电动汽车车内声品质评价研究进展

doi:10.19562/j.chinasae.qcgc.2024.08.010

Abstract

Abstract:

To thoroughly review the current state and identify future trends of the Interior Sound Quality of Electric Vehicles （EVs）， the research progress and distinctive features of interior sound quality of electric vehicles are introduced firstly in this paper. Then， the limitation of the A-weighted sound level in evaluating the interior sound quality of EVs is introduced in detail， alongside objective evaluation approaches that incorporate psychoacoustic parameters and several unconventional parameters. Following this， a summary of subjective evaluation methods for the interior sound quality of EVs is made， including the advantages and disadvantages. Then， the objective quantification models for the interior sound quality of EVs both at home and abroad are classified and summarized. Finally， a summary and outlook are made on the evaluation of the sound quality of electric vehicles. It is believed that in the future the evolution of this field will likely pivot towards adopting high-precision objective models over traditional subjective methods， aiming to diminish evaluation time and cost while improving accuracy.

Key words: electric vehicle, sound quality, subjective evaluation, psychoacoustic parameters, predictive models, neural networks, intelligent algorithm

Kun Qian,Ke Liu,Yanfu Wang,Haoyang Li,Jing Tan,Zhenghua Shen,Xikang Du,Jiying Duan,Jian Zhao. Progress in Research on Interior Sound Quality Evaluation of Electric Vehicles[J].Automotive Engineering, 2024, 46(8): 1431-1446.

Figures/Tables 34

References 63

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序号	局限因素	具体表现
1	声品质因素	声品质与频率内容和动态特性有关，而A声级只能提供总体的声强信息，无法提供声品质的详细信息，如谐波成分、突发声事件等。
2	频率响应因素	A声级能在一定程度上反映人耳对声音的响应，但这是基于对典型成人正常听力的平均响应曲线。并不完全适用于所有听众，也不可能准确地反映个体对于不同频率的敏感性差异。
3	心理声学因素	声音的感知还包含心理和情感反应。人们对声音的评价可能受到主观感受的影响，而通过客观测量得到的A声级无法捕捉到这些心理声学因素。
4	复杂声环境因素	实际道路条件下，电动汽车受到包括车速、道路表面、背景噪声等在内的多种因素影响。A声级无法准确反映这些复杂情境下的声品质。
5	动态特性因素	A声级通常是一个时域内的平均值，不能有效地反映电动汽车可能具有的明显动态变化，例如汽车加速时的声音变化。

评价方法	优点	缺点
语义细分法	多维度分析、直观易懂、灵活性高、结果可量化易分析	文化差异、词汇选择依赖大、主观性强、解释困难、中间偏置倾向
成对比较法	决策简化、精确度高、理解和实施简单、选择偏差少、群体通用性强	时间和资源密集、被试疲劳、缺乏绝对评价、可能的循环偏好或误判、数据分析复杂性高
等级评分法	简单直观、灵活性高、数据分析便利、对被试者要求低、多特性比较	粒度限制细微感知、中间偏置倾向、主观性强、解释困难、文化差异影响

等级程度	分值
等级程度	舒适的	强劲有力的
极其	4	4
非常	3	3
比较	2	2
有点	1	1
不确定	0	0
有点	-1	-1
比较	-2	-2
非常	-3	-3
极其	-4	-4
	烦恼的	虚弱无力的

组别	A好于B	A和B一样好	B好于A
N₁
N₂
?

烦恼度	评分
很高，须改进	1，2，3
临界情况	4，5
低，可以接受	6，7，8，9，10

Progress in Research on Interior Sound Quality Evaluation of Electric Vehicles

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评价分值	声品质主观感知描述	满意度
1	无法接受	没有声品质感
2	无法接受
3	很差
4	很差
5	须改进	有一定的声品质感，但仍须改进
6	可接受	声品质基本令人满意
7	好	声品质基本令人满意
8	很好	明显的声品质感
9	非常好	非常强烈的声品质感
10	好极了	非常强烈的声品质感

模型	优势	适用场景
传统机器学习模型	容易解释，适合处理小规模数据集，计算效率高。	适合于数据量不大、特征较少且需要模型可解释性强的车内声品质评价场景。
人工神经网络模型	擅长捕捉数据中的复杂模式，适合于非线性问题。	在有足够数据支持的情况下，适用于需要高度非线性建模的电动汽车车内声品质评价。
集成学习模型	通常比单一模型有更高的预测准确性，能有效处理各种数据类型，具有较好的泛化能力。	适用于中等规模的数据集，须平衡准确性和模型复杂度的电动汽车车内声品质评价。
深度学习模型	在处理大规模复杂数据（如图像、声音）时表现出色，能够自动提取和学习特征。	在有大量数据和足够计算资源的情况下，适用于复杂的电动汽车车内声品质评价问题，尤其是在处理音频、振动等多模态数据时。

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