In the UK, two electric vehicle manufacturers, academics, and a battery analytics specialist company are collaborating on an electric vehicle battery research program designed to predict battery life span – and they say they’ve succeeded.
Battery life span
Swindon-based battery analytics specialist Silver Power Systems (SPS), Imperial College London, the London Electric Vehicle Company (which makes electric London black cabs), and JSCA, the research and development division of Watt Electric Vehicle Company, are collaborating on an EV battery research program called REDTOP that aims to predict battery life span.
With the battery being by far the most expensive component of an electric vehicle, it’s critical for all sectors – from original equipment manufacturers and battery manufacturers to fleet owners and operators – to understand how the battery is performing and predict how much it’s likely to degrade over the vehicle’s lifetime.
Until now, predicting a battery’s life span 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 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.
The Real-time Electrical Digital Twin Operating Platform (REDTOP) automotive research program’s objective is to create the world’s most advanced battery “digital twin.” In other words, it’s a virtual model linked to a real battery.
Since January, around 50 London Electric Vehicle Company TX electric taxis and a new electric sports car from the Watt Electric Vehicle Company have collectively traveled more than 500,000 km (310,686 miles) as part of the program.
Each vehicle was fitted with Silver Power Systems’ data-collecting IoT device, which constantly communicates with the company’s cloud-based software.
The data was then analyzed by Silver Power System’s machine learning-powered platform EV-OPS, and together with Imperial College’s battery researchers, digital twins of EV batteries were created. The twins give not just a view of real-time battery performance and state of health, but also the potential to enable the battery models to predict battery life span.
Battery monitoring gives a complete 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.
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). Fleet owners can use the study’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.
Original equipment manufacturers and battery manufacturers can use the technology to enable more accurately underwritten battery warranties, setting warranties on a new battery or managing risk on an existing battery. Other sectors that can benefit include insurance providers, transport authorities, councils, and even private EV owners that can benefit from having access to data on their own vehicle’s battery performance.
Liam Mifsud, program manager at Silver Power Systems, said:
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 optimize the performance of the battery as the cells age and maximize battery life. For all automotive sectors, the potential to improve battery performance and overall useable life is revolutionary.
Photo: Silver Power Systems
FTC: We use income earning auto affiliate links. More.
Subscribe to Electrek on YouTube for exclusive videos and subscribe to the podcast.