Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM
Abstract
:1. Introduction
2. Pseudo-Siamese Nested LSTM Network
2.1. Nested LSTM Network
2.2. Model Architecture Proposed
3. Temperature Benchmark Data Set and Valuation Indicators
3.1. Temperature Benchmark Data Set
3.2. Evaluation Indicators
4. Learning Rate Optimization
5. Performance Assessment
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter Name | Symbol |
---|---|
Ambient temperature | |
Coolant temperature | |
Voltage d-component | |
Voltage q-component | |
Motor speed | |
Actual torque | |
Current d-component | |
Current q-component | |
Permanent Magnet temperature | |
Stator yoke temperature | |
Stator tooth temperature | |
Stator winding temperature | |
unique ID |
Hyper-Parameter | LSTM | LSTM-2 | NLSTM | PSNLSTM |
---|---|---|---|---|
Hidden layer | 3 | 4 | 4 | 4 |
Units | 64 | 64 | 64 | |
Time steps | 7 | 7 | 7 | |
Weight | normal | normal | normal | normal |
Optimizer | Nadam | Nadam | Nadam | Nadam |
Learning rate | 0.001 | 0.001 | 0.001 | 0.001 |
Warm-up epochs | 10 | 10 | 10 | 10 |
Epochs | 100 | 100 | 100 | 100 |
Gaussian noise | ||||
Drop out | 0.2 | 0.2 | 0.2 | 0.2 |
Model | MSE (%) | MAE (%) | RMSE (%) | STDPE | |
---|---|---|---|---|---|
LSTM | 0.0927 | 2.3625 | 3.0448 | 99.7374 | 0.0304 |
LSTM-2 | 0.0897 | 2.2004 | 2.9947 | 99.7460 | 0.3000 |
NLSTM | 0.0627 | 1.7879 | 2.5044 | 99.8223 | 0.0230 |
PSNLSTM | 0.0508 | 1.6860 | 2.2537 | 99.8561 | 0.0222 |
Model | MSE (%) | MAE (%) | RMSE (%) | STDPE | |
---|---|---|---|---|---|
LSTM | 0.1321 | 2.4961 | 3.6339 | 99.8435 | 0.0661 |
LSTM-2 | 0.1683 | 2.9113 | 4.1018 | 99.8006 | 0.0615 |
NLSTM | 0.0934 | 2.1675 | 3.0567 | 99.8892 | 0.0510 |
PSNLSTM | 0.0998 | 2.455845 | 3.1598 | 99.8816 | 0.0467 |
Model | MSE (%) | MAE (%) | RMSE (%) | STDPE | |
---|---|---|---|---|---|
LSTM | 0.4380 | 4.0302 | 6.6183 | 99.6873 | 0.0357 |
LSTM-2 | 0.3902 | 4.4855 | 6.2463 | 99.7215 | 0.0409 |
NLSTM | 0.2609 | 3.3056 | 5.1074 | 99.8138 | 0.0306 |
PSNLSTM | 0.2198 | 3.4557 | 4.6888 | 99.8430 | 0.0301 |
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Cai, Y.; Cen, Y.; Cen, G.; Yao, X.; Zhao, C.; Zhang, Y. Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM. World Electr. Veh. J. 2021, 12, 57. https://doi.org/10.3390/wevj12020057
Cai Y, Cen Y, Cen G, Yao X, Zhao C, Zhang Y. Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM. World Electric Vehicle Journal. 2021; 12(2):57. https://doi.org/10.3390/wevj12020057
Chicago/Turabian StyleCai, Yongping, Yuefeng Cen, Gang Cen, Xiaomin Yao, Cheng Zhao, and Yulai Zhang. 2021. "Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM" World Electric Vehicle Journal 12, no. 2: 57. https://doi.org/10.3390/wevj12020057