Identification of Rotational Speed and Contact Stiffness of EV Motor Bearings Using a Virtual Dynamic Model and a Physical Entity

Motor bearings are key transmission components in drive motors and they play an important role in the operational safety and stability of electric vehicles (EVs). The contact stiffness and rotational speed are key factors governing the frequency and vibration responses of bearing systems. Identifying these two parameters provides critical information for a better characterization of the dynamic behavior of bearings. In this study, we developed an efficient hybrid parameter identification method to estimate rotational speed and contact stiffness. An EV motor-bearing test rig with wireless data transmission was established, along with a four-degree-of-freedom dynamic bearing model. The rotational speed was estimated from vibration signals by time-frequency ridge tracking without a physical tachometer. The evolution of the effective equivalent contact stiffness was inversely identified by scaling the Hertzian stiffness coefficient using the artificial fish swarm algorithm. The proposed method is a potential technique for sequential updating of rotational speed and contact stiffness, which is suitable for assisting in the construction of a virtual–physical parameter-identification framework for bearing systems.