Digital twin ‘capable of predicting EV battery lifespan’

Electric Vehicles, News

A real-life trial of electric vehicles, involving battery analytics specialist Silver Power Systems (SPS), EV manufacturers and academics, claims to predict EV battery lifespan accurately.

With the rapid growth in electrification driven by the 2030 ban on new ICE (Internal Combustion Engine) 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.

Run over the last nine months, the pioneering 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 sophisticated virtual model, linked to a real battery. 

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’ data-collecting IoT device, which constantly communicates with the company’s cloud-based software.

This 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, Silver Power Systems claims. 

“This really is the holy grail,” explains 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.”


Chris Price
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