfortable drive range, and fast-charging batteries.
Likewise, today’s EV drivers demand a smooth navigation experience that will reliably lead them to a suitable charging point when they need it. And that’s where things start to go wrong. Unfortunately, the standard of charge point data has not kept pace with the quality of electric vehicles, and this is leading to frustration and inconvenience for drivers.
So what’s the problem with charge point data, and how do we fix it?
Two Key Issues with EV Charge Point Data Today
EV navigation systems often rely on data sourced from individual CPO networks. This data isn’t always in a consistent format, contains errors, and leaves important data fields empty. When these inaccuracies are reflected in navigation systems, EV drivers will suffer a really poor user experience.
So, let’s look at two key types of data that are important for EV drivers navigating to a charging point:
There’s nothing worse for an EV driver who is low on juice than turning up at a charge point to find… well, it’s not there. Problems with the location data can derive from an inaccurate address or GPS location, resulting in the driver arriving at the wrong place.
This might only be a matter of 200 metres’ difference, as CEO Herbert Diess of Volkswagen found on a recent holiday, but it’s still an inconvenience to the tired driver in an unfamiliar location.
In the worst case scenario, inaccurate or incomplete location data can lead to the driver looking for a “ghost” charge point that doesn’t exist at all. This can occur, for example, when a navigation system is fed outdated data containing charge points that have since been moved elsewhere or removed entirely.
Another common problem with the data is a lack of clarity about a charging point’s accessibility. How do I know I can actually use that specific charge point on the map once I get there?
The data will usually correctly distinguish between a public charging point at a motorway service station and a private one in a homeowner’s garage (important caveat: even this is actually a problem in some areas), but the information can often be ambiguous when it comes to “semi-public” charge points.
If these aren’t clearly labelled, the EV driver may arrive expecting to charge their vehicle – only to find that the facility is restricted to employees in an office building or to the opening times of a restaurant.
When you’re driving alone late at night and you’re low on battery, the last thing you need is to turn up at a charge point which is locked up behind a gate. And nor do you want to arrive unannounced at a stranger’s house. Unfortunately, these are still surprisingly common scenarios for EV drivers. For example, in the Netherlands, almost half of the charge points that are marked as available for third party charging in practice are private or have access limitations.
These two issues – location and accessibility – are just two examples of inconveniences faced by EV drivers when it comes to charge points. Though they may seem trivial when looking at the big picture, these issues are extremely frustrating when somebody experiences them.
Problems like these will compound over time to build a poor reputation, get in the way of mass market uptake, and give sceptics a reason to doubt the potential of the industry.
Moving Towards a New Standard of Charge Point Data
Eco-Movement is actively solving the problem of poor-quality, inaccurate, and incomplete data about charge points. We take data from 100+ different CPO networks and refine it, filter out the noise, enrich it, and leave it in a standardised user-friendly format. Navigation providers and MSPs can then employ this data to provide a new, high-level experience for their EV driving customers.
How do we do this? With a vast quantity of charge point data to sift through, we use machine learning to compare multiple different fields at once and identify incorrect data points. Machine learning enables us to improve accuracy to an extent that would be impossible with manual analysis, with the algorithm only getting smarter over time.
For example, we can use machine learning to detect when a charging point is listed with the wrong charger power. By comparing the power and location fields, machine learning can flag up an issue when a motorway charging point is categorised as “low” power – when others in that type of location are typically “rapid” or “fast” chargers.
Beyond correcting the data, we can also enhance it for a superior navigation experience. For instance, when planning a journey it’s useful for the EV driver to know how busy a charging point will be at a certain time. By using our algorithms, based on years of real-time data on the charge point status, we can accurately predict the availability of a charge point.
Through improving data and adding to the information in smart ways, we allow industry stakeholders to provide the high-quality navigation experience that mass market EV drivers are demanding.
Summary: The Demands of Mass Market EV
Early adopters of electric cars endured all kinds of inconvenience in order to own the latest technology that helps them live in a more environmentally conscious way. But mass market EV drivers demand more consistency, convenience, and added value. And quite simply – they want to charge their vehicle with the same ease of filling up a petrol car.
However, this is only possible if EV navigation systems can rely on a new standard of charge point data – complete, accurate, consistent, and enriched.
Eco-Movement uses machine learning to produce the highest quality charge point data, helping all service providers in the EV industry to get ready for the decade of mass market EV.