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Torque Vectoring Control for distributed drive electric vehicle is studied. A handling improvement algorithm for normal cornering maneuvers is proposed based on state variable feedback control: Yaw rate feedback together with steer angle feedforward is employed to improve transient response and steady gain of the yaw rate, respectively. According to the feedback coefficient's influence on the transient response, an optimization function is proposed to obtain optimum feedback coefficients under different speeds. After maximum feedforward coefficients under different speeds are obtained from the constraint of the motor exterior characteristic, final feedforward coefficients are calculated according to an optimal steering characteristic. A torque distribution algorithm is presented to help the driver to speed up during the direct yaw moment control.

Although Hydrostatic Transmission System (HTS) had been used in many places, such as machine tools, agriculture machinery, construction machinery, and vehicles, it had not been used in good performance. Twenty years ago many people began to design new hydrostatic transmission with higher efficiency. Hydrostatic Energy Storage Transmission System (HESTS) is one of new hydrostatic transmission system with higher efficiency. HESTS is more fit for being used in vehicle that is always running in undulating ground or starting and braking frequently. Construction of vehicular HESTS was analyzed, mathematical model of vehicular HESTS was established. The needed control strategies of vehicular HESTS were analyzed because there are many variables would be controlled in the new transmission system.

To develop a free-piston engine-alternator integrative power system for Hybrid Electric Vehicles, the key design parameters, such as reciprocating mass of the piston assembly, compression ratio, the ignition timing, the engine fuel consumption rate and power output, are studied based on the simulation. The results show that, the system simulation model of the free piston engine can predict the in-cylinder pressure vs. the piston's displacement being accurate enough as the test results from reported reference. The model can be employed to optimize the design parameters and to predict the fuel economy and power output. It provides the methods and bases for the free piston engine design and predicting the main performance parameters' values.

Adhesive bonding has many applications in the automotive industry. The single-lapped bonded joint is the most typically used among various bonding types. This paper presents experimental research for determining the strain field of the single-lapped joint under tensile loading. The materials for the joint are epoxy-based structural adhesive and low-carbon electrolytic zinc steel plate. In the study, a DIC (digital image correlation) system was adopted to measure the strain distribution of the bonded joint during a tensile test. The bonded steel coupons in the tensile test were prepared according to the ASTM standard. During the measurement, images of the coupon joint were taken before and after the deformation process. Then the DIC system measured the strain of bonded joint by comparing two consecutive images. The measured data from the DIC was compared to data taken simultaneously from a traditional extensometer.

Plug-in hybrid electric vehicles (PHEVs) provide significantly improvement in fuel economy over conventional vehicles as well as reductions in greenhouse gas and petroleum. Numerous recent reports regarding control strategy, power train configuration, driving pattern, all electric range (AER) and their effects on fuel consumption and electric energy consumption of PHEVs are reported. Meanwhile, the control strategy for engine start-stop and mileage between recharging events from the electricity grid also has an important influence on the petroleum displacement potential of PHEVs, but few reports are published. In this paper, a detailed simulation model is set up for a plug-in series hybrid electric vehicle (PSHEV) employing the AVL CRUISE. The model was employed to predict the AER of the baseline PSHEV using rule-based logical threshold switching control strategy.

In this paper, based on the plug-in series hybrid electric vehicle development project, the vehicle technology solutions and the match of power system parameters were analyzed. The vehicle control strategies were identified and optimized according to plug-in hybrid vehicle features. The plug-in series hybrid, rule-based logic threshold switching control strategy, charge depleting (CD) mode and charge-sustaining (CS) mode are chosen according to the key factors, such as the environment, performance requirements, technical requirements and cost. And then the structure and model of vehicle control strategy were established to carry out vehicle energy management and power system control. The parameter selection, electric drive system matching, energy storage system design based on the requirement of vehicle performance, system architecture and control strategy are presented.

An experimental study is presented on the evaporation of diluted droplet-laden two-phase jet flows within a heated air channel co-flow. In this study, n-heptane is pre-atomized by an ultrasonic nozzle to produce droplet cluster with a median diameter of about15μm, and a continuous cold air flow is applied to carry the fuel droplet cluster to emerge from a nozzle tube, producing a free turbulent jet of droplet stream. The droplet stream is then introduced as a central jet into a square-shaped channel with heated air co-flow for evaporation investigations. With flexibilities of the initial properties of droplet stream and surrounding conditions of channel flow, the axial evolution of droplet size is determined to characterize the evaporation behavior of n-heptane droplet stream under various boundary conditions. The equivalence ratios of droplet streams are varied by changing both the carrier-air flow rate and the fuel flow rate.

Vehicle and pedestrian detection technology is the most important part of advanced driving assistance system (ADAS) and automatic driving. The fusion of millimeter wave radar and camera is an important trend to enhance the environmental perception performance. In this paper, we propose a method of vehicle and pedestrian detection based on millimeter wave radar and camera. Moreover, the proposed method complete the detection of vehicle and pedestrian based on dynamic region generated by the radar data and sliding window. First, the radar target information is mapped to the image by means of coordinate transformation. Then by analyzing the scene, we obtain the sliding windows. Next, the sliding windows are detected by HOG features and SVM classifier in a rough detect. Then using the match function to confirm the target. Finally detecting the windows in a precision detection and merging the detecting windows. The target detection process is carried out in the following three steps.

Research studies have suggested that changes to the ignition system are required to generate a more robust flame kernel in order to secure the ignition process for the future advanced high efficiency spark-ignition (SI) engines. In a typical inductive ignition system, the spark discharge is initiated by a transient high-power electrical breakdown and sustained by a relatively low-power glow process. The electrical breakdown is characterized as a capacitive discharge process with a small quantity of energy coming mainly from the gap parasitic capacitor. Enhancement of the breakdown is a potential avenue effectively for extending the lean limit of SI engine. In this work, the effect of high-power capacitive spark discharge on the early flame kernel growth of premixed methane-air mixtures is investigated through electrical probing and optical diagnosis.

Idle stopping is one of the most important fuel saving methods for hybrid electric vehicle (HEV). While the enriched injection strategy which was employed to ensure reliable ignition of first cycle will leads to even more fuel film stayed in the intake port, all of the liquid film will evaporate randomly and interfere the mixture air-fuel ratio of the followed cycles. The fuel transport of the first cycle should be enhanced to reduce the residual fuel film, and then the control of the cycle-by-cycle air-fuel ratio will become easier and the combustion and HC emissions will also be better. In this paper the mixture preparation characteristics of the unfired first cycle, as well as the combustion and HC emissions characteristics of the fired first cycle under various injection timing strategies such as close-valve injection, mid-valve injection, and open-valve injection were investigated.

The vehicle system is actually a strongly nonlinear system with stochastic parameters. In this paper, the nonlinearities of suspension, tire and seat are analyzed, and the nonlinear dynamics model of driver-vehicle system with 8 degrees of freedom (DOFs) is built. The bifurcation and chaotic motion of the deterministic system under Sinusoid excitations considering the time delay between the front and rear tires are studied. Then, the stochastic feature of the equivalent stiffness and damping coefficients of suspension, tire and seat are assumed to be the normal distribution, and the nonlinear model with random parameters is obtained. The nonlinear dynamics of stochastic nonlinear driver-vehicle system is analyzed and compared through numerical simulation.

A cycle-resolved test system was designed in a Two Stage Direct Injection (TSDI) Gasoline engine to simulate the engine quick start process in an Integrated Start and Generator (ISG) Hybrid Electric Vehicle (HEV) system. Based on the test system, measurement of the in cylinder HC concentrations near the spark plug under different engine coolant temperature and cranking speed conditions were conducted using a Fast Response Flame Ionization Detector (FFID) with Sampling Spark Plug (SSP) fits, then the in-cylinder equivalence ratio near the spark plug was estimated from the measured HC concentrations. In addition, the effects of the 1st injection timing, 2nd injection timing, and total equivalence ratio on the mixture formation near the spark plug were analyzed by means of experiments.

In this paper, a new method for determining the forming limit diagram (FLD) of thin sheet metals, called DIC-Grid method, is proposed based on digital image correlation (DIC) technique. It's assumed that there exists one virtual grid with an initial diameter of 2.5mm, which is usually the same dimension as the grid in traditional circular grid analysis, close to the crack of specimen, and the limit strain point on FLD is determined by the deformation of this virtual grid. The DIC-Grid method has been compared with traditional circular grid analysis and the standard ISO/FDIS 12004-2 in Nakajima tests. The results show that the forming limit strains obtained by the newly proposed method are more stable and precise. Furthermore, DIC-Grid method can avoid the measurement error which exists in the circular grid analysis. Meanwhile, it overcomes the shortcomings of time-consuming data processing and non-applicable for unrealistic strain distribution in the method of ISO standard.

In the hybrid brake system of electric vehicle, due to the limitation of the motor braking force when the motor is at high speed and the failure of the regenerative braking force when the motor is at low speed, there are three transitional conditions in hybrid braking: the hydraulic brake system intervenes the braking, the hydraulic brake system withdraws the braking and the regenerative braking force withdraws the braking. Due to the response speed of the hydraulic system is slower than that of the motor, there is a large braking impact (the derivative of braking deceleration) in the transitional conditions of hybrid braking, which deteriorates the smoothness and comfort in braking. Aiming at the impact caused by the poor cooperation between the hydraulic braking force and the motor braking force, a coordinated strategy of double closed-loop feedback and motor force correction is proposed in this paper.

With the development of autonomous vehicle technology, there is an increasing tendency toward the application of intelligent sensors in environment-perception system on autonomous vehicle to assist vehicle in intelligent decision making relevant to autonomous driving. As for environment-perception system, a good track management method serves as the foundation, while multi-target tracking and multi-sensor data fusion are recognized as the key. In this paper, a track management method is proposed to deal with multi-target tracking based on the target-level data of multisource environmental sensors for autonomous vehicle. The track management includes four procedures as following: track initiation; point-track association; track update; track deletion. A modified fast algorithm for data association is applied in the point-track association procedure. Afterwards Kalman filter is implemented to update the track information of target. The algorithm has got through a simulation test.

The utilization of the SLAM (Simultaneous Localization and Mapping) technique was extended from the robotics to the autonomous vehicles for achieving the positioning. However, SLAM cannot obtain the global position of the vehicle but a relative one to the start. For sake of this, a fast and accurate system was proposed to obtain both the local position and the global position of vehicles based on mono-SLAM which realized the SLAM by using monocular camera with a lower cost and power consumption. Firstly, the rough latitude and longitude of current position was obtained by using common GPS without differential signal. Then, the Mono-SLAM operated on the consecutive video frames to generate the localization and local trajectory and its accuracy was further improved by utilizing the IMU information. After that, a piece of Map centered in the rough position obtained by common GPS was downloaded from the Open Street Map.

Automotive radar is an important environment perception sensor for advance driving assistance system. It can detect objects around the vehicle with high accuracy and it works in all bad weathers. For traditional automotive radar, it cannot measure the objects’ height. Thus, a manhole cover on the road surface or a guideboard high above the road would be taken erroneously as a non-moving car. In such cases, the adaptive cruise system would decelerate or stop the vehicle erroneously and make the driver uncomfortable. A 3D automotive radar with two-dimensional electronic scanning can measure the targets’ height as well as the targets’ azimuth angle. This paper presents a 79 GHz ultra-wide band automotive 3D imaging radar. Due to the 4 GHz wide bandwidth, the range resolution of this radar can be as small as 3.75 cm.

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm.

Real-time and reliable perception of the surrounding environment is an important prerequisite for advanced driving assistance system (ADAS) and automatic driving. And vision-based detection plays a significant role in environment perception for automatic vehicles. Although deep convolutional neural networks enable efficient recognition of various objects, it has difficulty in accurately detecting special vehicles, rocks, road pile, construction site, fence and so on. In this work, we address the task of traffic scene understanding with semantic image segmentation. Both driveable area and the classification of object can be attained from the segmentation result. First, we define 29 classes of objects in traffic scenarios with different labels and modify the Deeplab V2 network. Then in order to reduce the running time, MobileNet architecture is applied to generate the feature map instead of the original models.

Being one of the most simple and basic driving scenarios, highway scenario can be one of the first scenarios to achieve autonomous driving. Both car following (CF) and lane changing (LC) are the most basic and frequent maneuver during highway driving tasks, and therefore become two key issues to focus on in recent researches about autonomous vehicle (AV). Different from conventional CF and LC researches that attach much importance to the character, psychology, perception ability, and driving experience of human drivers, more timely and accurate reactions based on fast perception and communication technology as well as the automatic actuator are hypotheses for this research. Moreover, based on these hypotheses, a modified intelligent driver model (MIDM) is proposed for AVs’ following behavior to alleviate the fluctuations caused by lane changing behaviors.