Electric car manufacturer BYD has announced that it is installing electric vehicle (EV) chargers with a maximum output of 1360 kW in China – over 4000 of them – allowing electric cars to be charged as quickly as petrol cars. These ‘megawatt flash chargers’ can deliver around 250 miles of range in five minutes to BYD EVs that can accept that level of power – which is over four times faster than the typical chargers.
Even if BYD doesn’t install its chargers in the UK, there are plans to install chargers here with similar power for HGVs. But such chargers can take a long time to get grid connections in the UK – a number of years in some cases. So, what can be done to speed up the installation timescales – and to reduce the costs?
One solution to reduce the size and the cost of the grid connection for new charging infrastructure is ‘load balancing’ – distributing power across multiple charging stations to optimise charging capacity and reduce the load on the grid. However new technology that is more effective for managing megawatt and ultra rapid chargers has now been launched, harnessing artificial intelligence (AI) to deliver more intelligent energy compression than traditional load balancing.
The new technology from CrowdCharge can reduce the need for grid upgrades by using digital twin simulation during the planning stage of new charging projects, as well as helping to reduce energy costs and emissions during the operational phase. It can also optimise the revenue that can be made by providing grid services from charging projects that include elements such as battery storage and solar. The CrowdCharge technology has already been proven in real world settings.
Mike Potter, CEO of CrowdCharge, commented: ‘Megawatt chargers have the potential to revolutionise how quickly you can charge next generation electric cars, as well as electric trucks, but the installation of such chargers can be subject to significant delays due to grid connections taking a number of years in many cases. But thankfully there is a solution: new AI simulator technology can create a digital twin of a future project, allowing installers to reduce the scale of new grid connections, save energy costs, and unlock revenue from grid services.’
The new technology from CrowdCharge enables organisations such as charge point operators, fleets and local authorities to view a ‘digital twin’ of their low carbon technologies, with the ability to add new elements and see the impact on installation costs. The simulator can then be used to optimise existing and planned infrastructure development, before connecting new energy assets, potentially reducing project costs and timelines, as well as lowering emissions and achieving savings on energy bills.
The innovative CrowdCharge platform can evaluate many different complex variables, including multiple sites, reducing the need for grid upgrades for locations, and simulating the impact of vehicle to grid (V2G) charging. By optimising energy use and maximising flexibility, CrowdCharge can also reveal the scale of new revenue opportunities from grid services.
The AI powered technology with patented optimisation tools, developed via world leading research within the energy industry, helps to unlock maximum value from V2X (vehicle to everything) services across individual vehicles to fleets and workplace ecosystems, enabling organisations to better predict and manage usage, saving money and carbon. Organisations looking to save money on new energy infrastructure projects can arrange a free 30 day trial of the simulator by contacting CrowdCharge.
CrowdCharge is a partner in the Innovate UK V2VNY vehicle to grid (V2G) project which is trialling V2G using AC (alternating current), a more affordable solution than vehicle to grid charging using DC (direct current). CrowdCharge technology was previously validated in the Electric Nation smart charging and V2G projects.
CrowdCharge is a sister company to DriveElectric, a leading EV leasing broker specialising in supporting the transition to electric vehicles for organisations on their journey to net zero.
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