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The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function.

The technology of active sound generation (ASG) for automobiles is one of the most effective methods to flexibly achieve the sound design that meets the expectations of different user groups, and the active sound synthesis algorithms are crucial for the implementation of ASG. In this paper, the Kaiser window function-based the harmonic synthesis algorithm of automobile sound is proposed to achieve the extraction of the order sounds of target automobile. And, the suitable fitting functions are utilized to construct the mathematical model between the engine speed information and the amplitude of the different order sound. Then, a random phase correction algorithm is proposed to ensure the coherence of the synthesized sounds. Finally, the analysis of simulation results verifies that the established method of the extraction and synthesis of order sound can meet the requirements of target sound quality.

In this paper, we propose a method of dynamics simulation and analysis based on superelement modeling to increase the efficiency of dynamics simulation for vehicle body structure. Using this method, a certain multi-purpose vehicle (MPV) body structure was divided into several subsystems, and the modal parameters and frequency response functions of which were obtained through superelement condensation, residual structure solution, and superelement data restoration. The study shows that compared to the traditional modeling method, the computational time for vehicle body modal analysis can be reduced by 6.9% without reducing accuracy; for the purpose of structural optimization, the computational time can be reduced by 87.7% for frequency response analyses of optimizations; consistency between simulation and testing can be achieved on peak frequency points and general trends for the vibration frequency responses of interior front row floors under accelerating conditions.