Robust Design for Vehicle Ride Comfort and Handling with Multi-Objective Evolutionary Algorithm

Paper #:
  • 2013-01-0415

Published:
  • 2013-04-08
Citation:
Luo, Y., Wu, J., Fu, W., and Zhang, Y., "Robust Design for Vehicle Ride Comfort and Handling with Multi-Objective Evolutionary Algorithm," SAE Technical Paper 2013-01-0415, 2013, https://doi.org/10.4271/2013-01-0415.
Pages:
6
Abstract:
As is known to all, there are some contradictions between the handling and ride performance during the design process of vehicles. Sometimes owing to serious collisions of each criterion in the high-dimensional solution space, the common method to deal with the contradiction is to transform into a single target according to weights of each objective, which may not obtain a desired result. A multi-criteria approach is therefore adopted to optimize both properties and the result of a multi-criteria design is not a unique one but a series of balanced solutions.This paper is focused on the robust design of a simplified vehicle model in terms of not only ride comfort but also handling and stability using a multi-objective evolutionary algorithm (MOEA) method. Using the proposed method, the conflicting performance requirements can be better traded off. One of the most important indexes to characterize the vertical ride comfort is the acceleration of the sprung mass. Consequently, parts of the objectives interested are the vertical acceleration and pitch motion in center of sprung mass which are expressed in frequency domain. A simplified physical model with four degrees of freedom is adopted for ride analysis and improvement by the constraints of the partial natural frequency and damping ratio and suspension travel displacement. Additionally, we should ensure the handling stability, which affects both ease of operation and safety while traveling in high speed. Under the step input of steering wheel angle to a model with three degrees of freedom, the roll angle of vehicle body in the time domain is optimized. The objectives are performed on the basis of RMS and the corresponding standard deviations are estimated using the first-order Taylor approximation.The design variables or control factors are suspension stiffness and damping coefficient of both front axle and rear axle, while the noise factors are suspension mass and tire stiffness of both front axle and rear axle. The MOEA is employed in the paper, combining the ‘spider graphs’ to choose the most suitable solutions.
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