Interior Acoustic Control System Using Boundary Vibration with Giant Magnetostrictive Actuator: Improving Noise Reduction Through Actuator Placement and Secondary Path Construction

Ultra-compact electric vehicles (EVs) have gained popularity due to their compact size, lightweight design, affordability, and environmental friendliness. However, significant interior noise, mainly from road and wind noise, remains a challenge. To address this, we developed an active noise cancellation (ANC) system using a giant magnetostrictive actuator installed on the roof to generate counteracting sound waves through roof panel vibrations. This study employed a variant of the least mean squares (LMS) algorithm, the Filtered-X LMS algorithm, to achieve real-time noise cancellation by continuously updating the coefficients of the FIR adaptive filter. In addition, the influence of introducing and not introducing a secondary path estimation module in the noise reduction system on the overall noise reduction performance was also studied. Furthermore, this study analyzed the effects of different excitation points on noise reduction performance and proposed using the power spectral density method to examine the sound spectra and power density at various excitation points, aiming to explore the reasons behind how the actuator’s fixed position influences noise reduction performance. Finally, experiments on ANC of simulated road noise were conducted on an ultra-compact EV, and the results verified the effectiveness of incorporating secondary path characteristics and selecting an appropriate excitation point in enhancing active noise cancellation performance.

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Publication Details

Journal
Journal of Advanced Computational Intelligence and Intelligent Informatics
Published
2025-09-19
DOI
https://doi.org/10.20965/jaciii.2025.p1117
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
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article

Interior Acoustic Control System Using Boundary Vibration with Giant Magnetostrictive Actuator: Improving Noise Reduction Through Actuator Placement and Secondary Path Construction

Taro Kato, Kentaro Sawada, Ayato ENDO, Hideaki KATO et al.
Journal of Advanced Computational Intelligence and Intelligent Informatics
Advanced Adaptive Filtering Techniques
article

Interior Acoustic Control System Using Boundary Vibration with Giant Magnetostrictive Actuator: Improving Noise Reduction Through Actuator Placement and Secondary Path Construction

Taro Kato, Kentaro Sawada, Ayato ENDO, Hideaki KATO, Yudai Tanaka, Ikkei Kobayashi, Koichi Ikeda, Daigo Uchino, Kazuki Ogawa, Takayoshi NARITA, Jumpei Kuroda, W. Wu
article en

Abstract

Ultra-compact electric vehicles (EVs) have gained popularity due to their compact size, lightweight design, affordability, and environmental friendliness. However, significant interior noise, mainly from road and wind noise, remains a challenge. To address this, we developed an active noise cancellation (ANC) system using a giant magnetostrictive actuator installed on the roof to generate counteracting sound waves through roof panel vibrations. This study employed a variant of the least mean squares (LMS) algorithm, the Filtered-X LMS algorithm, to achieve real-time noise cancellation by continuously updating the coefficients of the FIR adaptive filter. In addition, the influence of introducing and not introducing a secondary path estimation module in the noise reduction system on the overall noise reduction performance was also studied. Furthermore, this study analyzed the effects of different excitation points on noise reduction performance and proposed using the power spectral density method to examine the sound spectra and power density at various excitation points, aiming to explore the reasons behind how the actuator’s fixed position influences noise reduction performance. Finally, experiments on ANC of simulated road noise were conducted on an ultra-compact EV, and the results verified the effectiveness of incorporating secondary path characteristics and selecting an appropriate excitation point in enhancing active noise cancellation performance.

Journal of Advanced Computational Intelligence and Intelligent InformaticsVol. 29(5)
Fukuoka Institute of Technology (JP), Hokkaido University of Science (JP), Tokai University (JP), Tokyo University of Technology (JP), National Institute of Technology, Numazu College (JP), Aichi University of Technology (JP)
Openalex Percentile: Top 73%
Advanced Adaptive Filtering Techniques
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