Junjian Hou

The body-in-white (BIW) is an important part of the electric vehicle body, its mass accounts for about 30% of the vehicle mass, and reducing its mass can significantly contribute to energy savings and emission reduction. In this paper, a collaborative optimization method combining the response surface method and genetic algorithm (RSM-GA) is developed to perform the lightweight optimization of the body-in-white of an electric vehicle. Seventeen design variables were screened by relative sensitivity calculations based on modal and stiffness sensitivity analysis, and the data samples were collected using the Taguchi experiment and Hammersley experiment during the designing of the experiment methods. To further maintain the accuracy rate, the least squares regression, moving least squares method, and radial basis function are applied to fitting data to obtain the response surface, and the error analysis of the fitting results is carried out to correct the response surface. Finally, the genetic algorithm based on the response surface is employed to optimize the structure of the body-in-white, and the results are compared with those of the adaptive response surface method and sequential quadratic programming method. Through comparison, the paper found that the optimization effect obtained by the proposed method has a relatively high accuracy rate.

Damping performance of the plates with constrained layer damping (CLD) treatment mainly depends on the layout of CLD material and the material physical properties of the viscoelastic damping layer. This paper develops a concurrent topology optimization methodology for maximizing the modal loss factor (MLF) of plates with CLD treatment. At the macro scale, the damping layer is composed of 3D periodic unit cells (PUC) of cellular viscoelastic damping materials. At the micro scale, due to the deformation of viscoelastic damping material affected by the base and constrained layers, the representative volume element (RVE) considering a rigid skin effect is used to improve the accuracy of the effective constitutive matrix of the viscoelastic damping material. Maximizing the MLFs of CLD plates is employed as the design objectives in optimization procedure. The sensitivities with respect to macrodesign variables are formulated using the adjoint vector method while considering the contribution of eigenvectors, while the influence of macroeigenvectors is ignored to improve the computational efficiency in the mesosensitivity analysis. The macro and meso scales design variables are simultaneously updated using the Method of Moving Asymptotes (MMA) to find concurrently optimal configurations of constrained and viscoelastic damping layers at the macro scale and viscoelastic damping materials at the micro scale. Two rectangular plates with different boundary conditions are presented to validate the optimization procedure and demonstrate the effectiveness of the proposed concurrent topology optimization approach. The effects of optimization objectives and volume fractions on the design results are investigated. The results indicate that the optimized layouts of the macrostructure are dependent on the objective mode and the volume fraction on the meso scale. The optimized designs on the meso scale are mainly related to the objective mode. By varying the volume fraction on the macro scale, the optimized designs on the meso scale are different only in their detailed size, which is reflected in the values of the equivalent constitutive matrices.