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This paper sets up a simplified dynamic model for simulating the yaw/roll stability of heavy tractor-semitrailer using Matlab/Simulink. A linear quadratic regulator (LQR) based on partial-state feedback controller is used to optimize the roll stability of the vehicle. The control objective for optimizing roll stability is to be reducing the lateral load transfer rate while keeping the suspension angle less than the maximum allowable angle. The simulation result shows that the LQR controller is effective in the active roll stability control of the heavy tractor-semitrailer.

Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

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.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

A TCS strategy of electric vehicle with 4 in-wheel motors is proposed in this paper. The control method consists of three parts: target slip rate calculation, target torque calculation and coordination control. By using Lyapunov stability analysis algorithm, the target slip rate boundary which makes the system stable is obtained. The target torque of each wheel is calculated by PI controller. According to the engineering experience, the TCS coordinated control strategy under split friction coefficient (split-μ) road, and friction coefficient jump(μ jump) road is proposed. The test results show that this strategy can improve the acceleration comfort and yaw stability of vehicles on uniform low friction coefficient (low μ) , split-μ and μ jump road.

Omni-directional electric vehicle built by our research group is an advanced electric vehicle whose four wheels can drive, steer and brake independently. The vehicle chassis system is composed of four in-wheel motors, four independent steer motors and electromagnetic brake system, and its control system is divided into logical control layer and underlying execution layer. The information exchange between these two layers is implemented by CAN bus. In this paper, the traction control logic for Omni-directional electric vehicle is developed. The study mainly involves two aspects: the vehicle states estimation and the traction control logic design. The vehicle states, including vehicle longitudinal velocity, lateral speed, side slip angle and yaw rate, etc, are estimated based on Extended Kalman Estimation and multiple degrees of freedom vehicle model.

Supercapacitor has the merits of low resistance and long lifecycle ability. When combined with battery, they can alleviate the burthen of battery, increase the battery's working efficiency and prolong its lifecycle. This paper introduces a control architecture based on balancing of SOC and algorithm based on fuzzy logic, Aiming at the two different cycles that have sufficient and insufficient energy that can be recovered from braking unlikely, this paper puts forward the methods of on-line adjusting fuzzy control parameters. Consequently, simulation was performed,and the results validate the effective adapting capacity of the control logic under different driving cycles.

Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

This paper summarized the development methodology and technical experiences on Formula Student racecar engines acquired by Jilin University from 2011 to 2015. This series of engines are all based on 600cc 4-cylinder motorcycle gasoline engines and were modified to turbocharged engines which met the Formula Student technical regulations, in order to achieve higher power output, wider torque band as well as lower fuel consumption. During the development process, multiple research projects have been conducted surrounding the turbocharging technology. These research projects have covered multiple areas including the matching of the flow rate characteristics of the engine and the turbocharger, the design of intake and exhaust systems, research on the wastegate as well as its actuator, the tuning and control of the boost pressure as well as the design of the lubrication system for the turbocharger, etc.

In this paper, tensile experiments were performed on the dorsal and ventral skin of rats, and the mechanical properties of the skin in these two sites were compared and analyzed. A three-factor experimental protocol of site (dorsal and ventral), strain rate (0.71s-1, 7.1×10-3s-1), and sampling orientation (0°, 45° and 90° relative to the spine) was established for tensile test using the L6(31×22) orthogonal table modified from the standard orthogonal table L4 (23). Uniaxial tensile experiments were performed on rat skin samples to calculate the stress-strain curve. The failure strain energy was selected as the index, and the sum of squared deviations of the factors to the index was calculated by analysis of variance (ANOVA), and the contributions of the factors to the failure strain energy were evaluated. The results showed that the site factor has the largest effect on the tensile strain energy with a contribution of 88.9% and a confidence level of 95%.

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.

During the vehicle braking, the Regenerative braking system (RBS) transforms the kinetic energy into electric power, storing it in the power sources. To secure the baking process, it is required to use hydraulic braking pressure to coordinately compensate the regenerative braking pressure. The traditional hydraulic pressure control algorithm which is used in regenerative braking system coordinated control has obvious laddering effect in braking. Unit control cycle pressure deviations seriously affect the comfort and the braking feeling on the vehicle.

The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L9(34) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance.

A full drive-by-wire electric vehicle, named Urban Future Electric Vehicle (UFEV) is developed, where the four wheels' traction and braking torques, four wheels' steering angles, and four active suspensions (in the future) are controlled independently. It is an ideal platform to realize the optimal vehicle dynamics, the marginal-stability and the energy-efficient control, it is also a platform for studying the advanced chassis control methods and their applications. A centralized control system of hierarchical structure for UFEV is proposed, which consist of Sensor Layer, Identification and Estimation Layer, Objective Control Layer, Forces and Motion Distribution Layer, Executive Layer. In the Identification and Estimation Layer, identification model is established by utilizing neural network algorithms to identify the driver characteristics. Vehicle state estimation and road identification of UFEV based on EKF and Fuzzy Logic Control methods is also conducted in this layer.

In order to improve the tractive ability, steering capability and directional stability, etc. of automated mechanical transmission (AMT) vehicles running on the wet and slippery road, the anti-slip regulation (ASR) technologies for AMT vehicles are developed. The significance of ASR for AMT vehicles is introduced; a road friction recognition method based on the deceleration of driving wheels is investigated; a fuzzy anti-slip control system based on adjustment of engine torque is developed and the corresponding experimental verification is conducted. The experimental results denote that the proposed method is effective to eliminate the excessive slip when the AMT vehicle travels on the low friction road.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

With rapid development of the automobile industry and the growing maturity of the automotive electronic technologies, the distributed-traction electric vehicle with four-wheel-distributed steering/braking/traction systems is regarded as an important development direction. With its unique chassis structure, it is the ideal benchmark platform used to evaluate active safety systems. The distributed-traction electric vehicle with four-wheel-distributed steering system is essentially full drive-by-wire vehicle. With its flexible chassis layout and high control degrees-of-freedom, the full drive-by-wire electric vehicle acted as a kind of redundant system is an ideal platform for the research of integrated control. In this treatise, the longitudinal dynamics of the electric vehicle as well as its lateral and yaw motions are controlled simultaneously.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

The Electromechanical Brake Booster system (EMBB) integrates active braking and energy recovery and becomes a novel brake-by-wire solution that substitutes the vacuum booster. While the intelligent unmanned vehicle is in unstable state, the EMBB can improve the vehicle yaw stability more quickly and safely. In this paper, a new type of integrated EMBB has been designed, which mainly includes two parts: servo motor unit and hydraulic control unit. Aiming at the dynamic instability problem of intelligent unmanned vehicle, a three-layer vehicle yaw stability control structure including decision layer, distribution layer and execution layer is proposed based on integrated EMBB. Firstly, the decision layer calculates the ideal yaw rate and the side slip angle of the vehicle with the classic 2DOF vehicle dynamics model. The boundary of the stable region is determined by the phase plane method and the additional yaw moment is determined by the feedback PI control algorithm.

Aiming at the abnormal vibration of driver seat of a passenger car in idle condition, vibration acceleration of engine, frame and seat rail was tested to identify vibration sources. Order tracking and spectrogram analysis indicated that the second order self-excitation of engine was the main cause. To solve the problem, semi-active controlled hydraulic engine mount with air spring of which characteristics could shift between a high dynamic stiffness and a low one was applied. Then the structure and principle of the mount with variable characteristics was introduced and control mode was analyzed. Dynamic characteristics were obtained by bench test. With sample mount applied, vibration of seat rail was tested again in multiple vehicle and engine working conditions. Dates showed that abnormal vibration in idle condition was extremely reduced and the mount could also meet the requirement of engine to dynamic stiffness in driving conditions.