Supervisory Real-Time Multidomain Modeling and Hardware Emulation of Fuel-Cell Hybrid Electric Bus Behavioral Transients

Supervisory Real-Time Multidomain Modeling and Hardware Emulation of Fuel-Cell Hybrid Electric Bus Behavioral Transients

Blog Article

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 Right Pedal Arm 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 $mu ext{s}$ for device-level transients.The results show that the hybrid Clever Pocket Tonies model has 96.

3% accuracy; furthermore, it significantly reduces the HEB emulation time compared to conventional EMT methods.