Multi-objective optimization of commercial vehicle cab parameters based on improved particle swarm optimization algorithm

In order to reduce the low-frequency, high-intensity vibration of a commercial vehicle cab and improve ride comfort, this paper proposes a multi-objective optimization method for cab parameters based on an improved particle swarm optimization (PSO) algorithm. The novelty of this work lies in the integration of a Kent chaotic map to initialize the particle swarm, which enhances population diversity and uniformity, thereby improving the algorithm’s ability to avoid local optima and increasing convergence stability. A multi-body dynamics model, constructed in ADAMS, is utilized to determine the cab’s key suspension parameters (stiffness, damping, and position). A multi-objective optimization model is then established with the objectives of minimizing the root mean square (RMS) value of vertical cab acceleration, minimizing the comprehensive weighted acceleration, and maximizing the decoupling degree of rigid body vibration modes. The improved PSO algorithm is used to solve the model and obtain the optimal cab parameter set. Experimental results, validated on a physical JH6 commercial vehicle under three distinct road conditions, show that this method effectively optimizes the cab parameters, leading to a measured ride comfort improvement of up to 82.5% and a significant enhancement in driving smoothness.