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The issue of ‘range anxiety’ is commonly recognised as a stumbling block to the transport system achieving full electrification. There is not yet the charging infrastructure or sufficient battery power to allow battery-electric vehicles (BEVs) to operate for long distances – something of particular concern for �����������’s haulage industry customers.

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����������� is working with UK-based start-up Spark EV Technology, which has developed a machine-learning model for energy and range prediction. It can accurately calculate how much energy a vehicle will use to complete a certain route, or how far a vehicle can travel in any direction based on its circumstances.

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“Range accuracy, vehicle specification and an effective charging strategy can have a significant impact on the commercial viability of a transition to a BEV fleet. We are harnessing machine learning to bring essential insights to drivers and fleet operators,” says Richard Welford, Chief Commercial Officer of Spark EV Technology.

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“It’s a sophisticated model that takes a lot of different data points and perspectives into consideration,” adds Erik Bråvander. “Such as how the vehicle and driver are behaving, traffic conditions, the topography of the route and the impact of the weather.”

Israeli company Autofleet helps asset-heavy fleets such as delivery & logistics fleets, taxi fleets and ride-sharing services optimise the usage of their vehicles. When it comes to electrification Autofleet’s platform is being used by fleets to plan their transition to electric vehicles and optimise vehicle routing and charging in live operations.

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“As part of this project, Autofleet has access to historical, real-life data from a combustion engine fleet of a ����������� customer, and using Autofleet’s platform, we are able to understand the impact of electrifying these heavy vehicles on the operation and make data-driven decisions on the optimal electrification plan for the fleet,” explains Erik Bråvander.

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The goal is that this will help �����������’s customers see which vehicles and routes could be electrified, and where there are advantages in using a ����������� BEV vehicle.

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“We are excited to partner with ����������� on this project, leveraging Autofleet’s optimisation platform, Fleet Planning Simulator and our team’s experience, this time in the context of heavy vehicle fleets and their path to electrification,” says Kobi Eisenberg, Autofleet’s CEO and Co-founder.

By providing smart charging services to EV drivers in partnership with OEMs, charger manufacturers and energy utilities. In this project their data modelling team are looking at the optimisation of charging and charging infrastructure placement, both in terms of charging depots and public charging facilities.

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“ev.energy also has access to the same customer data,” says Bråvander, “but its model looks at driving patterns and tries to help ����������� figure out where the best places would be for installing new charging hardware and perform charging in the smartest way possible.”

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“We’re delighted to be supporting ����������� and their customers with the transition to electrification,” adds William Goldsmith, Head of Grid & Data Services at ev.energy. “Applying our established expertise in domestic EV charging to commercial fleets is an exciting opportunity.”

“Combient Foundry helps quicken the time to market of new innovations and means companies can work together to help our customers shape their electrification strategies.” says Erik Bråvander. ��

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“Ultimately, it can be a catalyst to the transition that ����������� and the transport industry must make to achieve a sustainable transport system.”