Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks
Abstract
:1. Introduction
2. Methodology
2.1. Symmetric Data Point Coordinate Method
2.2. Convolutional Neural Network
2.2.1. Convolutional Kernel and Convolution Layer
2.2.2. Pooling Layer
2.2.3. Fully Connected Layer
3. Research System Architecture and Fault Design
3.1. Experimental System Process Architecture
3.2. Design of Experimental Fault Models for Lithium
3.2.1. Normal (Type 1)
3.2.2. Over-Discharging (Type 2)
3.2.3. Overcharging (Type 3)
3.2.4. Leakage (Type 4)
3.2.5. Aging (Type 5)
3.3. Fault Model Capacity Experiment
4. Experimental Results
4.1. Original Waveform Measurement
4.2. Application of the Symmetric Point Coordinate Method in Lithium Battery Modules
4.3. Recognition Results of the CNN
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Lithium Battery—Four Common Faults | |
---|---|
Type A | Normal (Type 1) |
Type B | Over-discharging (Type 2) |
Type C | Overcharging (Type 3) |
Type D | Leakage (Type 4) |
Type E | Aging (Type 5) |
Equipment | Model | Specification |
---|---|---|
Programmable DC Power Supply | ITECH-IT6302 | Output voltage: 0–30 V, Output current: 0–3 A, max power: 90 W |
Programmable Electronic Load | ITECH-IT8512A | Max input voltage: 120 V, Max input current: 30 A, Max input power: 300 W |
NI PXIe Chassis | NI PXI-5105 | 60 MHz sampling rate, 8 synchronized channels, and 12-bit resolution |
Signal Generator | Agilent 33220A | Frequency range: 1 µHz to 20 MHz Modulation: AM, FM, PM, FSK Waveforms: sine, square, ramp, and pulse |
Fault Types | Training Pattern | Testing Pattern | Accuracy (%) |
---|---|---|---|
Type A | 400 | 200 | 99.9 |
Type B | 400 | 199 | |
Type C | 400 | 200 | |
Type D | 400 | 200 | |
Type E | 400 | 200 |
Algorithm | Training Time (s) | Testing Time (s) | Epoch | Accuracy (%) |
---|---|---|---|---|
SDP + CNN | 121.9 | 0.3970 | 50 | 95.7 |
SDP + Res-Net18 | 232.0 | 0.7095 | 50 | 89.8 |
SDP + VGG-19 | 369.6 | 0.0107 | 50 | 94.4 |
SDP + CNN | 218.5 | 0.4457 | 100 | 99.9 |
SDP + Res-Net18 | 458.6 | 0.7602 | 100 | 91.5 |
SDP + VGG-19 | 723.5 | 0.0067 | 100 | 96.0 |
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Wang, M.-H.; Hong, J.-X.; Lu, S.-D. Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks. Sensors 2025, 25, 94. https://doi.org/10.3390/s25010094
Wang M-H, Hong J-X, Lu S-D. Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks. Sensors. 2025; 25(1):94. https://doi.org/10.3390/s25010094
Chicago/Turabian StyleWang, Meng-Hui, Jing-Xuan Hong, and Shiue-Der Lu. 2025. "Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks" Sensors 25, no. 1: 94. https://doi.org/10.3390/s25010094
APA StyleWang, M.-H., Hong, J.-X., & Lu, S.-D. (2025). Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks. Sensors, 25(1), 94. https://doi.org/10.3390/s25010094