Optimization of electric vehicle sound package based on LSTM with an adaptive learning rate forest and multiple-level multiple-object method

Abstract

The sound absorption and sound insulation performance of an acoustic package (AP) system directly affect the noise, vibration and harshness performance of a vehicle. Numerous studies have studied the optimization of vehicle sound package, however, there are two deficiencies in the current research of sound package: (1) The noise transmission path of acoustic package is complex and hierarchical. Most of the related works focus on the data-driven part while ignoring the knowledge attributes behind the acoustic package design problem, which limits the further improvement of prediction and optimization of acoustic package performance. (2) In using intelligent neural networks-based methods such as long short-term memory (LSTM), reducing the learning rate during training gradually narrows the search interval of a solution, and adjusting the learning rate in a small range may tend to trap local optima. In this study, a knowledge- and data-driven approach is proposed for the development of acoustic package systems. A multiple-level multiple-object method is proposed as the knowledge model, and a multilayer structure of the acoustic package system that contains the system, subsystem and component layers is developed. In addition, an improved long short-term memory model based on an adaptive learning rate forest, which can increase and decrease the learning rate adaptively, is proposed as the data-driven model. The knowledge- and data-driven method is applied to optimize the sound absorption and insulation of the acoustic package system. In the experimental validation, the effectiveness and robustness of the proposed method outperformed the traditional direct mapping method and the conventional long short-term memory method.