The current electrification trend involving hybrid and electric vehicles requires accurate tools to evaluate performance and reliability of electric powertrains’ control systems. Thanks to Hardware in the Loop (HiL) technique, verification, validation and virtual calibration of Electronic Control Systems can be performed without physical plants, addressing the need of frontloading, cost and time reduction of new vehicles control systems development. However, HiL applications with power electronics controllers brings several concerns due to the extremely low timestep needed for accurate simulation of electromagnetic phenomena, making FPGA-based simulation the only option. Moreover, thermal aspects of electric motors are very important from the control perspective as complex thermal management control strategies are implemented to improve the efficiency and to prevent overheating that can cause permanent damage to the electrical machine.
The aim of this work was to develop tools and methodologies for hybrid and electric powertrain control development and testing. In this paper a methodology for the parametrization, deployment and integration of a Permanent Magnet Synchronous Motor (PMSM) FPGA model equipped on the Fiat 500e, will be presented.
The original FPGA motor model, included in the dSPACE XSG Electric library, was modified to consider iron losses and a methodology to parametrize such models using Finite Elements Method (FEM) simulation results was developed. Moreover, a Real-Time thermal model of the electric motor was developed in GT-SUITE and executed on a dSPACE SCALEXIO Real-Time Processor in co-simulation with the electromagnetic model running on a DS6602 FPGA Board. The co-simulation accounts for the temperature dependency of the winding resistance, improving estimation accuracy of powertrain performances and enabling validation of thermal management control strategies.
The developed plant model was validated offline and online on a HiL machine coupled with the real Motor Control Unit (MCU) with respect to experimental data in real driving conditions.