Nonlinear vibration analysis of a magnetorheological–air spring hybrid cab mounting system for heavy duty trucks

Traditional system dynamics analysis often exhibits noticeable discrepancies due to neglected damper nonlinearities and the limited capability to predict coupled vibrations at distributed measurement locations. To address these gaps, this study develops a nonlinear multibody dynamic (MBD) model for a cab–seat–magnetorheological (MR) hybrid mounting system formulated via the Lagrange approach. A pivotal innovation of this study lies in the high-fidelity characterization of the nonlinear characteristics of the hybrid mount (comprising an MR mount and an air spring) and its seamless integration within the MBD framework. An experimental data-driven neural network model is employed to capture the MR mount’s nonlinear current–force–velocity relationship, while a polynomial model describes the air spring’s force–displacement behavior. By incorporating these high-fidelity component models, the proposed framework accurately represents multi-source nonlinearities and complex coupling effects. Furthermore, unlike conventional center of gravity (CG)-based evaluations, this model specifically focuses on seat-level dynamics to enhance vibration assessment at the driver-seat interface. Validation via ISO Class D road simulations at various vehicle speeds and bench tests demonstrates that the relative error of the seat vertical-acceleration RMS remains below 10% across all tested conditions. confirming the model’s superior prediction accuracy at critical measurement locations.