Identification and Optimization of Truck Suspension Parameters to Enhance Comfort using the QPSO Algorithm

Purpose Road irregularities generate vibrations and noise in vehicles, which are major occupational factors affecting drivers'' comfort. While many studies propose new suspension systems and use numerical models to optimize stiffness and damping parameters, they typically assume these parameters are known beforehand. This study addresses a gap by first identifying the actual effective stiffness and damping properties of an in-service truck from field measurements, and then using the experimentally calibrated model to optimize ride comfort. The main research question is whether this two-step approach can reduce vertical accelerations and improve comfort more effectively than conventional methods. Methods Accelerometers were installed at several points on the vehicle axles to collect experimental data during a test campaign at speeds of 15, 20, and 30 km/h. The Quantum Particle Swarm Optimization (QPSO) algorithm was employed to identify the suspension''s stiffness and damping parameters by minimizing the differences in vertical accelerations between the numerical model and experimental values. The performance of QPSO was compared against several other metaheuristics. Results QPSO exhibited faster convergence and lower final objective-function values than the other algorithms. The identified parameters yielded vertical accelerations that differed, on average, by 10.98%, 10.39%, and 10.47% from experimental measurements at 15, 20, and 30 km/h, respectively. Subsequent optimization for ride comfort resulted in a reduction of up to 18.41% in the RMS value of ISO 2631-weighted accelerations. Conclusion The proposed methodology successfully calibrates and optimizes suspension parameters using experimental data, leading to meaningful improvements in ride comfort. This approach offers a practical alternative to studies that rely on assumed parameters, with potential applications in vehicle design and performance verification.