e-MobilityVehicle Telematics

Spark EV launches artificial intelligence-based journey prediction telematics solution

Spark EV has launched its new artificial intelligence-based journey prediction telematics solution which would enable vehicles to complete more journeys between charges and remove range anxiety. The company claims that this would enable greater fleet utilisation, with up to 20% more journeys completed between charges.

A combination of easy to install sensor technology, cloud-based machine learning analysis software and a powerful smartphone app, Spark EV analyses live driver, vehicle and other data sources, such as the weather and congestion, and then uses its advanced AI software algorithms to increase the accuracy of journey predictions for electric vehicles. Using machine learning, Spark EV automatically updates predictions after each journey, continually improving efficiency.


Drivers and fleet managers simply enter their journey through the app, Spark EV’s web interface, or their existing fleet management software, and it advises whether they will be able to complete it, based on live data, previous trips and chargepoint locations. This delivers reassurance to fleet managers and drivers that they will be able to schedule and complete jobs without running out of charge, removing range anxiety while increasing the amount of potential vehicle journeys by an additional 2.8 per day. It even allows managers to add extra journeys or drop-offs to EV routes, based on their remaining capacity.

New legislative changes designed to cut vehicle pollution, combined with a growing public acceptance of EVs, are driving fleet operators, especially within the delivery, taxi, government and utility sectors, to increase their use of electric vehicles. For example, all new taxis licensed in London from 1 January 2018 will need to be zero emission, while the Ultra Low Emission Zone (ULEZ) will charge vehicles that do not meet strict environmental standards from 8 April 2019.

Delivered through a monthly subscription model, Spark EV easily integrates with existing fleet management/scheduling systems through its open API, or can be used as a standalone solution for smaller fleets and can be installed with all current EVs. It is already receiving strong interest in Scandinavia, where EV penetration is currently ahead of the UK.


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