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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.

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.

Crashworthiness is one of the most important performances of vehicles, and the front rails are the main crash energy absorption parts during the frontal crashing process. In this paper, the front rail was simplified to a thin-walled beam with a cross section of single-hat which was made of steel and aluminum. And the two boards of it were connected by riveting without rivets. In order to optimize its crashworthiness, the thickness (t), radius (R) and the rivet spacing (d) were selected as three design variables, and its specific energy absorption was the objective while the average impact force was the constraint. Considering the error of manufacturing and measurements, the parameters σs and Et of the steel were selected as the uncertainty variables to improve the design reliability. The algorithm IP-GA and the approximate model-RBF (Radial Basis Function) were applied in this nonlinear uncertainty optimization.

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.

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.

Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

For an electric vehicle driven by four in-wheel motors, the torque of each wheel can be controlled precisely and independently. A closed-loop control method of differential drive assisted steering (DDAS) has been proposed to improve vehicle steering properties based on those advantages. With consideration of acceleration requirement, a three dimensional characteristic curve that indicates the relation between torque and angle of the steering wheel at different vehicle speeds was designed as a basis of the control system. In order to deal with the saturation of motor's output torque under certain conditions, an anti-windup PI control algorithm was designed. Simulations and vehicle tests, including pivot steering test, lemniscate test and central steering test were carried out to verify the performance of the DDAS in steering portability and road feeling.

It’s significant to analyze the Experimental performance of active control system for road noise. In this paper, a 2-channel active control system of vehicle road noise based on FXLMS algorithm is established. The complexity of Filtered-x Least Mean Square algorithm (FXLMS) is analyzed. The bench test and road test are carried out to test and analyze the performance of the control system. Firstly, the general mathematic model of the multi-channel active control system based on FXLMS algorithm is established. The computational complexity of the algorithm is analyzed. Secondly, a hardware-in-the-loop (HIL) test bench based on multi-channel FXLMS algorithm and a measurement system based on DASP are set up, to measure the noise reduction performance of active noise control system under various working conditions. Finally, the bench test and the road test are carried out and the results are analyzed.

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.

This paper describes a methodology to estimate yaw rate of a 4-wheel-drive electric vehicle, in which wheel driven torque can be independently controlled by electric motor. Without non-driven wheels it would be difficult to estimate the vehicle yaw rate precisely, especially when some of the four wheels have large slip ratio. Therefore, a model based estimation methodology is put forward, which uses four wheel speeds, steering wheel angle and vehicle lateral acceleration as input signals. Firstly the yaw rate is estimated through three different ways considering both vehicle kinematics and vehicle dynamics. Vehicle kinematics based method has good estimation accuracy even when the vehicle has large lateral acceleration. However, it can not provide satisfying results when the wheel has large slip ratio. In contrast, vehicle dynamics based method is not so sensitive to wheel slip ratio.

Beamforming techniques are widely used today in aeroacoustic wind tunnels to identify wind noise sources generated by interaction between incoming flow and the test object. In this study, a planar spiral microphone array with 120 channels was set out-of-flow at 1:1 aeroacoustic wind tunnel of Shanghai Automotive Wind Tunnel Center (SAWTC) to test exterior wind noise sources of a production car. Simultaneously, 2 reference microphones were set in vehicle interior to record potential sound source signal near the left side view mirror triangle and the signal of driver’s ear position synchronously. In addition, a spherical array with 48 channels was set inside the vehicle to identify interior noise sources synchronously as well. With different correlation methods and an advanced algorithm CLEAN-SC, the ranking of contributions of vehicle exterior wind noise sources to interested interior noise locations was accomplished.

As is known to all, the structure of the chassis has been greatly simplified as the application of in-wheel motor in electric vehicle (EV) and distributed control is allowed. The micro EV can alleviate traffic jams, reduce the demand for motor and battery capacity due to its small size and light weight and accordingly solve the problem that in-wheel motor is limited by inner space of the wheel hub. As a result, this type of micro EV is easier to be recognized by the market. In the micro EV above, two seats are side by side and the battery is placed in the middle of the chassis. Besides, in-wheel motors are mounted on the rear axle and only front axle retains traditional hydraulic braking system. Based on this driving/braking system, distribution of braking torque, system reliability and braking intensity is analyzed in this paper.

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.

In this paper, a novel method is proposed to establish the vehicle yaw stability criterion based on the sideslip angle-yaw rate (β-r) phase plane method. First, nonlinear two degrees of freedom vehicle analysis model is established by adopting the Magic Formula of nonlinear tire model. Then, according to the model in the Matlab/Simulink environment, the β-r phase plane is gained. Emphatically, the effects of different driving conditions (front wheels steering angle, road adhesion coefficient and speed) on the stability boundaries of the phase plane are analyzed. Through a large number of simulation analysis, results show that there are two types of phase plane: curve stability region and diamond stability region, and the judgment method of the vehicle stability domain type under different driving conditions is solved.

In this paper, a speed tracking controller is designed for the All-terrain vehicles. The method of feedforward with state variable feedback based on conditional integrators is adopted by the proposed control algorithm. The feedforward is designed considering the influence of the road slope on the longitudinal dynamics, which makes the All-terrain vehicles satisfy the acceleration demand of the upper controller when it tracks the desired speed on the road with slope varying greatly. The road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimation requires an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. Based on the vehicle dynamic model and the nonlinear tire model, the method of conditional integration is used in the state variable feedback, which considers the saturation constraint of the actuator with the intention of preventing the divergent integral operation.

Path tracking is the rudimentary capability and primary task for autonomous ground vehicles (AGVs). In this paper, a novel four-wheel-independent-steering (4WIS) and four-wheel-independent-drive (4WID) electric vehicle (EV) is proposed which is equipped with steer-by-wire (SBW) system. For path-tracking controller design, the nonlinear vehicle model with 2 degrees of freedom (DOF) is built utilizing the nonlinear Dugoff tire model. The nonlinear dynamic model of SBW system is conducted as well considering the external disturbances. As to the path-tracking controller design, an integrated four-wheel steering (4WS) and direct yaw-moment control (DYC) system is designed based on the model predictive control (MPC) algorithm to track the target path described by desired yaw angle and lateral displacement. Then, the fast terminal sliding mode controller (FTSMC) is proposed for the SBW system to suppress disturbances.

For sensing system, the trustworthiness of the variant sensors is the crucial point when dealing with advanced driving assistant system application. In this paper, an approach to a hybrid camera-radar application of vehicle tracking is presented, able to meet the requirement of such demand. Most of the time, different types of commercial sensors available nowadays specialize in different situations, such as the ability of offering a wealth of detailed information about the scene for the camera or the powerful resistance to the severe weather for the millimeter-wave (MMW) radar. The detection and tracking in different sensors are usually independent. Thus, the work here that combines the variant information provided by different sensors is indispensable and worthwhile. For the real-time requirement of merging the measurement of automotive MMW radar in high speed, this paper first proposes a fast vehicle tracking algorithm based on image perceptual hash encoding.

BBW (Brake-by-wire) can increase the electric and hybrid vehicles performance and safety. This paper proposes a novel mechatronic booster system, which includes APS (active power source), PFE (pedal feel emulator), ECU (electronic control unit). The system is easily disturbed when the system parameters and the outside conditions change. The system performance is weakened. The cascade control technique can be used to solve the problem. This paper develops an adaptive cascade optimum control (ACOC) algorithm based on the novel mechatronic booster system. The system is divided into main loop and servo loop, both of them are closed-loop system. The servo-loop system can eliminate the disturbance which exists in the servo loop. So the robustness of the cascade control system is improved than which of the general closed-loop control system. Different control object is respectively chosen. The control-oriented mathematical model is designed.