Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~104 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardware-in-the-Loop (HiL) testbench, equipped with a minimal latency of 18μsec, is used for real-time (RT) model implementation of the EV’s HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition.
Original languageEnglish
Pages (from-to)115515-115536
Number of pages21
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 22 Jun 2020

    Research areas

  • DC/DC converter, Ev, Scalability, Optimization, Hardware-in-the-loop, wide bandgap (WBG) technologies

ID: 52476211