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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.

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.

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.

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.

This research examined the focused design, elastic design, energy decoupling, and torque roll axis (TRA) decoupling methods for mount optimization design. Requiring some assumptions, these methods are invalid for some load conditions and constraints. The linearity assumption is advantageous and simplifies both design and optimization analysis, facilitating engineering applications. However, the linearity is rarely seen in real-world applications, and there is no practical method to directly measure the reaction forces in the three locally orthogonal directions, preventing validation of existing methods by experimental results. For nonlinear system identification, there are additional challenges such as unobservable internal variables and the uncertainty of measured data.

The study of vehicle state estimation performance especially on the aspect of observer-based control for improving vehicle ride comfort and road handling is a challenging task for vehicle industry. Since vehicle roll behavior with various road excitations act an important part of driving safety, how to accurately obtain vehicle state under various driving scenes are of great concern. However, previous researches seldom consider coupling relation between vehicle vertical and lateral response with steering input under various road excitation. To address this issue, comprehension analyses on vehicle roll state estimation with coupled input are present in this paper. A full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first created to describe vehicle lateral and vertical coupling dynamics.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

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.

The ever-growing number of interacting electronic vehicle control systems requires new control algorithms to manage the increasing system complexity. As a result, torque-based control architecture has been popular for its easy extension as the torque demand variable is the only interface between the engine control algorithms and other vehicle control systems. Under the torque-based control architecture, the engine and AT coordinated control for upshift process is investigated. Based on the dynamics analysis, quantitative relationship between the turbine torque of HTC and output shaft torque of AT has been obtained. Then the coordinated control strategy has been developed to smooth the torque trajectory of AT output shaft. The designed control strategy is tested on a powertrain simulation model in MATLAB/Simulink and a test bench. Through simulation, the shift time range in which the engine coordinated control strategy is effective is acquired.

The cylinder-by-cylinder variations have many bad impacts on the engine performance, such as increasing the engine speed fluctuation, enlarging the torsional vibration and noise. To deal with this problem, the impact mechanism of cylinder-by-cylinder variations on low order torsional vibration has been studied in this paper, and subsequently a new individual cylinder control strategy was designed by processing the instantaneous crankshaft rotation speed signal, detecting the cylinder-by-cylinder variation and using feed-back control. The acceleration characteristics of each cylinder in each engine cycle were compared with each other to extract the variation index. The feed-back control algorithm was based on the regulation of the fuel injection according to the detected variation level.

The low cycle fatigue experiment is extensively used to test the reliability and durability of turbocharger. Low cycle fatigue test is mainly the switching between high and low speed. As the result of the experiment, the fatigue life is shorter as the difference between high and low speed becomes greater. In the traditional low cycle fatigue test, a large air compressor is needed to drive the turbocharger under different operating conditions, which consume large amounts of electric power. This paper presents a new experiment device which has double chambers and double turbochargers. This device can be self-circulating, without the large air compressor, to realize high and low speed switching on the premise of not exceeding the limitation of turbine entry temperature. First, a detailed model is established in GT-Power and self-circulation test data has been used to validate the model.

A multi-task control template has been established for diesel engine control system in this paper. By analyzing the function requirements and control strategies of electronic diesel engine control system, the data flow diagram has been obtained. Based on the mechanisms and characteristics of the RTOS as well as the design methods of real-time system software, modules in data flow graph has been divided further. So that the multi-tasks of the control system have been drawn. Then each task's properties have been designed. Afterwards, every task and the embedded RTOS have been transplanted into Freescale MPC5554 microcontroller. After that the multi-tasks have been tested and optimized in a Hardware-in-the-Loop Simulation (HILS) system of dSPACE AUTOBOX. The results show good reusability and real-time performance and it can increase the efficiency of the diesel engine control software development.

Based on MATLAB/Simulink, the ECU application software components library for diesel engine has been designed in this paper. The hierarchic and modularized components library is an open research platform for the model-based control software development. Using the components technology, the requirements of the diesel engine ECU application software have been analyzed, upon which the detailed components partition and the components library design have been accomplished. Besides, based on this components library, a control prototype for the diesel engine has been established quickly and verified through the Hardware-in-the-Loop test. The ECU software design and test process based on the components library show a good flexibility of the library, and it can improve the configurability and reusability of the software and increase the efficiency of the control software development.

Study and modeling of diesel combustion during transient operations is an important scientific objective. This is partially due to the fact that emissions under transient operations have aroused increasing attention by control groups during recent decades. The objective of this paper is to develop a combustion model to predict the peculiarities of transient combustion for developing and testing control strategies. To by-pass the complicated principles of transient combustion, the Neural Networks are applied to link the coefficients in an empirical combustion model with engine operating parameters. Finally, the Neural Networks combustion model would not only reflect the influence of turbocharge lag on combustion process during transient event, which cannot be predicted by its interpolation alternative, but also shown great potential for analyzing combustion characteristics during load increase transient event or other transient operations.

This paper proposes an effective nonlinear bicycle model including longitudinal, lateral, and yaw motions of a vehicle. This bicycle model uses a simplified piece-wise linear tire model and tire force tuning algorithm to produce closely matching vehicle trajectory compared to real vehicle for wide vehicle operation ranges. A simplified piece-wise tire model that well represents nonlinear tire forces was developed. The key parameters of this model can be chosen from measured tire forces. For the effects of dynamic load transfer due to sharp vehicle maneuvers, a tire force tuning algorithm that dynamically adjusts tire forces of the bicycle model based on measured vehicle lateral acceleration is proposed. Responses of the proposed bicycle model have been compared with commercial vehicle dynamics model (CarSim) through simulation in various vehicle maneuvers (ramp steer, sine-with-dwell).

Nowadays, the world is facing severe energy crisis and environment problems. Development of hydrogen fuel vehicles is one of the best ways to solve these problems. Due to the difficulties of infrastructures, such as the hydrogen transport and storage, hydrogen fuel vehicles have not been widely used yet. As a result, Hydrogen-gasoline dual-fuel vehicle is a solution as a compromise. In this paper, three way catalytic converter (TWC) was used to reduce emissions of hydrogen-gasoline dual-fuel vehicles. On wide open throttle and load characteristics, the conversion efficiency of TWC in gasoline engine was measured. Then the TWC was connected to a hydrogen internal combustion engine. After switching the hydrogen and gasoline working mode, emission data was measured. Experiment results show that the efficiency of a traditional TWC can be maintained above 85%., while it works in a hydrogen-gasoline dual-fuel alternative working mode.

The method and steps for software modeling of the embedded control systems for diesel engine based on UML are described in this paper. In order to meet the software function and the features of the system, object-oriented modeling for diesel engine embedded control software system has been implemented. Requirements are depicted by use case diagram and the logic structure is depicted by class diagram. According to the domain knowledge and the class diagram, the sequence diagram and state diagram are developed to describe the dynamic behavior of the system. The level of software development has been enhanced to the system level by software modeling. It focuses on the automotive field, and can be easy to grasp the problem from the overall perspective and discover software design problems at the early stage. It is also convenient to solve the problems caused by the change of requirements. The model has an excellent flexibility so that it can be applied to different software platforms.