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 to be able to understand how a 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 the same, or treated equally throughout their life - so they degrade at different rates underlining the need for real-world data to be combined with machine-learning based predictive technology to better understand how they degrade.
Run over the last nine months, the REDTOP (Real-time Electrical Digital Twin Operating Platform) automotive research programme has sought to bring about a step change in battery understanding, with the objective of creating the world’s most advanced battery ‘digital twin’ – a highly-sophisticated virtual model, linked to a real battery.
Part-funded by The Advanced Propulsion Centre UK (APC), the project, led by Silver Power Systems, has seen partners Imperial College, London Electric Vehicle Company (LEVC) and JSCA, the research and development division of the Watt Electric Vehicle Company, join forces on a real-world EV trial.
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.
This 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 now been created, giving not just a 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.”
Unparalleled monitoring will give 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 kind of battery ledger.
For electric vehicle manufacturers this monitoring capability gives insights into battery performance enabling them to accelerate the development of battery-powered vehicles. Fleet operators can gain a complete picture of EV health across their vehicle fleet enabling them to more efficiently run their vehicles (and potentially extend their life), while fleet owners can use SPS’s capabilities to predict the future residual value of vehicles based on future battery health. As the market transitions to EVs, this is set to become increasingly important.
OEMs and battery manufacturers can also use the technology to enable more accurately underwritten battery warranties, setting warranties on a new battery or managing risk on an existing battery, while other sectors who can benefit include insurance providers, transport authorities, councils and even private EV owners for whom having access to data on their own vehicle’s battery performance is beneficial.
