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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.

Lean combustion has been well known to be an effective method to improve the thermal efficiency. However, leaner mixture is prone to cause the unstable combustion and poorer unburned hydrocarbon (UTHC) emissions. Pre-chamber turbulent jet combustion has been proved to enhance the combustion stability under ultra-lean conditions. However, more NOx is formed during the combustion, resulting in the fact that the tailpipe NOx emission is too high to be still not available for the real application. In this report, in order to achieve a higher air excess ratio while keeping lower UTHC emissions, and especially NOx emission, a new combustion technique which combined pre-chamber jet combustion with fuel reforming was proposed and experimentally demonstrated on a pre-chamber engine.

The authors have reported significant smoke reduction in twin shaped semi-premixed diesel combustion with a newly designed combustion chamber to distribute the first and the second sprays into upper and lower layers. However, the first stage premixed combustion tends to advance far from the TDC, resulting in lowering of thermal efficiencies. In this report, improvement of thermal efficiency by optimizing the combustion phase with lower ignitability fuels was identified with the divided combustion chamber. The experiment was conducted with four fuels with different cetane numbers. The first stage premixed combustion can be retarded to the optimum phase with the fuel with cetane number 38, establishing high efficiencies.

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.

This investigation utilizes a correlated fluid-structure interaction (FSI) model of the torque converter and clutch assembly to perform a pseudo transient clutch engagement at steady state operating conditions. The pseudo transient condition consists of a series of nine steady state simulations that transition the torque converter clutch from fully released to near full lockup at a constant input torque and output speed representative of a highway cruising speed. The flow and pressured field of the torque converter torus and clutch are solved using a CFD model and then passed along to a transient structural model to determine the torque capacity of the lockup clutch. Bulk property assumptions regarding the friction material, deformation of the clutch plate, and deflection of supporting structures were made to simplify the model setup, run time, and solution convergence.

Owing to climate change issues caused by global warming, the role of alternative fuels, such as low-carbon and non-carbon fuels, is becoming increasingly important, particularly in the transportation sector. Therefore, hydrogen has emerged as a promising fuel for internal combustion engines because it does not emit carbon dioxide. Direct injection is mandatory for hydrogen-based internal combustion engines to mitigate backfires and low energy density. However, there is a lack of measurement of the equivalence ratio methodology because hydrogen has a higher diffusion rate than conventional fuels. The objective of this research is a feasibility study of laser-induced breakdown spectroscopy (LIBs) for measuring the equivalence ratio. The second harmonic ND-YAG laser was implemented to induce the atomic emission of hydrogen via the breakdown phenomenon. Simultaneously, the hydrogen jet structure was visualized in a constant volume vessel using Schlieren imaging.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

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.

In a natural gas fueled engine ignited by diesel fuel, the addition of hydrogen to the engine could be a possible way to improve thermal efficiency and reduce unburned methane which has a warming potential many times that of carbon dioxide as it promotes a more rapid and complete combustion. This study carried out engine experiments using a single cylinder engine with natural gas and hydrogen delivered separately into the intake pipe, and with pilot-injection of diesel fuel. The percentages of hydrogen in the natural gas-hydrogen mixtures were varied from 0% to 50% of the heat value. The results showed that the hydrogen addition has an insignificant effect on the ignition delay of the diesel fuel and that it shortens the combustion duration. The increase in the hydrogen ratio decreased the unburned hydrocarbon emissions more than the reduction of the amount of natural gas that was replaced by the hydrogen.

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.

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.

Incomplete-combustion and misfire are the hurdles in internal combustion engines to run on ultra-lean mixture, whereas high thermal efficiency has been achieved at lean mixture. The burning characteristics of n-heptane with 0% and 30% hydrogen additions were studied at 393K-453K and 100kPa-300kPa up to near lean burn limits, λ=0.8-2.0. The flame appeared in spherical shape only by 37-mJ ignition energy (IE) at λ=0.8-1.5, while further lean mixture, ≥1.6, could be ignited only by 3000-mJ with the distorted flame shape. The flame buoyed in the mixture when burning velocity calculated by kinetic mechanism was equal or less than 19.83 cm/s at the initial conditions of λ=1.8, 393K and 100kPa. The thermal instability under impact of initial pressure and temperature was higher at lean mixture than at stoichiometric mixture.