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Computer Aided Engineering (CAE) models have proven themselves to be efficient surrogates of real-world systems in automotive industries and academia. To successfully integrate the CAE models into analysis process, model validation is necessarily required to assess the models’ predictive capabilities regarding their intended usage. In the context of model validation, quantitative comparison which considers specific measurements in real-world systems and corresponding simulations serves as a principal step in the assessment process. For applications such as side impact analysis, surface deformation is frequently regarded as a critical factor to be measured for the validation of CAE models. However, recent approaches for such application are commonly based on graphical comparison, while researches on the quantitative metric for surface-surface comparison are rarely found.

This paper presents a novel lumped parameter model(LPM) and its parameter identification method for the hydraulic engine mount(HEM) with a suspended decoupler. In the new model the decoupler membrane’s variable stiffness caused by being contact with the metallic cage is considered. Therefore, the decoupler membrane in the model can be taken as a spring. As a result, two parameters of the decoupler’s variable stiffness and the equivalent piston area are added. Then the finite element method is employed to analyze the suspended decoupler membrane’s variable stiffness characteristics under the contact state with the metallic cage. A piecewise polynomial is used to fit the decoupler membrane’s variable stiffness. To guarantee the symmetry of the stiffness, the polynomial only keeps the odd power coefficients.

The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise order, and the resonance band worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant order, and the important resonance band. Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant order under a specific operating condition can be obtained after extraction.

The active engine mount (AEM) is developed in automotive industry to improve overall NVH performance. The AEM is designed to reduce major-order signals of engine vibration over a broad frequency range, therefore it is of vital importance to extract major-order signals from vibration before the actuator of the AEM works. This work focuses on a method of real-time extraction of the major-order acceleration signals at the passive side of the AEM. Firstly, the transient engine speed is tracked and calculated, from which the FFT method with a constant sampling rate is used to identify the time-related frequencies as the fundamental frequencies. Then the major-order signals in frequency domain are computed according to the certain multiple relation of the fundamental frequencies. After that, the major-order signals can be reconstructed in time domain, which are proved accurate through offline simulation, compared with the given signals.

Road NVH are becoming one of important performance controlled during passenger vehicle NVH development, especially for these EV vehicles due to lack of traditional gasoline or diesel engines noise sources. Generally speaking, traditional CAE and multi-body dynamic approaches have several drawbacks respectively, such as it is extremely difficult to get precise inputs as excitation to CAE model and non-linear parts in suspensions perform complex high frequency dynamic characteristics that is hard to be dealt with in multi-body software. Therefore, structure-borne road noise prediction has become one of difficult NVH problems in vehicle industry and eagerly, needs a systematic and scientific method. Under this circumstance, a new kind of high frequency road N&V co-simulation method has been introduced here to predict road NVH performance for one brand vehicle. This new approach includes three steps.

During current design process of vehicle engine exhaust system, the frequently-used approach mainly concerns an individual component, which usually results in not meeting the overall design requirements or unreasonable design parameters. Here a concept of comprehensive evaluation metrics for vehicle engine exhaust system was established, of which a new weight factor assignment method was proposed, named change rate method, as the core of evaluation system to be especially studied. Taking muffler as an instance, six weight factor assignment schemes were adopted to compare with each other. And the rationality and practicability of the change rate assignment method was verified by the muffler noise experiments. The results show that, the change rate method makes the weight assignment more scientific and rigorous. And the new method can reflect the wishes of designers and completely displays the performance comparison and evaluation between schemes.

As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the perceptual quality and intelligibility of speech. It has extensive applications in the field of intelligent car cabins. Although some end-to-end speech enhancement methods based on time domain have been proposed, there is often limited consideration given to designing model architectures based on the characteristics of the speech signal. In this paper, we propose a new U-Net based speech enhancement framework that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech.

During light to moderate braking at high speed, the local high temperature phenomenon can be observed on the brake disc surfaces, known as hot spots. The occurrence of hot spots will lead to negative effects such as brake performance fade, thermal judder and local wear, which seriously affect the performance of vehicle NVH. In this paper, based on the bench test of a ventilated disc brake, the basic characteristics of hot spots is obtained and the evolution process of temperature field and disc deformation is analyzed in detail. In temperature field, hot bands appear first and grow, migrate from inner and outer radius to the middle, with the growing temperature fluctuation and finally hot spots appear in the middle radius of the brake disc. The stable SRO waviness forms much earlier than the temperature fluctuation. In the stop brake studied in this paper, the SRO waviness stabilizes in main 7 order state which is lower than the final hot spot order.

Speech enhancement can extract clean speech from noise interference, enhancing its perceptual quality and intelligibility. This technology has significant applications in in-car intelligent voice interaction. However, the complex noise environment inside the vehicle, especially the human voice interference is very prominent, which brings great challenges to the vehicle speech interaction system. In this paper, we propose a speech enhancement method based on target speech features, which can better extract clean speech and improve the perceptual quality and intelligibility of enhanced speech in the environment of human noise interference. To this end, we propose a design method for the middle layer of the U-Net architecture based on Long Short-Term Memory (LSTM), which can automatically extract the target speech features that are highly distinguishable from the noise signal and human voice interference features in noisy speech, and realize the targeted extraction of clean speech.

An accurate finite element (FE) model is the basis for the numerical prediction of vibration and noise of permanent magnet synchronous motors (PMSMs). This paper provides an equivalent modeling method of PMSMs considering the orthotropic material parameters of the stator system. First, a theoretical analysis of the influence of orthotropic material parameters on modal characteristics is implemented. Subsequently, the influence of orthotropic material parameters on the modal frequency of the stator is analyzed through the FE method. Then, the modal parameters of the stator core and the stator assembly are obtained by modal tests. According to the equivalent FE model and modal parameters, the orthotropic material parameters of the stator system are acquired. Moreover, to save the calculation time and simplify the modal identification process, the influence of windings is taken into account through additional mass and additional stiffness during the modeling process.

Tire cavity noise of pure electric vehicles is particularly prominent due to the absence of engine noise, which are usually eliminated by adding Helmholtz resonators with arbitrary transversal section to the wheel rims. This paper provides theoretical basis for accurately predicting and effectively improving acoustic performance of wheel resonators. A hybrid finite element method is developed to extract the transversal wavenumbers and eigenvectors, and the mode-matching scheme is employed to determine the transmission loss of the Helmholtz resonator. Based on the accuracy validation of this method, the matching design of the wheel resonators and the optimization method of tire cavity noise are studied. The identification method of the tire cavity resonance frequency is developed through the acoustic modal test. A scientific transmission loss target curve and fitness function are defined according to the noise characteristics.

On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN.

The vibration characteristics of hybrid vehicles are very different from that of traditional fuel vehicles. In this paper, the active and passive control schemes are used to inhibit the vibration issues in vehicle hybrid powertrain system. Firstly the torsional vibration mechanical model including engine, motor and planetary gear subsystems is established. Then the transient vibration responses of typical working condition are analyzed through power control strategy. Consequently the active and passive control of torsional vibration in hybrid powertrain system is proposed. The active control of the motor and generator torque is designed and the vehicle longitudinal vibration is reduced. The vibration of the planetary gear system is ameliorated with passive control method by adding torsional vibration absorbers to power units. The vibration characteristics in vehicle hybrid powertrain system are effectively improved through the active and passive control.

Active engine mounts (AEMs) notably contribute to ensuring superior performance of vehicle’s noise, vibration, and harshness. This paper incorporates a filtered-x-least-mean-squares (FxLMS) controller into the active control engine mount system to attenuate the transmitted force to the body. To avoid the error caused by substituting the load cell for acceleration transducer, the FIR model of the secondary path was obtained by experiment. Finally, a hardware-in-the-loop testing system is built to verify the performance of the active engine mount. It can be observed from the test results that the vibration is reduced notably after control, which demonstrates the effectiveness of the active engine mount and the controller in vibration attenuation.

Manufacturers have been encouraged to accommodate advanced downsizing technologies such as the Variable Displacement Engine (VDE) to satisfy commercial demands of comfort and stringent fuel economy. Particularly, Active control engine mounts (ACMs) notably contribute to ensuring superior effectiveness in vibration attenuation. This paper incorporates a PID controller into the active control engine mount system to attenuate the transmitted force to the body. Furthermore, integrated time absolute error (ITAE) of the transmitted force is introduced to serve as the control goal for searching better PID parameters. Then the particle swarm optimization (PSO) algorithm is adopted for the first time to optimize the PID parameters in the ACM system. Simulation results are presented for searching optimal PID parameters. In the end, experimental validation is conducted to verify the optimized PID controller.

The turbocharger air intake noise during transient conditions like wide open throttle and tip-in/out affects the passenger ride comfort. This paper aims to study an optimized design of multi-chamber perforated resonators to attenuate this noise. The noise produced by a turbocharger in a test vehicle has been measured to find out the noise spectral characteristics which can be used to design the acoustic targets including the amplitude and frequency range of transmission loss (TL). The structural parameters of the resonators are optimized based on genetic algorithm (GA) and two-dimensional prediction theory of the resonator TL. The optimized resonators are installed on the test vehicle to verify the actual noise reduction effect. The results suggest that the broadband noise has been eliminated, and subjective feelings are greatly improved.

A 2 DOF nonlinear dynamic model of the automotive wiper system is established. Complex eigenvalues are calculated based on the complex modal theory, and the system stability as well as its dependence on wiping velocity is analyzed. Bifurcation characteristics of frictional self-excited vibration and stick-slip vibration relative to wiping velocity are studied through numerical analysis. Research of nonlinear vibration characteristics under various wiping velocities is conducted by means of phase trajectories, Poincaré map and frequency spectrum. The pervasive stick-slip vibration during wiping is confirmed, and its temporal and spatial distributions are analyzed by way of time history and contour map. Duty ratio of stick vibration and statistics of scraping residual are introduced as quantitative indexes for wiping effect evaluation. Results indicate that the negative slop of frictional-velocity characteristic is the root cause of system instability.

Noise signals of the driver’s right ear include those of engine, environment, chassis dynamometer, loaded gears and unloaded gears when they are recorded in full vehicle on chassis dynamometer in semi-anechoic room. Gear rattle noise signals of the driver’s right ear caused by unloaded gear pairs can’t be identified or quantified directly. To solve the problems, relative approaches are used to identify and quantify the gear rattle noise signals. Firstly, the rattle noise signals of the driver’s right ear are filtered by human ear characteristic functions and steady noise signals are extracted by regression and smoothing processes. The noise signals are regressed at 200ms interval in the hearing critical frequency bands and smoothed in the flanking frequencies. Then, the noise relative approaches are obtained by subtracting the steady noise signals from the filtered noise signals, which are the transient noise signals of the unloaded gear pairs inducing the rattle noise.

In this study, the vibroacoustic characteristics and torque fluctuation of switched reluctance motors (SRMs) with different phases and poles have been analyzed in detail. Also, the common four SRMs, i.e., three-phase 6/4 SRM, four-phase 8/6 SRM, five-phase 10/8 SRM, and six-phase 12/10 SRM, have been selected. First, the spatial-temporal distribution characteristics of radial force in SRMs were revealed by virtue of the analytical derivation, which was validated by the 2D Fourier decomposition based on the finite-element results of radial force. Second, a multiphysics model, which was composed of an electromagnetic field, a mechanical field, and an acoustic field, was established to predict the noise behaviors of SRMs with different phases and poles. Third, the relationship between the torque fluctuation and the phases / poles of SRMs, and the relationship between the noise and the radial force / phases / poles are all analyzed.

In this paper, a linear oscillatory actuator (LOA) with moving magnets used in active engine mount is modeled and theoretically analyzed considering its performance decline at high temperature. Firstly, a finite element model (FEM) of the LOA with moving magnets is established. The actuator force is decomposed to ampere force and cogging force through formation mechanism analysis. By using the FEM, ampere forces and cogging forces of the LOA with moving magnets under different current loads and different mover positions are calculated. The FEM and calculation method are validated by bench level test. The voice coil constant and cogging coefficient at normal temperature are identified, which indicates the actuator force is a linear model related to the current and the mover position.