Two-Level Battery Health Diagnosis Using Encoder-Decoder Framework and Gaussian Mixture Ensemble Learning Based on Relaxation Voltage

Accurate diagnosis of the state-of-health (SOH) of the lithium-ion battery is crucial for its safe and reliable operation. In this article, a two-level battery health diagnosis model is proposed using relaxation voltage. First, the health features of the relaxation voltage sequence are extracted usi...

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Published in:IEEE transactions on transportation electrification Vol. 10; no. 2; pp. 3966 - 3975
Main Authors: Xiang, Haoxiang, Wang, Yujie, Zhang, Xingchen, Chen, Zonghai
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aging
Algorithms
Batteries
Battery health diagnosis
Coders
data reconstruction model
Diagnosis
Electric potential
encoder–decoder framework
Ensemble learning
Feature extraction
Gaussian mixture ensemble learning (GMEL)
Integrated circuit modeling
Lithium-ion batteries
Machine learning
Probabilistic models
Rechargeable batteries
relaxation voltage
Root-mean-square errors
Voltage
ISSN:2332-7782, 2577-4212, 2332-7782
Online Access:Get full text
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Summary:Accurate diagnosis of the state-of-health (SOH) of the lithium-ion battery is crucial for its safe and reliable operation. In this article, a two-level battery health diagnosis model is proposed using relaxation voltage. First, the health features of the relaxation voltage sequence are extracted using the autoencoder based on the encoder-decoder framework, and the Gaussian mixture model (GMM) is used to cluster distinct aging levels of the battery, thereby enabling a preliminary diagnosis of battery SOH. Then, a novel Gaussian mixture ensemble learning (GMEL) method is presented that leverages prior knowledge for accurate diagnosis of battery SOH. Furthermore, the performance of the ensemble model is enhanced by the sequential model-based algorithm configuration (SMAC) algorithm to optimize the hyperparameters, resulting in mean-absolute-error (MAE) and root-mean-square-error (RMSE) of 0.736% and 1.013%, respectively. In addition, a data reconstruction model is developed using the encoder-decoder framework to address the challenge of obtaining complete relaxation voltage sequences in the real world. Utilizing only 4 min of incomplete relaxation voltage data, the presented model achieves the MAE of 1.265% and RMSE of 1.681% in diagnosing the battery SOH. Finally, the superiority of the proposed method is verified by several sets of experiments.
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ISSN:2332-7782
2577-4212
2332-7782
DOI:10.1109/TTE.2023.3317449