Domain Generalization Prognosis Method for Lithium-ion Battery State of Health with Transformer and Multi-kernel MMD

Yafei Zhu; Tianyi Guo; Xiang Li; Yewei Zhang; Wei Zhang

doi:10.37965/jdmd.2024.594

Authors

Yafei Zhu International Engineering College, Shenyang Aerospace University, Shenyang 110136, China
Tianyi Guo School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China https://orcid.org/0009-0008-9337-6475
Xiang Li Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China https://orcid.org/0000-0003-0569-2176
Yewei Zhang School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China
Wei Zhang School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China

DOI:

https://doi.org/10.37965/jdmd.2024.594

Keywords:

Battery health management, State of health prognosis, Transformer, Maximum mean discrepancy, Domain generation.

Abstract

In recent years, a number of intelligent algorithm have been proposed for forecasting the lithium-ion battery state of health (SOH). Due to the varying specifications and operating conditions of batteries, it is difficult to anticipate the health condition of lithium battery as it begins to deteriorate. There are still few studies on health state prediction models for different types of batteries. In this paper, 40 battery data from 5 public datasets are selected to carry out research, and a model architecture consisting of Denoising Autoencoder and Transformer is designed. One or two types of battery packs are identified as the source domain, and multiple types of battery packs are identified as the target domain. By employing Maximum Mean Discrepancy (MMD) on the Transformer architecture, the source and target domains were evaluated and found to converge as training continued. Finally, 29 transfer learning combination tasks were constructed. Results show that the model built with two kinds of batteries as the target domain has the best prediction accuracy and excels in prediction and is versatile in its application. The experimental results also reveal that this study provides a promising tool for predicting Lithium-ion batteries' SOH and strives to build a generalized model of the Lithium-ion batteries' SOH indicators.

Conflict of Interest Statement

The authors declare no conflicts of interest.