Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning

Abstract Accurate forecasting of state-of-health and remaining useful life of Li-ion batteries ensure their safe and reliable operation. Most previous data-driven prediction methods assume the same distributions between training and testing batteries. Because of different operating conditions and electrochemical properties of batteries, however, distribution discrepancy exists in real-world applications. To address this issue, we present a deep-learning-based health forecasting method for Li-ion batteries, including transfer learning to predict states of different types of batteries. The proposed method simultaneously predicts the end of life of batteries and forecasts degradation patterns with predictive uncertainty estimation using variational inference. Three types of batteries are used to evaluate the proposed model; one for source and the others for target datasets. Simulation results reveal that the proposed model reduces efforts required to collect data cycles of new battery types. Further, we demonstrate the generality and robustness of the proposed method in accurately forecasting the state-of-health of Li-ion batteries without past information, which applies to cases involving used batteries.