AI modelling predicts battery health using raw data

Estimating battery state of health (SOH) without interrupting battery operations or undergoing lengthy charge/discharge cycles is currently a challenge. The team from Heriot-Watt’s Smart Systems Group worked with researchers from the CALCE group at the University of Maryland to come up with a solution that built algorithms from the first principles of battery degradation. The algorithms are fed with the raw battery voltage and current operational data and can provide accurate SOH readings under all conditions, irrespective of battery design and chemistry.

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"To date, the progress of data-driven models for battery degradation relies on the development of algorithms that carry out inference faster,” explained Darius Roman, the Heriot-Watt PhD student that designed the AI framework. “Whilst researchers often spend a considerable amount of time on model or algorithm development, very few people take the time to understand the engineering context in which the algorithms are applied.