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This paper aims at the problem that the sideband vibration noise of induction motor caused by inverter pulse width modulation (PWM). The frequency distribution characteristics of the induction motor with 36 stator slots and 32 rotor slots (36/32 IM) are analyzed. Based on that, a frequency width selection method for the periodic signal-based modulation considering the characteristics of sideband electromagnetic force. Results show that this method can effectively reduce the peak value of the sound power level (SWL) of sideband noise of IM at different speeds. This method is also applicable to IMs with different pole-slot match.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm. In this paper the authors present an ultra-light heuristic method for generating optimal Eco-Driving traces for Connected Autonomous Vehicles (CAVs) which indirectly minimizes the 1-norm of acceleration. This novel method produces EE improvements in line with previously implemented meta-heuristic methods while executing in a fraction of the time.

Premium instrument panels (IPs) contain passenger airbag (PAB) systems that are typically comprised of a stiff plastic substrate and a soft ‘skin’ material which are adhesively bonded. During airbag deployment, the skin tears along the scored edges of the door holding the PAB system, the door opens, and the airbag inflates to protect the occupant. To accurately simulate the PAB deployment dynamics during a crash event all components of the instrument panel and the PAB system, including the skin, must be included in the model. It has been recognized that the material characterization and modeling of the skin tearing behavior are critical for predicting the timing and inflation kinematics of the airbag. Even so, limited data exists in the literature for skin material properties at hot and cold temperatures and at the strain rates created during the airbag deployment. This paper presents tensile test results of one typical skin material conducted at four different strain rates of 0.01/s, 1/s, 10/s, and 100/s. Challenges in testing are discussed. A material modeling methodology is proposed that accounts for anisotropy, loading rate sensitivity and failure, and is verified by comparison of results from simulation and physical tests. Finally, recommendations for setting proper contact parameters between different parts in the model and for proper representation of the adhesive between the instrument panel substrate and skin are presented.

In research and development of any automotive industry the main challenge is to virtually simulate probable failures rather than relying on physical testing which consumes time and resources. It is even more challenging when it comes to failure prediction of ABS plastic parts due to its complexity in material, behavior and assembly variations. ABS material is used extensively in automotive vehicles especially in motorcycles and scooters due to its visual and structural benefits at moderate cost. In this paper, the work showcases a methodology to predict failure of ABS parts. In order to do so, understanding the shortcomings in the current system is necessary. With the help of testing database history of various vehicles on proving grounds, root causes and drawback with respect to current simulation process are identified for failure of ABS parts. The input excitations from proving ground are probabilistic, thus, random vibration fatigue process is then introduced to calculate life. By correlating the simulation predicted life with testing, the required factor of safety to include residual stresses, assembly stresses are calculated and passing limit in terms of stress is derived for ABS parts to further reduce the simulation run time. Using six sigma methods, the accuracy in simulation is improved with which cycle time, prototype cost and testing time is reduced.

The physical characteristics of Mars's soil have an impact on how easily a spacecraft can land and navigate the planet's surface. On the surface of Mars, wheeled robots known as "rovers" were planted to carry out scientific investigations on the planet's historical temperature, surface geology, and possibilities for past or current life. The challenges of guiding mobile robots across terrain that is sloping, rocky, and deformable have brought to light the significance of creating precise simulation models of the tire and mars soil interaction. In this paper, current efforts to create a terramechanics-based model of rover movement using a Non-Pneumatic (NP) tire on planetary surfaces are discussed. Since no rocks or soils have been brought back to Earth from Mars, Martian simulants are frequently used for testing rovers and other devices for Mars terrain research. Using a Finite Element Analysis-based NP tire model that is modeled and tested, in addition to a dry loose Martian soil that is modeled using Smoothed-Particle Hydrodynamics (SPH) technique and calibrated using pressure-sinkage and direct shear test. The rolling resistance coefficient on a Mars simulant can be investigated and analyzed. Results obtained from this research will validate the sustainability of NP tires for future Exploration of Mars.

Typical two-site storage-based SCR plant models in literature consider NH3 stored in the first site to participate in NH3 storage, NOx conversion and second site to only participate in NH3 storage passively. This paper focuses on quantifying the impact of stored NH3 in the second site on the overall NOx conversion for an ultra-low NOx system due to intra site NH3 mass transfer. Accounting for this intra site mass transfer leads to better prediction of SCR out NH3 thus ensuring compliance with NH3 coverage targets and improved dosing characteristics of the controller that is critical to achieving ultra-low NOx standard. The stored NH3 in the second site undergoes mass transfer to the first site during temperature ramps encountered in a transient cycle that leads to increased NOx conversion in conditions where the dosing is switched off. The resultant NH3 coverage fraction prediction is critical in dosing control of SCR. This phenomenon is evaluated and quantified with different aging conditions, where the increased second site storage and reduced standard SCR activity due to hydrothermal aging leads to further increase in the reported phenomena. Although this phenomenon was observed for both light-off SCR (Lo-SCR) and downstream SCR based on analysis of the data, the impact on Lo-SCR performance was found to be higher compared to the downstream system due to the transient thermal conditions and higher temperatures experienced by the Lo-SCR system. This mass transfer mechanism also plays a role in determining NH3 slip characteristics of Lo-SCR for real world conditions where the gradual transfer of NH3 in the axial direction leads to NH3 slip. This phenomenon is demonstrated using experimental data collected on a production engine for a set of HFTP, CFTP, RMC and LLC cycles

A flow channel design of the battery liquid cooling plate is carried out through the variable density topology optimization method according to the heat dissipation requirements of lithium-ion power batteries under actual working conditions. Firstly, given the non-uniform heat generation of lithium battery cells, the heat generation mechanism is studied so that the battery electro-thermal model is established, then the distribution regularity of heat generation rate in the cell at different discharge rates is obtained. Subsequently, through COMSOL Multiphysics simulation software, the multi-objective topology optimization of the primary configuration radiator is conducted. The weights of the optimization objectives minimum temperature and minimum flow resistance are determined by practical engineering application. Finally, an optimized model with a volume fraction of 50% was obtained. Based on this optimized design result, research and analysis of related fluid flow and heat transfer characteristics were conducted through numerical simulation. The results show that, under the same battery cooling demand, compared with the traditional design of the serpentine radiator, the energy consumption and the pressure drop decrease by 90.22% and 41.35% respectively at a slight cost of temperature rise. It has been proved that the topology-optimized cooling plate proposed in this paper help improve the comprehensive performance of lithium battery thermal management.

Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NOX standards and a system with advanced aftertreatment and engine technologies designed to meet low NOX 2031+ emissions standards. For the production system, eco-driving on an urban cycle resulted in a CO2 reduction of 8.4% but an increase of 18% in brake specific NOX over the baseline cycle. With the low NOX system, eco-driving achieved a similar reduction in CO2. NOX emissions increased 108% over the baseline but remained below the low NOX standard. The eco-driving cycles generated lower exhaust temperatures than the baseline cycles, which inhibited SCR catalyst performance and increased tailpipe NOX. Conversely, a port drayage cycle with eco-driving showed improvements in both CO2 and NOX emissions over the baseline. The results demonstrate that eco-driving algorithms can be a technological enabler to meet current and potential future emissions targets for heavy-duty applications.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance.Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead. Firstly, a vehicle data acquisition platform is built based on a Logitech HD camera, which is used to synchronously collect road image information and vehicle status signals. Secondly, aiming at the problem of unclear road edges of unstructured roads, threshold segmentation and connected domain analysis method are used to detect the road region in the image, and Canny operator is combined to realize road edge detection and road boundary fitting. Then, a road classification and post-processing algorithm based on vision and mileage information is proposed to resist the impact of sudden change of recognition results on continuous sections of complex unstructured roads.Finally, the algorithm is tested and verified based on the collected real vehicle data playback. The results show that the designed unstructured road classification algorithm can accurately identify the changes of road categories, with an average accuracy of 94.57% on asphalt road, brick road, dirt road, and gravel road.

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming.However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles. To update J2365 and other task analyses, estimates were developed for (1) cognitive activities (mental, search, read), (2) low-level 2D elements (Press, Tap, Double Tap, Drag, Zoom, Press and Hold, Rotate, Turn Knob, Type and Keypress, and Flick), (3) complex 2D elements (handwrite, menu use), and (4) for 2D/3D elements (Reach, Swipe, Dwell/Hold, Grab/Grip/Grasp, Release, Draw, Pinch and Spread, and Wave/Shake). A future paper will provide estimates for complex 2D elements and cognitive activities. Most of the time estimates are for young people (ages 18-30) because those data were available. Methods are provided to estimate times for other age groups. These estimates were drawn from recognized data sources including, (1) industrial engineering predetermined time systems (e.g., Methods-Time- Measurement 1 (MTM-1), (2) the Keystroke-Level Model (KLM), (3) the Model Human Processor (MHP), (4) SAE J2365, (5) human-computer interaction studies, and (6) driver-interface studies concerned with estimating and validating task times on touch screens.

In the present paper the environmental impact of a gas-steam combined cycle, in terms of CO2 emissions has been supplemented with the energetic analysis of the cycle. The gas turbine based triple-pressure reheat combined cycle incorporates, vapor compression inlet air cooling and air-film turbine blade cooling, to study the improvement in plant performance and sustainability. A parametric study of the effect of compressor pressure ratio (rp,c), compressor inlet temperature (CIT), turbine inlet temperature (TIT), inlet temperature ratio (rIT), ambient relative humidity and ambient temperature on performance and sustainability has been carried out. The integration of inlet air cooling and gas turbine blade cooling results in a significant reduction in CO2 emission per unit plant output. The integration of vapor compression inlet air cooling to gas turbine based combined cycle, has been observed to improve the specific work by more than 10 %. The plant efficiency increases significantly with increase in TIT. For all values of TIT, there exists an optimum rp,c at which the plant efficiency is maximum. The cost of environmental impact due to CO2 emission reduces with increase in TIT and decrease in CIT.

Intake system is an important noise source for commercial vehicles, which has a significant impact on their NVH performance. To predict the intake noise more accurately, a new one-dimensional prediction model is proposed in this paper. An air compressor model is introduced into the traditional model, and the acoustic properties of the intake system are simulated by GT-power. The simulation data of the inlet noise is obtained to make a comparison with the inlet noise data acquired from a test. The result shows that the proposed model can make a more precise prediction of the inlet noise. Compared with the traditional model, the proposed model can identify the noise coming from the air compressor, and achieve a more accurate prediction of the total sound pressure level of the inlet noise.

As a zero-carbon fuel and a hydrogen derivative, ammonia is promising for large-scale use in internal combustion engines under the global decarbonization background. Although ammonia fuel itself does not contain elemental carbon and cannot produce carbon dioxide, it contains elemental nitrogen and produces nitrogen oxides (NOX) emissions during combustion. Accordingly, it is essential to understand the formation and evolution of NOX during ammonia oxidation as a prerequisite for finding solutions to control NOX emissions. Since the emission formation is chemically reaction-driven, this paper investigates the ammonia low and high temperature oxidation processes via laminar flame and ideal reactor models, which can provide steady-state NOX formation characteristics to be studied and eliminate unpredictable turbulence and gradients of species concentration and temperature in the engine combustion chamber. Moreover, this study investigates the ammonia combustion process under thermodynamic conditions representative of the engine in-cylinder environment. One challenge in understanding the NOX formation mechanism during ammonia combustion is the coupling of fuel NOX (i.e., nitrogen from ammonia) and thermal NOX (i.e., nitrogen from the atmosphere). The main innovation of this article is the introduction of a methodology to decouple fuel nitrogen and atmospheric nitrogen. The results prove that this method is effective regardless of the operating conditions. In addition, unlike the thermal NOX whose concentration is related to temperature and residence time, fuel NOX, especially nitric oxide (NO) and nitrous oxide (N2O), are important intermediate species and are active in the reaction zone and during ignition. Furthermore, the concentration of fuel NOX and thermal NOX are of comparable order of magnitude and they are sensitive to the combustion boundary conditions (e.g., temperature, equivalence ratio, and hydrogen addition). Specifically, increasing the temperature favors the thermal NOX formation, and fuel-rich operation reduces both fuel NOX and thermal NOX concentrations. Also, mixing ammonia with hydrogen can increase fuel NOX and thermal NOX levels simultaneously. Consequently, the cost of using hydrogen as a combustion promoter to improve the ammonia chemical reactivity is to increase the difficulty of NOX emission control. Overall, all of these findings support the need for further fundamental research on ammonia combustion to accelerate the engine transition to carbon neutrality.

The demand for clean energy efficient transportation is rapidly increasing to meet greenhouse gas emissions reduction and promote sustainability. Gasoline direct injection engines (GDI) have high thermal efficiency thus low greenhouse gas emissions compared with conventional port fueled engines. However, during cold start conditions GDI engines produce harmful emissions, including nitrogen oxides, hydrocarbons, and particulate matter. Thus, high pressure fuel system development for direct injection of gasoline is being conducted to reduce emissions during engine cold start. This paper summarized the effect of high fuel pressure and multiple injections on cold catalyst light-off strategy for rapid heating of the catalyst. Experiments were carried out using light-duty four-cylinder engine at cold catalyst light-off conditions with coolant temperature at 30 °C. Fuel injection strategy optimization was carried out considering (a) 25 MPa - two injections (b) 25 MPa - three injections and (c) 35 MPa - three injections, and engine emission, combustion stability, as well as fuel consumption data analyzed for each case. Experimental results show that 25 MPa and 35 MPa fuel injection pressure with three injection pulse have 60% and 80% reduction of HC and NOx combined relative to 25MPa fuel injection pressure and two injection pulse. It is expected that the improved homogeneous mixing realized by controlling spray penetration with multiple injections as well as the better spray atomization due to high fuel pressure, led to further HC reductions in the presence of high internal residual gases. Moreover, it has been also found that the combustion stability, and fuel consumption were similar for all three cases considered in this study. Experimental results show the benefits of using high pressure fuel injector with multiple injection strategies to realize reductions in engine emissions.

This paper describes a Hardware-In-the-Loop (HIL) platform based on the dual-axis dynamometer for development and validation of ABS/TCS controllers. Antilock Braking System (ABS) and Traction Control System (TCS) are standard equipment for passenger vehicles. The ABS, an anti-skid braking assistance system, promotes safety by preventing the locking of wheels during braking. TCS is a control system that prevents the wheels from slipping by moderating driving power to the one that is losing its grip on the road. The real-time platform is based on a dSPACE vehicle model and the simulation environment, and it consists of an actual drive motor, hydraulic braking system and Chroma dual-axis dynamometer test bench, which provide more realistic and complicated conditions than the one-axis platform. With dual-axis architecture, it could effectively perform simulation results of model on two axes. In addition, this HIL system could evaluate the different control logics and performance of developed controllers on the real drive and brake control unit.

A state-of-the-art review of the technical meaning and application of the term ‘maneuver’, used by the U.S. Army and ground vehicle engineering communities, was performed with regard to various military activities, including modeling and simulation (M&S), to focus on the value and applicability of the term to military vehicle dynamics. As shown, U.S. military doctrine has built through history and experience a unique concept of maneuver-in-general and its application in U.S. Army unified land operations. Yet, the term ‘maneuver’ needs further technical categorization and characterization for the purpose of dynamics of military unmanned ground vehicles (UGVs) and vehicle design for maneuver. While the NHTSA and SAE standards and definitions provide solid foundations for M&S of cars and trucks to enhance the safety of those vehicles (manned and autonomous), occupants, and pedestrians on roads, the standards cannot address all needs of military vehicles in maneuver. Military UGVs are designed to operate in hyper-dynamic battlefield and tactical conditions on severe terrains where manned systems cannot operate. These operational conditions require a different approach to modeling, simulation, and real-time UGV-self-assessment of its dynamic behavior to be technically capable to fulfill autonomous missions and tasks for the sake of the safety of warfighters and the UGV itself. In the paper, a technical definition for a military vehicle maneuver is presented with the purpose of encompassing vehicle agile movements with extended safety due to controllable instability and also unsafe movements on a need basis. Sub-element definitions of a vehicle maneuver and new ideation of agile movement is proposed to narrow the scope to vehicle military tasks in austere environments. Along with formulation, a graphical interpretation is provided to illustrate advantages of the proposed approach for planning UGV motion using geometric and kinematics characteristics. The contextual application is shown in an operation study to illustrate where the terms can improve M&S.

This paper reports on a transient, three-dimensional computational fluid dynamics (CFD) study of flow and heat transfer in the complete fuel system of an inline 6-cylinder, direct injection (DI) diesel engine used in commercial applications. The CFD software Simerics-MP+ was used for this purpose. Diesel engine development, to meet fuel economy and exhaust emission standards, requires the precise integration of each component in the fuel system in order to reliably deliver the fuel to the combustion chamber as a function of crank angle to the combustion chamber, at the specified injection pressure. Both the model set-up and run times are practical, thus the simulation tool can play a key role in the design and development of diesel engine fuel systems.

The side door closing effort is one of the main evaluating parameters which demonstrates the build quality of the vehicle. The side door hinge axis inclination is one of the key attributes that affect the side door closing effort. Commonly, the hinge axis is inclined in two directions of a vehicle to have necessary door rise during the door opening event. Due to the process and assembly variations in the door assembly, the upper and lower hinge axis of the side door deviates from the design axis. In this paper, the deviations in the side door hinge axis and its effects on the side door closing velocity is discussed. The deviations of the side door hinge axis are studied with a coordinate measuring machine. The side door closing velocity of the vehicle is measured with the velocity meter. The study revealed that side door closing velocity is increasing with an increase in the deviation of the top and bottom door hinge axis from the design hinge axis. The hinge axis skewness between the top and bottom door hinge also increases the side door closing velocity. Based on the study, it is evident that the process and assembly deviations in the door assembly need to be controlled to have the desired door closing velocity.

Magneto-Rheological (MR) Fluid started to be used for industrial applications in the last 20 years, and, from that moment on, innovative uses have been evaluated for different applications to exploit its characteristic of changing yield stress as a function of the magnetic field applied. Because of the complexity of the behavior of the MR fluid, it is necessary to perform lots of simulations, combining multi-physical software capable of evaluating all the material’s characteristics.The paper proposes a strategy capable of quickly verifying the feasibility of an innovative MR system, considering a sufficient accuracy of the approximation, able to easily verify the principal criticalities of the innovative applications concerning the MR fluid main electromagnetic and fluid-dynamic capabilities. The procedure follows the main steps: 1. design the solution (functionally related to the MR principles); 2. steady-state analyses (performed respectively with Altair Flux and Altair Acusolve) needed for defining the electromagnetic and the fluid-dynamic behavior of the fluid application; 3. after the basic feasibility definition, material, and geometrical optimization are performed for maximizing the MR capabilities into the innovative system design.The proposed strategy has been used to validate the feasibility analysis of a Magneto-Rheological Brake for automotive applications, particularly for electric vehicles. Once the design phase defines a preliminary functional geometry for the application taken into consideration, it is needed to validate (through software analysis) the effectiveness of the innovative layout and its capabilities to reach the expected results, before developing a prototype that requires an important economical effort.

The influence of engine cooling fan on the working state of engine cooling system under different driving forms and control strategy is studied, and a simulation model of engine thermal management system of a commercial vehicle is established. The model takes into account the measured performance parameters of the cooling system components, the gear shift logic of the transmission, the effect of vehicle speed on the airflow rate of the radiator, and proposes a modeling method for different cooling fan driving forms. The performance parameters such as engine outlet coolant temperature and corresponding cooling fan speed under different vehicle speeds and engine loads are calculated and analyzed by using the established model. The road measurement test of the engine thermal management system under the same working condition was carried out to read the relevant data from the engine ECU and confirm the reliability of the data. The correctness of the model is proved by the comparison between the model calculation results and the test results. Based on the established model, the working characteristics of the cooling fan driven by the electronically controlled silicone oil clutch are analyzed, and the improvement method of the control strategy of the electronically controlled silicone oil clutch is proposed. The results show that the cooling fan driven by the electronically controlled silicone oil clutch can adaptively adjust the fan speed, reduce the power consumption of the engine, and make the engine work at the optimal temperature by using the improved control strategy of the electronically controlled silicone oil clutch established in this paper.

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate. This study will primarily assist in traffic modeling for Fuel Efficiency (FE) of individual Connected and Autonomous Vehicles (CAV) estimation (road safety and fleet-wide efficiency are out of the scope). We propose a process to identify typical characteristics of trips that are most critical for CAV’s FE from single-vehicle data. We then use this data along with vehicle counts to calibrate the traffic model such that the drive cycle characteristics of the Vehicle Under Test (VUT) are matched with the data collected from the real test. This calibration procedure ensures that the vehicle in the simulation environment observes speed profiles that allow realistic FE estimates. In this paper, the traffic modeling calibration is performed in Simulation of Urban MObility (SUMO) where we demonstrate the approach for the Columbus, OH metropolitan area. The available data is in the form of edge-based traffic count commonly known as Annual Average Daily Traffic (AADT), provided by the Ohio Department of Transportation (ODOT).

As the world moves towards a decarbonized motorization, Hydrogen became a strong candidate to replace Diesel and Gasoline. Possibly used in a DI configuration, a huge challenge is the injection and mixing process of the hydrogen in the combustion chamber. In this paper we will focus on the characterization of a compressed hydrogen jet using Schlieren imaging technique and image processing. The injector used in those tests is designed and manufactured by BorgWarner to be used specifically with Compressed Hydrogen Gas (CHG). It operates at medium pressure. Two injection pressures had been used to study the jet development in different conditions. The cylinder pressure (back pressure) will vary between 1.2 bar and 15 bar while the temperature will go from 20°C to 150°C. The discussed tests were made in full nitrogen conditions to avoid any ignition of the hydrogen jet. The results show that both injection pressure and back pressure in the chamber have a huge effect on the jet development. Indeed, the velocity of the jet decreases when the back pressure gets higher or when the injection pressure decreases. Those parameters also have impact on the width of the jet and the timing at which it collapses. However, if we focus on the temperatures impact, the effect on the jet penetration or area is more moderate.

Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS) are being actively developed to prevent traffic accidents. As the complexity of AD/ADAS increases, the number of test scenarios increases as well. An efficient development process that meets AD/ADAS quality and performance specifications is thus required. The European New Car Assessment Programme (Euro NCAP®1) and the Japan Automobile Manufacturers Association (JAMA®2) have both defined test scenarios, but some of these scenarios are difficult to carry out with real-vehicle testing due to the risk of harm to human participants. Due to the challenge of covering various scenarios and situations with only real-vehicle testing, we utilize simulation-based testing in this work. Specifically, we construct a Model-in-the-Loop Simulation (MILS) environment for virtual testing of AD/ADAS control logic.When the collision scenarios defined by Euro NCAP in detail with collision condition, are utilized in simulation-based testing, problems arise due to the difficulty of manually generating scenarios in which the collision position and collision timing need to be adjusted.In this paper, we therefore propose an automatic scenario generation method that satisfies the intended collision condition, by means of inverse calculation that adjusts the collision position and timing automatically. This process consists of two steps. First trajectories are obtained by conducting simulations, utilizing the input with inputed scenario component and parameter variation based on the Euro NCAP. Second, the target’s position offset and timing offset that satisfy the ego car’s collision condition, are calculated from the trajectories. By applying position offset and timing offset to the base scenario, collision scenarios are generated. We also came up with a function that validates the Euro NCAP measurement error for thegenerated scenarios. Our automatic scenario generation method results in a collision error rate of about 0.2 cm for 98.02% of the Euro NCAP scenarios we tested. By utilizing the proposed scenario generation and error validation, the labor hours is expected to be reduced by up to 1/6 compared to the manual process. Our method is also applicable to custom scenarios that require precise adjusting of collision positions and timing.In the EuroNCAP test scenarios, the target’s speed and acceleration are both constant, but for AD/ADAS robustness testing, irregular behavior scenarios are required. We therefore added disturbances to the collision scenario by traffic flow simulation, where multiple vehicles are generated as disturbances and the target’s irregular behavior under the influence of multiple vehicles is generated.In this work, EuroNCAP and JAMA test scenarios became able to generate automaticaly, in addition collision target's irregular behavior can be considered for AD/ADAS robustness testing.

In this paper, an innovative EGR valve, containing an integrated self-cleaning function to mitigate the progressive buildup of carbon deposits, is described. Conventional EGR valves use Butterfly and Poppet mechanisms to open and close a round shaped exhaust gas port with limited ability to remove exhaust carbon deposits from the affected surfaces and mechanical elements.The self-cleaning EGR valve, instead, combining both rotational and linear motions, continuously sweeps its internal passages while delivering the gas flow, removing the carbon deposits before they can adhere to the internal surfaces.

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario. In addition, hypothesis tests are used to reveal over-represented element conditions that separate a specific crash scenario from others. A case study is given on fatal crashes with a single vehicle and a single-child pedestrian to demonstrate the proposed framework. Insights are obtained on the similarities and more interestingly the differences in the context among crash scenarios. For example, compared to crashes noted with “Non-Motorist Contributing Factors” (actions and/or circumstances that may have contributed to the crash) for child pedestrians, crashes without the type of factors noted were associated with a significantly higher proportion of driver violations charged and/or driving under the influence. When involved in a crash, child pedestrians who failed to yield the right-of-way were significantly more likely to be young teens (13-14 years) while those in the roadway improperly were more likely playing toddlers (1-3 years). We expect the work to serve as a fundamental and practical tool for further examination of crash context and causation, especially those involving children, and to improve their safety traveling on the road.

Remote Monitoring and Teleoperation (RMTO) of Autonomous Vehicles (AV) is advancing rapidly in the industry. Researchers and industrial partners explore the role RMTO plays in helping AV navigate complicated situations, among many others. At the heart of this lies the problem of potential pathways and attack vectors or threat surfaces by which a malicious attack can be carried out on an RMTO and an AV. The separation of cybersecurity considerations in RMTO is barely considered, as so far, most available research and activities are mainly focused on AV. The main focus of this paper is addressing RMTO cybersecurity utilising an adaptable security-by-design approach, although security-by-design is still in the infant state within automotive cybersecurity. An adaptable security-by-design approach for RMTO covers Security Engineering Life-cycle, Logical Security Layered Concept, and Security Architecture. Based on the international automotive cybersecurity standards - ISO/SAE 21434, a Threat Analysis and Risk Assessment (TARA) with a formalisation of the highest level of threats identified from the TARA of the RMTO system is carried out, with corresponding mitigation actions as per UNECE WP29. The adaptable security-by-design approach has been then applied to a prototype RMTO system developed by an industrial partner. Finally, penetration testing has been carried out where the results verify the capability of the adoptable security-by-design to reinforce the security of the RMTO systems against some of the identified risks and threats.

This work is a part of medium-duty Low NOx technology development project with a focus on evaluating a combination of engine and advanced aftertreatment for 0.02 g/bhp-hr NOx regulation proposed by CARB (California air resource board). In this project, a control oriented chemical kinetics model of SCR (Selective catalytic reduction) was used in the aftertreatment controller that is susceptible to performance degradation due to hydrothermal and chemical aging. This paper focuses on modeling the NOx conversion and NH3 storage characteristics using a controls oriented SCR plant model which is further used for a model-based urea dosing scheme. A set of steady state reactor tests were used to calibrate the SCR performance at degreened, hydrothermal only and hydrothermal + chemical aging conditions and also to determine inhibition factors related to aging. The resultant model is capable of simulating SCR performance deterioration such as a reduction in NOx conversion and NH3 storage. A non-linear aging profile was observed for Lo-SCR and downstream SCR showing a change in the NOx conversion in the aged system when compared to a degreened system. Upon chemical aging further deterioration of low temperature performance was observed. This aging phenomenon impacts the dosing control strategy of the system. The results on controller performance for a set of Heavy Duty Federal Test Protocol(FTP), Ramp Modal Cycle (RMC), and Low Load Cycle (LLC) are presented.

Accurate prediction of RF interference (RFI) requires high fidelity measured data. There are many ways transmitters can interfere with receivers. The output spectrum of transmitting radios is made up of the fundamental signal, harmonics, spurious emissions, and broadband noise. Receiving radios are capable of detecting extremely low-level signals in their pass band, but can also be sensitive to signals outside the pass band such as mixer products and spurious responses. Manual measurements to obtain this data is very time consuming, especially when the radios can operate over hundreds or thousands of channels. This paper discusses an Automated Radio Measurement System (ARMS) that can accurately perform these broadband measurements with high dynamic range.

Forward Collision Warning System is an important part of vehicle active safety system, it can reduce the occurrence of rear-end collision accidents with high fatality rate and improve the safety of driving. At present, there are still some outstanding issues to be addressed among the existing forward collision warning systems, such as the high cost of information acquisition based on LiDAR and other high-definition sensors, and the poor real-time performance of target detection based on vision. In view of the aforementioned issues and in order to improve the detection accuracy and real-time requirements of the target detection function of the early warning system, this paper proposes an enhanced deep learning model-based vehicle target detection method, and improves the key techniques of target detection, ranging and speed measurement and early warning strategy in the warning system. Then, a target positioning scheme by visual fusion method is employed to improve the accuracy of distance detection, followed by an improved multi-target tracking algorithm.to realize the speed estimation of the front vehicle. Finally, the proposed forward collision warning strategy is demonstrated through real world case studies and results are given in the end.

The bolted joints in suspension systems are subjected to severe external service loads during vehicle operation. To prevent the loaded joint from loosening and allowing it to retain its potential energy stored during assembly, a holistic design approach is needed. This paper explains the methodology to design and optimize bolted joints for the suspension systems of a modern 7-seater sports utility vehicle. The optimization technique consists of - 1Extensive benchmarking of global benchmark vehicles with similar suspension architecture and gross vehicle weights to derive preliminary torque and joint preloads.2Measuring external loads acting in x, y and z directions using a wheel force transducer on various durability tracks.3Performing Multi body dynamic simulations to obtain the loads at various bolted joint locations.4Taking the input of the external loads acting on the individual joints and perform a simulation to evaluate slip at joinery for a given preload.5Bolt characterization measurements to establish the torque to be applied to achieve the required preload at the joint.Based on the results, joinery design approaches are discussed which focus on increasing the resistance against slip and utilizing the optimum preload applied during assembly. For critical joints experiencing high service loads, the torque to yield (torque plus angle) method is prescribed instead of the elastic tightening method along with reduction of the axial gap. For double shear joints having steel sleeve and sheet metal interface, improvements seen with the addition of serrations/knurling on the contact surface of the sleeve are discussed. The stiffness of mating parts is increased for proper bolt load transfer. With the incorporation of these design modifications in the joinery, extensive vehicle durability tests are performed in the vehicle, and no incidences of joinery loosening/slipping are observed. The efficacy of the design improvement was reflected in the residual torque values which showed no reduction after the test compared to the start of the test.

As the AVN display in the car interior becomes larger and located above the center fascia, the driver's visual visibility is becoming important. In addition, since an expensive touch sensor is installed, a transparent electrode cost reduction technology for a display touch sensor that can replace an indium material, which is an expensive rare metal, is required. In this paper, we developed new transparent electrode materials and manufacturing methods for the touch sensor film which light reflectance is low and flexible without a separate low-reflection multi-layer, so that the design freedom is high and the material cost is low. By optimizing the amount of fluorine doping ratio in tin oxide, excellent electrical conductivity and high optical transmittance are secured, and the surface reflectance is reduced by adjusting the diameter and length of the silver nanowire. As a result, it was shown that the AVN display image and font readability was improved. In addition, we verified that the material has probability to adoption to a curved and flexible display applications for future mobility based on autonomous driving.

Combustion in a lean atmosphere diluted with a large amount of air can greatly improve fuel efficiency by reducing cooling loss [1, 2]. On the other hand, when air-fuel mixture in cylinder becomes lean, the turbulent combustion speed will decrease, resulting in problems such as the generation of unburned hydrocarbon (HC) and combustion instability [3, 4]. In order to solve these problems, it is important to increase the turbulence intensity and combustion speed [5, 6, 7, 8, 9, 10].When designing combustion in cylinder by using Computational Fluid Dynamics (CFD), K-epsilon model is widely used for a turbulence model, and the calculated turbulence energy k or turbulence intensity u’ have been used as important indices of combustion velocity [11, 12]. However, it has been confirmed by measurements that the flow will conversely weaken near the top dead center and the combustion duration will become longer when the air flow in the cylinder is extremely strengthened by improved intake port. This phenomenon can be expressed by using Large Eddy Simulation (LES) instead of K-epsilon model as a turbulence model, so K-epsilon model cannot be used for future combustion development including lean burn, and LES is critical. This paper proposes a model-based development method for designing engine combustion by using LES and Kolmogorov scale.

Following the recent trend in the automotive industry, hybrid and pure electric powertrain systems are more and more preferred over conventional combustion powertrain systems due to their significant potential to reduce greenhouse-gas emissions. Although electric powertrains do not produce direct emissions during their operational time, the indirect emissions over their whole life cycle have to be taken into consideration. In this direction, the carbon footprint due to the electrification of the hand-held power tool industry needs to be examined in the preliminary design phase. In this paper, after defining the carbon footprint calculation framework, assumptions and simplifications used for the calculations, a direct comparison of the total carbon dioxide equivalent (CO2eq) emissions of three equivalent power and range powertrain systems - a combustion-driven, a hybrid-driven, and a cordless electric-driven - is presented. The relative comparison of their life cycle CO2eq emissions delivers important insights for the future design considerations of hand-held power tools. Furthermore. as the energy storage system has the leading influence on CO2eq emissions for the hybrid and electric powertrains, a sensitivity analysis by examining different battery charging conditions and scenarios is presented. The aim of this study is to introduce useful knowledge of life cycle assessment for these small powertrains and forward an argumentation for different powertrain alternatives in the hand-held tool industry.

Platoon is a system that connects vehicles through vehicle-to-vehicle (V2V) communication technology to maintain a short distance between vehicles while driving on the road. To improve fuel efficiency, many automotive original equipment manufacturers (OEMs) are interested in developing and demonstrating real-world platoon system. However, it is hard for heavy duty trucks to develop this system due to the difficulty of maintaining the targeted intervehicle distance not only for fuel efficiency but also for safety in case of emergency braking. Because of this critical safety issue in the emergency situation, the platoon system for heavy duty trucks can be hardly demonstrated or tested in real vehicle environment. The relatively complex system and the slow response characteristic of commercial vehicles makes this even more difficult.In this paper, focusing on the emergency braking function implemented through the V2V communication interface, we introduce the platoon system developed by Hyundai Motor, and explain the system configuration, technology, and control strategy. While there have been various efforts to develop the emergency braking system of the platoon system in a simulation environment in previous studies, we conduct real vehicle-in-the-loop (VIL) test with three semi-trailer trucks. Through repeated VIL tests, we could identify certain vehicle data to be transmitted and received via V2V communication during emergency braking situation and the corresponding signals were properly tailored to reduce the inherent delay. Finally, by reducing the delay of the front vehicle’s deceleration signal, the safe distance gap between vehicles is secured even after the emergency braking. VIL test results of the system are also included to validate the effectiveness of the proposed platoon system.

The energy consumption of a vehicle is typically determined either by testing or in simulation. While both approaches are valid, they only work for a specific drive cycle, they are time intensive, and they do not directly result in a closed-form relationship between key parameters and consumption. This paper presents an alternative approach that determines the consumption based on a simple analytical model of the vehicle and statistical parameters of the drive cycle, specifically the moments of the velocity. This results in a closed-form solution that can be used for analysis or synthesis.The drive cycle is quantified via its moments, specifically the average speed, the standard deviation of the speed as well as the higher order moments skewness, and the kurtosis. A mixed quadratic term is added to account for acceleration or aggressiveness, but it is noticeably distinct from the conventional metric of positive kinetic energy (PKE). The vehicle is quantified using a polynomial model of the traction force and of the primary energy consumption of the powertrain. This model form fits both conventional and electrified powertrains, including all the component efficiencies.Through a statistical analysis of the model, the primary energy consumption can be related to both the model parameters and the statistical properties of the drive cycle. This result can be useful for the analysis of a drive cycle, for the analysis of a powertrain, for economy optimization, and for control purposes. An example of a Nissan LEAF powertrain is presented over different cycles.

Under the proposed Green Deal program, the European Union will aim to achieve zero net greenhouse gas (GHG) emissions by 2050. The interim target is to reduce GHG by 55% by 2030. In the current debate concerning CO2-neutral powertrains, bio-fuels and e-fuels could play an immediate and practical role in reducing lifecycle engine emissions. Hydrogen however, is one of the few practical fuels that can result in near zero CO2 emissions at the tailpipe, which is the main focus of current legislation. Compared to gasoline, hydrogen presents a higher laminar flame speed, a wider range of flammability and higher auto-ignition temperatures, making it among the most attractive of fuels for future engines. As a challenge, hydrogen requires a very low ignition energy. This may imply an increased susceptibility to Low Speed Pre-Ignition (LSPI), surface ignition and back-fire phenomena. In order to exploit hydrogen’s potential, the injection system plays an extremely important role. This paper focuses on the experimental characterization of an H2 Direct Injection (DI) injector in order to provide a wide and robust dataset to be used in three dimensional Computational Fluid Dynamics (3D CFD) simulation correlations. For reasons of safety and practicality, Schlieren measurements are performed mainly using helium whereas the transposition to hydrogen is conducted via comparison of helium vs. hydrogen measurements as well as 3D CFD simulations. Injection simulations will help to set targets for new combustion chamber architectures and assess mixture preparation formation to support next generation injector development for a high performance oriented H2 engine.

Overcharging lithium-ion batteries is a failure mode that is observed if the battery management system (BMS) or battery charger fails to stop the charging process as intended. Overcharging can easily lead to thermal runaway in a battery. In this paper, nickel manganese cobalt (NMC) battery modules from the Chevrolet Bolt, lithium manganese oxide (LMO) battery modules from the Chevrolet Volt, and lithium iron phosphate (LFP) battery modules from a hybrid transit bus were overcharged. The battery abuse and emissions tests were designed to intentionally drive the three different battery chemistries into thermal runaway while measuring battery temperatures, battery voltages, gaseous emissions, and feedback from volatile organic compound (VOC) sensors. Overcharging a battery can cause lithium plating and other exothermic reactions that will lead to thermal runaway. During the testing, VOC sensors were used to determine what, if any, amount of forewarning they may provide in the event the battery enters thermal runaway. Additionally, three different fire suppressant agents were also used to judge whether one is more effective than the other in extinguishing the battery fires. The fire suppressants were engaged sixty seconds after thermal runaway was detected, and their effectiveness was judged by visually evaluating whether the fire was extinguished and remained extinguished. Data is analyzed to extract comparisons in peak thermal runaway temperatures, the amount of forewarning the battery may provide preceding thermal runaway as measured by the pre-thermal runaway temperatures and VOC sensors, and other qualitative metrics observed during the testing. The emissions collected during the overcharge testing are summarized for all the three chemistries and various suppressants.

Fatigue analysis of pistons is reliant on an accurate representation of the high temperatures to which they are exposed. It can be difficult to represent this accurately, because instrumented tests to validate piston thermal models typically include only measurements near the piston crown and there are many unknown backside heat transfer coefficients (HTCs). Previously, a methodology was proposed to aid in the estimation of HTCs for backside convection boundary conditions of a stratified charge compression ignition (SCCI) piston. This methodology relies on Bayesian inference of backside HTC using a co-simulation between computational fluid dynamics (CFD) and finite element analysis (FEA) solvers. Although this methodology primarily utilizes the more computationally efficient FEA model for the iterations in the calibration, this can still be a computationally expensive process. In this paper, several data reduction methods, such as principal component analysis, data clustering and resampling, sensor reduction, and uniform bin sampling are investigated to improve computation time while minimizing reduction in accuracy of the inference results. Each data reduction method is compared to a control case to determine change in accuracy and improvement in run time. Results indicate that most reduction methods were no more effective than using a smaller Latin hypercube design to inform the Gaussian process within the Bayesian inference code. Reduced error was observed for the structured sensor reduction method, indicating that further studies on the value of individual sensor locations to the overall calibration might be a viable path to reduce the computation time of the calibration methodology without compromising accuracy.

Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance. Validation results with several data sets indicate that the proposed direct and inverse identification models using ANFIS can be used to predict the nonlinear dynamic performance of MR fluid dampers accurately and can work as a damper controller.

In the last few years, the number of Advanced Driver Assistance Systems (ADAS) on road vehicles has been increased with the aim of dramatically reducing road accidents. Therefore, the OEMs need to integrate and test these systems, to comply with the safety regulations. To lower the development cost, instead of experimental testing, many virtual simulation scenarios need to be tested for ADAS validation. The classic multibody vehicle approach, normally used to design and optimize vehicle dynamics performance, is not always suitable to cope with these new tasks; therefore, real-time lumped-parameter vehicle models implementation becomes more and more necessary. This paper aims at providing a methodology to convert experimentally validated light commercial vehicles (LCV) multibody models (MBM) into real-time lumped-parameter models (RTM). The proposed methodology involves the definition of the vehicle subsystems and the level of complexity required to achieve a good match between the simulation results obtained from the two models. Thus, an automatic vehicle model converter will be presented together with the assessment of its accuracy. An optimization phase is included into the conversion tool, to fine-tune uncertain vehicle parameters and to compensate for inherent modelling differences. The objective function of the optimization is based on typical performance indices used for vehicle longitudinal and lateral dynamics assessment. Finally, the simulation results from the original and converted models are compared during steady-state and transient tests, to prove the conversion fidelity.

This paper presents calculations of external car aerodynamics by using the Partial-Averaged Navier-Stokes (PANS) variable resolution model in conjunction with the finite volume (FV) immersed-boundary method. The work presented here is the continuation of the study reported in Basara et al. [1]. In that work, it was shown that the same accuracy of predicted aerodynamic forces can be achieved by using Reynolds-Averaged Navier-Stokes (RANS) k-ζ-f model on both types of meshes, the standard body-fitted (BF), and on the immersed boundary (IB) mesh. Due to all well-known shortcomings of the steady state approach, in this work we deal with the Partially Averaged Navier-Stokes (PANS), which belongs to the hybrid RANS-LES (scale resolving / high fidelity) methods. This approach was developed to resolve a part of the turbulence spectrum adjusting seamlessly from RANS to DNS (Direct Numerical Simulation). The PANS model variant used for the present calculations is based on the near-wall RANS k-ζ-f model. The numerical implementation of the wall treatment on the IB meshes is the same as in the previously reported RANS calculations. The significant speed-up of the PANS calculations was achieved by using the fractional step method. Comparisons of the PANS results on both types of meshes with the experimental data for the well-known DrivAer notchback model (Hupertz et al. [2]) demonstrate the predictive capability of PANS in the IB framework.

Digital technologies are capable of making a significant contribution to improving large internal combustion engine technology. In particular, methods from the field of artificial intelligence are opening up new avenues. So-called “intelligent” engine components rely on advanced instrumentation and data analytics to create value-added data, which in turn can serve as the basis for applications such as condition monitoring, predictive maintenance and controls. For related components and systems, these data may also allow for novel condition monitoring approaches. This paper describes the use of value-added data from an intelligent diesel fuel injection valve that give detailed information about the injection process for real-time prediction of key combustion parameters such as indicated mean effective pressure, maximum cylinder pressure and combustion phasing. These parameters are usually involved in combustion controls and power unit condition monitoring and normally acquired using in-cylinder pressure indication systems, which are costly and prone to wear. On the one hand, a data-driven model for key combustion parameters based on an intelligent fuel injection valve could replace an indication system. On the other hand, such a model may enable backup functionality and mutual condition monitoring of the fuel injection valve and the indication system. The data required for model building were acquired from a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 dm3. Different machine learning methods are compared to obtain an accurate yet reliable model for each of the desired combustion parameters. In addition to the value-added injection data, readily available parameters on production engines serve as model inputs (e.g., engine speed, charge air and exhaust gas pressures). Based on the results, the quality of the model predictions is evaluated, and it is assessed whether the approach might be useful for series engine applications.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing. Statistical feature vectors comprising of Kurtosis, Skewness, RMS, Crest Factor, Mean, Peak-peak factor etc. have been extracted from the 1-D time series data for different defect cases. These features are then fed as input vectors to algorithms comprising of Support Vector Machines (SVM’s) and Multi-layered Perceptron (MLP) for defect classification. A dedicated hardware setup has been built to test the efficiency of the developed algorithms in real-time. These algorithms have been evaluated based on two criteria – examining the simultaneous defect classification accuracy for two sets of bearings and individually monitoring the class labels for a particular defect. It was observed that the developed framework was able to classify between different bearing defects with a classification accuracy of 97.8%.

The towbarless aircraft taxiing system (TLATS) consists of the towbarless towing vehicle (TLTV) and the aircraft. The tractor realizes the towing work by fixing the nose wheel. During the towing process, the tractor driver may cause the aircraft to collide with an obstacle because of the blind spot of vision leading to the accident. The special characteristics of aircraft do not allow us to modify the structure of the aircraft to achieve collision avoidance. In this paper, three degrees of freedom (DOE) kinematic model of the tractor system is established for each of the two cases of pushing and pulling the aircraft, and the relationship between the coordinates of each danger point and the relatively articulated angle of the TLATS and the velocity of the midpoint of the rear axle is derived. Considering that there is an error between the velocity and relatively articulated angle measured by the sensor and the actual one, the effect of velocity and relatively articulated angle uncertainty on the traction trajectory of the aircraft-tractor system is investigated based on the Chebyshev expansion function. The calculation efficiency and the result range of Chebyshev method and those of Monte Carlo(MC) method are compared. An aircraft collision avoidance model on the apron is established based on Simulink. The simulation results show that the method can achieve the measurement of aircraft attitude and position and realize the anti-collision.

With the introduction of vehicular digitization and automation, there has been significant growth in the number of Electronic Control Units (ECUs) inside vehicles, followed by the broader use of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADSs). The growth of the number of ECUs has also significantly increased the number of user interfaces. To conduct safe driving, in addition to those related to the real-time control of the vehicle, a driver also needs to be able to digest information effectively and efficiently from various ECUs via the Human-Machine Interface (HMI). To evaluate the safety of ADS, including its interactions with system users, some work has suggested that they will need to be driven for over 11 billion miles. However, the number of test miles driven is not a meaningful metric for judging safety. Instead, the types of scenarios encountered by the driver-vehicle interactions during testing are critically important. With a hazard-based testing approach, this paper proposes that the extent to which testing miles are ‘smart miles’ that reflect hazard-based scenarios relevant to potential unsafe driver-vehicle interactions is fundamental. The authors proposed an extension based on STPA’s Human Mental Model to create hazard-based test scenarios related to human-machine interactions. The proposed approach has been applied to a real-world project associated with the testing of an SAE-Level 4 Autonomous Vehicle (AV) during its prototyping phase, which involves the interactions between the safety driver and the AV’s ADS and X-by-Wire system. The authors also proposed an extension to the Scenario Description Language (SDL) that can be used to define hazard-based test scenarios. The test scenarios generated from the extended SDL have been used for scenario-based testing in real-world and simulation environments.

Air foil bearings are gradually applied in air compressors in fuel cell vehicles for the advantages of high speed, oil-free and non-contact. Advanced air foil bearings with different structures are used to improve the performance of air compressor. Accurate modeling of the complex structures in air foil bearings has become a research hotspot in recent years. This paper presents a theoretical model for a double-top-foil air foil journal bearing (DAFJB) for centrifugal air compressors used in fuel cell vehicles. The foil structure is modeled by finite element method (FEM) using shell elements. Coulomb law and penalty function method are applied to model the tangential and normal behavior of the contact areas. The local contact between the middle top foil and the bump foil, the bump foil and the bearing sleeve are modeled using node-to-segment contact method. The large-area contact behavior between two layers of top foils is modeled by simplified surface-to-surface contact scheme. The hydrodynamic air film is modeled by two-dimensional isothermal compressible Reynolds equation, and the fluid-structure coupling calculation is carried out within the foil and the rotor. Results show that the DAFJB has better static characteristics than the first-generation air foil bearing, because the double-layer structure increases the thickness of the foil and introduces additional friction between the two layers of top foil. Greater stiffness results in less foil deformation, so its hydrodynamic film distribution is more uniform, so the load-carrying characteristics are improved. The DAFJB can be applied in more complex and high-load working conditions, also improve the stability of rotor.

Electric vehicles (EVs) and the development around them has been rapid in recent years. As the battery is the most essential component of an electric vehicle, a lot of research and analysis has been focused on ensuring safe and reliable performance of batteries. Considering the location, size, and operating conditions for EV batteries, they must be designed with an in-built safety infrastructure keeping in mind certain realistic scenarios such as fire exposure, mechanical vibration, collisions, over-charging, single cell failures, and others. In this paper, we discuss an overview of various EV battery failure mechanisms, present current safety and abuse testing methods and standards associated with such mechanisms and discuss the need for the development and implementation of additional testing standards to better characterize the safety performance of EV battery packs.

Contribution to carbon neutrality is one of the most important challenges for the automotive industry. Though CO2 emission has been reduced through electrification, internal combustion engines equipped in vehicles such as Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicle (PHEV) are still necessary for the foreseeable future, and continuous efforts to improve fuel economy are demanded. To improve powertrain thermal efficiency, direct-injection turbocharged gasoline engines have been widely utilized in recent years. Super lean-burn combustion engine has been being researched as the next generation of turbocharged gasoline engines. It is known that an increase of the boost pressure causes deposit formation, which decrease the turbocharger efficiency, in the turbocharger compressor housing. To avoid the efficiency loss due to deposit, air temperature at compressor outlet has to be limited low. In this paper, the methodology was constructed to predict compressor efficiency loss, based on the mechanism of turbocharger deposit formation in gasoline engines.Test procedure to reproduce compressor deposit in turbocharger unit test equipment was developed. The correlation between the temperature of compressor housing inner surface and the rate of compressor efficiency loss was clarified. In addition, the correlation between the location where deposit was formed and the compressor efficiency loss was also investigated. As opposed to steady-state operation such as engine dyno tests, vehicle operations in actual markets are transient, and the boost pressure of turbocharger and its outlet air temperature can vary widely. Due to the thermal capacity of compressor housing, the temperature of housing changes after the outlet air temperature changes with certain time delay. The temperature model considering this delay has been developed and enabled to estimate the deposit formation, turbocharger efficiency loss and engine performance deterioration in transient conditions.

NVH predictions have gained importance since last few decades due to increased focus on ride comfort as competitive advantage within cost target. Hence, NVH CAE has become integral part of product development process for early issue identification and solutions. To make NVH simulation robust, continuous improvement is required in existing CAE processes.In automobile powertrains, components are connected with each other through fasteners. Gaskets are sandwiched at periphery between two mating components, while the components are bolted together with required torque. Gaskets are used to prevent leakage of oil and gases into the atmosphere while operation. NVH CAE modeling practices globally are evolved with considering gaskets as one of the less important modelled components. However, these practices miss to consider its effects considerably on NVH performance.Scope of this paper is to address the gaps present in the existing powertrain modeling techniques with the introduction of gasket. First the impact of gasket inclusion is studied at the experimental level. Then modified CAE modeling process is proposed with modal correlations perspective.

Backlash is the movement between the gear teeth that allows them to mate without binding. Backlash can cause large torque fluctuations in vehicle powertrains when the input torque changes direction. These fluctuations cause a jerk and shuddering, which negatively affects drive quality. Input torque frequently changes direction in electric vehicles due to regenerative braking. Limiting zero crossings is an option for better drive quality; however, this leads to decreased vehicle efficiency. Because of this, modulating the torque through the backlash region is preferred, yet, if done poorly, it can result in sluggish torque response.This paper proposes a torque-shaping algorithm for an electric motor and gear/differential system to reduce backlash in electric vehicles. The control algorithm modulates the commanded torque’s rate of change based on the vehicle speed and zero-crossing torque. The torque change is dynamically de-rated over the backlash region, and the gears are gently re-engaged to transmit the torque. The algorithm considers the variability in electric machine designs and e-gear drives and provides calibration tables to change the base rate. This rate is modulated and filtered before applying to commanded torque in both directions. The initial parameters were calculated using model-in-loop simulations. They are later calibrated in closed course testing with specific tip-in and tip-out conditions at various speeds and accelerator pedal positions.The algorithm was applied on a modified 2019 Chevrolet Blazer with a P0-P4 parallel hybrid architecture using a GVM210-150 Parker Hannifin motor connected to a BorgWarner single-speed gear unit on the rear axle. The algorithm reduced shudder during tip-in and tip-out torque commands from the driver, which yielded a 70% reduction in jerk while maintaining responsive control of the vehicle. The vehicle was subjected to various customer-focused evaluations as part of the DOE/GM EcoCAR program.