Search Results

The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

To reduce the energy consumption level of electric vehicles, the working range of the regenerative braking system will gradually expand to the high state of charge of the battery. The time delay in the control signal transmission path of the high state of charge regenerative braking control process will affect the regenerative braking. At the same time, regenerative braking under a high state of charge puts forward higher requirements for the control accuracy of regenerative current. In the research of this paper, the motor model, battery model, and vehicle dynamics model are firstly established by using MATLAB/Simulink, and the dynamic relationship between regenerative current and regenerative braking torque is analyzed at the same time. Considering the system time delay, this paper proposes a high-charge regenerative braking control strategy (SPPC) that combines Smith prediction and prescribed performance control.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

The principal objective of the present work is to investigate the fundamental characteristics of a commercially available outwardly opening twin-fluid injector, which utilizes air-assisted atomization principle to attain pulse-type injection of fuel-air mixture. The electromagnetic characteristics of this injector were simulated and the effects of dominating parameters on the electromagnetic force to drive injector were ascertained. On that basis, this paper elaborates on the fundamental characteristics of air-assisted spray using gasoline and kerosene with the employment of two types of optical testing techniques. The spray morphological evolution under varied fuel injection durations and ambient pressures were captured with high-speed shadowgraph thus the corresponding external macroscopic characteristics were obtained and further compared. Spray droplet velocity and diameter at fixed monitoring location were measured by using PDPA (Phase Doppler Particle Analyzer).

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s.

As a new type of energy, free-piston linear generator (FPLG) attracts more research on its stable operation and power performance, while less on its combustion and emission performance. So, in this paper, the emission characteristics of FPLG in two different modes are studied through a port fuel injection (PFI) mode which was verified by the experiment and a gasoline direct injection (GDI) mode. The results showed that: both the GDI mode and the PFI mode produced large amounts of nitrogen oxide (NOx) during the working process. But the GDI mode produced before the PFI mode and it produced nearly 2 times than the PFI mode. However, the formation rate of NOx in GDI mode is much lower than that in PFI mode. Meanwhile, in both modes, 90% of NOX was generated in the cylinder at the temperature higher than 1750K, and only about 10% of NOX was generated at a temperature lower than 1750K.

The deformation of injector components cannot be disregarded as the pressure of the system increases. Deformation directly affects the characteristics of needle movement and injection quantity. In this study, structural deformation of the nozzle, the needle and the control plunger under different pressures is calculated by a simulation model. The value of the deformation of injector components is calculated and the maximum deformation location is also determined. Furthermore, the calculated results indicates that the deformation of the control plunger increases the control chamber volume and the cross-section area between the needle and the needle seat. A MATLAB model is established to The influence of structural deformation on needle movement characteristics and injection quantity is investigate by a numerical model. The results show that the characteristic points of needle movement are delayed and injection quantity increases due to the deformation.

An opposed piston two-stroke engine is more suitable for use in an unmanned aerial vehicle because of its small size, excellent self-balancing, stable operation, and low noise. Consequently, in this study, based on experimental data for a prototype opposed piston two-stroke engine, numerical simulation models were established using GT-POWER for 1D simulation and AVL-FIRE for 3D CFD simulation. The mesh grid and solver parameters for the numerical model of the CFD simulation were determined to guarantee the accuracy of the numerical simulation, before studying and optimizing the ventilation efficiency of the engine with different dip angles. Furthermore, the fuel spray and combustion were analyzed and optimized in details.

Hydrogen fuel is a future energy to solve the problems of energy crisis and environmental pollution. Hydrogen internal combustion engines can combine the advantage of hydrogen without carbon pollution and the main basic structure of the traditional engines. However, the power of the port fuel injection hydrogen engines is smaller than the same volume gasoline engine because the hydrogen occupies the volume of the cylinder and reduces the air mass flow. The turbocharger can increase the power of hydrogen engines but also increase the NOx emission. Hence, a comprehensive controlling strategy to solve the contradiction of the power, BTE and NOx emission is important to improve the performance of hydrogen engines. This paper shows the controlling strategy for a four-stroke, 2.3L hydrogen engine with a turbocharger. The controlling strategy divides the operating conditions of the hydrogen engine into six parts according to the engine speeds and loads.

HTL is a kind of biodiesel converted from wet biowaste via hydrothermal liquefaction (HTL), which has drawn increasing attention in recent years due to its wide range of raw materials (algae, swine manure, and food processing waste). However, from the previous experiments done in a constant volume chamber, it was observed that the presence of 20% of HTL in the blend produced as much soot as pure diesel at in chamber environment oxygen ratio of 21%, and even more soot at low oxygen ratios. It was also observed that n-butanol addition could reduce the soot emission of diesel significantly under all tested conditions. In this work, the spray and combustion characteristics of HTL and diesel blends with n-butanol added were investigated in a constant volume chamber. The in-chamber temperature and oxygen ranged from 800 to 1200 K and 21% to 13%, respectively, covering both conventional and low-temperature combustion (LTC) regimes.

Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

Flash boiling has drawn much attention recently for its ability to enhance spray atomization and vaporization, while providing better fuel/air mixing for gasoline direct injection engines. However, the behaviors of flash boiling spray with multi-component fuels have not been fully discovered. In this study, isooctane, ethanol and the mixtures of the two with three blend ratios were chosen as the fuels. Measurements were performed with constant fuel temperature while ambient pressures were varied to adjust the superheated degree. Macroscopic and microscopic characteristics of flash boiling spray were investigated using Diffused Back-Illumination (DBI) imaging and Phase Doppler Anemometry (PDA). Comparisons between flash boiling sprays with single component and binary fuel mixtures were performed to study the effect of fuel properties on spray structure as well as atomization and vaporization processes.

The spray structure and vaporization processes of flash-boiling sprays in a constant volume chamber under a wide range of superheated conditions were experimentally investigated by a high speed imaging technique. The Engine Combustion Network’s Spray G injector was used. Four fuels including gasoline, ethanol, and gasoline-ethanol blends E30 and E50 were investigated. Spray penetration length and spray width were correlated to the degree of the superheated degree, which is the ratio of the ambient pressure to saturated vapor pressure (pa/ps). It is found that parameter pa/ps is critical in describing the spray transformation under flash-boiling conditions. Three distinct stages namely the slight flash-boiling, the transition flash-boiling, and the flare flash-boiling are identified to describe the transformation of spray structures.

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

The advanced Pedestrian Legform Impactor (aPLI) incorporates a number of enhancements for improved lower limb injury prediction capability with respect to its predecessor, the FlexPLI. The aPLI also incorporates a simplified upper body part (SUBP), connected to the lower limb via a mechanical hip joint, that expands the impactor’s applicability to evaluate pedestrian’s lower limb injury risk also in high-bumper cars.As the aPLI has been developed to be used in standardized testing, further considerations on the impactor’s manufacturability, robustness, durability, usability, and repeatability need to be accounted for.. The aim of this study is to define and verify, by means of numerical analysis, a battery of design modifications that may simplify the manufacturing and use of physical aPLIs, without reducing the impactors’ biofidelity. Eight candidate parameters were investigated in a two-step numerical analysis.

ISO 26262, an international functional safety standard of electrical and/or electronic systems (E/E systems) for motor vehicles, was published in November 2011 and it is expected that the scope will be extended to motorcycles in a second edition of ISO 26262 going to be published in 2018. ISO/DIS 26262 second edition published in 2016 has Part 12 as a new part in order to apply ISO 26262 to motorcycle. Proper estimation of Exposure, Controllability, and Severity in accordance with ISO/DIS 26262 Part 12, are key factors to determine Motorcycle Safety Integrity Level. To estimate precise these factors, there would be a case that it might not be appropriate to apply studies done for passenger car to motorcycle, and it would be necessary to apply motorcycle specific knowledge and estimation methods. In our previous studies we clarified these motorcycle specific issues and studied the method for the adaptation.

The electronic control of direct injection fuel system, which could improve engine fuel efficiency, dynamics and engine emission performance through good atomization, precise control of fuel injection time and improvement of fuel-gas mixture, is the key technology to achieve the stratified combustion and lean combustion. In this paper, a direct injection injector that based on voice coil motor was designed aiming at the technical characteristics of one 800cc two-stroke cam-less engine. Prior to a one - dimensional simulation model of injector was established by AMEsim and the maximal fuel injection demand was met via the optimization of the main parameters of the injector, the structure of the voice coil motor was optimized by magnetic equivalent circuit method. After that, the maximal flow rate of the injector was verified by the injector bench test while the atomization characteristic of the injector was verified by using a high-speed camera.

The control valve is the most important implementation part of a high pressure common rail system, and its flow characteristics have a great influence on the performance of an injector. In this paper, based on the structure and the working principle of an electromagnetic injector in a high pressure common rail system, a simulation model of the injector is established by AMESim software. Some key parameters of the control valve, including the volume of the control chamber, the diameter of the orifice Z (feeding orifice), the diameter of the orifice A (discharge orifice) and the hole diameter of the fuel diffusion hole are studied by using this model. The results show that these key structural parameters of the control valve have a great influence on the establishment of the control chamber pressure and the action of the needle valve.

Diesel Particulate Filter (DPF) is a very effective aftertreatment device to limit particulate emissions from diesel engines. As the amount of soot collected in the DPF increases, the pressure loss increases. Therefore, DPF regeneration needs to be performed. Injected fuel into the exhaust line upstream of the Diesel Oxidation Catalyst (DOC), hydrocarbons are oxidized on the DOC, which increases the exhaust gas temperature at the DPF inlet. It is also necessary that the injected fuel is completely vaporized before entering the DOC, and uniformly mixed with the exhaust gases in order to make the DOC work efficiency. However, ensuring complete evaporation and an optimum mixture distribution in the exhaust line are challenging. Therefore, it is important that the fuel spray feature is grasped to perform DPF regeneration effectively. The purpose of this study is the constructing a simulation model.

Considering the randomness and instability of the oil pressure in the lubrication system, a new approach for fault detection and diagnosis of diesel engine lubrication system based on support vector machine optimized by particle swarm optimization (PSO-SVM) model and centroid location algorithm has been proposed. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters. It can improve the prediction accuracy of the model. The results show that the classify accuracy of PSO-SVM is improved compared with SVM in which parameters are set according to experience. Then, the support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, diagnose algorithm is achieved through analyzing the centroid movement of features. According to Performance degradation data, degenerate trajectory model is established based on centroid location.