Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model
Due to the disparate timescale behavior in the electrical and thermal aspects, achieving a balance between simulation efficiency and accuracy in electrothermal analysis of insulated gate bipolar transistor (IGBT) modules has been a challenging task. A physical-based electrothermal stress evaluation...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Power Electronics |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10214390/ |
| _version_ | 1797679494760235008 |
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| author | Yiping Lu Enyao Xiang Ankang Zhu Hongyi Gao Haoze Luo Huan Yang Rongxiang Zhao |
| author_facet | Yiping Lu Enyao Xiang Ankang Zhu Hongyi Gao Haoze Luo Huan Yang Rongxiang Zhao |
| author_sort | Yiping Lu |
| collection | DOAJ |
| description | Due to the disparate timescale behavior in the electrical and thermal aspects, achieving a balance between simulation efficiency and accuracy in electrothermal analysis of insulated gate bipolar transistor (IGBT) modules has been a challenging task. A physical-based electrothermal stress evaluation approach combining with artificial neural network (ANN) model is proposed in this article, which significantly improves performance in circuit simulation. The training data for ANN models are derived from the Hefner physical model, a well-established model integrated in Saber. By re-expressing the Hefner model using MATLAB scripts, high-precision data can be efficiently obtained. Double-pulse experiments show that the switching transient characterized by the Hefner model have high precision, with an error within 5% compared to the experimental data. Additionally, the transient behavior of IGBT devices is further described by a two-layer feed-forward ANN, trained using datasets obtained by varying parasitic or operating parameters in the re-expressed Hefner model. Combining the physical model with the ANN models, the proposed approach can simulate not only transient electrical behavior but also long-term thermal behavior with accurate switching energy. This approach has been implemented in MATLAB/Simulink and verified with Saber for system-level circuit simulation. The electrothermal stress evaluation results show that the simulation efficiency is significantly improved (180 times faster than Saber under the simulation settings in this article), while maintaining high precision, and the error is within 2.5%. Experimental results also validate the accuracy of proposed model in predicting the voltage and current stress, with a maximum error of 1.5%. |
| first_indexed | 2024-03-11T23:15:30Z |
| format | Article |
| id | doaj.art-5d369db798ff40e585dc7bf9c09c4ce4 |
| institution | Directory Open Access Journal |
| issn | 2644-1314 |
| language | English |
| last_indexed | 2024-03-11T23:15:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Power Electronics |
| spelling | doaj.art-5d369db798ff40e585dc7bf9c09c4ce42023-09-20T23:00:37ZengIEEEIEEE Open Journal of Power Electronics2644-13142023-01-01474075110.1109/OJPEL.2023.330401610214390Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network ModelYiping Lu0https://orcid.org/0000-0003-4241-1185Enyao Xiang1https://orcid.org/0009-0000-7431-5919Ankang Zhu2https://orcid.org/0000-0002-2837-4587Hongyi Gao3https://orcid.org/0009-0001-0716-0897Haoze Luo4https://orcid.org/0000-0001-5103-5068Huan Yang5https://orcid.org/0000-0001-5388-0687Rongxiang Zhao6Zhejiang Provincial Key Laboratory of Electrical Machine Systems and the College of Electrical Engineering, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Electrical Machine Systems and the College of Electrical Engineering, Zhejiang University, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Electrical Machine Systems and the College of Electrical Engineering, Zhejiang University, Hangzhou, ChinaZhejiang Provincial Key Laboratory of Electrical Machine Systems and the College of Electrical Engineering, Zhejiang University, Hangzhou, ChinaDue to the disparate timescale behavior in the electrical and thermal aspects, achieving a balance between simulation efficiency and accuracy in electrothermal analysis of insulated gate bipolar transistor (IGBT) modules has been a challenging task. A physical-based electrothermal stress evaluation approach combining with artificial neural network (ANN) model is proposed in this article, which significantly improves performance in circuit simulation. The training data for ANN models are derived from the Hefner physical model, a well-established model integrated in Saber. By re-expressing the Hefner model using MATLAB scripts, high-precision data can be efficiently obtained. Double-pulse experiments show that the switching transient characterized by the Hefner model have high precision, with an error within 5% compared to the experimental data. Additionally, the transient behavior of IGBT devices is further described by a two-layer feed-forward ANN, trained using datasets obtained by varying parasitic or operating parameters in the re-expressed Hefner model. Combining the physical model with the ANN models, the proposed approach can simulate not only transient electrical behavior but also long-term thermal behavior with accurate switching energy. This approach has been implemented in MATLAB/Simulink and verified with Saber for system-level circuit simulation. The electrothermal stress evaluation results show that the simulation efficiency is significantly improved (180 times faster than Saber under the simulation settings in this article), while maintaining high precision, and the error is within 2.5%. Experimental results also validate the accuracy of proposed model in predicting the voltage and current stress, with a maximum error of 1.5%.https://ieeexplore.ieee.org/document/10214390/Artificial neural network (ANN)electrothermal stressphysical modelinsulated gate bipolar transistor (IGBT) |
| spellingShingle | Yiping Lu Enyao Xiang Ankang Zhu Hongyi Gao Haoze Luo Huan Yang Rongxiang Zhao Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model IEEE Open Journal of Power Electronics Artificial neural network (ANN) electrothermal stress physical model insulated gate bipolar transistor (IGBT) |
| title | Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model |
| title_full | Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model |
| title_fullStr | Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model |
| title_full_unstemmed | Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model |
| title_short | Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model |
| title_sort | physics based electrothermal stress evaluation approach of igbt modules combined with artificial neural network model |
| topic | Artificial neural network (ANN) electrothermal stress physical model insulated gate bipolar transistor (IGBT) |
| url | https://ieeexplore.ieee.org/document/10214390/ |
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