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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

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.

In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

In this paper, the dynamic stress of the front subframe of a passenger car was obtained using modal stress recovery method to estimate the fatigue life. A finite element model of the subframe was created and its accuracy was checked by modal test in a free hanging state. Furthermore, the whole vehicle rigid-flexible coupling model of the passenger car was built up while taking into account the flexibility of the subframe. Meanwhile, the road test data was used to verify the validity of the dynamic model. On this basis, the modal displacement time histories of the subframe were calculated by a dynamic simulation on virtual proving ground consisting of Belgian blocks, cobblestone road and washboard road. By combining the modal displacement time histories with modal stress tensors getting from normal mode analysis, the dynamic stress time histories of the subframe were obtained through modal stress recovery method.

With the increasingly complex traffic environment, the vehicle AEB system needs to go through a large number of testing processes, in order to drive more safely on the road. For speeding up the development process of AEB and solve the problems of long cycle, high cost and low efficiency in AEB testing, in this paper, a millimeter wave radar turntable is built, and a high-precision detection algorithm of turntable encoder is designed, at the same time, a test method of vehicle AEB based on the detection data of radar turntable encoder is designed. The verification results show that methods described in this paper can be used to develop the vehicle AEB test algorithm efficiently.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

Proton exchange membrane fuel cells (PEMFC), which convert the chemical energy into electrical energy directly through electrochemical reactions, are widely considered as one of the best power sources for new energy vehicles (NEV). Some of the major advantages of a PEMFC include high power density, high energy conversion efficiency, minimum pollution, low noise, fast startup and low operating temperature. The Membrane Electrode Assembly (MEA) is one of the core components of fuel cells, which composes catalyst layers (CL) coated proton exchange membrane (PEM) and gas diffusion layers (GDL). The performance of MEA is closely related to mass transportation and the rate of electrochemical reaction. The MEA plays a key role not only in the performance of the PEMFCs, but also for the reducing the cost of the fuel cells, as well as accelerating the commercial applications. Commercialized large-size MEA directly plays a major role in determining fuel cell stack and vehicle performance.

Proton Exchange Membrane Fuel Cell (PEMFC) uses hydrogen and oxygen for fuel, the whole energy conversion process almost has no negative impact on the environment. The PEM fuel cell stack with the advantages of low-operating temperature, high current density and fast start-up ability is considered to be the next generation of new electric vehicle power. However, due to the limited current output, it is difficult for a single cell to meet the practical application requirements. The actual fuel cell stack is formed by many single cells assembled together. The assembly process is often related to load transfer, material transfer, energy exchange, multi-phase flow, electrochemical reaction and other factors. The performance of MEA (Membrane Electrode Assembly), sealing gaskets and other components will change during the assembly process, which makes the fuel cell stack assembly process more complex.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

The noise and vibration of electric vacuum pump (EVP) become a major problem for electric vehicles when the vehicle is stationary. This paper aims at the EVP’s abnormal noise of an electric vehicle when stationary. Driver’s right ear (DRE) noise was tested and spectrogram analysis was carried out to identify the noise sources. In order to attenuate this kind of abnormal noise, a new EVP rubber mount with a segmented structure was introduced, which optimized the transfer path of vibration. Then dynamic stiffness and fatigue life of the EVP mount with different rubber hardness were calculated through finite element analysis (FEA) approach. Bench tests of fatigue life and DRE noise were performed to validate the FEA results. Test data of the sample mount shows that sound pressure level of DRE was dramatically attenuated and thus passengers’ ride comfort was enhanced.

The vehicle pull problem is very important to driving safety. Major factors that may cause the pull problem related to tire include variations of geometric dimension (e.g. RPK) and stiffness (e.g. cornering stiffness, aligning stiffness), plysteer and conicity. In previous research, the influencing mechanism of these factors was well studied. But in fact, vehicle pull problem caused by tire is probabilistic. When we assemble four tires onto the car, there could be 384 different assembly arrangements. If there are significant differences among these four tires, there will also be significant differences in the influence of different tire assembly schemes on vehicle pull, which has not been systematically discussed in previous studies. If we want to evaluate the pull performance of all these arrangements by vehicle test, it will be a time consuming process which will take almost 24 working days, along with a high test expense.

Blower is one of the main noise sources of fuel cell vehicle. In this paper, a narrowband active noise control (ANC) model is established based on adaptive notch filter (ANF) to control the high-frequency noise produced by the blower. Under transient conditions, in order to reduce the frequency mismatch (FM) of ANC for blower, a new Frequency Mismatch Filtered-Error Least Mean Square algorithm (FM-FELMS) is proposed to attenuate blower noise under transient conditions. According to the theoretical analysis and simulation, the proposed algorithm has an excellent noise reduction performance at relatively high blower speed. While for the low speed working condition, the Normalized Least Mean Square (NLMS) algorithm is applied to attenuate noise. The two algorithms could be jointly utilized to control the blower noise actively.

A compiled method of the programmed load spectrum, which can simplify and accelerate the fatigue bench test of a car body, is proposed and its effectiveness is checked by the fatigue simulation. By using the multi-body dynamics model with a satisfactory accuracy, the virtual iteration is applied to cascade body loads from the wheel hubs. Based on the rain-flow counting method and statistics theory, the distributions of the body loads are analyzed, and then the programmed load spectrum is compiled and simplified. Through comparative study, the simulation results of random and programmed load spectrum are found to agree well with each other in terms of the damage distribution and fatigue life, which demonstrates the effectiveness of the presented method.

As a novel assist actuator of brake system, the electromechanical brake (EMB) booster has played a significant role in the battery electric vehicles and automatic driving vehicles. It has advantages of independent to vacuum source, active braking, and tuning pedal feeling compared with conventional vacuum brake booster. In this article, a novel EMB booster system is proposed, which is consisted of a permanent magnet synchronous motor (PMSM), a two-stage reduction by gears and ball screw, a servo body, and a reaction disk. Together with the hydraulic control unit, it has two working modes: active braking for automatic drive and passive braking for driver intervention. The structure and work principle of the electric brake booster system is first introduced. The precise control from pedal force to hydraulic pressure is the key for such a power-assisted brake actuator. We translate the control problem of force feedback control to position tracking control.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

The Automatic Parking System (APS) is consisted of environmental perception, path planning and path following. As one of the key technologies in APS, path following module controls the lateral movement of the vehicle during the parking process. A mature path following module should meet all the performance indexes of high precision, fast convergence, convenient tuning and good passenger comfort. However, the current path following control methods can only meet parts of the performance indexes, instead of all. In order to satisfy all the performance indexes above, a path following control method based on Linear Quadratic Regulator (LQR) is proposed in this paper. Firstly, the linearization of the non-linear vehicle kinematic model was done to establish a linear system of the path following error. Secondly, LQR optimal control was used to achieve the closed-loop control of this linear system to guarantee its stability and fast convergence property.

The linear internal combustion engine-linear generator integrated system (LICELGIS) is an innovative structure as a range-extender for the hybrid vehicles, which contains two opposed free piston engines and one linear generator between them. The LICELGIS is a promising power package due to its high power density and multi-fuel flexibility. In the combustion process of linear engines, the top dead center (TDC) position is not stable in different cycles, which significantly affects system operations. Otherwise, pistons move away from the TDC with high-speed because of the tremendous explosive force, which incurs the short residence time of pistons around the TDC and rapid decrease of in-cylinder temperature, pressure and the combustion efficiency. In order to address this problem, a scientific simulation model which includes dynamic and thermodynamic models, is established and a combustion optimization control strategy is proposed.

Startup from subzero temperature is one of the major challenges for polymer electrolyte membrane fuel cell (PEMFC) to realize commercialization. Below the freezing point (0°C), water will freeze easily, which blocks the reactant gases into the reaction sites, thus leading to the start failure and material degradation. Therefore, for PEMFC in vehicle application, finding suitable ways to reach successful startup from subfreezing environment is a prerequisite. As it’s difficult and complex for experimental studies to measure the internal quantities, mathematical models are the effective ways to study the detailed transport process and physical phenomenon, which make it possible to achieve detailed prediction of the inner life of the cell. However, review papers only on cold start numerical models are not available. In this study, an extensive review on cold start models is summarized featuring the states and phase changes of water, heat and mass transfer.

Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors.