  Hundreds of millions of people lack access to electricity. Decentralised
solar-battery systems are key for addressing this whilst avoiding carbon
emissions and air pollution, but are hindered by relatively high costs and
rural locations that inhibit timely preventative maintenance. Accurate
diagnosis of battery health and prediction of end of life from operational data
improves user experience and reduces costs. But lack of controlled validation
tests and variable data quality mean existing lab-based techniques fail to
work. We apply a scaleable probabilistic machine learning approach to diagnose
health in 1027 solar-connected lead-acid batteries, each running for 400-760
days, totalling 620 million data rows. We demonstrate 73% accurate prediction
of end of life, eight weeks in advance, rising to 82% at the point of failure.
This work highlights the opportunity to estimate health from existing
measurements using `big data' techniques, without additional equipment,
extending lifetime and improving performance in real-world applications.
