Machine learning takes charge

The research from the team at Oxford takes a completely new approach to estimating the lifespan of a battery. Traditionally you'd fully charge a battery, use it, then see how much that affects the maximum capacity. Once you've done that a few times you can predict how long your battery will last by extending that same decrease in maximum capacity forward in time.

The new approach takes real-world battery information from many batteries to train a machine learning model to make better predictions.


It turns out that predicting battery life is not straightforward, especially when a battery is middle-aged or nearing expiration.

There are many factors that cause a battery to lose functionality and these can come into play at different points in its lifespan.

High currents, high temperatures and the passage of time all contribute to battery degradation. The harder you use a battery, the faster it will deplete, but the way in which a battery is used throughout its lifetime can change too. For example, downloading a TV series on your mobile phone will run it down much faster than making a phone call, but you're probably not going to be doing that everyday.

Oxford University's model incorporates all these variables and expresses the results in terms of 'cell capacity' - a well-recognised indicator of battery health.

Fluent took their core application, built in Python, and 'surfaced' the outputs from calculations in a series of visualisations that encourage experimentation and interaction. We implemented the software so it could expand and accommodate future research findings.

The project was given a new, more user-friendly identity - ‘Terminal Q’ - which, it is hoped, will help generate interest on the topic of battery degradation beyond academic and industry circles.

TerminalQ factors designed by Fluent, Cambridge

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