Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries

TL;DR

This paper introduces a novel transfer learning approach using Vision Transformer for predicting the state of health in lithium-ion batteries, enhancing feature extraction and prediction accuracy over existing methods.

Contribution

It combines Vision Transformer with transfer learning to improve SOH prediction accuracy and effectiveness in battery management.

Findings

Outperforms existing deep learning methods in feature extraction.

Achieves better prediction accuracy and transferability.

Effective in early cycle SOH prediction.

Abstract

In recent years, significant progress has been made in transportation electrification. And lithium-ion batteries (LIB), as the main energy storage devices, have received widespread attention. Accurately predicting the state of health (SOH) can not only ease the anxiety of users about the battery life but also provide important information for the management of the battery. This paper presents a prediction method for SOH based on Vision Transformer (ViT) model. First, discrete charging data of a predefined voltage range is used as an input data matrix. Then, the cycle features of the battery are captured by the ViT which can obtain the global features, and the SOH is obtained by combining the cycle features with the full connection (FC) layer. At the same time, transfer learning (TL) is introduced, and the prediction model based on source task battery training is further fine-tuned according to the early cycle data of the target task battery to provide an accurate prediction. Experiments show that our method can obtain better feature expression compared with existing deep learning methods so that better prediction effect and transfer effect can be achieved.