Uncertainty optimization of pure electric vehicle interior tire/road noise comfort based on data-driven

Abstract

Without the masking effect of engine noise, tire/road (TR) noise is increasingly becoming noticeable in pure electric vehicles (PEVs) and represents a primary concern for drivers and passengers. Currently, numerous works have studied PEV motor and powertrain noises, but few studies have investigated the tire/road structure-borne (TRS) noise of PEVs. Therefore, in this paper, the sound quality of TRS noise is studied through objective and subjective evaluations, and the group paired comparison method (GPCM) is proposed to evaluate a large noise sample set. The correlation between sound quality metrics and the subjective annoyance of TRS noise is analyzed, and the contribution of chassis dynamic parameters to the TRS noise of PEVs is quantified. In addition, because of the nonlinear transfer and complex characteristics of TRS noise, the expected design results will easily be affected by material, processing and manufacturing uncertainties, which are difficult to process with conventional optimization methods. Therefore, to overcome the uncertainty problem, an improved interval analysis method (IIAM) is proposed. This method is used to optimize the interior TR sound quality of PEVs while treating riding comfort as a constraint. The optimized result of the IIAM is compared with that of the advanced genetic algorithm (GA) optimization method. Through real vehicle verification, the proposed IIAM outperforms the GA method in terms of accuracy and robustness.