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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.

The rapid compression expansion machine (RCEM) was used to investigate the temporal variations of the spray flame and wall heat flux in the diesel engine combustion process by using 120 MPa and 180 MPa common rail pressure. A stepped cavity was applied to investigate spray and flame behavior under the pilot, pre and main multiple injection strategy. Wall heat flux sensors were installed in the piston cavity and the cylinder side. The injector has 3 holes with the neighboring angle in the left direction and another 3 holes in the right direction to simulate the spray interaction in the 10-hole injector combustion system in the actual diesel engine. The spray and flame behavior were taken by a high-speed video camera with direct photograph. A two-color analysis was applied to investigate gas temperature and KL factor distribution. The effect of locations and common rail pressure on heat transfer was investigated.

In this study, we determined the detailed reaction mechanism of CO/NO/O2 for automotive three way catalysts. The N2O formation process obtained from measurements of the reaction properties and the formation process of adsorbed NCO species obtained from surface analysis of platinum group metals were added to a previous detailed surface reaction mechanism. The computational accuracy of the developed reaction mechanism was verified by the one-dimensional simulation software BOOST, and it was found to be sufficient for any combination of platinum group metals and gas concentrations.

To achieve higher thermal efficiency for spark- ignition (SI) engines, advanced rapid combustion technology under high compression ratio is needed. The results of single-cylinder preliminary engine tests using E.U. commercial fuel at 96 RON show that the higher the compression ratio, the faster the combustion speed. Additional engine test and computations using S5R five-component surrogate gasoline with reliable chemistry under various temperature and pressure conditions implied that the autoignition assisted flame played significant role under higher compression ratio conditions, i.e., high temperature and pressure conditions, where apparent increases in laminar flame speeds compared to conventional combustion.

To achieve carbon-neutrality, internal combustion engines need to further improve their thermal efficiency to reduce CO2 emissions. To accomplish this, it is necessary to quantify and enhance five factors that control indicated thermal efficiency: compression ratio, specific heat ratio, combustion duration, combustion timing, and heat transfer to wall. In this work, quantitative targets for each factor were defined, which were derived from a simulation that considered the influence of heterogeneity of diesel combustion on thermal efficiency. The simulation utilized a two-zone combustion model. In particular, the targets for the combustion duration, combustion timing and heat transfer to wall were increased significantly compared to those for a conventional engine, in anticipation of an expansion of the load range of premixed charge compression ignition (PCI) combustion to higher loads.

Automobiles will have to be applied strict regulations such as Euro7 against PM, HC, CO. The generation of these components are related to fuel deposition to the wall surface of the combustion chamber. Therefore, the fuel injection model of engine combustion CFD requires accurate prediction about the deposition and vaporization of fuel on the combustion chamber. In this study, multiphase flow numerical analysis that simulates fuel behavior on the wall surface was conducted first. Then, two model formulae about the contact area and the heat flux of a liquid film was constructed based on the result of multiphase flow numerical analysis method. Finally, the new film heat transfer model was constructed from these model formulae. In addition, it was confirmed that new heat transfer model can predict the liquid film temperature obtained by multiphase flow numerical analysis method accurately.

As a technology to reduce the heat loss of engines, heat insulation coating to the surface of combustion chamber has been received a lot of attention. In order to maximize the thermal efficiency improvements by the technology, it is important to clarify the location where heat insulation coating can reduce heat loss more effectively, considering the impact on abnormal combustion etc. In this study, transient behavior of wall heat flux distribution on the piston was analyzed using 3D Computational Fluid Dynamics (CFD) for three combustion modes (spark ignition combustion (SI), homogenous charge compression Ignition (HCCI) and spark controlled compression ignition (SPCCI)).

Operating condition of rotor embedded magnet materials for permanent magnet synchronous motor (PMSM) critically affect electric vehicle (EV) range and dynamic characteristics. The rotor liquid cooling technique has a deep influence on PMSM performance improvement, and begin to be studied and applied increasingly in EV field. Here, the fluid, thermal, and electromagnetic characteristics of motor with and without hollow-shaft cooling are researched comprehensively based on 100 kW PMSM with housing water jacket (HWJ) and hollow-shaft rotor water jacket (SWJ). The solid models are constructed considering temperature-dependent power loss and anisotropic thermal conductivity. After the fluid models are set up by using Reynolds stress model (RSM), conjugate heat transfer is conducted through computational fluid dynamics (CFD) simulation, and is verified by real PMSM test bench experiments.

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.

Gasoline-related factors that affect low-speed pre-ignition (LSPI) include the distillation properties of gasoline, manganese (Mn), ethanol, diesel fuel, detergent for aftermarket, and iron (Fe). The combined effect of Mn with ethanol or high calcium engine oil (high-Ca oil) has not been sufficiently clarified. Therefore, appropriate countermeasures for LSPI have not yet been implemented. To clarify the effect of the gasoline properties and additives on LSPI, engine tests were conducted using gasoline with different “PM Index” values, an indicator of distillation properties, different concentrations of Mn, ethanol, diesel fuel, detergent, Fe, and high-Ca oil. The results showed that the LSPI frequency tended to increase with the PM Index, Mn up to 60 ppm, diesel fuel up to 2 vol.%, and detergent up to three times the standard amount.

TWC exposure to extreme temperature could result in irreversible damage or thermal failure. Thus, a strategy embedded in the engine control unit (ECU) called catalyst overheating protection (COP) will be activated to prevent TWC overheating. When COP is activated, the command air-fuel ratio will be enriched to cool the catalyst monolith down. Fuel enrichment has been proven a main prerequisite for ammonia formation in hot TWCs as a by-product of NOx reduction. Hence, COP events could theoretically be a source of post-catalyst ammonia from petrol vehicles, but this theory is yet to be confirmed in published literature. This paper validated this hypothesis using a self-programmed chassis-level test. The speed of the test vehicle was set to constant while the TWC temperature was raised stepwise until a COP event was activated.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

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).

Gear rattle noise is one of the important characteristics of manual and dual-clutch transmission，it is generated by the impact of unloaded meshing gear pairs in the transmission due to engine torsional vibration. Based on a front-drive manual transmission and a five dynos drivetrain NVH test bench with high-speed sine wave generator function, this paper designs an experimental program suitable for transmission rattle noise. By driving dynamometer to simulate the torque fluctuation of real engine, the main research is to study the characteristics of the transmission rattle noise under different excitation amplitudes and different excitation frequencies, and the sensitivity of rattle noise under different gears, different oil temperatures, different excitation amplitudes and excitation frequencies is analyzed. Finally, the transmission maps of rattle noise in different gears can be obtained.

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.

Owing to the small size of engines and high injection pressures, it is difficult to avoid the fuel spray impingement on the combustion cylinder wall and piston head in Direct Injection Spark Ignition (DISI) engine, which is a possible source of hydrocarbons and soot emission. As a result, the droplets size and distribution are significantly important to evaluate the atomization and predict the impingement behaviors, such as stick, spread or splash. However, the microscopic behaviors of droplets are seldom reported due to the high density of small droplets, especially under high pressure conditions. In order to solve this problem, a “spray slicer” was designed to cut the spray before impingement as a sheet one to observe the droplets clearly. The experiment was performed in a constant volume chamber under non-evaporation condition, and a mini-sac injector with single hole was used.

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.

The aims of this study are to understand the mechanism of dew condensation and try to prevent it on automobile headlamp. In this case, it is necessary to build the computational model of the headlamp with all details accurately. In addition, the simulation framework for predicting the turbulent flow field has to be accompanied with the high temperature heat source and consider high accuracy of the wall heat transfer in the running environment of a vehicle. Moreover, it is a challenging task that using CFD (Computational Fluid Dynamics) to understand the mechanism of the flow field inside the light emitting diode (LED) headlamp with a rotating fan to cool the light sources because of the complicated internal structures and significant heat transfer. In this paper, the method of compressible turbulence has been constructed which based on hierarchical cartesian grid using the HPC (High Performance Computing) environment.