Battery intelligence software could slash the time it takes a typical electric vehicle (EV) to charge by up to 25%.
Elysia, from WAE Technologies (WAE), uses a suite of algorithms to unlock extra performance from an EV battery.
The company estimates that a typical electric vehicle could rapid charge 25% faster if it was equipped from the factory with its software, reducing the time spent on a charger by 10 minutes.
Elysia commercial lead Joe Jones said: “To protect the life of the battery, an electric car currently leaves the factory with a predetermined set of parameters that govern its performance, including how quickly it can be rapidly charged.
“Elysia offers car makers a much more sophisticated approach, unlocking improved performance safely and with no impact on battery life. Our intelligent charging algorithms allow the Battery Management System (BMS) to understand what conditions have the potential to damage the battery, and then using this information stay closer to the peak charging rate for longer, without ‘overstepping’ these limits.
“By reducing the time spent on a rapid charger by 10 mins for a typical electric vehicle, Elysia can increase the number of cars each charger can service in a day. The benefit here being twofold: happier electric car owners and more efficient utilisation of the existing rapid charging network.”
Elysia is built upon a combination of AI and data science, fused with electrochemical modelling expertise. It is the first mass market software offering from WAE.
Elysia Embedded is a suite of battery management algorithms, which run locally on a vehicle’s BMS. The algorithms help OEMs to increase usable energy, enable faster charging, and maximise battery power, enabling them to maximise utilisation of the battery asset and enable greater flexibility in the services the battery can deliver, without compromising safety or battery life.
In applications to date, this has shown the ability to bring up to a 30% increase in battery life, whilst also enabling dramatically reduced fast charge times and higher peak power output when compared to traditional battery management algorithms.
