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Fleet benefits predicted from battery analytics trial results

Date: 22 September 2021   |   Author: Sean Keywood

A real-world trial has been conducted of battery analytics technology which could lead to accurate predictions of EV battery lifespan, it has been said.

The trial has involved battery analytics company Silver Power Systems (SPS), along with Imperial College, LEVC, and JSCA, the research and development division of Watt Electric Vehicle Company.

According to SPS, until now predicting battery lifespan has been difficult, as while digital models of EV batteries have been created, they have lacked accurate real-world data to back them up.

In addition, batteries degrade at different rates due to differences in driving style and charging routines.

Over the past nine months, the real-time electrical digital twin operating platform (REDTOP) trial has seen 50 LEVC TX electric taxis and a Watt electric sports car travel over 500,000km while fitted with SPS data-gathering devices.

The resulting data has been analysed by SPS's machine learning-powered platform EV-OPS, and with help from Imperial College researchers, the world's most advanced 'digital twins' of actual EV batteries are said to have been created.

This is said to provide not only an unprecedented view of real-time battery performance and state-of-health, but also the potential to enable these models to predict battery lifespan.

According to SPS, the technology would allow fleet operators to get a complete picture of battery health across their fleet, enabling them to run their vehicles more efficiently and potentially extend their life.

In addition, fleet owners could use SPS's capabilities to predict the future residual value of vehicles based on future battery health.

SPS 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.

"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."