Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model
With the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus. However, strong temporal dependencies in battery operation data and the scarcity of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5795 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129671199391744 |
|---|---|
| author | Shaofan Liu Tianbao Xie Yanxin Li Siyu Liu |
| author_facet | Shaofan Liu Tianbao Xie Yanxin Li Siyu Liu |
| author_sort | Shaofan Liu |
| collection | DOAJ |
| description | With the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus. However, strong temporal dependencies in battery operation data and the scarcity of fault samples hinder the accuracy and robustness of existing methods. To address these challenges, this paper proposes a deep learning-based fault detection model that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. The GAN is employed to augment minority-class fault samples, effectively mitigating the class imbalance in the dataset. Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. Experiments conducted on real-world datasets collected from electric vehicles demonstrate that the proposed model achieves a Precision of 95.23%, Recall of 87.23%, and F1-Score of 91.12% for fault detection. Additionally, it yields an Average Precision (AP) of 97.45% and an Area Under the ROC Curve (AUC) of 99%, significantly outperforming conventional deep learning and machine learning baselines. This study provides a practical and high-performance solution for fault detection in power battery systems, with promising application potential. |
| format | Article |
| id | doaj-art-f07dfffef31b4aa98d556fa6f583f45f |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-f07dfffef31b4aa98d556fa6f583f45f2025-08-20T02:32:54ZengMDPI AGApplied Sciences2076-34172025-05-011511579510.3390/app15115795Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid ModelShaofan Liu0Tianbao Xie1Yanxin Li2Siyu Liu3School of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaWith the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus. However, strong temporal dependencies in battery operation data and the scarcity of fault samples hinder the accuracy and robustness of existing methods. To address these challenges, this paper proposes a deep learning-based fault detection model that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. The GAN is employed to augment minority-class fault samples, effectively mitigating the class imbalance in the dataset. Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. Experiments conducted on real-world datasets collected from electric vehicles demonstrate that the proposed model achieves a Precision of 95.23%, Recall of 87.23%, and F1-Score of 91.12% for fault detection. Additionally, it yields an Average Precision (AP) of 97.45% and an Area Under the ROC Curve (AUC) of 99%, significantly outperforming conventional deep learning and machine learning baselines. This study provides a practical and high-performance solution for fault detection in power battery systems, with promising application potential.https://www.mdpi.com/2076-3417/15/11/5795power batteryfault detectionimbalanced datasetmachine learningdeep learninggenerative model |
| spellingShingle | Shaofan Liu Tianbao Xie Yanxin Li Siyu Liu Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model Applied Sciences power battery fault detection imbalanced dataset machine learning deep learning generative model |
| title | Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model |
| title_full | Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model |
| title_fullStr | Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model |
| title_full_unstemmed | Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model |
| title_short | Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model |
| title_sort | fault detection for power batteries using a generative adversarial network with a convolutional long short term memory gan cnn lstm hybrid model |
| topic | power battery fault detection imbalanced dataset machine learning deep learning generative model |
| url | https://www.mdpi.com/2076-3417/15/11/5795 |
| work_keys_str_mv | AT shaofanliu faultdetectionforpowerbatteriesusingagenerativeadversarialnetworkwithaconvolutionallongshorttermmemorygancnnlstmhybridmodel AT tianbaoxie faultdetectionforpowerbatteriesusingagenerativeadversarialnetworkwithaconvolutionallongshorttermmemorygancnnlstmhybridmodel AT yanxinli faultdetectionforpowerbatteriesusingagenerativeadversarialnetworkwithaconvolutionallongshorttermmemorygancnnlstmhybridmodel AT siyuliu faultdetectionforpowerbatteriesusingagenerativeadversarialnetworkwithaconvolutionallongshorttermmemorygancnnlstmhybridmodel |