Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks
This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/94 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548921410682880 |
|---|---|
| author | Meng-Hui Wang Jing-Xuan Hong Shiue-Der Lu |
| author_facet | Meng-Hui Wang Jing-Xuan Hong Shiue-Der Lu |
| author_sort | Meng-Hui Wang |
| collection | DOAJ |
| description | This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-5dda81411c18417ea0cc67213a2ca2d5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5dda81411c18417ea0cc67213a2ca2d52025-01-10T13:20:51ZengMDPI AGSensors1424-82202024-12-012519410.3390/s25010094Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural NetworksMeng-Hui Wang0Jing-Xuan Hong1Shiue-Der Lu2Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanThis paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method.https://www.mdpi.com/1424-8220/25/1/94convolutional neural networklithium batterysymmetrized dot patternfault diagnosis |
| spellingShingle | Meng-Hui Wang Jing-Xuan Hong Shiue-Der Lu Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks Sensors convolutional neural network lithium battery symmetrized dot pattern fault diagnosis |
| title | Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks |
| title_full | Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks |
| title_fullStr | Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks |
| title_full_unstemmed | Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks |
| title_short | Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks |
| title_sort | fault diagnosis of lithium battery modules via symmetrized dot pattern and convolutional neural networks |
| topic | convolutional neural network lithium battery symmetrized dot pattern fault diagnosis |
| url | https://www.mdpi.com/1424-8220/25/1/94 |
| work_keys_str_mv | AT menghuiwang faultdiagnosisoflithiumbatterymodulesviasymmetrizeddotpatternandconvolutionalneuralnetworks AT jingxuanhong faultdiagnosisoflithiumbatterymodulesviasymmetrizeddotpatternandconvolutionalneuralnetworks AT shiuederlu faultdiagnosisoflithiumbatterymodulesviasymmetrizeddotpatternandconvolutionalneuralnetworks |