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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.

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.

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.

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.

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.

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.

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.

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.

With the increasing frequency of fire incidents of electric vehicles, the safety of power batteries has attracted more and more attention. At present, the research on the safety of power batteries is mainly focused on fresh batteries. As the state of health of batteries deepens, how the safety of the battery evolves is not clear enough so far. This paper analyzes the external short-circuit safety of a NCM/C rectangular battery under different state of charges. The results show that when the cycle number is less than 800, the maximum temperature of the battery during short-circuit is below 130 °C. The main failure mode of the battery is bulging in volume or opening of the explosion-proof valve and there is no obvious regularity between the failure mode with the cycle life. However, when the cycle number reaches 1000, the battery goes into thermal runaway during the safety test.

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.

In this paper, we present a framework for the robot to learn how to place objects to a workpiece by learning from humans in smart manufacturing. In the proposed framework, the rational scene dictionary (RSD) corresponding to the keyframes of task (KFT) are used to identify the general object-action-location relationships. The Generalized Voronoi Diagrams (GVD) based contour is used to determine the relative position and orientation between the object and the corresponding workpiece at the final state. In the learning phase, we keep tracking the image segments in the human demonstration. For the moment when a spatial relation of some segments are changed in a discontinuous way, the state changes are recorded by the RSD. KFT is abstracted after traversing and searching in RSD, while the relative position and orientation of the object and the corresponding mount are presented by GVD-based contours for the keyframes.

It’s important to predict human actions in the industry assembly process. Foreseeing future actions before they happened is an essential part for flexible human-robot collaboration and crucial to safety issues. Vision-based human action prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations among time steps for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in the past human motions and construct the future action based on limited accessible human demonstrations.

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.

Light-duty China-6, which is among the most stringent vehicle exhaust emission standards globally, mandates the monitoring and reporting of real driving emissions (RDE) from July, 2023. In the process of regulation promulgation and verification, more than 300 RDE tests have been performed on over 50 China-5 and China-6 certified models. This technical paper endeavors to summarize the experience of RDE practice in China, and discuss the impacts of some boundary conditions (including vehicle dynamic parameters, data processing methods, hybrid propulsion and testing altitude) on the result of RDE measurement. In general, gasoline passenger cars confront few challenges to meet the upcoming RDE NOx requirement, but some China-5 certified samples, even powered by naturally-aspirated engines may have PN issues. PN emissions from some GDI-hybrid powertrain systems also need further reduction to meet China-6 RDE requirements.

In this research, propane flash boiling sprays discharged from a five-hole gasoline direct injector were studied in a constant volume vessel. The fuel temperature (Tfuel) ranged from 30 °C to 90 °C, and the ambient pressure (Pamb) varied from 0.05 bar to 11.0 bar. Different flash boiling spray behavior compared to that under sub-atmospheric conditions was found at high Pamb. Specifically, at the sub-atmospheric pressures, the individual flashing jets merged into one single jet due to the strong spray collapse. In contrast, at Pamb above 3.0 bar and Tfuel above 50 °C, the spray collapse was mitigated and the flashing jets were separated from each other. Further analyses revealed that the mitigation of spray collapse at high Pamb was ascribed to the suppression of jet expansion. In addition, it was found that the spray structure was much different at similar Rp, indicating that Rp lacked the generality in describing the structure of flash boiling sprays.

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.

Methanol has been regarded as a potential transportation fuel due to its advanced combustion characteristics and flexible source. However, it is suffering from misfire and high HC emissions problems under cold start and low load conditions either on methanol SI engine or on methanol/diesel dual fuel engine. Hydrogen is a potential addition that can enhance the combustion of methanol due to its high flammability and combustion stability. In the current work, the effect of hydrogen fraction on the laminar flame characteristics of methanol- hydrogen-air mixture under varied equivalence ratio was investigated on a constant volume combustion chamber system coupled with a schlieren setup. Experiments were performed over a wide range of equivalence ratio of the premixed charge, varied from 0.8 to 1.4, as well as different hydrogen fraction, 0%, 5%, 10%, 15% and 20% (n/n). All tests were carried out at fixed temperature and pressure of 400K and 0.1MPa.

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%.