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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.

In the intelligent airbag system, the detection accuracy of occupant position is the precondition and plays a vital role to control airbag detonation time and inflated strength during the crash. Through accurately analyzing the seat surface pressure distributions of different occupant sitting position and types, an occupant position recognition approach which purely uses occupant pressure distribution information measured by seat pressure sensors is presented with the method of Support Vector Machine. In the end, the distribution samples with different occupant sitting position and types are used to train and test the recognition approach, and the good validity and accuracy are shown in the experiments.

Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles.

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, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

Embedded bus monitoring and fault diagnosis system, which was used on the hybrid vehicle was designed in this paper. And this system took the 32-bit embedded one as a hardware platform, customized a WinCE6.0 operation system and used EVC as the tool to design the embedded application. The functions of CAN communication, protocol defamations etc were realized. Good human-computer interaction is developed and the system has already been applied on the bus.

The traditional energy management algorithm is mainly based on a single driving cycle, it is obvious that many factors might be often neglected by designer, such as different driving cycles would suit for different control strategies. But they tend to make decisions on the balance of torque distribution and battery power that based on a single driving cycle. Therefore, it is very difficult to achieve the optimal control in each case. In this paper we introduce a new design concept of Multi-operating mode energy management, a mathematical model of the energy management applied to a hybrid vehicle system is presented. Results of simulations using the model with the Multi-operating mode energy management were compared with results of simulations using a model with the single mode energy management, allowing the energy efficiency evaluation of the proposed energy management system.

Based on traditional heavy commercial vehicles, a hydraulic hub-motor drive vehicle (HHMDV) is equipped with a set of hydraulic hub-motor auxiliary system (HHMAS) to improve the traction performance and adaptability under complex conditions. In the case of low-speed operation or mechanical transmission failure, the creep mode (CM) can be used to drive the vehicle. Aiming at a common hydraulic system problem that flow loss increases due to temperature variation, a temperature compensation control strategy of the CM is proposed in this paper. By analyzing the speed regulation characteristics of the closed loop of the system in the CM, combined with the efficiency of the hydraulic variable pump (HP) and the hydraulic quantitative motor (HM), and aiming at adjusting the engine work in the optimal curve of the engine, the temperature compensation factor is introduced to control the HP displacement with hydraulic stepless speed regulation.

In this paper, based on the research of the first generation of a hybrid electric city bus, principle researches have been offered on the parameters matching of the power-train and main components for the new generation of hybrid electric city bus. The vehicle system was designed integrated, and then simulation platform was developed and established. The dynamic and economy performance can be predicted based on the above work, results show that the designed hybrid electric city bus can meet the performance demands and validate rationality of the parameters matching.

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.

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.

The high popularity of automobiles has led to frequent collisions. According to the latest statistics of the United Nations, about 1.25 million people worldwide die from road traffic accidents each year. In order to improve the safety of vehicles in driving, the active safety system has become a research hotspot of various car companies and research institutions around the world. Among them, the more mature and popular active security system are Forward Collision Warning(FCW) and Autonomous Emergency Braking(AEB). However, the current active safety system is based on traditional sensors such as radar and camera. Therefore, the system itself has many limitations due to the shortage of traditional sensors. Compared to traditional sensors, Vehicle to Everything (V2X) technology has the advantages of richer vehicle parameter information, no perceived blind spots, dynamic prediction of dangerous vehicle status, and no occlusion restriction.

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.

To reach the goal of optimal performance match between engine and transmission, the dynamic characteristics of engine should be taken into consideration. In the paper, the dynamic torque and fuel consumption models of engine, described by a multi-layers feed forward neural network, were established. Based on that, the methods used to calculate the optimal dynamic and economical shift schedules with dynamic 3-parameters were put forward. The shift schedule with dynamic 3-parameters based on neural network model is proven to be superior to the shift schedule with only 2-parameters in both dynamic performance and fuel economy by the test.

Hydraulic retarder, as an auxiliary braking device, is widely used in commercial vehicles. Nowadays, the hydraulic retarder’s internal oil is mainly cooled by the coolant circuit directly. It not only aggravates the load of engine cooling system, but also makes the abundant heat energy not be recycled properly. In this study, an independent energy supply device with organic Rankine cycles is applied to solve the problems above. In the structure of this energy supply device, the evaporator’s inlet and outlet is connected in parallel with the oil outlet and inlet of the retarder respectively. A part of oil enters the evaporator to transfer heat with the organic fluid, and the rest of oil enters the oil-water heat exchanger to be cooled by the coolant circuit. According to the different braking conditions of the retarder, the oil temperature in the inlet of the hydraulic retarder can be kept within the proper range through adjusting the oil flow rate into the evaporator properly.

With the rapid development of modern economy and society, traffic congestion has become an increasingly serious problem. Vehicle cooperative driving can alleviate traffic congestion and improve road traffic capacity. Compare with vehicle separate control, cooperative driving combines various vehicle systems, and highly integrates information on obstacle location, vehicle status and driving intention. Then the controller uniformly issues instructions to ensure the orderly driving of the platoon. In the cooperative driving platoon, the displacement difference and the speed difference between vehicles have a certain relationship, which reduces the possibility of traffic accidents and then improves the safety of driving. In the process of cooperative driving, if there are multiple vehicles whose speeds don’t meet the current lane requirements, or if there are obstacles ahead, multi-vehicle lane change measures must be taken.

With the rapid development of science and technology, the automobile industry is developing rapidly, and intelligent networking and autonomous driving have become new research hotspots. The safety and efficiency of vehicle driving has always been an important research topic in the transportation field. Due to reducing the participation of drivers, autonomous vehicles can reduce traffic accidents caused by human factors. While the development of intelligent networking can achieve information sharing between vehicles, and improve driving efficiency to a certain extent. Based on the game theory and the minimum safe distance condition, this paper establishes a lane changing decision model of intelligent network-connected autonomous vehicles, puts forward a game payoff function and analyzes the game strategy.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

Adaptive Cruise Control (ACC) includes three modes: cruise control, car following control, and autonomous emergency braking. Among them, the car following control mode is mainly used to manage the speed and vehicle spacing approach the preceding vehicle within the range of smooth acceleration changes. In addition, although the motion information signal of the preceding vehicle can be collected by auxiliary equipment, it is still a random variable and normally regarded as a disturbance to affect the performance of vehicle controller. Therefore, this paper proposed an ACC strategy considering the disturbance of the preceding vehicle and multi-objective optimization.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.