【摘要】 为评价混合动力汽车声品质特性,并对混合动力汽车声品质进行优化,建立了粒子群和遗传算法优化的神经网络混合动力汽车声品质客观评价模型。计算响度、AI指数、波动度、粗糙度、尖锐度和A声压级对声品质的贡献度,提出了利用聚酯与聚丙烯多孔吸音材料,联合丁基橡阻尼材料对混合动力汽车进行宽频域的减振降噪来提升车内声品质。通过对车内声品质优化试验结果的对比分析发现,优化后的混合动力汽车的车内声品质客观参数明显改善,声品质预测值提升了24.8%,说明所采取的声品质优化策略可靠有效。更多还原

【Abstract】 In order to evaluate the sound quality characteristics of HEV and optimize the sound quality of HEV, a neural network HEV sound quality objective evaluation model optimized by particle swarm optimization and genetic algorithm is established. The contributions of loudness, AI index, fluctuation, roughness, sharpness and A-weighted sound pressure level are calculated based on the established model. According to the contributions of objective parameters of sound quality, polyester and polypropylene porous sound-absorbing materials and butyl rubber damping materials are used to reduce vibration and noise of HEV in a wide frequency domain to improve the HEV interior sound quality. Through the comparative analysis of the test results before and after the interior sound quality optimization, the results show that all the objective parameters about the interior sound quality of the optimized HEV have been significantly improved, and the predicted value of the sound quality has increased by 24.8%, which shows the effectiveness of the sound quality optimization strategy.更多还原