Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Abstract

Knowing the long-term degradation trajectory of Lithium-ion (Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system (BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health (SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell’s health at a very early stage of Li-ion battery usage. A transferred convolutional neural network (CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.