Search Results

With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs.

In the diagnosis of membrane flooding and drying faults in a Proton Exchange Membrane Fuel Cell (PEMFC) through Electrochemical Impedance Spectroscopy (EIS), this paper proposes a Genetic Algorithm (GA)-based feature selection method for selecting the required frequency points of failure, to reduce the measurement time taken by EIS while ensuring high diagnostic accuracy. This feature selection method searches the feature space through GA and proposes an encoding method tailored to this problem. During the searching process, three algorithms, i.e., Backpropagation Neural Network (BPNN), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost), are used to extract various features and select higher diagnostic rates of feature frequencies.

Engine operation produces particles that contaminate the lubricating oil and can damage the engine's internal components. This paper presents a model for a three-coil inductive metal particle sensor and verifies the rationality and accuracy of the model by simulating the motion of a single spherical iron particle passing through the sensor. On this basis, the simulation of coupling double particles with different sizes, distances, and shapes is carried out. The study explores the influence of particle motion on the sensor-induced signal under various conditions. The research shows that when two particles pass through the sensor, the induced voltage signal will produce superposition when the distance between the two particles is small. The peak value of the induced voltage is 1-2 times the peak value of the induced voltage of a single particle. As the distance increases, the peak value of the induced voltage initially decreases, then slowly increases, and finally stabilizes.

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.

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.

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.

Thermal propagation test of lithium-ion battery is an important method to verify the safety of battery system, and how to effectively trigger the thermal runaway of a cell and minimize the energy introduced into the system become the key of test method design. In this work, the influence of different heating area and different heating power on thermal runaway of prismatic cells and pouch cells is studied. The results show that when the heating area is fixed, the heating power increases, the heating time required to trigger the thermal runaway of the cells becomes shorter. The energy needed to be introduced becomes smaller, but there will be a minimum value of the introduced energy. On the other hand, the thermal runaway results of prismatic cells are more sensitive to the change of heating area, and the thermal runaway results of pouch cells are more sensitive to heating power.

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.

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.

Low combustion efficiency and high hydrocarbon emissions at low loads are key issues of natural gas and diesel (NG-diesel) dual fuel engines. For better engine performance, two diesel-spray-orientated (DSO) bowls were developed based on the existing diesel injector of a heavy-duty diesel engine with the purpose of placing more combustible natural gas/air mixture around the diesel spray jets. A protrusion-ring was designed at the rim of the piston bowl to enhance the in-cylinder flame propagation. Numerical simulations were conducted for a whole engine cycle at engine speed of 1200 r/min and indicated mean effective pressure (IMEP) of 0.6 MPa. Extended coherent flame model 3 zones (ECFM-3Z) combustion model with built-in soot emissions model was employed. Simulation results of the original piston bowl agreed well with the experimental data, including in-cylinder pressure and heat released rate (HRR), as well as soot and methane emissions.

In this paper, the differences between multi-hole and single-hole spray contour under the same conditions were compared by using high-speed photography. The difference between the contour area of multi-hole and that of single-hole spray was used as a parameter to describe the degree of spray collapse. Three dimensionless parameters (i.e. degree of superheat, degree of undercooling, and nozzle pressure ratio) were applied to characterize inside-nozzle thermodynamic, outside-nozzle thermodynamic and kinetic factors, respectively. In addition, the relationship between the three dimensionless parameters and the spray collapse was analyzed. A semi-empirical equation was proposed for evaluation of the degree of collapse based on dimensionless parameters of flash and non-flash boiling sprays respectively.

The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability.

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.