Guangbo Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Guangbo Lin has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Changzhi Lv
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
1 shared publication - Di Fan
College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
1 shared publication - Dongxin Xu
Department of Equipment Engineering, Shandong Urban Construction Vocational College, Jinan 250103, China
1 shared publication - Zhongxin Song
Shandong Enpower Electric Co., Ltd., Heze 274000, China
1 shared publication
Latest Publications
- Open Access
- 22 September 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (9) , 541
Prediction of Motor Rotor Temperature Using TCN-BiLSTM-MHA Model Based on Hybrid Grey Wolf Optimization Algorithm
The permanent magnet synchronous motor (PMSM) is the core of new energy vehicle drive systems, and its temperature status is directly related to the safety of the entire vehicle. However, the temperature of rotor permanent magnets is difficult to measure directly, and traditional sensor schemes are costly and complex to deploy. With the development of Artificial Intelligence (AI) technology, deep learning (DL) provides a feasible path for sensorless modeling. This paper proposes a prediction model that integrates a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory Network (BiLSTM), and multi-head attention mechanism (MHA) and introduces a Hybrid Grey Wolf Optimizer (H-GWO) for hyperparameter optimization, which is applied to PMSM temperature prediction. A public dataset from Paderborn University is used for training and testing. The test set verification results show that the H-GWO-optimized TCN-BiLSTM-MHA model has a mean absolute error (MAE) of 0.3821 °C, a root mean square error (RMSE) of 0.4857 °C, and an R2 of 0.9985. Compared with the CNN-BiLSTM-Attention model, the MAE and RMSE are reduced by approximately 11.8% and 19.3%, respectively.
- 22 September 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (9) , 541
Open AccessPrediction of Motor Rotor Temperature Using TCN-BiLSTM-MHA Model Based on Hybrid Grey Wolf Optimization Algorithm
The permanent magnet synchronous motor (PMSM) is the core of new energy vehicle drive systems, and its temperature status is directly related to the safety of the entire vehicle. However, the temperature of rotor permanent magnets is difficult to measure directly, and traditional sensor schemes are costly and complex to deploy. With the development of Artificial Intelligence (AI) technology, deep learning (DL) provides a feasible path for sensorless modeling. This paper proposes a prediction model that integrates a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory Network (BiLSTM), and multi-head attention mechanism (MHA) and introduces a Hybrid Grey Wolf Optimizer (H-GWO) for hyperparameter optimization, which is applied to PMSM temperature prediction. A public dataset from Paderborn University is used for training and testing. The test set verification results show that the H-GWO-optimized TCN-BiLSTM-MHA model has a mean absolute error (MAE) of 0.3821 °C, a root mean square error (RMSE) of 0.4857 °C, and an R2 of 0.9985. Compared with the CNN-BiLSTM-Attention model, the MAE and RMSE are reduced by approximately 11.8% and 19.3%, respectively.