Aiming at the consensus problem of slow convergence for the active noise control (ANC) model based on standard FxLMS algorithm that leads to performance degradation, this paper takes the error signal and its variation as the inputs of fuzzy logic control, and proposes an improved FxLMS algorithm by fuzzy control mechanism with two-input-two-output TSK fuzzy rules (TSK-FxLMS); In addition, the four-channel ANC models based on standard FxLMS and TSK-FxLMS are constructed using the noise signals from four measuring points inside an electric bus under uniform and variable speed conditions, respectively. Ultimately, the offline simulation and acoustic parameter calculation results indicate that the A-weighted sound pressure level (ASPL) and loudness of the two FxLMS models within the low and middle frequencies are significantly reduced, whereas the TSK-FxLMS model has faster convergence rate, higher average reduction percentage of ASPL and loudness, which proves that the established four-channel TSK-FxLMS model has a better sound quality improvement effect than the standard FxLMS.

The research on vehicle sound quality is an emerging and highly promising field. This paper proposes a solution for sound quality improvement based on sensitivity analysis of psycho-acoustic parameters, in which the main acoustic techniques involved include noise signal acquisition, subjective and objective evaluations, sound quality modelling and prediction, parameter sensitivity and spectral analysis, and fuzzy comprehensive evaluation. In addition, taking 64 noise samples of electric bus as an application case, the presented sound quality analysis steps are implemented successively, and the obtained test results of improving the whole vehicle sound quality verify the feasibility and effectiveness of the proposed scheme.

This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.