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Porous wall permeability is one of the most critical factors for the estimation of backpressure, a key performance indicator in automotive particulate filters. Current experimental and analytical filter models could be calibrated to predict the permeability of a specific filter. However, they fail to provide a reliable estimation for the dependence of the permeability on key parameters such as wall porosity and pore size. This study presents a novel methodology for experimentally determining the permeability of filter walls. The results from four substrates with different porosities and pore sizes are compared with several popular permeability estimation methods (experimental and analytical), and their validity for this application is assessed. It is shown that none of the assessed methods predict all permeability trends for all substrates, for cold or hot flow, indicating that other wall properties besides porosity and pore size are important.

Permanent magnet (PM) electrical machine has far-reaching impacts in aviation electrification due to the continuous development in high power density and high efficiency electrical drives. The primary barrier to acceptance of permanent magnet machines for safety-critical starter-generator systems is its low fault-tolerance capability and low reliability (for the conventional designs). This article investigates a modular triple three-phase PM starter-generator comprehensively, including the tradeoff of fault-tolerant topology, optimization design process, analysis of electromagnetic (highlight the post-fault analysis) and thermal behavior, respectively. The triple three-phase segmented topology proposed meet the fault-tolerant requirement along with complete electrical, magnetic, and thermal isolation. There would be cost penalty on the proposed topology, but it gets offset by the ease of manufacturing of coils and their insertion.

Electric machines in aerospace applications are subjected to extremely high operating temperatures. This increases coercivity or decreases saturation flux density of the electrical steel resulting in increased core loss. The need for high power density and increased operating speed favours the use of thin gauge Silicon Steel (Si-Fe) and Cobalt Iron (Co-Fe) laminations for aerospace applications. Therefore, the variation in iron loss is studied for three grades of Si-Fe laminations by subjecting them to controlled ageing in laboratory. The analysis is also provided over a range of flux density and frequency to generalize the phenomenon over the operating domain. The results of ageing the laminations are in turn used to predict the degradation in performance of a 1.15 MW, 16-pole 48-slot propulsion machine for aerospace application. The degradation is estimated in terms of variation in iron loss.

Evolving of aircraft design towards further electrification requires safe and fault-free operation of all the components. More electric aircraft are increasingly utilizing electro-mechanical actuators (EMA). EMAs are prone to jamming and subsequent failure due to large forces on the shaft. Large forces are generated due to the high reflected inertia of the electric machine rotor. To limit the force acting on the shaft, a torque limiting device is connected to the power train which can separate the rotating mass of the electric machine from the power train. In this paper, a concept of integration of torque limiter and the electric machine rotor is presented to reduce overall volume and mass. It is connected closely with the rotor, within the motor envelope. A commercially available torque limiter and an electric machine designed for actuator application are used to demonstrate the concept. While essential for safety, the torque limiter adds to the mass and size of the overall EMA.

The increased severity and prevalence of insoluble deposits formed on fuel injectors in gasoline direct injection (GDI) engines precipitates negative environmental, economic and healthcare impacts. A necessary step in mitigating deposits is to unravel the molecular compositions of these complex layered materials. But very little molecular data has been acquired. Mass spectrometry shows promise but most techniques require the use of solvents, making them unsuited for analyzing insoluble deposits. Here, we apply the high mass-resolving power and in-situ analysis capabilities of 3D OrbitrapTM secondary ion mass spectrometry (3D OrbiSIMS) to characterize deposits formed on the external tip and internal needle from a GDI injector. This is the first application of the technique to study internal GDI deposits. Polycyclic aromatic hydrocarbons (PAHs) are present up to higher maximum masses in the external deposit.

Automotive Seat design is driven by various design factors considering occupant safety, occupant comfort, road visibility etc. Seats are generally a combination of structural frames, foams, suspension wires, adjustment mechanisms etc. to support safety, comfort & road visibility. Most of the regulatory tests on seats are related to safety which demands structural integrity of seat frames, welds and bolts etc. And few tests demand occupant comfort and seat functionality involving foams, suspension wires & adjustment mechanisms. There are few tests which needs design consideration on all seat components discussed above. FMVSS 202a Back Set Retention is one among them. It is a static test with multiple loads in sequence of loading and unloading steps on the seat back and head restraint. Stiffness of the foams, lumbar mats, seat adjustment mechanisms, seat frames contribute a lot to meet performance targets set by the regulation.

Engine research has increasingly focused on emission of sub 23 nm particulates in recent years. Likewise, current legislative efforts are being made for particulate number (PN) emission limits to include this previously omitted size range. In Europe, PN measurement equipment and procedures for regulatory purposes are defined by the particle measurement programme (PMP). Latest regulation drafts for sub 23 nm measurements specify counting efficiencies with a 65% cut-off size at 10 nm (d65) and a minimum of 90% above 15 nm (d90). Even though alternative instruments, such as differential mobility spectrometers (DMS), are widely used in laboratory environments, the interpretation of their sub 23 nm measurements has not yet been widely discussed. For this study, particulate emissions of a 1.0L gasoline direct injection (GDI) engine have been measured with a DMS system for low to medium speeds with two load steps.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Through improving the 48V hybrid vehicle archetype, governmental emission targets could be more easily met without incurring the high costs associated with increasing levels of electrification. The braking energy recovery function of hybrid vehicles is recognised as an effective solution to reduce emissions and fuel consumption in the short to medium term. The aim of this study was to evaluate methods to maximise the braking energy recovery capability of the 48V hybrid electric vehicle over pre-selected drive cycles using appropriately sized electrified components. The strategy adopted was based upon optimising the battery chemistry type via specific power capability, so that overall brake power is equal to the maximum battery charging power in a typical medium-sized passenger car under typical driving. This will maximise the regenerative braking energy whilst providing a larger torque assistance for a lower battery capacity.

Latches are generally used to lock/unlock a component against each other. In the automotive industry, latches are widely used in doors and seats. Seat latches have to secure the seat safely to the body in the event of a crash and at the same time they have to be locked/unlocked with easy efforts. Seat latches are mostly supplier designed parts. Supplier latch effort calculations involve only latch components. Actual latch effort calculations should be done with seat structures, foams, trims and body environments. Hence OEMs are responsible to provide easily lockable/unlockable seats to their customers. Customers nowadays, are raising complaints regarding latching issues to respective automotive industry which in turn costs more due to after sales services/warranty claims. Therefore, automotive industries must spend a significant amount of time and capital on physical test and method development for calculating the latch efforts.

Dynamical Energy Analysis (DEA) has recently been introduced as a mesh-based high frequency method modelling structure borne sound for complex built-up structures. Using DEA, the structure-borne sound of an assembled agricultural tractor was calculated and good agreement between measurement and DEA calculations has been shown. However, it is still difficult to model a solid structure as currently DEA is based on wave-transmission calculations through plates and plate-to-plate junctions. Additionally, it is often difficult to generate accurate FE meshes of assembled complex structures because of welds, bolts, and rubber brushes between components. In this paper, we propose a novel method to generate DEA elements based on measurement data in order to model solid parts of a complex structures. The method of Advanced Transfer Path Analysis (ATPA) is employed to extract energy-transmission characteristics of a structure.

There has been a great effort expended in identifying causes of Hydro-Carbon (HC) and Particulate Matter (PM) emissions resulting from poor spray preparation, leading to characterization of fueling behavior near nozzle. It has been observed that large droplet size is a primary contributor to HC and PM emission. Imaging technologies have been developed to understand the break-up and consistency of fuel spray. However, there appears to be a lack of studies of the spray characteristics at the End of Injection (EOI), near nozzle, in particular, the effect that tip deposits have on the EOI characteristics. Injector tip deposits are of interest due to their effect on not only fuel spray characteristics, but also their unintended effect on engine out emissions. Using a novel imaging technique to extract near nozzle fuel characteristics at EOI, the impact of tip deposits on Gasoline Direct Injection (GDI) fuel injectors at the EOI is being examined in this work.

Characterization of soot nanoparticle morphology can be used to develop understanding of nanoparticle interaction with engine lubricant oil and its additives. It can be used to help direct modelling of soot-induced thickening, and in a more general sense for combatting reductions in engine efficiency that occur with soot-laden oils. Traditional 2D transmission electron microscopy (TEM) characterization possesses several important shortcomings related to accuracy that have prompted development of an alternative 3D characterization technique utilizing electron tomography, known as 3D-TEM. This work details progress made towards facilitating semi-automated image acquisition and processing for location of structures of interest on the TEM grid. Samples were taken from a four cylinder 1.4 L gasoline turbocharged direct injection (GTDI) engine operated in typically extra-urban driving conditions for 20,284 km, with automatic cylinder deactivation enabled.

Automotive Applications of Hardware-in-the-Loop (HIL) Simulation shines a light on HIL simulation testing methodology commonly used in the automotive industry for conventional, electrification and autonomy applications and can serve as an introductory resource for college students looking to join the automotive industry or experienced technical professionals who need a deeper understanding on what is HIL simulation, what are its benefits and how can it be used in their respective organizations.

With the recent advancements in sensing and processing capabilities of consumer mobile devices (e.g., smartphone, tablet, etc.), they are becoming attractive choices for pervasive computing applications. Always-on monitoring of human movement patterns is one of those applications that has gained a lot of importance in the field of mobility and transportation research. Automatic detection of the current transportation mode (e.g., walking, biking, riding a shuttle, etc.) of a consumer using data from their smartphone sensors enables delivering of a number of customized services for multi-modal journey planning. Most accurate models for automatic mode detection are trained with supervised learning algorithms. In order to achieve high accuracy, the training datasets need to be sufficiently large, diverse, and correctly labeled.

Accurate road friction coefficient is crucial for the proper functioning of active chassis control systems. However, road friction coefficient is difficult to be measured directly. Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. The proposed estimation algorithm is validated by both simulations and tests on a scaled model vehicle.

Autonomous vehicles and advanced driver assistance systems have functionality realized across numerous distributed systems that interact with a dynamic cyber-physical environment. This complexity raises the potential for emergent behaviours which are not intended for the system’s operational use. The need to analyze the intended functionality of these emergent behaviours for potential hazards, which may occur in absence of faults, are aspects of the ISO PAS 21448, Safety of the Intended Functionality (SOTIF) [1]. The Safety of the Intended Functionality or SOTIF is a framework for developing systems which are free from unreasonable risk due to the intended functionality or performance limitations of a system which is free from faults. This is meant to complement Functional Safety which is covered in ISO 26262 [2]. The major focus of SOTIF is to aid in the functional development of a system.

One of the ways to reduce city congestion is to balance the traffic flow on the road network and maximally utilize all road capacities. There are examples showing that, if the drivers are not competitive but cooperative, the road network usage efficiency and the traffic conditions can be improved. This motivates the idea of designing a cooperative routing algorithm to benefit most vehicles on the road. This paper presents a semi-cooperative social routing algorithm for large transportation network with predictive traffic density information. The goal is to integrate a cooperative scheme into the individual routing and achieve short traveling time not only for the traveler itself, but also for all vehicles in the road network. The most important concept of this algorithm is that the route is generated with the awareness of the total travel time added to all other vehicles on the road due to the increased congestion.

As automakers begin to design the autonomous vehicle (AV) for the first time, they must reconsider customer interaction with the Automatic Speech Recognition (ASR) system carried over from the traditional vehicle. Within an AV, the voice-to-ASR system needs to be capable of serving a customer located in any seat of the car. These shifts in focus require changes to the microphone selection and placement to serve the entire vehicle. Further complicating the scenario are new sources of noise that are specific to the AV that enable autonomous operation. Hardware mounted on the roof that are used to support cameras and LIDAR sensors, and mechanisms meant to keep that hardware clean and functioning, add even further noise contamination that can pollute the voice interaction. In this paper, we discuss the ramifications of picking up the intended customer’s voice when they are no longer bound to the traditional front left “driver’s” seat.

Air filter elements have been around since the dawn of automotive development. The function of an air induction system and the filter element in particular is to remove particulates such as dust, soot, and relatively minor contaminants from the air flow. This protects the engine, turbocharger, and other components from wear. However, sometimes severe duty cycles may cause large amounts of dust, mud, and water to enter the air induction system (AIS). This can cause filter degradation and even rupture or deformation, leading to highly increased engine and turbocharger wear. One example of this extreme loading is the tar sands region of Alberta, Canada, where trucks can accumulate over 1000 pounds of mud on a vehicle during normal usage over a few weeks’ time. Significant amounts of this mud also get ingested into the AIS. This study attempts to analyze different aspects of filter design to increase robustness to severe usage, particularly mud.