# Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks
## 1 Introduction
The artificial neural network (ANN) is known as a broadly used approach in vehicle engineering and acoustical engineering, and the relationship between the inputs and outputs in ANNs is nonlinear. Meanwhile, it is possible to design an SQ estimation model by utilizing an ANN to simulate the subjective assessment model in an approximation process of the subjective perception. Many researchers have applied ANNs to SQ evaluation, and they have achieved reliable results [15; 16; 17; 18; 19; 20]. [PERSON] and [PERSON] [15] used ANNs to predict the annoyance evaluations of the pass-by noise of EVs and found that most of the parameters have no linear influence on the annoyance perception. [PERSON] et al. [16] developed an SQ prediction model of heavy commercial vehicles based on the back propagation artificial neural network (BP-ANN), and their proposed model showed high precision and good generalization. Moreover, [PERSON] et al. [17] established an SQ evaluation model based on the BP-ANN to  
Figure 1: Comparison of the interior noise between electric vehicles (EVs) and internal combustion engine vehicles (ICEVs): (**a**) comparison of the interior noise level between EVs and ICEVs at different constant speeds; (**b**) comparison of the noise spectrum in EVs and ICEVs at a certain constant speed. SPL, sound pressure level. evaluate and predict the abnormal noise of permanent magnet synchronous motors in the steady-state working condition of EVs, and [PERSON] et al. [18] evaluated the SQ of EVs under steady-state conditions and established an SQ valuation model based on an ANN. However, the evaluation of the SQ under unsteady conditions needs to be further studied. [PERSON] et al. [19] proposed a method for predicting the SQ of vehicle interior noise using a BP-ANN that is based on particle swarm optimization (PSO). In addition, [PERSON] et al. [20] established an SQ evaluation model of hub-driven motors based on a BP-ANN, and they also proposed the limitations of the BP-ANN model. In order to improve the prediction accuracy, the structural parameters of BP-ANNs need to be optimized, but this process consumes a lot of time. Generally, it is feasible to describe the subjective perception using objective measurable physical parameters and psychoacoustic parameters. Intelligent algorithms and artificial neural network technologies can be used to complete the comprehensive evaluation of the SQ of EVs instead of the subjective evaluation of humans, where an objective quantitative model for the subjective evaluation of SQ in EVs can be established based on the physiological and psychological characteristics of auditory systems. In this paper, an SQ estimation model was designed to assess the SQ in EVs by means of the GA-BP ANN. The paper is organized as follows. Section 2 describes the interior sound measurement of EVs, the subjective jury test, and the subjective and objective evaluation of the SQ. In Section 3, the MLR and the GA-BP ANN models of the SQ in EVs are established, respectively, according to the subjective and objective evaluation results. Section 4 discusses the advantages of the GA-BP ANN model in detail by comparing it with the MLR model, and the final section presents the conclusion of the research.