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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.

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.

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.

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.

Particulate matter emissions are becoming a big issue for GDI engines as the emission regulations being more stringent. Methanol has been considered to be an important alternative fuel to reduce soot emissions. To understand the effect of methanol addition on soot and polycyclic aromatic hydrocarbons (PAHs) formation, the 2-D distributions of soot volume fraction and different size PAHs relative concentrations in methanol/gasoline laminar diffusion flames were measured by TC-LII and PLIF techniques. The effect of methanol was investigated under the conditions of the same carbon flow and the same flame height. The methanol volume fraction was set as M0/20/40/60/80. The results showed that the natural luminescent flame lift-off height and soot lift-off height increases consistently with the increasing methanol content due to the increase of outlet velocity of fuel vapor.

CNG-diesel dual fuel combustion mode has been regarded as a practical operation strategy because it not only can remain high thermal efficiency but also make full use of an alternative fuel, natural gas. However, it is suffering from misfire and high HC emissions under cold start and low load conditions. As known, hydrogen has high flammability. Thus, a certain proportion of hydrogen can be added in the natural gas (named HCNG) to improve combustion performance. In this work, the effect of hydrogen volume ratio on combustion characteristics was investigated on an optically accessible single-cylinder CNG-diesel engine using a Phantom v7.3 color camera. HCNG was compressed into the tank under different hydrogen volume ratios varied from 0% to 30%, while the energy substitution rate of` HCNG remained at 70%.

The diesel low temperature combustion (LTC) can keep high efficiency and produce low emission. Which has been widely studied at home and abroad in recent years. The combustion control parameters, such as injection pressure, injection timing, intake oxygen concentration, intake pressure, intake temperature and so on, have an important influence on the combustion and emission of diesel LTC. Therefore, to realize different combustion modes and combustion mode switch of diesel engine, it is necessary to accurately control the injection parameters and intake parameters of diesel engine. In this work, experimental study has been carried out to analyze the effect of intake oxygen concentration, intake pressure and intake temperature in combustion and emission characteristics of diesel LTC, such as in-cylinder pressure, temperature, heat release rate, NOx and soot emission.

Soot emission, known as PM (particulate matter), is becoming a big issue for GDI engines as the emission regulations being increasingly stricter. It is found that ethanol, as an oxygenated bio-fuel, can reduce the soot emission when added to gasoline. In order to fully understand the effect of ethanol on soot reducing, the soot characteristics of ethanol/gasoline blends were studied on laminar diffusion flames. In this experiment, the blending ratio of ethanol/gasoline was set as E0/20/40/60/80. Considering the carbon content decreasing due to ethanol addition, carbon mass flow rate was remained constant. The two-dimensional distributions of soot volume fraction were measured quantitatively by using two-color laser induced incandescence technique. The results showed that ethanol is able to decrease the soot significantly, but the effect of ethanol on soot reduction is weakened with the increasing ethanol ratio.

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.

Dual-fuel combustion combining a premixed charge of compressed natural gas (CNG) and a pilot injection of diesel fuel offer the potential to reduce diesel fuel consumption and drastically reduce soot emissions. In this study, dual-fuel combustion using methane ignited with a pilot injection of No. 2 diesel fuel, was studied in a single cylinder diesel engine with optical access. Experiments were performed at a CNG substitution rate of 70% CNG (based on energy) over a wide range of equivalence ratios of the premixed charge, as well as different diesel injection strategies (single and double injection). A color high-speed camera was used in order to identify and distinguish between lean-premixed methane combustion and diffusion combustion in dual-fuel combustion. The effect of multiple diesel injections is also investigated optically as a means to enhance flame propagation towards the center of the combustion chamber.

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.

Along with the booming expansion of private car preservation, many Chinese cities are now struggling with hazy weather and ground-level ozone contamination. Although central government has stepped up efforts to purify skies above China, counter-strategies to curb ground-level ozone is comparatively weak. By using maximum incremental reactivity (MIR) method, this paper estimated the ozone forming potential for twenty-five Euro-3 to Euro-5 passenger cars burning conventional gasoline, methanol-gasoline, ethanol-gasoline, neat methanol and compressed natural gas (CNG). The results showed that, for all the fuel tested, VOC/NOx ratios and SR values decreased with the upgrading of emission standard. Except for Euro-3 M100 and Euro-4 M85, SR values for alternative fuel were to different degrees smaller than those for gasoline. When the emission standard was shifted from Euro-4 to Euro-5, OFP values estimated for gasoline vehicle decreased.

The effect of temporally-splitting high pressure injection on Diesel spray combustion and soot formation processes was studied by using the high-speed video camera. The spray was injected by the single-hole nozzle with a hole diameter of 0.11mm into the high-pressure and high-temperature constant volume vessel. The free spray and the spray impingement on the two dimensional (2D) piston cavity wall were examined. Injection pressures of 100 and 160 MPa for the single injection and 160 MPa for the split injection were selected. The flame structure and soot formation process were examined by using the two-color pyrometry. The soot generated in the flame under the split injection under 160 MPa becomes higher than that of the single injection under 160 MPa.

As a probable solution to both energy and environmental crisis, methanol and methanol gasoline have been used as gasoline surrogates in several provinces of China. Most recently, the Ministry of Environmental Protection of China is drafting a special emission standard for methanol-fueled light-duty vehicles. Given the scarcity of available data, this paper evaluated regulated emissions, carbonyl compounds and particulate matter from a China-5 certificated gasoline/methanol dual-fuel vehicle over New European Driving Cycle (NEDC). The results elucidated that in context with gasoline mode, CO emitted in methanol mode decreased 11.2%, while no evident changes of THC and NOx emissions were noticed with different fueling regimes. The total carbonyls and formaldehyde have increased by 39.5% and 19.8% respectively after switching from gasoline to methanol. A remarkable decrease of 65.6% in particulate matter was observed in methanol mode.

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.