Spark EV launches to increase productivity for electric vehicle fleet
Aimed at fleet operators moving to electric vehicles (EVs), Spark EV today
launched its new artificial intelligence-based journey prediction telematics
solution. Spark EV enables vehicles to complete more journeys between
charges, removes range anxiety and enables 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.
“Fleet managers understand that the future increasingly revolves around
electric vehicles, due to new legislation coming into force around the
world, a move away from diesel and rapid growth in EV sales,” said Justin
Ott, CEO, Spark EV Technology (pictured). “However, existing methods of
predicting range between charges are not accurate enough for fleet use,
leading to range anxiety and a consequent drop in productivity as managers
cut back the number of journeys to avoid potentially running out of power.”
“Spark EV increases productivity through more accurate predictions that
enable 20% more journeys between charges, enabling EVs to deliver greater
efficiency for businesses, while meeting tightening emissions legislation,”
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.
About Spark EV Technology
Driven by air quality concerns, global legislation is accelerating the
adoption of electric vehicles (EVs). However, fleet operators face multiple
challenges with EVs, including range anxiety, ensuring high utilisation
rates and optimising productivity.
Built specifically for fleets, Spark EV Technology helps operators overcome
these issues. An artificial intelligence-powered journey prediction and
route planning solution, it uses machine learning to deliver more accurate
range predictions, enabling operators to increase journeys by 20%, removing
range anxiety and maximising driver and vehicle time.
Developed in Cambridge, UK, Spark EV combines the company’s own research
with work from the University of Essex. Available now, it is already
receiving strong interest across Scandinavia. A regional finalist in the
Shell Springboard competition and an alumni member of Accelerate Cambridge,
Spark EV Technology has received investment from the Low Carbon Innovation
Spark EV Technology
Maximising utilisation for electric vehicles ...
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