| Autor: |
Chengzhang Lyu, Venkata R Dinavahi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 85975-85985 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3189471 |
| Popis: |
Real-time device-level multi-domain emulation can provide an accurate insight into behavioral transients of the hydrogen fuel-cell hybrid electric bus (HEB). However, the conventional electromagnetic transient (EMT) simulation suffers from the computation burden caused by the complex multi-domain subsystems. This paper develops a hybrid recurrent neural network (RNN) and EMT method for device-level multi-domain emulation for fuel-cell and battery HEB. Two recurrent neural networks (RNN) are designed and trained to create device-level models of permanent magnet synchronous motor (PMSM) and the modular multilevel converter (MMC), respectively. The IGBTs’ behavioral transients and thermal performance are integrated into the RNN-based MMC model. Moreover, the EMT models represent the energy behaviour of onboard fuel-cell stacks and battery stacks. The proposed multi-domain hybrid models are implemented on the Xilinx Versal™ adaptive compute acceleration platform (ACAP), where multiple AI engines and the programmable logic deal with the RNN and EMT models, respectively. The real-time hardware emulation is carried out at the time-step of 0.1 $\mu \text{s}$ for device-level transients. The results show that the hybrid model has 96.3% accuracy; furthermore, it significantly reduces the HEB emulation time compared to conventional EMT methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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