An Application of Hardware-in-the-Loop Simulation in Fault Detection and Diagnosis Algorithm Development – An EV PMSM Case Study

Condition monitoring (CM) is vital for maintaining the reliability and safety of high-dependence systems. This process encompasses the detection, identification, isolation, and prediction of system degradation. Over time, significant research has advanced the development of CM algorithms, with fault detection and diagnosis (FDD) emerging as the most mature area. However, most FDD-CM techniques predominantly rely on signal processing and machine learning, which often require extensive, and frequently unavailable, datasets for model training. To address this limitation, this study proposes using a low-cost Hardware-in-the-Loop (HIL) platform to replicate the real-time dynamic behavior of a system of interest. In this paper, Permanent Magnet Synchronous Motors (PMSMs), commonly used in Electric Vehicle (EV) traction systems, serve as a case study. Given the continuous operational requirements for CM of EV PMSMs, a recursive parameter estimation principle is employed for FDD algorithm development. Specifically, the Recursive Least Squares (RLS) algorithm is applied to demonstrate the HIL platform’s effectiveness in supporting FDD algorithm development. This support comes from its ability to generate an indefinite training dataset, which offers a promising opportunity for real-time implementation on low-cost microprocessors.