Early prediction of remaining useful life for lithium-ion batteries based on CEEMDAN-transformer-DNN hybrid model

Yuxiang Cai; Weimin Li; Taimoor Zahid; Chunhua Zheng; Qingguang Zhang; Kun Xu

doi:10.1016/j.heliyon.2023.e17754

Early prediction of remaining useful life for lithium-ion batteries based on CEEMDAN-transformer-DNN hybrid model

Heliyon. 2023 Jul 3;9(7):e17754. doi: 10.1016/j.heliyon.2023.e17754. eCollection 2023 Jul.

Authors

Yuxiang Cai^{1

2}, Weimin Li², Taimoor Zahid³, Chunhua Zheng², Qingguang Zhang^{1

2}, Kun Xu²

Affiliations

¹ Department of Materials Science and Engineering, Southern University of Science and Technology, 518055, Shenzhen, China.
² Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
³ College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Pakistan.

Abstract

A reliable and safe energy storage system utilizing lithium-ion batteries relies on the early prediction of remaining useful life (RUL). Despite this, accurate capacity prediction can be challenging if little historical capacity data is available due to the capacity regeneration and the complexity of capacity degradation over multiple time scales. In this study, data decomposition, transformers, and deep neural networks (DNNs) are combined to develop a model of RUL prediction for lithium-ion batteries. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used for battery capacity sequential data to account for the capacity regeneration effect. The transformer networks are leveraged to predict each component of capacity regeneration thus improving the model's ability to handle long sequences while reducing the amount of data. The global degradation trend is predicted using a deep neural network. We validated the early prediction performance of the model using two publicly available battery datasets. Results show that the prediction model only uses 25%-30% data to achieve high accuracy. In the two public data sets, the RMSE errors were 0.0208 and 0.0337, respectively. A high level of accuracy is achieved with the model proposed in this study, which is based on fewer capacity data.

Keywords: CEEMDAN; Capacity regeneration; Deep neural networks; Early prediction; Lithium-ion batteries; RUL; Transformer.