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
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Abstract	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.
AbstractList	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.
Author	Chen, Zonghai Wang, Yujie Zhang, Xingchen Xiang, Haoxiang
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SubjectTerms	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
Title	Two-Level Battery Health Diagnosis Using Encoder-Decoder Framework and Gaussian Mixture Ensemble Learning Based on Relaxation Voltage
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