本文關鍵詞： 電動車動力總成 噪聲品質 粒子群優(yōu)化 支持向量機 敏感頻帶能量比　出處：《西安交通大學學報》2016年01期 　論文類型：期刊論文

【摘要】：為了實現(xiàn)電動車動力總成噪聲品質的預測,以某集中驅動式電動車為例,在考慮動力總成輻射噪聲品質頻域特性和已設立的敏感頻帶能量比這一客觀評價參數(shù)的基礎上進行了心理聲學參數(shù),即響度、尖銳度、粗糙度、抖動度、語音清晰度等與主觀評價的相關性分析,由此建立了電動車動力總成噪聲品質粒子群支持向量機預測模型,內容涉及采用支持向量機建立噪聲品質預測模型、利用粒子群優(yōu)化算法對向量基懲罰因子及核函數(shù)參數(shù)進行優(yōu)化,最后驗證了敏感頻帶能量比評價參數(shù)的有效性。研究結果表明:敏感頻帶能量比與主觀評價相關度達到0.946,可以較好地反映主觀感受;基于粒子群支持向量機的噪聲品質預測模型的平均相對誤差和最大相對誤差分別為2.0%和6.7%,表明以敏感頻帶能量比作為輸入特征的粒子群優(yōu)化支持向量機模型,在電動車動力總成噪聲品質的預測精度上優(yōu)于基于遺傳算法優(yōu)化及網格搜索優(yōu)化的預測模型。
[Abstract]:In order to predict the noise quality of electric vehicle powertrain, a centralized drive electric vehicle is taken as an example. On the basis of considering the frequency domain characteristic of radiated noise quality of power assembly and the objective evaluation parameter of sensitive band energy ratio, the psychoacoustic parameters, namely loudness, sharpness, roughness and jitter, are carried out. Based on the correlation analysis between speech articulation and subjective evaluation, a prediction model of noise quality particle swarm optimization support vector machine (PSO) for electric vehicle powertrain is established, which involves the establishment of noise quality prediction model using support vector machine (SVM). The particle swarm optimization algorithm is used to optimize the vector basis penalty factor and kernel function parameters. Finally, the validity of the evaluation parameters of the sensitive band energy ratio is verified. The results show that the correlation degree between the sensitive band energy ratio and the subjective evaluation is 0.946, which can better reflect the subjective feeling. The average relative error and maximum relative error of the noise quality prediction model based on particle swarm optimization support vector machine are 2.0% and 6.7% respectively. It is shown that the particle swarm optimization support vector machine model is based on the sensitive band energy ratio as the input feature. The prediction accuracy of the noise quality of electric vehicle powertrain is better than the prediction model based on genetic algorithm optimization and grid search optimization.
【作者單位】： 同濟大學新能源汽車工程中心;同濟大學汽車學院;同濟大學中德學院;
【基金】：國家“863計劃”資助項目(20U11AA11A265) 國家自然科學基金資助項目(51205290) 中央高�；究蒲袠I(yè)務費專項資金資助項目(1700219118)
【分類號】：U469.72
【正文快照】： 大量的聲學研究發(fā)現(xiàn),A計權聲壓級不能完全反映人對噪聲的主觀感受。在這種情況下,噪聲品質這個現(xiàn)代噪聲研究的全新概念應運而生,它指出人對噪聲的感覺是受心理和生理因素的共同影響。噪聲品質的準確預測是對產品聲學優(yōu)化設計的重要前提。噪聲品質預測研究包括車內噪聲[1-2]、

【共引文獻】

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本文編號：1461994