This paper examines the analysis of NVH (noise, vibration, and harshness) phenomena generated by vehicle drivetrains powered by internal combustion engines and electric motors. It considers the identification, evaluation, and optimization of these phenomena. Firstly, it introduces the current understanding of the vibrational performance, sources, and classification as well as designation methods in the field of vehicle drivetrain, along with the related issues of stochastic dynamic. On this basis of theory, machine learning is introduced to obtain an automatic, real-time identification method. Secondly, the paper discusses new technologies and directions for optimizing NVH performance of multi-source vehicle drivetrains. These include structural optimization, passive and active control approaches. The paper provides guidance for the advanced technological development in vibration reduction and noise abatement. Finally, a multi-dimensional evaluation of vehicle drivetrains vibration is described, encompassing time-frequency, order, and energy domains, transient and steady states, deterministic and stochastic phenomena, as well as subjective and objective aspects. This contributes to the creation of an evaluative standard system that guides research on structural safety and passenger comfort. In conclusion, the overview of the above three parts of research contributes to the achievement of efficient and intelligent optimization of NVH performance on multiple kinds of vehicles.
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