Why is digital twin tech needed for EV batteries?

Digital twin is the solution for continuous battery monitoring for electric and plug-in hybrid vehicles, especially for operators of large fleets.
5 November 2021 | 1 Shares

Why is digital twin tech needed for EV batteries? Source: Silver Power Systems

  • A battery analytics firm Silver Power Systems (SPS) claims it has achieved the “holy grail of battery modelling” with a new platform that can predict the usable lifespan of an EV battery. 
  • Digital twin tech also offers insights, recommendations, and tools that can also optimize battery behavior and performance and predict potential breakdowns.

Digital twin, as the name would suggest, is a digital representation of a physical object, process, or even service. A digital twin tech allows for a virtual, real-time computerized double of anything. Without a doubt, it is one of the most important Industry 4.0 technologies currently available. 

That is because, more than a virtual simulation of a physical asset, digital twins also provide insights that can be used to make better decisions. Especially given the advancement of machine learning and factors such as big data, these virtual models have become a staple in modern engineering to drive innovation and improve performance.

While the technology is applicable to almost any industry, in recent years, digital twins have grown particularly popular within the electric vehicle sector.

A better EV battery lifespan with digital twin tech

Back in September this year, a battery analytics firm claimed that it had achieved the “holy grail of battery modelling” under a project known as the Real-Time Electrical Digital Twin Operating Platform (REDTOP). Silver Power Systems (SPS) claimed that the project has succeeded in creating the “world’s most advanced digital twins of actual EV batteries,” which could offer not only an accurate view of real-time battery performance and state-of-health but also the potential to predict battery lifespan. 

The new platform was invented by SPS in partnership with Imperial College, the London Electric Vehicle Company (LEVC), and JSCA, the research-and-development arm of the Cornwall-based Watt Electric Vehicle Company (WEVC). The ninth-month project gathered data from 50 LEVC TX range-extender taxis and a WEVC coupé electric sports car, which travelled collectively over 500,000km. 

Each vehicle was equipped with SPS’s own data-collection device, which communicates over-the-air with the company’s cloud-based software. The collected data was then analyzed by the company’s EV-OPS battery management platform and processed by Imperial College researchers.  

CTO Pete Bishop said, “This really is the holy grail. 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.”

The "holy grail of battery modelling"

The “holy grail of battery modelling”
Source: Silver Power Systems

For electric vehicle manufacturers, SPS said 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’ capabilities to predict the future residual value of vehicles based on future battery health,” the statement reads.

What makes the technology more interesting is that, on top of using a combination of real-world data, machine learning and the digital twin to predict future battery degradation, SPS said the technology can be used 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. 

SPS also noted that the new ‘digital twin’ development concept can be applied to any EV battery to predict battery lifespan, but never really mentioned when the tech will be commercialised. Perhaps, as the market transitions to EVs, this technology is set to become of ever-increasing importance.