## 1 Introduction
[25] proposed an active sound quality control algorithm based on local sub-path estimation and filtering error structure, which designed and realized powerful sound quality of real vehicle internal noise during acceleration. [PERSON] et al. [26] created an analytical model of the giant magneto strictive actuator and considered the magneto strictive force and the properties of the proposed giant magneto strictive actuator through electromagnetic field analysis. An active noise control (ANC) system that utilizes the wall vibrations generated by the GMA is proposed to achieve a noise-cancelling effect. [PERSON] et al. [27] introduced the design of an active and robust sound control system, and proposed a multi-input multi-output robust feedback control strategy to reduce the sound pressure level of all occupant ears. [PERSON] et al. [28] considered a new algorithm for actively controlling sound quality in the cabin using a virtual error microphone (VEM). The VEM-based ASQC (VEM-ASQC) algorithm in the study is designed to relocate the controlled sound region formed at the wrong microphone position to the driver's ear position where the virtual wrong microphone is located. The experimental results show that the performance of the VEM-ASQC algorithm achieves the required sound quality within a small error range. [PERSON] et al. [29] studied interior noise sound quality when engine idle at a constant speed in different speed operation. Vehicle sound quality was studied using the support vector machine (SVM) model. The results showed that car interior sound quality was mainly affected by four psycho-acoustical parameters: A-weighted sound pressure level, loudness, roughness, and jitter degree. [PERSON] et al. [30] proposed a sound quality index for evaluating in-vehicle noise based on full-open test conditions and constant speed test conditions, determined the most critical factors under the two test conditions, and obtained the corresponding sound quality equation by multi-factor regression method. [PERSON] et al. [31] proposed a novel AST system configured with the frequency domain inverse Model LMS (FDIMLMS) algorithm to reshape vehicle power system responses according to predetermined internal vehicle sound quality standards. The effectiveness of the proposed system Is verified by numerical simulations using measured powertrain noise signals under constant and starting engine running conditions. [PERSON] et al. [32] optimized the rear door of eVs running at a constant speed of 40 km/h by means of experiments and simulations,and added a dynamic vibration absorber to make its modal frequency avoid the modal frequency of the acoustic cavity, so as to reduce the noise inside the vehicle. At 42 Hz, the noise of the whole vehicle is reduced by 8 dBA, and the ear pressure is reduced, thereby improving the noise, vibration, and harshness (NVH) performance of the vehicle. With the improvement of the requirements for comfort and sound quality, the research on sound quality of EVs at constant speed and the development of ASGS based on subjective evaluation are more worthy of attention. Therefore, this paper carries out research on the evaluation method and the development of ASGS for EVs under uniform speed condition and proposes a sound design method for the vehicle under uniform speed, which focuses on three dimensions: engine order composition, spectral energy distribution, and sound amplitude enhancement in typical speed range. The control hardware and software of the in-vehicle ASGS are developed, the ASGS based on the in-vehicle sound system is constructed, and the experimental verification is carried out, which lays the foundation for the sound quality research of EVs under uniform speed conditions.