Multi-parameter optimization based on a surrogate model for improving vehicle dynamics and reducing wheel wear in high-speed EMUs

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With the continuous development of China's high-speed railways, the problems of vehicle dynamics and wheel wear in high-speed electric multiple units (EMUs) are becoming increasingly prominent, and the suspension parameters of these vehicles have a significant effect on improving vehicle dynamics performance and reducing wheel wear. This paper first establishes a vehicle dynamics model using the Ultra-Latin Hypercube sampling method to select suspension parameters, and targets the improvement of vehicle dynamics performance and the reduction of wheel wear, and finally a Kriging Surrogate Model-Compression Particle Swarm Optimization (KSM-CPSO) model used to optimise the suspension parameters. The vehicle dynamics and wheel wear of the before and after optimisation are analysed. The results showed that the use of optimised suspension parameters effectively increased the vehicle's critical speed. The optimised parameters further improved the vehicle's ride index, safety index, and reduced the lateral force of the wheel axle. In addition, the optimised parameters suppressed the lateral vibration of the vehicle. When the wear mileage reached 200,000 km, the wheel wear depths before and after optimisation were 1.086 and 0.9806 mm, respectively. Therefore, the optimisation of the suspension parameters can effectively improve the vehicle dynamics performance and subsequently reduce wheel wear.