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The body strength, stiffness and crashworthiness are the key aspects for the mass reduction of the commercial bus body frame. Heavy computation cost is one of the critical problems by the finite element (FE) method to accomplish a high-efficient multi-objective optimizing design. Starting from this point, in this paper, the surrogate model method is adopted to optimize the electric bus frame to reduce the mass as possible while guaranteeing the side-impact strength. The optimizing objective comprises the total mass and side-impact intrusion while the performances of static strength and stiffness in bending and torsion conditions are chosen as the constraints in optimization. First, an FE model is developed to perform the static strength analysis, modal analysis and side-impact strength analysis. Nine groups of candidate variables are determined as the optimizing design variables by sensitivity analysis.

This work focuses on the optimizing design of the bolt joint of the steel-aluminum body frame of an electric bus. First, a finite element (FE) model is established for the T-joint part of the bus body frame and nonlinear quasi-static analysis is carried out by HyperWorks to understand the effect of the structural parameters of the bolt joint on the concerned performance such as strength and stiffness. Five design variables those are bolt diameter, center distance between the bolts, the thickness of the connecting part, the area of the ribbed plate of the connecting part and the thickness of the connected aluminum part are introduced for optimizing. The maximum deformation and the maximum stress of the joint are chosen as the indicators. Then the function relations between the performance indicators and the design variables are formulated by the polynomial regression analysis method with the data obtained from 25 groups of orthogonal experiment.

This work focuses on the robust optimization of the bolted T-joint part of the steel-aluminum body frame of an electric bus, aiming to improve the performance of fatigue durability of the local structure of the bolted T-joint part. First, finite element model is built for the bolted T-joint part connecting the chassis and the side of the body frame for fatigue durability analysis. Surrogate model for design optimization is fitted by the Kriging method based on the finite element (FE) analysis data. Then, a multi-objective optimization problem is formulated to enhance the fatigue life of the element with the worst fatigue durability performance, and to decrease the deformation of the element with the largest deformation, by choosing the thickness of the beams of the T-joint part as the design variables. A deterministic multi-objective optimization problem is performed by the adaptive simulated annealing (ASA) method.

The traditional design optimization of the bus body frame are mainly limited to the optimization of the thickness of the parts. In this work, we perform the optimization design of the bus body frame by optimizing the sectional shape of the tube beams based on the mesh morphing technology. Several groups of finite element analysis are performed for the body frame and the sectional sizes of the rectangular tube beams of the chassis and the side structure of the body that have a greater impact on the body performance are selected for optimization. The mesh morphing technology is used to establish shape design variables for the selected tube beams, and the design variables are comprised of the length, width, and thickness of the sections of the selected tube beams. Based on the entropy weight method and the order preference by similarity to the ideal solution (TOPSIS) comprehensive weight method, the design variable with a higher comprehensive contribution is obtained.

This work aims to perform the optimization of the iron-aluminum lightweight body frame of a commercial electric bus orienting the static performance (e.g., strength and stiffness), side-impact safety, and possible reduction in mass. Firstly, both the static and side-impact finite element (FE) models are established for the electric bus body frame. The body frame is partitioned according to the deformation and the thickness of the square tube beams, and the contribution is analyzed by the relative sensitivity and the Sobol index methods. The thickness of the tube beams in the nine regions is selected as the design optimization variables. After data sampling by the Hamersley method and conducting design of experiments (DOE), the surrogate models for optimization are fitted by the least square method.