Multi-Objective Optimization of the Electric Bus Body Frame Based on the PSO-BP-MOMVO Approach

<div class="section abstract"><div class="htmlview paragraph">This paper focuses on the design optimization of a commercial electric bus body frame with steel-aluminum heterogeneous material orienting the performances of strength, crashworthiness and body lightweight. First, the finite element (FE) model of the body frame is established for static and side impact analysis, and the body frame is partitioned into several regions according to the thickness distribution of the components. The thicknesses of each region are regarded as the variables for the sensitivity analysis by combining the relative sensitivity method and the Sobol index method, and nine variables to which the performance indexes are more sensitive are selected as the final design variables for design optimization. Then the surrogate models are developed, and in order to improve the accuracy of the surrogate models, a model-constructing method called the particle swarm optimization BP neural network (PSO-BP) data regression prediction is proposed and formulated. In this method, the particle swarm optimization (PSO) algorithm is adopted to optimize the weights of each node of the BP neural net to improve the prediction accuracy of the output response of the surrogate models. Subsequently, a multi-objective multi-constraint optimization problem is established with the objective of overall minimization of the vehicle mass, the frame stress under torsional conditions and the side impact intrusion. By fusing the trained PSO-BP model into the multi-objective multi-verse optimization (MOMVO) algorithm, the optimization problem is solved. The optimization results are evaluated by FE simulations, and it is revealed that the mass, the stress and the side impact intrusion are reduced by 4.8%, 10.5% and 15.2% respectively after optimization.</div></div>