Simulation And Experimental Study On Ride Comfort Optimization Of A Mine Car

To solve the problem of mine car uncomfortable ride, a suspension parametersoptimization method was put forward. Combining the ride comfort test method, improvedparticle swarm optimization, sensitivity analysis and approximation theory were applied tosolve the problem of suspension multi-parameter optimization.In this paper, the problem was analyzed according to the ideas from low degree offreedom (DOF) model to high DOF model and from simple to complex. Firstly,1/2vehiclemodel were established, body weighted root mean square (RMS) acceleration wasconsidered as the objective function, and suspension deflection and tire dynamic load wereconsidered as the constraints. Standard particle swarm optimization (SPSO) was used tooptimize the four DOF suspension parameters. Because SPSO is slow speed onconvergence and is easy to be trapped in local optima, four methods were adopted toimprove PSO convergence accuracy and convergence rate. They were inertia weightadjustment strategy, velocity and position selection strategy for particles beyond theboundaries, introducing chaotic mutation to strengthen local search and adjustingneighborhood selection strategy of chaos particle swarm optimization (CPSO). And thenCPSO with exponential function to adjust the inertia weight and the local neighborhood(ICPSO) was proposed. Simulation results show that ICPSO can greatly improveconvergence speed and optimizing precision in solving suspension optimization problem.Considering vehicle pitch and roll vibration, seven DOF vehicle model as established.After this, ISIGHT software was used to analysis parameters sensitivity. The parametershaving greatest impact on vehicle ride comfort were chose as the optimization variables. Toaccelerate the optimization speed, a regression model was obtained by response surfacemethod. Based on the regression model, ICPSO was applied to optimize suspensionparameters. Simulation results show that the approximate optimization model can greatlyreduce the optimization time and improve the optimization efficiency. The ideal match ofsuspension parameters was obtained.Finally, the improved and the original vehicles were tested on the random road andpulse road, according to the ride comfort test method GB/T4970-2009. Test results show that the improved suspension can greatly reduce the weighted RMS acceleration on randomroad and the maximum acceleration response on pulse road. So the test proves that thesuspension multi-parameter optimization based on PSO can improve vehicle ride comfortlargely.