This Guidance demonstrates how to use historical battery health data with artificial intelligence and machine learning (AI/ML) algorithms to improve the accuracy of battery State of Health (SoH) and Remaining Useful Life (RUL) estimations. Currently, these estimations largely rely on a static formula-based approach, which can provide near-term battery health information. Using this Guidance, automotive original equipment manufacturers (OEMs) can predict battery SoH and RUL into the future with easy-to-train AI/ML models built using historical data stored in the Cloud.
