# Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks
## 1 Introduction  
Nowadays, electric vehicles (EVs), which are driven by electric motors, are becoming very popular because they do not emit pollutants to the environment. Owing to the lack of the internal combustion engine noise and its masking effect in EVs, the noise level of EVs is much lower than that of conventional internal combustion engine vehicles (ICEVs), as shown in Figure 0(a). In addition, the aerodynamic noise and road noise contribution to the interior noise in EVs is significant in comparison with ICEVs. Moreover, on the basis of the new noise components of electric motors, the sound characteristics are different, as shown in Figure 0(b). The electromagnetic noise, which is unique to EVs, makes the subjective perception very irritating [1, 2]. For example, drivers and passengers are often plagued by \"howling\" and \"buzzing\" sounds under certain EV operating conditions [3, 4]. This largely contributes to the fact that the SQ in EVs has become a major concern for their drivers and passengers [5]. Moreover, the experience in estimating noise in ICEVs cannot be simply used with EVs [6]. Therefore, a new SQ evaluation method is needed for EVs. At present, the subjective SQ evaluation in EVs is carried out by organizing jury tests, where listening tests are conducted, and each sound sample is subjectively scored and evaluated [7, 8]. However, listening tests are not only time-consuming, but they are also influenced by external interferences [9]. If a universally applicable intelligent evaluation model can be established, the subjectiveperception of SQ can be directly obtained from the objective parameter of the sound signals in the case of similar events, which can greatly save manpower and material resources. Therefore, on the basis of the physiological and psychological characteristics of auditory systems, it is necessary to study the whole process from the subjective evaluation of sound signal detection and noise processing to modeling so as to realize a rapid and accurate evaluation of the intelligent SQ for EVs. Most of the existing SQ estimation models were constructed using the multiple linear regression (MLR) method [10; 11; 12; 13; 14]. In [10], a mathematical model of the preference and annoyance between the objective psychoacoustic parameters and the subjective evaluation was established using the MLR method. Moreover, using correlation and regression analyses, a subjective annoyance model of EVs, which can describe the correlation between the subjective evaluation and the objective parameters, was established [11]. In [12], an SQ subjective evaluation of hybrid electric vehicles was quantified based on the tone, roughness, and sharpness, and a linear regression model of the sample was established. In addition, in [13], through curve estimation and linear regression analysis, an SQ evaluation model of EVs was proposed, where the modeling error between the subjective perception value and the objective parameters was less, and the subjective perception was mainly affected by the loudness, sharpness, and A-weighted sound pressure level (SPL). In [14], a sound index for estimating annoyance using independent variables was developed to evaluate the SQ in trains using statistical analyses. However, the existing MLR model of the SQ cannot directly map the complex nonlinear relationship between the subjective perception and the objective evaluations, and its evaluation accuracy is also relatively low. This is because the relationship between the human subjective perception and the objective parameters is nonlinear and has strong randomness. Generally, there are many factors that lead to differences in the subjective perception of SQ, and these factors often have a nonlinear relationship with subjective perception. Therefore, it is necessary to establish a more accurate nonlinear model. With the advancement of machine learning and artificial networks, this problem can be well solved.