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Remaining useful-life prediction of lithium battery based on neural-network ensemble via conditional variational autoencoder

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Abstract

Ensemble learning using deep neural networks has become prevalent in predicting the Remaining Useful Life (RUL) of Lithium Batteries (LiBs). However, owing to the predominant linearity of ensemble learning, capturing nonlinear relationships among base learners remains a persistent challenge. This study presents an RUL-prediction method for LiBs based on a neural-network ensemble via a Conditional Variational Autoencoder (CVAE). The proposed method serves as a nonlinear ensemble learning method and promises enhanced prediction performance while maintaining ease of implementation. The methodology entails several key steps. First, data smoothing is conducted via local weighted linear regression. Subsequently, a preliminary linear-ensemble phase is executed through an attention mechanism, which filters out extraneous information in the features and bolsters the importance of valid features. Subsequently, a nonlinear ensemble is accomplished by utilizing the CVAE, with truth labels serving as conditions. Finally, the efficacy of the proposed method is substantiated through experimentation, demonstrating its superior performance compared to the candidate methods.

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Data availability and access

The data that support the findings of this study are openly available at https://data.matr.io/1/ and https://www.batteryarchive.org/snlstudy.html

Abbreviations

CNN:

Convolutional Neural Network

CVAE:

Conditional Variational Autoencoder

DNN:

Deep Neural Network

GPR:

Gaussian Process Regression

GRU:

Gate Recurrent Unit

LCO:

LiCoO2

LiBs:

Lithium Batteries

LSTM:

Long Short-Term Memory

LWLR:

Locally Weighted Linear Regression

MIT-ST:

Massachusetts Institute of Technology-Stanford University-Toyota Research Center

NTM:

Nickel-Rich Ternary Material

OSELM:

Online Sequential Extreme Learning Machine

RF:

Random Forest

RMSE:

Root Mean Square Error

RUL:

Remaining Useful Life

Sandia:

Sandia National Laboratories

SOH:

State-of-Health

SVM:

Support Vector Machine

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Acknowledgements

This work was supported in part by the Key R & D project of Shaanxi Province(2023-YBGY-030), the Key Industrial Chain Core Technology Research Project of Xi’an (23ZDCYJSGG0028-2022), the National Natural Science Foundation of China (62272387). This paper is supported by the Project serving to locations in Shaanxi province (NO: 19JC036). We would like to thank Editage for providing English language editorial assistance.

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Contributions

Hengshan Zhang: Conceptualization, Methodology. Kaijie Guo: Data curation, Writing- Original draft preparation. Yanping Chen and Jiaze Sun: Validation, Writing- Reviewing and Editing, Supervision.

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Correspondence to Kaijie Guo.

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Zhang, H., Guo, K., Chen, Y. et al. Remaining useful-life prediction of lithium battery based on neural-network ensemble via conditional variational autoencoder. Appl Intell 55, 34 (2025). https://doi.org/10.1007/s10489-024-05885-1

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