作者：廖连莹1,2, 左言言2, 周翔1, 孟浩东1, 廖旭晖1, 吴赛赛2
作者单位：1. 常州工学院汽车工程学院, 江苏 常州 213032;
2. 江苏大学振动噪声研究所, 江苏 镇江 212013
关键词：混合动力汽车;声品质;匀速工况;遗传算法;BP神经网络
摘要：
为快速准确评价混合动力汽车车内声品质，在分析BP神经网络和遗传算法（GA）特点的基础上，利用遗传算法对BP神经网络的权值和阈值进行优化，从而建立GA-BP的混合动力汽车声品质客观评价模型。利用此模型进行混合动力汽车匀速工况车内声品质预测后，把GA-BP模型预测结果与多元线性回归模型和传统BP神经网络模型预测结果进行比较。对比结果显示GA-BP模型预测结果精度最高。证明所建立的GA-BP声品质预测模型的有效性，说明该模型较适用于混合动力汽车车内声品质预测。
Sound quality evaluation model of hybrid electric vehicle in constant speed working conditions based on GA-BP neural network
LIAO Lianying1,2, ZUO Yanyan2, ZHOU Xiang1, MENG Haodong1, LIAO Xuhui1, WU Saisai2
1. School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China;
2. Institute of Noise and Vibration, Jiangsu University, Zhenjiang 212013, China
Abstract: In order to quickly and accurately evaluate the sound quality of hybrid electric vehicle (HEV), after analyzing the BP neural network and genetic algorithm (GA) characteristics, GA-BP sound quality objective evaluation model of HEV was established through using genetic algorithm to optimize the weights and thresholds of BP neural network. Using this model, the sound quality of HEV in constant speed working conditions is predicted. The prediction results of GA-BP model are compared with the results of multiple linear regression model and traditional BP neural network model. The results show that the GA-BP model has the best effect. At the same time, it proves the effectiveness of the GA-BP model, which indicates that the model is suitable for predicting the interior sound quality of HEV.
Keywords: hybrid electric vehicle;sound quality;constant speed working condition;GA;BP neural network
2019, 45(5):128-133 收稿日期: 2017-09-03;收到修改稿日期: 2017-11-16
基金项目: 国家自然科学基金（51575238）；江苏省博士后科研资助计划资助项目（1601064C）
作者简介: 廖连莹(1978-),男,福建长汀县人,副教授,博士,主要从事车辆振动与噪声控制研究
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