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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.

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.

To improve the cold start performance and to reduce the misfire occurrence at cold start, the start-up strategy of total stoichiometric ratio combined with local rich mixture was applied in the study. The effect of injection strategy (the 1st injection timing, 2nd injection timing, 1st and 2nd fuel injection proportion and ignition timing) on the cold start HC emissions in the initial 10 cycles were investigated in a Two stage direct injection (TSDI) gasoline engine. The transient HC and NO emissions in the initial 10 cycles were analyzed, when the fuels are injected in the only 1st cycle and in the followed all cycles. The transient misfiring HC emissions were compared between the single and two-stage injection modes. In addition, the unburned HC (UBHC) emissions in the 1st cycle are compared among the TSDI engine, Gasoline direct injection (GDI) engine, Port fuel injection (PFI) engine and Liquefied petroleum gaseous (LPG) engine at the stoichiometric ratio.

In view of the problem of low-frequency (less than 10Hz, such as 0.5Hz, 1.15Hz, 8Hz in this paper) longitudinal vibration exists in a pure electric vehicle, modeling methods of drive-line torsion vibration system are conducted. Firstly, dynamometer test is performed, signals of motor speed and seat rail acceleration are obtained, the frequency characteristics of flutter is determined using the order analysis and time frequency analysis. Then four types of modeling and analysis are investigated facing the drive-line torsion vibration problem, including single model without electromagnetic stiffness, branch model without electromagnetic stiffness, single model considering electromagnetic stiffness and branch model considering electromagnetic stiffness. The results show that, modeling taking into account the electromagnetic stiffness and branches can reflect more low-frequency characteristics helps to reveal the low-frequency longitudinal flutter of the researched electric vehicle.

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.

In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.

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.

Millimeter wave automotive radar is one of the most important sensors in the Advanced Driver Assistance System (ADAS) and autonomous driving system, which detects the target vehicles around the ego vehicle via processing transmitted and echo signals. However, the sampling rate of classical radar signal processing methods based on Nyquist sampling theorem is too high and the resolution of range, velocity and azimuth can’t meet the requirement of highly autonomous driving, especially azimuth. In spatial domain, targets are sparse distribution in the detection range of automotive radar. To solve these problems, the algorithm for targets location based on compressed sensing for automotive radar is proposed in this paper. Besides, the feasibility of the algorithm is verified through the simulation experiments of traffic scene. The range-doppler-azimuth model can be used to estimate the distance, velocity and azimuth of the target accurately.

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.

Connected and autonomous vehicles are different from traditional vehicles. The communication between vehicles (V2V) or between vehicles and infrastructures (V2I) renders it possible to convey traffic information (e.g. signal timing or speed advisory) from signal controllers to vehicles as well as vehicles to vehicles in real time. Taking this advantage, this paper aims to developing an algorithm which enables the interconnected autonomous vehicles running efficiently on arterials. A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating.

Automatic lane change of intelligent vehicles is a complex process. Besides of safety, feelings of the driver and passengers during the lane change are also very important. In this paper, a lane change trajectory planner is designed to generate an ideal collision-free trajectory to satisfy the driver’s preference. Various lane changing modes, gentle lane change, general lane change, radical lane change and personalized lane change, are designed to meet the needs of different passengers on vehicles simultaneously. In this paper, the condition of the two-lane change is studied. One vehicle is in front of the ego vehicle at the same lane and one is at the rear of the ego vehicle at the target lane. A trajectory planning method is then established based on constant speed offset and sine curve, vehicle distances and speed difference, etc. The key factors which can reflect drivers’ lane change characteristics are then acquired.

Spray characteristics and spray wall-impingement events are the key factors for the direct injection spark ignition (DISI) engines, affecting fuel/air mixture preparation and its combustion process. Thus, the spray characteristics of a multi-hole injector for DISI engines, such as spray tip penetration and spray cone angle were investigated in an optical chamber employing the high-speed shadow photography. Furthermore, the effects of the injection pressure, ambient pressure and piston top shape on the impinging spray development were studied in the optical chamber, when the impinging distance is 26.1 mm, corresponding to about 60 CAD ATDC. In addition, the SMD and wall film thickness of the spray impinging on the piston top were studied by means of CFD technique. The results showed that the ambient pressure had the greater effect on the changes of the spray penetration and spray cone angle than the injection pressure.

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.

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.

There is an enormous effort to implement safety functionality into battery systems to prevent any accidents with the poisonous and inflammable ingredients of the electrolytes and electrode materials. But not only the safety regulation for lithium ion batteries will be different in comparison to the home electronics application, also the operating strategy must be different to guaranty the required lifetime in the automotive industry up to 10-12 years. This paperwork will show an approach to get offline (on test benches) and/or online (installed inside the car) information regarding the current healthy and state inside the cell. As an approach modeling of physical effects by the help of electro impedance spectroscopy (EIS) will be applied.

This set combines the book Road Vehicle Dynamics with its corresponding workbook companion, Road Vehicle Dynamics: Problems and Solutions. Road Vehicle Dynamics provides a detailed overview of the dynamics of road vehicle systems, giving readers an understanding of how physical laws, human factor considerations, and design choices affect ride, handling, braking, acceleration, and vehicle safety. Chapters cover analysis of dynamic systems, tire dynamics, ride dynamics, vehicle rollover analysis, handling dynamics, braking, acceleration, total vehicle dynamics, and accident reconstruction. The workbook will enable students and professionals from a variety of disciplines to engage in problem-solving exercises based on the material covered in each chapter of that book. It presents systematic rules of analysis that students can follow in a step-by-step manner to understand the efficiencies or shortcomings of various techniques.

This workbook, a companion to the book Road Vehicle Dynamics, will enable students and professionals from a variety of disciplines to engage in problem-solving exercises based on the material covered in each chapter of that book. Emphasizing application more than theory, the workbook presents systematic rules of analysis that students can follow in a step-by-step manner to understand the efficiencies or shortcomings of various techniques. Readers will gain a greater understanding of the factors influencing ride, handling, braking, acceleration, and vehicle safety.

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.

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.

To accurately and efficiently predict the temperature fields inside a lithium-ion battery is key technology for the enhancement of battery thermal management and the improvement of battery performances. The dimensional analysis method is applied to derive similarity criterions and the similarity coefficients of battery interior temperature fields, based on the governing partial differential equations describing the three dimensional transient temperature field. To verify the correctness of similarity criterions and the similarity coefficients, 3D finite element models of battery temperature field are established with a prototype and scale model, on the assumption that the battery cell has single-layer structure and multi-layers structure separately. The simulation results show that the similarity criterions and the similarity coefficients are correct.