By Tom Gray, transport market development lead at Ordnance Survey (OS)
From company car fleets to delivery vans and service vehicles, businesses are electrifying at pace to meet sustainability targets, reduce long-term costs, and prepare for tightening emissions regulations. But as commercial fleets move faster towards electrification, a key challenge is becoming increasingly clear: charging infrastructure is not always being deployed where it is needed most.
The UK’s charging network is expanding, but not in a way that consistently supports fleet operations. For vehicles running fixed routes, covering defined service areas or returning to homes and workplaces overnight, gaps in local provision quickly translate into higher costs, detours and lost time. Department for Transport figures show that in April 2026 the UK had 119,080 public electric vehicle (EV) chargers – 13% more than a year earlier. But month-on-month installations are becoming less consistent, which points to structural challenges in how and where infrastructure is planned.
To reach the Government’s minimum target of 300,000 public chargers by 2030, around 211,000 additional devices will need to be installed over the next four years. For fleets the core issue is not one of scalability, but instead, compatibility. Infrastructure must reflect operational realities, from route density and dwell times, to where vehicles are parked at the end of the day. This is where geospatial data becomes essential: providing a joined-up view of real-world vehicle journeys, parking access and likely charging pressure to inform infrastructural placement decisions.
Fleet electrification relies on charging across several environments: depots, workplaces, public networks and drivers’ homes. While depot investment is growing, many operating models still depend on overnight home charging, which is not always practical.
Drivers with driveways or off-street parking can charge at home, often at far lower cost than public alternatives. But millions of businesses and households, particularly in urban areas, terraced housing, or flats, do not have that option. This leaves fleets more exposed to the public network which can quickly become an operational issue.
Where local provision is limited, drivers are often pushed towards rapid or ultra-rapid chargers at motorway services or commercial sites. While essential for longer journeys, the electricity tariffs at these charge points are significantly higher, and can add time to daily operations due to reroutes.
Charging infrastructure is often built where deployment is easiest or commercially attractive, rather than where operational demand is greatest. As a result, some areas risk being overserved while others, particularly communities with limited off-street parking, remain underserved. For fleets, the result is straightforward: higher running costs, reduced vehicle utilisation and added pressure on drivers.
Planning EV infrastructure requires understanding how multiple datasets interact across a physical landscape. Housing types, parking access, traffic flows, grid capacity and travel patterns all influence how charging demand builds across different areas, and at different times. Geospatial data brings these factors together, turning fragmented inputs into clear decisions about where investment will have the greatest impact.
Better spatial planning also helps avoid costly deployment errors. Chargers installed in poorly chosen locations may remain underused, tying up investment that could have been directed elsewhere. At the same time, poorly planned installations can place unnecessary pressure on local electricity networks, triggering delays or expensive grid upgrades. For local authorities, this is increasingly about reducing risk as much as delivering coverage.
Ordnance Survey’s work reflects how mapping itself has evolved to support these challenges. Today, OS maintains a digital database containing more than 600 million location features across Great Britain, updated around 30,000 times every day. Artificial intelligence is now being used to extract insight from this data to support infrastructure planning.
Working with Transport for the North, Ordnance Survey developed a machine learning model to identify where households can and cannot access home or off-street EV charging. By analysing driveways across the UK, this model highlights neighbourhoods where reliance on public charging is likely to be highest and helps local authorities plan infrastructural investment based on real-world needs rather than assumptions.
As the UK moves towards its 2030 charging targets, success will not be measured by charger numbers alone. It will depend on whether the network supports reliable, efficient operation at scale. For fleets already leading the charge, this difference will be felt directly in cost, utilisation and day-to-day performance.
Biography: Tom Gray is transport market development lead at Ordnance Survey (OS), where he leads market strategy across the transport sector, with a focus on electric vehicle infrastructure, fleet decarbonisation and digital transport systems. He works closely with public sector bodies, fleet operators and industry partners to identify where geospatial data can better support infrastructure planning and operational decision-making.
Tom is also an EV owner, currently driving a battery electric vehicle for work and personal travel, giving him first-hand experience of workplace, home and public charging environments alongside his professional focus on infrastructure planning.
