A technique for estimating air-borne noise propagation characteristics from the battery unit to the vehicle interior to consider high-frequency ripple noise

Kenya Fujii (Honda Motor)･Hitoshi Taira (AUTO TECHNIC JAPAN)･Naoki Toyama (Honda Motor)

We developed CAE technology to predict the air propagation characteristics of high-frequency ripple noise generated by the high-voltage battery unit of an electric vehicle. The development method used FEM and coupled analysis of the exterior acoustics, structure, and interior acoustics models. By appropriately considering the Biot parameters of the carpet and the interior sound absorption coefficient, we obtained CAE results that showed good agreement with actual measurements in the high-frequency.

NVH Development Process and Modeling Techniques for Electric Vehicles Using a Driving Simulator

Kenji Torii･Shion Mise･Fujii Kenya (Honda Motor)･Sellerbeck Philip･Philippen Bernd (HEAD acoustics)･Kenta Tanabe (HEAD acoustics Japan)

In the development of electric vehicles, there is a strong demand for enhanced development efficiency through shorter development cycles and reduced costs and labor. To address this need, the authors have established a development process that utilizes a driving simulator to evaluate NVH performance from the early design before the production of prototype vehicles. This presentation introduces an overview of the proposed development process and the modeling techniques that support it.

Development of a Machine Learning Model to Predict Engine Noise Perception Based on Cabin Noise and Vehicle Parameters

Shinichi Suganuma (Chuo University, Graduate School of Science and Engineering)･Shimpei Nagae (Nissan Motor)･Takeshi Toi (Chuo University)

Interior cabin noise and vehicle parameters were recorded simultaneously during on-road driving while drivers rated engine-noise annoyance on a three-level scale. A machine-learning model using only cabin noise as input achieved 56% accuracy in predicting these ratings. Adding engine speed, driveshaft torque, and vehicle speed increased accuracy to 65%. These findings demonstrate that vehicle parameters, in addition to cabin noise inputs, significantly influence perceived engine-noise annoyance.

Development of a Machine Learning Model to Predict Engine Noise Perception Considering Temporal Driving Conditions

Shinichi Suganuma (Chuo University, Graduate School of Science and Engineering)･Shimpei Nagae (Nissan Motor)･Takeshi Toi (Chuo University)

Engine speed, driveshaft torque, and vehicle speed—the driving parameters most strongly associated with subjective engine-noise annoyance—were used as inputs to a machine-learning model that predicted three-level annoyance with 65 % accuracy. Adding a five-second history of engine speed preceding the subjective evaluation raised the accuracy to 73 %. These results quantitatively demonstrate that short-term temporal factors significantly influence perceived engine-noise annoyance during driving.

Development of automatic evaluation system for BSR (2nd Report)

Tatsuya Sakuishi･Kazutaka Yonemori･Takaaki Yamanaka･Yoshinari Tokunaga･Yohei Kurami (Nissan Motor)

At the 2024 Autumn Conference, we reported on the development of an automatic evaluation system for abnormal noises, specifically BSR (buzz, squeak, and rattle), caused by rattling and friction. To apply this system to vehicle development, we worked on automatic classification of noise occurrence locations and utilized past knowledge to propose causes and countermeasures. Additionally, we enhanced noise detection performance through advanced signal processing. The details of these developments were presented at the conference.

Study on Psychoacousic Indices for Evaluating Annoyance Caused by Fluctuating Wind Noise

Tomoya Washizu (Nissan Motor)･Toshihiko Komatsuzaki (Kanazawa University)･Takuya Yoshimura (Tokyo Metropolitan University)･Akiyoshi Iida (Toyohashi University of Technology)･Toru Yamazaki (Kanagawa University)･Yuichi Matsumura (Gifu University)･Takenori Miyamoto･Keiichiro Iida (Suzuki Motor)･Keiichi Taniguchi (Nissan Motor)

A large scale auditory evaluation test was conducted to investigate the characteristics of fluctuating wind noise, which is unpleasant for passengers during high-speed driving.
Psychoacoustic indices highly correlated with the annoyance got from the auditory evaluation test were studied using psychoacoustic evaluation quantities, including sharpness and roughness, as well as modulation spectrum analysis.