Abstract:Accurate vehicle dynamic parameter identification is a prerequisite for vehicle-bridge coupling vibration analysis and bridge health monitoring. This study proposes a rapid identification method for the vehicle dynamic parameters based on complex modal analysis and multi-core parallel genetic algorithm. Firstly, an algorithm combining the complex modal theory with the finite element method is improved to calculate the natural frequencies, damping ratios, and modal shapes of vehicles. Subsequently, a multi-core parallel genetic algorithm for the vehicle dynamic parameter identification is proposed, in which the fitness evaluation of multiple objectives including natural frequencies, damping ratios and mode shapes is adopted, and the dynamic parameters including stiffness coefficient, damping coefficients and moments of inertia of the vehicle model can be rapidly and accurately identified. Finally, the wheel-drop-vibration experiment and ambient-excitation peak method are used in modal analysis of practical vehicle to obtain the measured natural frequencies, damping ratios and vibration modes, which are used in the fitness evaluation. The above methods have been validated by the dynamic parameters identification of the practical light car and heavy truck, and the results show that: vehicle vibration modes calculated with the identified vehicle dynamics parameters are in good agreement with the measured vibration modes, in which the maximum error of the first three natural frequency is 0.8%, the maximum error of the damping ratio is 1.3%, and the cosines of the angle between the calculated and measured vibration mode vectors are close to 1; incorporating body torsional damping is critical to accurately capture the torsional vibration characteristics of real vehicles; the multi-core parallel algorithm greatly accelerates the identification process. The acceleration ratio of 16-core CPU reaches the maximum value of 12.5 when 15 cores are in parallel. Therefore, the multi-core parallel algorithm is very effective in multi-objective and multi-parameter identification of complex vehicles.
