Skip to main content

Health and care organisation adopt Spark EV AI-based technology

UK-based health and care organisation Provide has adopted Spark EV’s artificial intelligence-based technology with the intention of removing range anxiety for drivers in its electric vehicle (EV) fleet rollout. The technology is said to enable the cars to complete 20% more journeys between charges. Called Spark, the system collects live driver, vehicle and other data sources through an in-car sensor. It uses cloud-based machine learning algorithms to provide more accurate journey predictions for EVs.
March 7, 2018 Read time: 2 mins

UK-based health and care organisation Provide has adopted Spark EV’s artificial intelligence-based technology with the intention of removing range anxiety for drivers in its electric vehicle (EV) fleet rollout. The technology is said to enable the cars to complete 20% more journeys between charges.

Called Spark, the system collects live driver, vehicle and other data sources through an in-car sensor. It uses cloud-based machine learning algorithms to provide more accurate journey predictions for EVs.

Fleet managers and drivers can enter their proposed journey into the solution’s smartphone app to obtain advice on whether they will be able to complete it based on live data, previous trips and charge point locations.

Provide is also implementing charge points five of its sites and is planning to expand its fleet of Nissan Leaf EVs. It will also use Spark’s improved journey predictions based on previous trips to optimise vehicle usage.

Spark is available through a monthly subscription and is intended to integrate with existing fleet management/ scheduling systems through its open application programming interface. It can also be used as a standalone solution for smaller fleets.

Philip Richards, executive finance director and company secretary, at Provide, said: “We understand the benefits of EVs in terms of increasing efficiency and demonstrating our green credentials to the communities we serve. At the same time our patients rely on us being in the right place at the right time, meaning it is vital that our EV fleet is used efficiently, without staff needing to worry about not being able to complete their schedules due to running out of charge. By quickly providing us with more accurate journey predictions Spark EV removes range anxiety and is therefore accelerating our EV adoption.”

Related Content

  • Econolite keeps an open mind
    May 11, 2021
    If we’re going to take advantage of new technologies to improve safety, collaboration at the traffic management cabinet edge is vital, thinks Eric Raamot of Econolite
  • Cooperative infrastructure systems waiting for the go ahead
    February 3, 2012
    Despite much research and technological promise, progress towards cooperative infrastructure system deployment is still slow. Here, Robert Cone and John Miles take a considered look at how and when it might come about. From a systems engineering viewpoint it looks logical and inevitable that vehicles should be communicating between themselves and with the road infrastructure. But seen from a business viewpoint the case is not proven.
  • Connexionz awarded contract to connect multiple transit agencies across three States
    November 22, 2017
    Provider of smart transit innovations Connexionz has been awarded a contract to deliver multi-agency regional passenger information system to connect several transport networks across three US States. It will initially manage and support seven partner agency fleets, with potential to scale and link up to 18 separate transport operators across Washington, Oregon and Idaho. Called iTransit NM it is designed with the intention of enabling passengers convenient access to real-time information on all rural and
  • Vehicle analytics market ‘to grow by 26 per cent by 2022’
    September 19, 2017
    A new market research report by MarketsandMarkets estimates that the market for vehicle analytics will grow from US$1124.1 million in 2017 to US$3637.4 million by 2022, at a Compound Annual Growth Rate (CAGR) of 26.5 per cent. According to the report, the major driving factor for this market remains advances in technologies, such as machine learning, artificial intelligence (AI) and predictive maintenance to enhance fleet management, as well as increasing use of real-time data collected from sensors and