A novel prediction model of total drag coefficient of an MPV vehicle based on LSA optimization

Abstract

Environmental degradation and energy scarcity are caused by the ongoing rise in the number of automobiles. The most efficient way to lower air resistance while driving is to boost vehicle energy efficiency and reduce energy consumption. In this case, a specific MPV model serves as the study subject in this work, and we use CFD simulation to estimate the drag coefficient along with the selection of important factors. Furthermore, to increase prediction accuracy, a lightning search algorithm (LSA)-based BP-neural-network prediction model is created and developed. The results show that the average relative error reduction of 2.83% in the drag resistance model optimized by the lightning search method represents a significant improvement in accuracy. This phenomenon can be explained by the LSA’s ability to resolve the BP neural network’s initial weight and threshold uncertainty as well as the issue of local minima being quickly reached. The high prediction accuracy drag prediction model, obtained herein, is useful for creating and producing automobile shapes with low drag coefficients.