Predicting battery end of life from solar off-grid system field data using machine learning

Off-grid solar-battery systems provide clean electricity, enabling education and enterprise. However, these systems are in remote areas, and it can be difficult to replace failed batteries. To improve reliability and cost-effectiveness, a noninvasive method to estimate battery health is required. We demonstrate how realworld operating data may be used to infer health and detect end of life. This work highlights the opportunity to analyze field data with machine learning to understand battery aging. Antti Aitio, David A. Howey