Multi-objective optimization design for steel-aluminum lightweight body of pure electric bus based on RBF model and genetic algorithm

Abstract

In order to solve the problem of insufficient range caused by the excessive weight of the pure electric bus, a multi-objective genetic algorithm (GA) and radial basis function (RBF) model are combined in this paper to realize the lightweighting of steel and aluminum hybrid body of the pure electric bus. First, the upper and lower frames of the pure electric bus body are initially designed with aluminum alloy and steel materials respectively to meet the lightweight requirements. Second, a finite element (FE) model of the bus body is established, and the validity of the model is validated through physical tests. Then, the sensitivity analysis is performed to identify the relative importance of individual design parameters over the entire domain. The Hamosilei sampling method is selected for the design of the experiment (DOE) because users can specify the number of experiments and ensure that the set of random numbers is a good representative of real variability, and the RBF model is adopted to approximate the responses of objectives and constraints. Finally, the multi-objective optimization (MOO) method based on GA with RBF model is used to solve the optimization problem of the lightweight steel-aluminum hybrid bus body. The results show that compared with the traditional fully steel body, the use of the aluminum alloy lower-frame structure can reduce body mass by 38.4%, and the proposed optimization method can further reduce the mass of the steel-aluminum body to 4.28% without affecting the structural stiffness and strength performance of the body.