The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assem...The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.展开更多
In the design of the motor used for electric vehicles(EVS),vibration and noise problems are often ignored,which reduce the reliability and service life of the motor.In this paper,an interior permanent magnet synchrono...In the design of the motor used for electric vehicles(EVS),vibration and noise problems are often ignored,which reduce the reliability and service life of the motor.In this paper,an interior permanent magnet synchronous motor(IPMSM)with high power density is taken as an example,and its electromagnetic vibration and noise problem is investigated and optimized.Firstly,the factors that generate the electromagnetic force harmonic of IPMSM are analyzed by theoretical derivation.Furthermore,the mode and electromagnetic harmonic distribution of the motor are calculated and analyzed by establishing the electromagnetic-structure-sound coupling simulation model.Then,by combining finite element method(FEM)with modern optimization algorithm,an electromagnetic vibration and noise performance optimization method is proposed in the electromagnetic design stage of the motor.Finally,an IPMSM is optimized by this method for electromagnetic vibration and noise performance.The results of comparison between before and after optimization prove the feasibility of the method.展开更多
电驱动力总成悬置系统高频结构噪声控制是电动汽车噪声、振动和声振粗糙度(noise,vibration and harshness,简称NVH)开发与研究的重要内容,悬置动特性试验因设备技术更新换代而大幅提升测试频率上限至3 kHz。以某电动汽车橡胶悬置为研...电驱动力总成悬置系统高频结构噪声控制是电动汽车噪声、振动和声振粗糙度(noise,vibration and harshness,简称NVH)开发与研究的重要内容,悬置动特性试验因设备技术更新换代而大幅提升测试频率上限至3 kHz。以某电动汽车橡胶悬置为研究对象,进行高频动特性试验研究。首先,分析悬置动特性试验边界条件,指出悬置的胶体刚度、支架模态以及试验夹具模态是影响高频动特性的3个主要因素;其次,研究橡胶悬置高频动刚度的主要特点,通过附加质量的方案对动刚度峰值及频率与影响因素的对应关系进行了辨识;然后,总结了3个主要因素对动特性的影响规律,提出电动车悬置及其支架的设计与优化原则;最后,用悬置刚度和支架质量对整车主减速器阶次噪声的改善效果进行试验,验证了橡胶悬置高频动特性研究的必要性和结论的正确性。展开更多
为了探究电驱动总成对车内噪声的影响,对某纯电动汽车进行急加速工况下的试验研究。基于阶次分析确定车内噪声与电驱动总成振动噪声之间的关联,并识别电驱动总成对车内噪声影响较大的激励;基于奇异值分解改进的工况传递路径分析(Operati...为了探究电驱动总成对车内噪声的影响,对某纯电动汽车进行急加速工况下的试验研究。基于阶次分析确定车内噪声与电驱动总成振动噪声之间的关联,并识别电驱动总成对车内噪声影响较大的激励;基于奇异值分解改进的工况传递路径分析(Operational Transfer Path Analysis,OTPA)方法,分析对车内噪声影响最大的激励通过结构路径和空气路径对车内噪声的贡献情况。结果表明由空间0阶径向电磁力引起的频率24阶激励和48阶激励对车内噪声影响较大,其中24阶激励影响最大。在低转速区间,24阶振动激励和24阶声学激励通过结构路径对车内噪声贡献和通过空气路径基本一致;在中高转速区间,24阶声学激励通过空气路径对车内噪声贡献较大;在高转速区间,24阶振动激励通过后悬置Z方向结构路径对车内噪声贡献较大。研究结果从激励源和传递路径两个方面为降低纯电动汽车车内噪声指明方向。展开更多
基金The author received the funding from Sichuan Natural Science Foundation(2022NSFSC1892).
文摘The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2019YJS181).
文摘In the design of the motor used for electric vehicles(EVS),vibration and noise problems are often ignored,which reduce the reliability and service life of the motor.In this paper,an interior permanent magnet synchronous motor(IPMSM)with high power density is taken as an example,and its electromagnetic vibration and noise problem is investigated and optimized.Firstly,the factors that generate the electromagnetic force harmonic of IPMSM are analyzed by theoretical derivation.Furthermore,the mode and electromagnetic harmonic distribution of the motor are calculated and analyzed by establishing the electromagnetic-structure-sound coupling simulation model.Then,by combining finite element method(FEM)with modern optimization algorithm,an electromagnetic vibration and noise performance optimization method is proposed in the electromagnetic design stage of the motor.Finally,an IPMSM is optimized by this method for electromagnetic vibration and noise performance.The results of comparison between before and after optimization prove the feasibility of the method.
文摘电驱动力总成悬置系统高频结构噪声控制是电动汽车噪声、振动和声振粗糙度(noise,vibration and harshness,简称NVH)开发与研究的重要内容,悬置动特性试验因设备技术更新换代而大幅提升测试频率上限至3 kHz。以某电动汽车橡胶悬置为研究对象,进行高频动特性试验研究。首先,分析悬置动特性试验边界条件,指出悬置的胶体刚度、支架模态以及试验夹具模态是影响高频动特性的3个主要因素;其次,研究橡胶悬置高频动刚度的主要特点,通过附加质量的方案对动刚度峰值及频率与影响因素的对应关系进行了辨识;然后,总结了3个主要因素对动特性的影响规律,提出电动车悬置及其支架的设计与优化原则;最后,用悬置刚度和支架质量对整车主减速器阶次噪声的改善效果进行试验,验证了橡胶悬置高频动特性研究的必要性和结论的正确性。
文摘为了探究电驱动总成对车内噪声的影响,对某纯电动汽车进行急加速工况下的试验研究。基于阶次分析确定车内噪声与电驱动总成振动噪声之间的关联,并识别电驱动总成对车内噪声影响较大的激励;基于奇异值分解改进的工况传递路径分析(Operational Transfer Path Analysis,OTPA)方法,分析对车内噪声影响最大的激励通过结构路径和空气路径对车内噪声的贡献情况。结果表明由空间0阶径向电磁力引起的频率24阶激励和48阶激励对车内噪声影响较大,其中24阶激励影响最大。在低转速区间,24阶振动激励和24阶声学激励通过结构路径对车内噪声贡献和通过空气路径基本一致;在中高转速区间,24阶声学激励通过空气路径对车内噪声贡献较大;在高转速区间,24阶振动激励通过后悬置Z方向结构路径对车内噪声贡献较大。研究结果从激励源和传递路径两个方面为降低纯电动汽车车内噪声指明方向。
