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Ensuring spray robustness of gasoline direct injection (GDI) is essential to comply with stringent future emission regulations for hybrid and internal combustion engine vehicles. This study presents experimental and numerical assessments of spray for lateral-mounted GDI sprays with two different plume arrangements to analyze spray collapse characteristics, which can significantly deteriorate the atomization performance of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. A series of computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization. Spatio-temporal plume dynamics of conventional triangle-pattern spray are evaluated and compared to a plume pattern with an inversed T pattern that has more open space between plumes.

Wet-sump transmissions are widely used in heavy duty and medium duty vehicles. As these transmissions do not have a dedicated forced lubrication system, it is important that the gear train, shafts, and enclosure are designed appropriately so that enough oil splashes to critical locations to ensure sufficient lubrication. The lubrication effectiveness of such transmissions can be studied through detailed tests or numerical simulations. Often, the vehicle, and therefore the transmission, encounters some severe operating conditions, such as climbing on an incline, driving downhill, etc. Studying these conditions through tests is an expensive process and this imposes the need for an analysis first approach. In this paper, the 3D multiphase Volume of Fluid (VOF) method is used to examine two such extreme cases: an 8-degree tilted installation of transmission in a vehicle, and an inclined condition of transmission during a 10-degree uphill climb.

Roots blower is a rotary positive displacement pump which operates by pumping a fluid with a pair of meshing lobes. Recent trends in automotive industry demands high power density solutions for various applications. In comparison with legacy applications, compressors for high power density applications demand continuous operation with harsher duty cycle as well as demand higher pressure ratios. Because of longer duty cycles, it will be subjected to high heat loads which will cause a rise in temperatures of timing gears, bearings, and other components within the assembly. Accurate prediction of thermal performance is critical to design a durable and efficient roots blower for high power density applications. Thermal analysis of an assembly of roots blower involves modelling of multi-physics phenomena. This paper details a coupled CFD analysis approach to predict temperatures of roots blower components and timing gear case oil. Timing gears are lubricated using wet sump lubrication.

The use of electrical contacts in aerospace applications is crucial, particularly in connectors that transmit signal and power. Crimping is a widely preferred method for joining electrical contacts, as it provides a durable connection and can be easily formed. This process involves applying mechanical load to the contact, inducing permanent deformation in the barrel and wire to create a reliable joint with sufficient wire retention force. This study utilizes commercially available Abaqus software to simulate the crimping process using an explicit solver. The methodology developed for this study correlates FEA and testing for critical quality parameters such as structural integrity, mechanical strength, and joint filling percentage. A four-indenter crimping tool CAD model is utilized to form the permanent joint at the barrel-wire contact interfaces, with displacement boundary conditions applied to the jaws of the tool in accordance with MIL-C-22520/1C standard.

With the advent of wide band gap semiconductor devices like SiC based MOSFETs/Diodes, there is a growing demand for utilizing electrical power instead of the conventional fuel-based power generation in both automotive and aerospace industry. In automotive/aerospace industry the focus on electrification has resulted in a need for sub-systems like inverters, power distribution units, motor controllers, DC-DC converters that actively utilize SiC based power electronics devices. To address the growing power density requirements for electronics in next generation product families, more efficient & reliable thermal management solution plays a critical role. The effective thermal management of the power electronics is also critical aspect to ensure overall system reliability. The conventional thermal management system (TMS) optimization targets heat sink/ cold plate design parameters like fin spacing, thickness, height etc. or sizing of the required cooling pump/fan.

Direct injection strategies have been successfully used on spark ignited internal combustion engines for improving performance and reducing emissions. Among the different technologies available, outward opening injectors seem to have found their place in renewable applications running on gaseous fuels, including natural gas or hydrogen, as well as in a few specific liquid fuel applications. In order to understand the key operating principles of these devices, their limitations and the resulting sprays, it is necessary to accurately describe the pintle dynamics. The pintle’s relative position with respect to the injector body defines the internal flow geometry and therefore the injection rates and spray characteristics. In this paper both numerical and experimental investigations of the dynamics of an outward opening injector pintle have been carried out.

To cope with regulatory standards, minimizing tailpipe emissions with rapid catalyst light-off during cold-start is critical. This requires catalyst-heating operation with increased exhaust enthalpy, typically by using late post injections for retarded combustion and, therefore, increased exhaust temperature. However, retardability of post injection(s) is constrained by acceptable pollutant emissions such as unburned hydrocarbon (UHC). This study provides further insight into the mechanisms that control the formation of UHC under catalyst-heating operation in a medium-duty diesel engine, and based on the understanding, develops combustion strategies to simultaneously improve exhaust enthalpy and reduce harmful emissions. Experiments were performed with a full boiling-range diesel fuel (cetane number of 45) using an optimized five-injections strategy (2 pilots, 1 main, and 2 posts) as baseline condition.

The latest edition of the US Department of Energy's (DOE) Advanced Vehicle Technology Competition (AVTC) series is the EcoCAR Mobility Challenge (EMC). In the third year of the EMC, the Mississippi State University (MSU) team developed and tested a perception system and a longitudinal controller to achieve SAE level 2 autonomy. Our team leveraged the model-based design approach to iterate between developing software components and executing tests in multiple environments in the loop (XIL) to verify that design requirements are met. This workflow allowed us to detect and resolve issues early in the development process. The perception system is composed of a sensor fusion and tracking algorithm. It relies on detections from a front facing camera and radar to generate tracks for a leading vehicle. The tracks from the perception system are used by a model predictive controller (MPC) to maintain a safe distance to the leading vehicle.

The EcoCAR Mobility Challenge (EMC) is the latest edition of the Advanced Vehicle Technology Competition (AVTC) series sponsored by the US Department of Energy. This competition challenges 11 North American universities to redesign a stock 2019 Chevrolet Blazer into an energy-efficient, SAE level 2-autonomous mild hybrid electric vehicle (mHEV) for use in the Mobility as a Service (MaaS) market. The Mississippi State University (MSU) team designed a P4 electric powertrain with an 85kW (113.99 HP) permanent magnet synchronous machine (PMSM) powered by a custom 5.4 kWh lithium-ion energy storage system. To maximize energy efficiency, Model Based Design concepts were leveraged to optimize the overall gear ratio for the P4 system. To accommodate this optimized ratio in the stock vehicle, a custom offset gearbox was designed that links the PMSM to the rear drive module.

Injector performance in gasoline Direct-Injection Spark-Ignition (DISI) engines is a key focus in the automotive industry as the vehicle parc transitions from Port Fuel Injected (PFI) to DISI engine technology. DISI injector deposits, which may impact the fuel delivery process in the engine, sometimes accumulate over longer time periods and greater vehicle mileages than traditional combustion chamber deposits (CCD). These higher mileages and longer timeframes make the evaluation of these deposits in a laboratory setting more challenging due to the extended test durations necessary to achieve representative in-use levels of fouling. The need to generate injector tip deposits for research purposes begs the questions, can an artificial fouling agent to speed deposit accumulation be used, and does this result in deposits similar to those formed naturally by market fuels?

The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions.

The current simulation models of EV and ICE Vehicles are well known in industry for their use in estimating the fuel economy or Range benefits because of controller calibrations and component sizing. However, there is a gap in understanding the behavior of accessories such as HVAC, power steering and other such auxiliary loads and the energy losses associated with them. Impact of thermal behavior of electronics on vehicle range also needs to be studied in detail. These kinds of studies help OEM and tier 1 manufactures in improving their design concepts significantly with minimum cost and development time. Hence, the focus of this study is on building simulation models of thermal, electrical, traction and control circuits of a typical electric vehicle. These models are then integrated, and analysis is performed to understand vehicle system level performance metrics.

Commercial vehicles require fast aftertreatment heat-up in order to move the SCR catalyst into the most efficient temperature range to meet upcoming NOX regulations. Today’s diesel aftertreatment systems require on the order of 10 minutes to heat up during a cold FTP cycle. The focus of this paper is to heat up the aftertreatment system as quickly as possible during cold starts and maintain a high temperature during low load, while minimizing fuel consumption. A system solution is demonstrated using a heavy-duty diesel engine with an end-of-life aged aftertreatment system targeted for 2027 emission levels using various levels of controls. The baseline layer of controls includes cylinder deactivation to raise the exhaust temperature more than 100° C in combination with elevated idle speed to increase the mass flowrate through the aftertreatment system. The combination yields higher exhaust enthalpy through the aftertreatment system.

The autonomous collision avoidance problem for multi-trailer vehicle maneuvering is investigated in this paper. Different from conventional vehicle systems that contain one single moving part or multi-parts that can be considered as one rigid body, the interconnection between the tractor and each trailer, and interactions between trailers in the multi-trailer system introduce a high dimensional and highly complex dynamic system for the controller design. The external disturbance and parametric uncertainties further increase the difficulty in system identification and state space formulation. To implement a real time control system for various scenarios where the locations and states of the obstacles are not known beforehand, a supervisory algorithm is designed to convert the control problem to a discrete event system. The model predictive control (MPC) using limited lookahead policy is employed in the proposed algorithm.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer.

In this work, a process-structure-property relationship for additively manufactured Al-Si-Mg alloy was constructed, with the aid of an integrated multi-physics model. Specifically, first, a series of thermal simulations were performed to understand molten pool geometry under different additive manufacturing (AM) operating conditions, including laser beam power, scanning speed, and hatch spacing. The porosity formation was predicted based on thermal simulation results, which yield molten pool dimension information for predicting the lack-of-fusion porosity. Dream.3D was utilized to reconstruct synthetic microstructures with different volume fraction of porosity.

A theoretical and experimental analysis is conducted to study the influences of key design parameters on the backlash of the roller enveloping hourglass worm gear. Two equations, the gear engagement equation and the tooth profile equation have been derived and represented in terms of four key parameters arising from the backlash of the worm gear by applying the gear meshing theory. Based on the derived equations, an efficient approach for reducing or eliminating the backlash of such a novel warm gear is developed. Specifically, the influences of center distance, roller radius, transmission ratio, and the radius of base circle on the contact curves and the tooth profile have been systematically investigated through numerical analysis, modeling and simulation. Next, a roller enveloping hourglass worm gear is manufactured and used for assessing the efficiency of the developed method in reducing and/or eliminating the backlash.

The increasing number of gasoline direct injection (GDI) vehicles on the roads has drawn attention to their particulate matter (PM) emissions, which are greater both in number and mass than port fuel injected (PFI) spark ignition (SI) engines [1]. Regulations have been proposed and implemented to reduce exposure to PM, which has been shown to have negative impacts on both human health and the environment [2, 3]. Currently, the gasoline particulate filter (GPF) is the proposed method of reducing the amount of PM from vehicle exhaust, but modifications to improve the filtration efficiency (FE) and reduce the pressure drop across the filter are yet needed for implementation of this solution in on-road vehicles. This work evaluates the impacts of wall thickness and cell density on filtration efficiency and backpressure using a benchtop filtration system.

Diesel engine cylinder deactivation (CDA) can be used to reduce petroleum consumption and greenhouse gas (GHG) emissions of the global freight transportation system. Heavy duty trucks require complex exhaust aftertreatment (A/T) in order to meet stringent emission regulations. Efficient reduction of engine-out emissions require a certain A/T system temperature range, which is achieved by thermal management via control of engine exhaust flow and temperature. Fuel efficient thermal management is a significant challenge, particularly during cold start, extended idle, urban driving, and vehicle operation in cold ambient conditions. CDA results in airflow reductions at low loads. Airflow reductions generally result in higher exhaust gas temperatures and lower exhaust flow rates, which are beneficial for maintaining already elevated component temperatures. Airflow reductions also reduce pumping work, which improves fuel efficiency.