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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.

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 passive thermal management system (TMS) using composite phase change material (PCM) for large-capacity, rectangular lithium-ion batteries is designed. A battery module consisting of six Li-ion cells connected in series was investigated as a basic unit. The passive TMS for the module has three configurations according to the contact area between cells and the composite PCM, i.e., surrounding, front-contacted and side-contacted schemes. Firstly, heat generation rate of the battery cell was calculated using the Bernardi equation based on experimentally measured heat source terms (i.e. the internal resistance and the entropy coefficient). Physical and thermal properties such as density, phase change temperature, latent heat and thermal conductivity of the composite PCM were also obtained by experimental methods. Thereafter, thermal response of the battery modules with the three TMS configurations was simulated using 3D finite element analysis (FEA) modeling in ANSYS Fluent.

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.

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.

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.

Wireless charging system for vehicular power batteries is becoming more and more popular. As one of important issues, charging power regulation is indispensable for online control, especially when the distance or angle between chassis and ground changes. This paper proposes a novel power regulation method named Z-Source-Based Pulse-Amplitude-Modulation (ZSB-PAM), which has not been mentioned in the literatures yet. The ZSB-PAM employs a unique impedance network (two pairs of inductors and capacitors connected in X shape) to couple the cascaded H Bridge to the power source. By controlling the shoot-through state of H bridge, the input voltage to H bridge can be boosted, thus the transmitter current can be adjusted, and hence, charging current and power for batteries. A LCL-LCL resonant topology is adopted as the main transfer energy carrier, for it can work with a unity power factor and have the current source characteristic which is suitable for battery charging.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

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.

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.

Lithium-ion batteries (LIBs) with relatively high energy, power density and eco-friendly characteristic are considered as a vital energy source in consumer market of portable electronics and transportation sector especially in electric vehicles (EVs). To meet the higher capacity requirements, the nickel-rich LIBs with higher capacity has been used as the commercial power batteries. However, the battery with higher energy density is more destructive, which could result in thermal runaway accidents and make the battery safety issues become more and more prominent. Thermal runaway of LIBs is one of the key scientific problems in safety issues. Until now, the inducement of thermal runaway process is complicated which perplex researchers and industry a lot. On the one hand, the internal mechanism about thermal runaway should be deeply studied. On the other hand, in-situ monitoring should be developed to supply the mechanism study and early warning.

The proton exchange membrane fuel cell (PEMFC) system has emerged as the state-of-art power source for the electric vehicle, but the widespread commercial application of fuel cell vehicle is restricted by its short service life. An enabling high accuracy model holds the key for better understanding, simulation, analysis, subsystem control of the fuel cell system to extract full power and prolong the lifespan. In this paper, a quasi-dynamic lumped parameters model for a 3kW stack is introduced, which includes filling-and-emptying volume sub-models for the relationships between periphery signals and internal states, static water transferring sub-model for the membrane, and empirical electrochemical sub-model for the voltage response. Several dynamic experiments are carried out to identify unknown parameters of the model.

In order to study the fast filling problem of large-capacity on-board hydrogen storage tank for highway passenger cars, a computational fluid dynamics (CFD) simulation model of 134L large-capacity hydrogen storage tank was established. By simulating different pre-cooling temperatures and mass flow rates, the temperature distribution and thermal transmission in the tank were observed. Due to the large ratio of length to diameter of the hydrogen tank, the temperature distribution is extremely uneven during the whole filling process, and the high temperature area is mainly concentrated in the tank tail. And the heat transfer between the gas and the tank wall is not obvious under the low and constant mass flow rate. The temperature rise process during the whole filling process under different mass flow conditions was simulated to satisfy the highest safe temperature limit.