Multitarget prediction and optimization of pure electric vehicle tire/road airborne noise sound quality based on a knowledge- and data-driven method

Abstract

With the promotion of pure electric vehicles (PEVs), the overall interior noise level has been gradually reduced. Tire/road noise is increasingly becoming noticeable in PEVs and represents a primary concern for passengers. Vehicle acoustic packages are crucial for suppressing tire/road noise, and numerous studies have focused on improving the acoustic package performance and sound quality of tire/road noise. However, the prediction and optimization of tire/road acoustic comfort have two deficiencies: (1) The characteristics and transfer paths of tire/road noise are complex. Using knowledge-driven methods (such as simulation models) and data-driven methods (such as neural networks) for analysis has difficulty in accuracy parameter acquisition and unexplainable models. (2) The sound quality of tire/road noise is multidimensional. In the development of multitarget prediction models, when multiple targets contradict each other, prediction bias may result from wide differences among targets in the gradient value of the loss function in training. Therefore, in this paper, a knowledge- and data-driven method is proposed, which introduces the knowledge graph technique to develop a vehicle tire/road noise knowledge graph architecture and uses an improved residual network to drive reasoning in the domain knowledge graph. In addition, a multitarget prediction method based on the adaptive balanced learning mechanism for a residual network is proposed, which uses the dynamic weighted average method to adaptively adjust the loss weight of each target according to the convergence speed and learning difficulty of each target. The proposed dual-drive method is applied to predict and optimize the performance of vehicle acoustic package and to further improve the multiple sound quality metrics of tire/road noise. In the experimental validation, the proposed method outperforms the traditional prediction and optimization methods in effectiveness and robustness.