CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine
<p>Powertrain of an electric vehicle.</p> "> Figure 2
<p>Typical topology of a bidirectional DC-DC converter used in electric vehicles.</p> "> Figure 3
<p>Electrical model of an electrolytic capacitor.</p> "> Figure 4
<p>Accelerated aging test data acquisition system.</p> "> Figure 5
<p>Experimental setup of the accelerated aging test.</p> "> Figure 6
<p>Charging and discharging cycle of electrolytic capacitor.</p> "> Figure 7
<p>Proposed approach.</p> "> Figure 8
<p>CNN-LSTM NN architecture.</p> "> Figure 9
<p>LSTM memory cell.</p> "> Figure 10
<p>Experimental setup to acquire data from DC-DC converter.</p> "> Figure 11
<p>Measured and estimated values during the accelerated aging test. (<b>a</b>) Capacitance; (<b>b</b>) ESR.</p> "> Figure 11 Cont.
<p>Measured and estimated values during the accelerated aging test. (<b>a</b>) Capacitance; (<b>b</b>) ESR.</p> "> Figure 12
<p>Prediction of the future values of (<b>a</b>) Capacitance; (<b>b</b>) Equivalent series resistance of the electrolytic capacitor.</p> "> Figure 13
<p>Measured signals versus the estimated ones with the CNN-LSTM NN. (<b>a</b>) Input voltage; (<b>b</b>) Input current; (<b>c</b>) Output voltage; (<b>d</b>) Output voltage.</p> "> Figure 13 Cont.
<p>Measured signals versus the estimated ones with the CNN-LSTM NN. (<b>a</b>) Input voltage; (<b>b</b>) Input current; (<b>c</b>) Output voltage; (<b>d</b>) Output voltage.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Powertrain of an Electric Vehicl Jmghj
Electrolytic Capacitor
2.2. Accelerated Aging Tests
Laboratory Setup
2.3. Proposed Approach
2.3.1. Parameter Estimation Based on Nonlinear Least Squares Optimization
2.3.2. Capacitance and ESR Forecasting Based on CNN-LSTM
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Parameter | Minimum | Maximum | Optimal |
---|---|---|---|
Kernel size | 1 | 15 | 4 |
LSTM neurons | 1 | 100 | 31 |
n | 20 | 100 | 56 |
m | 1 | 40 | 12 |
Method | Capacitance | ESR | Time Elapsed | ||
---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | ||
CNN-LSTM | 0.00042 | 0.995 | 0.0016 | 0.990 | 12.2 s |
NARX | 0.00080 | 0.825 | 0.0055 | 0.846 | 66 s |
LSTM | 0.00056 | 0.961 | 0.0035 | 0.944 | 8.03 s |
CNN | 0.00121 | 0.774 | 0.0094 | 0.790 | 12.8 s |
ARIMA | 0.00065 | 0.949 | 0.0032 | 0.951 | 4.7 s |
Kalman filter | 0.00071 | 0.938 | 0.0041 | 0.932 | 2.8 s |
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Rojas-Dueñas, G.; Riba, J.-R.; Moreno-Eguilaz, M. CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine. Sensors 2021, 21, 7079. https://doi.org/10.3390/s21217079
Rojas-Dueñas G, Riba J-R, Moreno-Eguilaz M. CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine. Sensors. 2021; 21(21):7079. https://doi.org/10.3390/s21217079
Chicago/Turabian StyleRojas-Dueñas, Gabriel, Jordi-Roger Riba, and Manuel Moreno-Eguilaz. 2021. "CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine" Sensors 21, no. 21: 7079. https://doi.org/10.3390/s21217079