Exploration on sound quality prediction and evaluation of a range-extended electric vehicle under idle conditions

This paper investigates the impact of in-vehicle noise on ride comfort of range-extended electric vehicles (REEVs) under the parked idle condition. Aiming at the problem of the need to improve the noise quality of the range extender, systematic subjective and objective sound quality evaluation experiments were carried out, and a grid-search-optimized support vector regression model (GS-SVR) was constructed for sound quality prediction and analysis. Steady-state noise samples of the range extender were collected in a semi-anechoic environment. Psychoacoustic parameters and key physical acoustic features were extracted, and a correlation analysis was conducted in combination with subjective scores to screen out special acoustic indicators significantly related to auditory perception. Subsequently, multiple groups of sound quality prediction models were established with traditional indicators, special indicators, and their combinations as inputs, respectively, and a grid-search-optimized random forest model was introduced for comparison. The results show that the GS-SVR model performs best when using special indicators as inputs. Metrics such as RMSE, MAE, R 2 , and precision are superior to those of other models, demonstrating stronger nonlinear fitting ability and generalization stability. This study not only validates the effectiveness of the GS-SVR model in range extender sound quality modeling but also establishes a set of special acoustic indicator systems with clear physical meaning and engineering applicability, providing a methodological reference for the quantitative evaluation of REEV noise quality and the optimization of subjective comfort.