The time is now. With a perfect storm of government regulation, investment, and rising electric...
Great news, the UK has hit 50,000 chargers in 2023! But before we congratulate ourselves too much let’s remember there’s still a long way to go from 50k to 500k public EV Chargers. To reach that goal by 2030 lets do some simple mathematics – we need to be putting about 300 chargers in the ground each day, every day; so let’s celebrate the achievement but not the goal. Or let’s look at it another way: for the next seven years we need to achieve every year what we have achieved so far. As Dodona expands to the USA, the daily amount of chargers needed to be installed is about 1000 a day which presents an even bigger challenge.
If we want consumers and businesses to have the confidence to embrace the rEVolution, to transition to cleaner transportation then they need to know that there is a pure public charging network that supports those vehicles’ operation. As ⚡ Sam Clarke ⚡ Clarke has as his tagline -’I’ve come too far to come this far’ and we feel exactly the same way.
The key challenge CPOs continue to grapple with is achieving scale while maintaining accuracy in network planning (and individual site assessment). Our customers are growing at twice the market rate and yet we are still collectively and individually missing targets. So how can we meet the bigger targets when we’re missing the smaller ones?
Better collective buy in from stakeholders in the planning process
Achieving these ambitious targets requires buy in from all stakeholders. This includes CPOs, local authorities, financiers, and the broader community. Charge point deployment targets are part of a shared goal, all stakeholders must commit to doing their part in sharing information, resources and incentives to overcome these challenges. We believe that an evidence (data and Data Science) based approach remains one of the best ways to not only select the right sites but also speed up the approval and financing across the value chain.
Data based decision-making
Making informed decisions at scale demands comprehensive, accurate and recent data as well as harnessing Data Science in order that you have a Data Model that is arguably more valuable than the data itself. CPOs must leverage data analytics to understand usage patterns, predict current and future demand and optimise site selection. Achieving scale is not something to think about later, as along with accuracy, it has to be an essential part of a network planning approach.
AI models to make it scale
Planning for the present is difficult enough and planning for the distant future is that much harder. The scale of the task requires using advanced technologies. AI models should play a key role in scaling planning processes as they can analyse vast amounts of data, identify trends and optimise efficiency. The landscape is evolving and navigating it is a pretty much impossible task without the use of advanced technology.
A holistic approach to network planning is essential, considering every factor from traffic and demographic considerations to energy grid capacity and future EV adoption trends. Failing to do this will lead to more missed targets, wasted resources and insufficient infrastructure to support the shared goal. The challenge is difficult and we should not underestimate the enormity of what needs to happen, but we can help.
About Dodona Analytics
Dodona Analytics are Europe’s leading EV network-planning platform for charging infrastructure and work with some of the most ambitious and successful Charge Point Operators to help deploy many tens of thousands of chargers every year. As Data Scientists that are experts in Future Mobility we are changing the way we move people, goods and services and we are passionate about building a better future! We have recently launched the Dodona eMobility Platform for the USA, are already working with first American customers and we want to help drive the mobility rEVolution across the world.
This article was contributed by our expert content, communications and PR team.