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Evaluation of electric steering (EPAS) system performance using vehicle specific load conditions is important for steering system design validation and vehicle steering performance tuning. Using real-time vehicle dynamics mathematical models is one approach for generating steering loads in steering hardware-in-the-loop (HIL) testing. However achieving a good correlation of simplified mathematical models with real vehicle dynamics is a challenge. Using rack force models from measured steering tie rod forces or from simulations using a high-fidelity vehicle dynamics model is an effective data-driven modelling method for testing EPAS systems under vehicle specific load conditions. Rack force models are identified from physical measurements or validated vehicle simulations of selected steering test maneuvers. The rack force models have been applied in steering system performance evaluation, benchmarking, and steering model validation.

Distinguishing physical modes from mathematical modes in the modal analysis of complex systems, such as full vehicle structures, is a difficult and time-consuming process. The major tools frequently used are stabilization diagrams, mode indicator functions, or modal participation factors. When closely spaced modes are to be identified, the stabilization diagrams and mode indicator functions are no longer effective. Even the reciprocities of mode shapes and modal participation factors cannot be well satisfied to indicate whether a mode is a physical one, when measurement errors are large. To overcome these difficulties, an optimization procedure is developed, whereby physical modes can be sorted out in a given frequency range while the error between measured and synthesized frequency responses is minimized. An optimal subset selection algorithm is used in this procedure.