Vehicle structural road noise prediction based on an improved Long Short-Term Memory method

Xiongying  Yu; Ruxue  Dai; Jian  Zhang; Yingqi  Yin; Sha  Li; Peisong  Dai; Haibo  Huang

doi:10.59400/sv2022

Vehicle structural road noise prediction based on an improved Long Short-Term Memory method

Xiongying Yu Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd, Chongqing 400023, China; State Key Laboratory of Vehicle Noise, Vibration and Harshness (NVH) and Safety Technology, Chongqing 401120, China
Ruxue Dai School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Jian Zhang Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd, Chongqing 400023, China
Yingqi Yin School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Sha Li Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd, Chongqing 400023, China
Peisong Dai School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Haibo Huang School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Article ID: 2022

DOI: https://doi.org/10.59400/sv2022

Keywords: NVH; road noise; empirical knowledge; LSTM; neural network

Abstract

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.

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Published

2025-01-09

How to Cite

Yu, X., Dai, R., Zhang, J., Yin, Y., Li, S., Dai, P., & Huang, H. (2025). Vehicle structural road noise prediction based on an improved Long Short-Term Memory method. Sound & Vibration, 59(1), 2022. https://doi.org/10.59400/sv2022

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