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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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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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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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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DOI: https://doi.org/10.1007/s10489-024-05885-1