Thanks to their economic and ecological benefits, electric vehicles are surging in popularity. In 2021 alone, global EV sales hit a record $6.9 million, up 107 percent from 2020. As governments around the world look to provide manufacturers and consumers with incentives to curb emissions, these numbers will only climb.
Charging stations for EVs are also multiplying. In the U.S. alone, the number of sites has increased by 38 percent in just the last year. Similar trends can be tracked internationally. In 2021, for example, publicly available EV charging stations rose by 40 percent the world over.
Organizations hoping to capitalize on this explosion in demand need the right tools to analyze, plan, and implement effective EV charging networks. The main driver for this is location data, which can help organizations to select sites that serve the most customers. Let’s dive into the variables that should be considered for a successful EV charging site, then into how geospatial data can supercharge the selection process.
Despite the rising number of EV charging stations, demand for them still outweighs supply. So, as organizations rush to expand their EV charging networks, what indicators should they consider for site selection?
When evaluating a potential location, developers need to take into account a variety of factors, including the site’s proximity to other points of interest. For example, it’s not only important for an EV charging station to be next to major roads and highways. Site traffic is also likely to rise if the location is close to well-equipped facilities, such as bathrooms, cafes, gyms, and supermarkets. As EV charging is often time-consuming, it’s critical to provide would-be customers with places to wait, shop, or relax as their batteries fill.
Other, more granular factors offer routes for maximizing ROI on EV site development. For example, it’s advisable to break ground on locations that take advantage of the benefits of peak-valley electricity pricing. It’s also helpful to choose a site that’s well connected to grid infrastructure, as doing so saves money on equipment costs and fortifies capital investment.
Finally, EV charging station developers need to remain aware of the competition. For each site under consideration, how far away are competing stations? Are they public or private? Do they offer Level 1, Level 2, or Level 3 (DC and Tesla Supercharging) capabilities? Knowing these details can be the difference between capturing market share and over- or underinvesting.
As EVs hit the road every day, developers need tools for making smart site selections for charging stations that will allow them to grow their networks at scale.
Because of this, when evaluating a potential site, companies regularly turn to various forms of data to make decisions, including data on grid infrastructure, road networks, land use, and vehicle adoption. They might also use publicly available datasets on charging networks, such as those provided by the U.S. Department of Energy.
Another holistic solution for planning EV charging networks is location data. Different forms of geospatial insights — for example, mobility data, which shows where people go, or point of interest, or POI, data, which shows where businesses and other sites of interest are — allow companies to shift decision making into a higher gear.
With mobility data, it becomes possible to track traffic patterns (and by extension road infrastructure) in a given region. This data can be paired with POI data, which details how a potential site might benefit from its proximity to local attractions, businesses (including the competition), or housing density. Together, these indicators make it possible to develop global competitive intelligence that promotes return on site investment.
Imagine that a developer wants to expand their network in Los Angeles. Looking at accurate mobility and POI datasets, they discover that the area surrounding the popular shopping hub The Grove is surprisingly underserved and decide to find a site nearby. With this data, they’re also capable of visualizing the competition, understanding how traffic flows in and out of the area, and pinpointing the perfect address to intercept customers
Unfortunately, these various forms of data are often difficult to make sense of and to use. For instance, a company might struggle to derive insights from the sheer number of data sources available. As they attempt to make insight-forward decisions, they might encounter inaccuracies across sources or out-of-date information. More damaging is when these same businesses unknowingly act on bad data, which might lead them to build stations in suboptimal locations, creating long-term gaps in revenue opportunities.
For this reason, it’s crucial that organizations shore up site selection by first verifying the accuracy of their data or enhancing it themselves. Once they’ve done so, they can confidently take their place in the green revolution.