Use Cases
Footfall

Solving EV range anxiety with smarter location data

The solution to range anxiety lies not just in bigger batteries, but a charging infrastructure built on location intelligence.

3 min read
- Published on
May 9, 2025

Why is range anxiety an issue?

EVs aren’t a new thing. Roads across North America, Europe, and Asia are populated with them at a growing rate. China is leading the charge with countless new brands and cars rolling onto highways domestic and abroad at a fast rate. In Europe, manufacturers like BYD and Zeekr have established a strong foothold amongst its European competitors and pushing EV adoption further.

Despite its popularity, a common issue among EV owners is range anxiety. This is the fear that an EV vehicle won’t have sufficient charge to reach a destination. It’s a layered issue that goes beyond battery capacity. It’s about infrastructure confidence. If drivers aren’t sure they can find a charging station when and where they need it, they won't buy an EV.

Home EV charging alleviates range anxiety for those who can afford it, a reality not everyone shares. Developing a robust EV infrastructure is crucial for widespread adoption and eliminating range anxiety. James May, an EV owner known for co-hosting the automotive shows Top Gear and Grand Tour, views range anxiety as a significant problem that will worsen without proper infrastructure and technological advancements. 

From his viewpoint, it’s less about range anxiety and more about recharge anxiety. Long road trips are planned around charging stations, and it shouldn't be that way. Compared to a petrol-engined car, EVs take longer to charge, have less range, and the charging point infrastructure isn’t where it needs to be. While charging time and range have to do with battery technology, charging point infrastructure is all about location. 

The approach to infrastructure planning isn’t about putting chargers everywhere. Charging stations should be located in the right places, where people need them most. Location data and mobility insights play a critical role in tackling this aspect of range anxiety. 

Infrastructure gaps aren’t always visible

EV Infrastructure planning often relies on high-level demographic data, traffic volume, and anecdotal and outdated data. This can lead to underused stations in low-demand areas, gaps in underserved communities, and inefficient investments that don’t solve the range anxiety problem. 

Smarter location data, smarter planning

Smart location data starts with POI data. This data, layered with foot traffic, dwell time, and commuter patterns, lets planners and automakers gather key insights. This includes identifying true demand areas and understanding where and when charging is actually needed (e.g., overnight in residential areas vs during the day near workplaces or shopping centers). Combined, these insights help optimize placements to serve real-world user behavior. 

At Echo, we’ve seen research institutes and manufacturers leverage granular POI and mobility data to develop EV charging station infrastructure in the US. For a particular use case, a leading research institute wanted to place EV charging stations for urban commuters and underserved communities. With Echo’s data, they could optimize their infrastructure planning, achieving a balance between sustainability and equitable access, reducing EV range anxiety for everyone.

Optimize strategy with foot traffic insights.
Optimize strategy with foot traffic insights.

Benefits of smarter EV infrastructure with location data

Location data is a must-have for EV ecosystems

The growing adoption of electric vehicles underlines an urgent need for a robust and strategically planned charging infrastructure. 

Developments in battery technology will help extend vehicle ranges and are sure to mitigate the range anxiety issue. However, as of right now, range anxiety remains a significant barrier for many potential EV adopters. Overcoming this challenge requires a strategic shift in infrastructure planning, moving beyond rudimentary demographic data. 

Granular POI data, enriched with mobility patterns such as foot traffic data, offers a transformative approach. A data-driven methodology that allows the identification of true demand centers. It reveals not just where chargers are needed, but also when they are most wanted. Higher utilization rates hinge on optimizing charging station placement based on real-world behavior. It reduces the risk of stranded drivers and fosters greater confidence in the EV ecosystem.

A location-data-driven approach directly addresses range anxiety. It can enable access to charging facilities, extending the benefits of electric mobility in underserved communities. The integration of high-quality geospatial data into infrastructure planning will transition it from a valuable tool to an absolute necessity. 

Collaboration between OEMs, governments, and charging network providers obviously has a large role in this as well. All parties need the right data to make collaborative decisions and build an EV future where range anxiety is seen as a problem from a bygone era.  

The solution to range anxiety lies not just in bigger batteries, but in the data-driven confidence that a charging solution is readily available, precisely where and when it's needed.

If you’re planning EV infrastructure or advising stakeholders, Echo Analytics can help. Our POI + mobility insights turn planning into precision.

Authors
Marc Kranendonk
Content Manager
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Use Cases
Footfall

Solving EV range anxiety with smarter location data

The solution to range anxiety lies not just in bigger batteries, but a charging infrastructure built on location intelligence.

3 min read
- Published on
May 9, 2025

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