In conventional gasoline automobiles, the engine powers the air conditioning system and engine noise can somewhat mask the noise and vibration of the air conditioning system. In pure electric vehicles, however, the absence of an engine makes the air conditioning system’s noise more noticeable, concentrated in a limited frequency range at constant speeds. As a result, aerodynamic noise from the air conditioning system is a primary noise source in electric vehicles. Pipeline silencers are the main method for reducing this noise. The current silencer design uses plane wave acoustic theory but when cavity modal resonance occurs, the transmission loss error is relatively high. This article addresses the issue of non-planar wave cavity resonance, studying the cavity modal of a muffler using the finite element method to reveal the transmission loss under cavity mode resonance. A dual cavity expansion structure of an impedance composite muffler is proposed, with sound-absorbing materials placed in the cavity to enhance acoustic performance. The analysis of the transmission loss characteristics of the impedance composite muffler provides a theoretical basis for noise control in pure electric vehicle air conditioning systems.

Clarifying the noise source and the contribution of each path is essential for the system’s noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network’s linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method’s outcomes are most accurate when the converter cabinet’s path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems.

A common engineering challenge is that a single sound-absorbing material cannot adequately cover the full frequency range in broadband noise control of commercial vehicle cabs; with the aim of addressing this challenge, this study employs a synergistic noise reduction method integrating hybrid finite element–statistical energy analysis (FE–SEA) with foam mixture design. A hybrid FE–SEA cab model is first established, and panel contribution analysis is used to identify key radiating areas such as the floor and rear panel, as well as their corresponding energy-concentrated frequency bands. High- and low-flow-resistance polyurethane foams and melamine foam are selected as base materials. A simplex-centroid mixture design is adopted to construct a Scheffé regression model using the average noise reduction contribution in frequency bands as the response, and Derringer–Suich multi-response optimization is applied to obtain the optimal proportions. Full-vehicle simulation results show that the optimized formulation achieves noise reduction contributions of 13.87, 16.42, and 10.84 dB in the 200–500 Hz, 800–1600 Hz, and 2000–3150 Hz bands, respectively, without evident weak-band performance, reducing the overall SPL to 68.25 dB(A). The proposed method establishes a bidirectional linkage between vehicle acoustic response and material formulation, providing a reproducible framework for the forward design and broadband acoustic optimization of commercial vehicle acoustic packages.