Multi-scenarios transferable learning framework with few-shot for early lithium-ion battery lifespan trajectory prediction

Abstract

Capturing the lifespan trajectory of lithium-ion (Li-ion) batteries in the early stage is critical for the operation and maintenance of battery energy storage systems (BESSs). Recently, data driven model is a promising solution to implement this task, yet the battery's early cycling stage can only provide very limited information in the training phase and the scenarios of the BESS applications are not immutable. To alleviate the above issues, this paper proposes a multi-scenario transferable learning framework with few-shot to predict the Li-ion battery lifespan trajectory. An easily constructed model is chosen to generate various pseudo trajectories from only the full lifespan trajectory of a single cell. Then, a transferred deep learning method integrating the gate recurrent unit (GRU) and one-dimensional convolutional neural network (1D CNN) is proposed to utilize the pseudo curves, whose training ability can be adjusted to a new scenario using simply the first 100 cycling data and a pre-trained strategy. Finally, the proposed framework can accurately predict the whole Li-ion battery aging trajectory for multi-scenarios in two different datasets.