Data-driven estimation of battery state-of-health with formation features

He，Weilin1; Li，Dingquan2; Sun，Zhongxian1; Wang，Chenyang3; Tang，Shihai3; Chen，Jing3; Geng，Xin3; Wang，Hailong3; Liu，Zhimeng3; Hu，Linyu4; Yang，Dongchen5; Tu，Haiyan1; Lin，Yuanjing4; He，Xin1,3

2024-07-01

10.1088/1361-6439/ad520c

Journal of Micromechanics and Microengineering   影响因子和分区

0960-1317

1361-6439

Accurately estimating the state-of-health (SOH) of a battery is crucial for ensuring battery safe and efficient operation. The lifetime of lithium-ion batteries (LIBs) starts from their manufacture, and the performance of LIBs in the service period is highly related to the formation conditions in the factory. Here, we develop a deep transfer ensemble learning framework with two constructive layers to estimate battery SOH. The primary approach involves a combination of base models, a convolutional neural network to combine electrical features with spatial relationships of thermal and mechanical features from formation to subsequent cycles, and long short-term memory to extract temporal dependencies during cycling. Gaussian process regression (GPR) then handles SOH prediction based on this integrated model. The validation results demonstrate highly accurate capacity estimation, with a lowest root-mean-square error (RMSE) of 1.662% and a mean RMSE of 2.512%. Characterization on retired cells reveals the correlation between embedded formation features and their impact on the structural, morphological, and valence states evolution of electrode material, enabling reliable prediction with the corresponding interplay mechanism. Our work highlights the value of deep learning with comprehensive analysis through the relevant features, and provides guidance for optimizing battery management.

ensemble model formation cycle mechanical feature state-of-health estimation thermal feature

SCI ; EI

英语

其他

20242516296531

Battery management systems ; Convolutional neural networks ; Deep learning ; Mean square error

Ergonomics and Human Factors Engineering:461.4 ; Secondary Batteries:702.1.2 ; Mathematical Statistics:922.2

ENGINEERING

2-s2.0-85196391991

Scopus

1.College of Electrical Engineering,Sichuan University,Chengdu,610065,China
2.Peng Cheng Laboratory,Shenzhen,518055,China
3.School of Chemical Engineering,Sichuan University,Chengdu,610065,China
4.School of Microelectronics,Southern University of Science and Technology,Shenzhen,518055,China
5.Faculty of Engineering,Architecture and Information Technology (EAIT),University of Queensland,St Lucia,4072,Australia

He，Weilin,Li，Dingquan,Sun，Zhongxian,et al. Data-driven estimation of battery state-of-health with formation features[J]. Journal of Micromechanics and Microengineering,2024,34(7).

He，Weilin.,Li，Dingquan.,Sun，Zhongxian.,Wang，Chenyang.,Tang，Shihai.,...&He，Xin.(2024).Data-driven estimation of battery state-of-health with formation features.Journal of Micromechanics and Microengineering,34(7).

He，Weilin,et al."Data-driven estimation of battery state-of-health with formation features".Journal of Micromechanics and Microengineering 34.7(2024).