A world-first, real-life trial of electric vehicles, involving leading battery analytics specialist Silver Power Systems (SPS), EV manufacturers and academics, has brought closer” the holy grail of battery modelling“, as a press release states: accurately predicting EV battery lifespan.
With the rapid growth in electrification driven by the 2030 ban on new ICE sales combined with the battery being by far the most expensive component of an EV, it is critical for all sectors – from OEMs and battery manufacturers to fleet owners and operators – to understand how the battery is performing and predict how much it is likely to degrade over the vehicle’s lifetime.
Until now, predicting lifespan has been difficult. While digital models of EV batteries have been created, they have lacked accurate real-world data to back them up. What’s more, not all batteries are born equal, and not all batteries are treated equally throughout their life, degrading at different rates, subject to different drivers and charging routines, further underlining the need for real-world data to be combined with machine-learning based predictive technology.
Since January, some 50 LEVC TX electric taxis and a new EV sports car from the Watt EV Company have collectively travelled over 500,000km as part of the programme. Each vehicle has been fitted with Silver Power Systems’ state-of-the-art data-collecting IoT device, which constantly communicates with the company’s cloud-based software
All the crucial data has been analysed by SPS’s machine learning-powered platform EV-OPS, and together with Imperial College’s battery researchers, the world’s most advanced digital twins of actual EV batteries have been created, giving not just an unprecedented view of real-time battery performance and state-of-health, but also the potential to enable these highly sophisticated battery models to predict battery lifespan.
“This really is the holy grail,” explained Pete Bishop, CTO of Silver Power Systems. “Understanding how an electric vehicle’s battery is performing right now – and predicting how it will perform over the coming years – is absolutely critical for many sectors. But to date there has been a lack of data and predictive modelling has been largely lab-based. By combining a robust real-world trial with our EV-OPS machine-learning analytics capability through the REDTOP programme, we have not only been able to unlock an unprecedented view of real-time battery performance and state-of-health but also create the world’s most advanced digital twin enabling prediction of battery future life.”
SPS’s technology has enormous benefits for a wide variety of sectors. Unparalleled monitoring gives a total picture of battery activity, identifying differences between batteries (whether performance or charging capability) and – in the long term – building up a complete picture of battery health over the life of the vehicle: a battery ledger.
Liam Mifsud, Program Manager, Silver Power Systems added: “On top of using a combination of real-world data, machine learning and the digital twin to predict future battery degradation, we can use this technology to update an EV’s software via the cloud to change algorithms or parameters to optimise the performance of the battery as the cells age and maximise battery life. For all automotive sectors the potential to improve battery performance and overall useable life is revolutionary.”