The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers.

This paper compares Permanent-Magnet Synchronous Motor (PMSM) torque waveforms measured on a dynamometer test bench and generated by a real-time Hardware-in-the-Loop (HiL) simulation. A nonlinear PMSM model, parameterised using measurements from the physical motor and incorporating magnetic saturation through Look-Up Tables, was implemented on a dSpace HiL platform. Torque signals were evaluated at steady-state operating points of 20, 100, and 200 rpm using time-domain errors, Pearson and Spearman correlations, and FFT-based spectral analysis. An affine gain–offset transformation was applied to separate calibration-related discrepancies from differences caused by dynamic behaviour and unmodelled physical effects. The maximum observed initial discrepancy between dynamometer-measured and HiL-estimated torque was 0.638 Nm. Affine calibration reduced the maximum error by up to 85%, demonstrating that gain and offset mismatches were major contributors to the original error. However, correlation values remained low, and spectral analysis identified substantial differences in harmonic content. The dynamometer signals contained speed-dependent components, including a component near the 24th mechanical harmonic, whereas the HiL signals were dominated by the first mechanical harmonic. The proposed workflow supports mean-torque-oriented HiL validation and identifies priorities for improving model representation, channel scaling, and dynamic testing procedures.