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Wheel Aerodynamics is an important part of vehicle aerodynamics. The wheels can notably influence the total aerodynamic drag, lift and ventilation drag of vehicles. In order to simulate the real on-road condition of driving cars, the moving ground and wheel rotation is of major importance in CFD. However, the wheel rotation condition is difficult to be represented exactly, so this is still a critical topic which needs to be worked on. In this paper, a study, which focuses on two types of cars: a fastback sedan and a notchback DrivAer, is conducted. Comparing three different wheel rotating simulation methods: steady Moving wall, MRF and unsteady Sliding Mesh, the effects of different methods for the numerical simulation of vehicle aerodynamics are revealed. Discrepancies of aerodynamic forces between the methods are discussed as well as the flow field, and the simulation results are also compared with published experimental data for validation.

Distributed drive electric vehicle (EV) is driven by four independent hub motors mounted directly in wheels and retains traditional hydraulic brake system. So it can quickly produce driving/braking motor torque and large stable hydraulic braking force. In this paper a new control allocation strategy for distributed drive electric vehicle is proposed to improve vehicle's lateral stability performance. It exploits the quick response of motor torque and controllable hydraulic pressure of the hydraulic brake system. The allocation strategy consists of two sections. The first section uses an optimal allocation controller to calculate the total longitudinal force of each wheel. In the controller, a dynamic efficiency matrix is designed via local linearization to improve lateral stability control performance, as it considers the influence of tire coupling characteristics over yaw moment control in extreme situations.

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.

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.

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 adhesion control is the basic technology of active safety for the four-wheel driven EV. In this paper, a novel adhesion control method based on fuzzy logic control is proposed. The control system can maximize the adhesion force without road condition information and vehicle speed signal. Also, the regulation torque to prevent wheel slip is smooth and the vehicle driving comfort is greatly improved. For implementation, only the rotating speed of the driving wheel and the motor driving torque signals are needed, while the derived information of the wheel acceleration and the skid status are used. The simulation and road test results have shown that the adhesion control method is effective for preventing slip and lock on the slippery road condition.

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.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.

Based on the emergency lane change cases extracted from the China naturalistic driving data, the driving steering behavior divides into three phases: collision avoidance, lateral movement and steering stabilization. Using the steering primitive fitting by Gaussian function, the distribution of the duration time, the relationship between steering wheel rate and deflection were analyzed in three phases. It is shown that the steering behavior essentially is composed of steering primitives during the emergency lane-change. However, the combination of the steering primitives is different according to the specific steering constraints in three phases. In the collision avoidance phase, a single steering primitive with high peak is used for the fast steering; in the lateral movement and stabilization phase, a combination of two or even more steering primitives is built to a more accurate steering.

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 recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.

The prevention and control of brake judder and its various negative effects has been a key target of vehicle production. One of the effects is the steering wheel vibration during vehicle braking. Experimental and theoretical investigation into “steering wheel vibration due to brake judder” is extensively presented in this paper. The vehicle road test is carried out under controlled braking conditions. During the test, the accelerations of brake caliper assembly, suspension low and upper control arm, steering arm, tie rod and steering wheel, left and right wheel rotary speed, are measured by a multi-channel data acquisition system. The data processing focuses on order tracking analysis and transfer path analysis to work out the related resonant components. A disc brake assembly, with deliberately designed disc thickness variation and surface run-out combinations, is tested on a brake dynamometer.

In order to improve the robustness and stability of autonomous vehicle at high speed, a path tracking approach which combines front steering and differential braking is investigated in this paper. A bicycle model with 3-DOFs is established and a linear time-varying predictive model using front steering as its control input can be derived. Based on model predictive theory, the path tracking issue using linear time-varying model predictive control can be transformed into an online quadratic programming problem with constraints. The expected front steering angle can be obtained from online moving optimization. Then the direct yawing control is adopted to treat two types of differential braking control. The first one investigates steady-state gain of yaw rate in linear 2-DOFs vehicle model, and designs a stable differential braking controller which is based on reference yaw rate.

During a car launch, the driving torque from driveline acts on brake disk, and may lead the pad to slip against the disk. Especially with slow brake pedal release, there is still brake torque applies on the disk, which will retard the rotation of disk, and under certain conditions, the disk and pad may stick again, so the reciprocated stick and slip can induce the noise and vibration, which can be transmitted to a passenger by both tactile and aural paths, this phenomenon is defined as brake groan. In this paper, we propose a nonlinear dynamics model of brake for bidirectional, and with 7 Degrees of Freedom (DOFs), and phase locus and Lyapunov Second Method are utilized to study the mechanism of groan. Time-frequency analysis method then is adopted to analyze the simulation results, meanwhile a test car is operated under corresponding conditions, and the test signals are sampled and then processed to acquire the features.

A composite steering control strategy, which combines four-wheel steering (4WS) and differential steering, is proposed in this paper, to optimize steering maneuverability in the conditions where the vehicle speed is below 15 Km/h, mainly for U-turning and parking conditions. A dynamic model is developed for the steering system and the tire system. Taking different steering wheel inputs into consideration, a 4WS control strategy proportional to the front wheel steering angle is quoted to improve the steering maneuverability in the low speed conditions and guarantee the manipulability by controlling the side slip of the vehicle. Based on the 4WS system, this paper explores the possibility of further improving the low-speed maneuverability of the vehicle through differential steering. And the differential steering control strategy is developed, including four hub-motor output modes. A composite steering controller is designed based on the 4WS-4WD electric vehicle platform.