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Vehicles equipped with one or several functions of Advanced Driver Assistant System (ADAS) and autonomous driving (AD) technology are more mature and prevalent nowadays. Vehicles being smarter and driving being easier is an unstoppable trend. In the near future, intelligent vehicles will be mass produced and running on the road. However, before the mass-production of intelligent vehicles, a lot of experimental tests and validations need to be carried out to insure the safety and reliability of ADAS and AD technology. Although the road test of real vehicles is the most reliable and accurate test method, it cannot meet the need of rapid development of technology research due to high time and financial cost. Therefore, a high-efficient design and evaluation methodology for ADAS and AD development and test is a must. In this paper, a virtual co-simulation platform based on MATLAB/Simulink, OpenModelica and Unity 3D game engine (MOMU) is proposed.

To wheel driven electric vehicle, besides road unevenness excitation, the electric motor torque fluctuation has great effects on the whole body vibration (WBV) according to prior research.[1],[2] To evaluate and further reduce the influence of torque fluctuation on the whole body vibration (WBV), vibration characteristic of rear suspension is analyzed thoroughly and finally optimized in this paper. Firstly, based on an existing eccentrical motor driven electric vehicle, a rear twist beam suspension suitable for high frequency analysis is modeled in Adams/View. Secondly, the vibration characteristic is simulated using Adams/Vibration module and verified with natural modal parameters of system. Finally, the vibration characteristic is optimized by adjusting the parameters of bushings using DOE method and proven to be effective to reduce WBV caused by torque fluctuation.

Powertrain system of series-parallel hybrid vehicle contains multiple excitation sources like engine, motor and generator. The reduction of noise and vibration is quite difficult during multiplex working modes or the switch of modes. Aiming at Series-parallel hybrid powertrain system which contains engine, motor and planetary gear subsystems, this paper considered a typical working condition which is based on the power control strategy and established the torsional vibration mechanical model of hybrid powertrain system. The inherent characteristics and transient vibration response of the electric mode, hybrid mode and parking charging mode were studied and it was discovered that the repetitive frequency of the powertrain system under the three working modes is the same which is only related to inertia and meshing stiffness of planetary gear system. The non-repetitive frequency and corresponding vibration modes under the electric mode and parking charging mode are both close.

Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance.

High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object detector-based advanced driver assistance systems (ADAS) and autonomous driving systems. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the motion of the vehicle and the position of objects in the traffic scene, which limits the consistence between the resulting augmented images and traffic scenes. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes to generate more realistic augmented images and achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene.

In order to avoid the influence of the change of the camera pitch angle and the variation of the height of the ground on the ranging accuracy, improve the real-time performance of the algorithm by substituting the current widely-used monocular vision ranging algorithm which builds the optical model based on the shadow of the vehicle floor and the lane line, as well as avoid the classification of vehicle detection, a vehicle distance measurement algorithm based on monocular vision and license plate width is established. Firstly, the target image acquisition and preprocessing are studied. Then the paper studies the license plate image location segmentation method based on accelerated template matching. On this basis, the algorithm for obtaining the ratio of license plate width to image width is studied, and the function of vehicle distance and license plate ratio width is established.

In the autonomous driving perception task, the accuracy of target detection is an essential evaluation, especially for small targets. In this work, we propose a multi-sensor fusion neural network that combines radar and image data to improve the confidence level of the camera when detecting targets and the accuracy of the prediction box regression. The fusion network is based on the basic structure of single-shot multi-box detection (SSD). Inspired by the attention mechanism in image processing, our work incorporates the a priori knowledge of radar detection in the convolutional block attention module (CBAM), which forms a new attention mechanism module called radar convolutional block attention module (RCBAM). We add the RCBAM into the SSD target detection network to build a deep neural network fusing millimeter-wave radar and camera.

Fuel cell electric vehicles (FCEVs) require multiple components to operate properly, and the fuel cell stack—the source of power—is one of the most important components. While the number of enterprises manufacturing and selling fuel cell stacks is increasing globaly year after year, the residual challenges of core components and technologies still need to be resolved in order to keep pace with the development of lithium-ion batteries (i.e., its primary competitor). Additionally, many production and distribution standards are seen as unsettled. These barriers make large-scale commercialization an issue. Use of Proton-exchange Membrane Fuel Cells in Ground Vehicles explores the opportunities and challenges within the PEMFC industry. With the help of expert contributors, a critical overview of fuel cells and the FCEV industry is presented, and core technology, applications, costs, and trends are analyzed.

An electronic waste-gated turbocharger for automotive application can accurately control the boost pressure and effectively reduce turbo-lag. It can improve the transient responsive performance of engine and the acceleration performance of vehicle, which makes vehicle have a better adaptation to the complex traffic environment. A detailed analysis of aerodynamic working principle of electronic wastegate is the foundation for designing the control strategy of electronic wastegate. Putting turbine with electronic wastegate under unsteady condition that simulates the pulse exhaust gas of engine and studying influences of different valve opening on the performance of turbine has the practical value. This paper sets fixed and periodical unsteady conditions and adopts numerical methods to explore the performance of turbine in twin-entry turbocharger and the flow loss of bypass. Steady simulation structure is given for reference.

The transient characteristics of combustion and emissions during the engine start up at different higher cranking speeds for hybrid electric vehicle (HEV) applications were presented in this paper. Cycle-by-cycle analysis was done for each start up case. Intake air mass during the first several cycles decrease as the engine was cranked at higher speed. Ignition timing is delayed with higher cranking speed, which leads to an increase of exhaust temperature. For various start up cases, similar quantity of fuel is injected at the first cycle, but the ignition timing is significantly delayed to meet the acceleration requirement when cranking speed enhanced. Because of the deterioration of intake charge, the air-fuel mixture is over-enriched in the first several cycles for the cases at higher cranking speed. With cranking speed is increased, the in-cylinder residual gas fraction rises, which leads to poor combustion and decrease of mass fraction of burned fuel.

The motor has vibration characteristics of order and multi-band in the frequency domain, which is different from the internal combustion engine when it is used as the vehicle’s drive. These characteristics cannot be briefly attenuated by general methods, but make the phononic crystal (PC) an ideal solution to eliminate the vibration transmission of the motor, because the concentrated vibration peak can easily be blocked by the bandgap. In this paper, one dimensional locally resonant phononic crystal (LRPC) which has low-frequency bandgaps are arranged on the automotive subframe to absorbing vibration. The partial coherence analysis is used to analyze the transfer characteristic of vibration on the subframe. Then, 6 main paths are selected from the 18 vibration transmission paths, based on its high ratio of partial coherence coefficient in a certain frequency, and the arranged position, the spring stiffness and the resonator’s mass of the LRPCs are chosen based on this result.

Highly accurate parking slot detection methods are crucial for Automated Valet Parking (AVP) systems, to meet their demanding safety and functional requirements. While previous efforts have mostly focused on the algorithms’ capabilities to detect different types of slots under varying conditions, i.e. the detection rate, their accuracy has received little attention at this time. This paper highlights the importance of trustworthy slot detection methods, which address both the detection rate and the detection accuracy. To achieve this goal, an accurate slot detection method and a reliable ground-truth slot measurement method have been proposed in this paper. First, based on a 2D laser range finder, datapoints of obstacle vehicles on both sides of a slot have been collected and preprocessed. Second, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm has been improved to efficiently cluster these unevenly-distributed datapoints.

It is becoming an increasingly important issue to improve NVH performance of electric drive motor for electric vehicle as the market grows rapidly. The correlation between stiffness of rear cover of motor, rotor eccentricity and noise of an electric drive is discussed in this paper which was few mentioned before. Poor stiffness of bearing chamber of rear cover may cause rotor eccentricity, which would lead to additional orders of electromagnetic noise. Stiffness optimization model of rear cover of motor was established, and the Optistruct of Hyper works software was used to improve stiffness as well as mode frequency by designing circular and radial ribs to surround bearing chamber of rear cover under guidance of topology. As compared to basis model with same mass, the 1st and 2nd strict mode frequencies of optimized rear cover separately increased by 11% and 12.5% with numerical simulations.

Lithium ion battery is considered as one of the most possible energy storage equipment for new energy vehicles (EV, HEV, etc.) because of the advantages of long cycle life, high power density and low self-discharge rate. However, under freezing condition high power battery suffers of significant performances losses. For example, they would suffer from significant power capability losses and poor rate performance, which would restrict the availability to delivery or to gain of high current in transient conditions. To evaluate those performance drawbacks and to make an efficient design, good mathematical models are required for system simulation especially for battery thermal management. In this paper, a three-dimensional homogenization thermal model of a 20 Ah prismatic lithium ion battery with LiFePO4 (LFP) cathode is described.

As one of the most crucial components in electric vehicles, power batteries generate abundant heat during charging and discharging processes. Thermal management system (TMS), which is designed to keep the battery cells within an optimum temperature range and to maintain an even temperature distribution from cell to cell, is vital for the high efficiency, long calendar life and reliable safety of these power batteries. With the desirable features of low system complexity, light weight, high energy efficiency and good battery thermal uniformity, thermal management using composite phase change materials (PCMs) has drawn great attention in the past fifteen years. In the hope of supplying helpful guidelines for the design of the PCM-based TMSs, this work begins with the summarization of the most commonly applied heat transfer enhancement methods (i.e., the use of thermally conductive particles, metal fin, expanded graphite matrix and metal foam) for PCMs by different researchers.

The exhaust aftertreatment strategy is one of the most fundamental aspects of spark ignition engine technologies. For various types of engines (e.g., carburetor engine, PFI engine and GDI engine), measuring, purifying, modeling, and control strategies regarding the exhaust aftertreatment systems vary significantly. The primary goal of exhaust aftetreatment systems is to reduce the exhaust emission levels of NOx, HC and CO as well as to lower combustion soot. In general, there is a tradeoff among different engine performance aspects. The exhaust catalytic systems, such as the three way catalyst (TWC) and lean NOx trap (LNT) converters, can be applied together with the development of other engine technologies (e.g., variable valve timing, cold start). With respect to engine soot, some advanced diagnosing techniques are essential to obtain thorough investigation of exhaust emission mechanisms.

The cab is an essential part of a light truck, and its fatigue durability performance plays an important role in the design and development stage. Accelerated fatigue bench test has been widely applied to product development of carmakers for its low cost and short development cycle. However, in reality, interference exists generally in torsional conditions for the light truck cab when tested on the 4-post vehicle road simulation system. To solve this problem and minimize the lateral force applied on the hydraulic cylinders, the direction and size combinations of displacement release about front and rear suspensions were discussed based on multi-body dynamics simulation and fixture design theory in this paper. Through comparative study, the optimum design and layout scheme of fixtures was determined to conduct the next test procedure. The weak positions of the light truck cab were firstly predicted by utilizing finite element method (FEM) and fatigue analysis theory.

In this paper, a case study of Shanghai HEVs application and its effects on the social and environmental benefits are presented based on the multi views on the different aspects, such as, not only for the fuel consumption saving, but also emissions reduction and health effect, agriculture loss and cleaning cost. The results show that the potential benefits for the society from HEVs application are markedly with the increase of the ratio of HEV in the population of vehicle. Based on this, the policy to promote the HEV purchased by consumers is very important at the beginning of HEV into market.

In recent years, one of the keys to achieving energy conservation and emission reduction and practicing sustainable development strategies is the wide-area access of large-scale electric vehicles. The charging behavior of large-scale electric vehicles has brought great challenges to the load management and adjustment capacity determination of the power system. Therefore, the prediction of adjustable ability of electric vehicle aggregator based on deep-belief-network is proposed in this paper. First of all, this paper selects the indicators related to the load of the electric bus station: including the arrival time, departure time, and daily mileage of the electric vehicle, from which the SOC variation trend and accurate charging demand of the single electric vehicle are obtained.

Centrifugal Pendulum Vibration Absorber (CPVA for short) is used to absorb torsional vibrations caused by the shifting motion of the engine. It is increasingly used in modern powertrains. In the research of the dynamic characteristics of the CPVA, it is necessary to obtain the real motion of the pendulum to compensate the fitting performance of mathematical model. The usual method is to install an angle sensor to measure the movement of the pendulum. On the one hand, the installation of the sensor will affect its movement to a certain extent, so that the measurement results do not match the actual motion. On the other hand, the motion of the pendulum is not only the rotational motion around the rotational axis of the CPVA rotor, but also has translation relative to it. As a result, it is difficult to obtain accurate motion only by the angle sensor. We proposed a non-contact centrifugal pendulum motion measurement method.