Predicting future capacity of lithium-ion batteries using transfer learning method

Abstract

Lithium-ion (Li-ion) batteries have numerous applications, such as electric vehicles, power tools, medical devices, drones, satellites, and utility-scale storage. Remaining useful life (RUL) prediction is a challenging task due to the highly nonlinear degradation behavior and prediction of batteries dealing with unknown future data. Online RUL prediction for Li-ion batteries plays an important role in proper battery health management. To improve the prediction accuracy of RUL, we propose a novel hybrid method based on transfer learning to predict the future capacity of Li-ion batteries. The proposed method consists of integrating an ensemble empirical mode decomposition algorithm, a support vector regression model, and a bidirectional long short-term memory with attention mechanism model to predict the state of health (SOH), where SOH is the battery cycle life and discharge capacity measured in number of cycles. In addition, we also consider keen-onset effects. The performance of RUL predictions using different starting RUL prediction points is compared with experiments. The proposed method shows that the larger the cycle number of the RUL prediction starting point, the more accurate the RUL prediction results. The relative error values for the three different charging policy target batteries using the proposed method are 6.96 %, 0.6 %, and 6.25 %, respectively. The proposed method is effective for predicting the future capacity of Li-ion batteries and can be applied to predictive maintenance.