A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot
Abstract
:1. Introduction
2. Voltage Prediction Model Development
2.1. Description of the Data Acquisition
2.2. NARX Architecture
2.3. Model Training and Voltage Prediction Based on the NARX
2.3.1. Determination of the Time Delays and Hidden Layer Size
2.3.2. Determination of the Feedback Mode
2.3.3. Determination of the Train Function
2.3.4. Predictive Performance Evaluation
3. Voltage Prediction Results and Discussion
3.1. Voltage Prediction Results
3.2. The Comparison of NARX with Back Propagation Neural Network
4. Battery Fault Diagnosis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Items | Parameters |
---|---|
Battery type | 1865 EH |
Nominal voltage | 3.2 V |
Charge cut-off voltage | 3.65 ± 0.05 V |
Discharge cut-off voltage | 2.0 ± 0.05 V |
Nominal rated capacity | 32 Ah |
Test Mode | Temperature/°C | Number of Cells | Cell Index |
---|---|---|---|
1 | −20 | 2 | 1,2 |
2 | −10 | 2 | 3,4 |
3 | 0 | 5 | 5,6,7,8,9 |
4 | 10 | 2 | 10,11 |
5 | 25 | 3 | 12,13,14 |
6 | 40 | 4 | 15,16,17,18 |
7 | 55 | 3 | 19,20,21 |
8 | 60 | 2 | 22,23 |
Cell Index | RMSE (V) | MAE (V) | Computational Time (s) | |
---|---|---|---|---|
1 | 0.042273 | 0.027807 | 0.93504 | 14.69 |
12 | 0.0092318 | 0.0064715 | 0.89038 | 13.23 |
13 | 0.0079855 | 0.0052514 | 0.94849 | 11.84 |
14 | 0.0096344 | 0.0060982 | 0.92077 | 12.51 |
22 | 0.0076182 | 0.0053174 | 0.88271 | 13.02 |
Alarm or Warning Thresholds | Fault Types | Alarm or Warning Levels |
---|---|---|
Overvoltage fault | 1 | |
Open-circuit fault | 2 | |
Potential open-circuit fault | 3 | |
Normal | Safe | |
Potential short-circuit fault | 3 | |
Short-circuit fault | 2 | |
Undervoltage fault | 1 |
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Qiu, Y.; Sun, J.; Shang, Y.; Wang, D. A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot. Symmetry 2021, 13, 1714. https://doi.org/10.3390/sym13091714
Qiu Y, Sun J, Shang Y, Wang D. A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot. Symmetry. 2021; 13(9):1714. https://doi.org/10.3390/sym13091714
Chicago/Turabian StyleQiu, Yan, Jing Sun, Yunlong Shang, and Dongchang Wang. 2021. "A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot" Symmetry 13, no. 9: 1714. https://doi.org/10.3390/sym13091714
APA StyleQiu, Y., Sun, J., Shang, Y., & Wang, D. (2021). A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot. Symmetry, 13(9), 1714. https://doi.org/10.3390/sym13091714