This is a three-day course which provides a comprehensive and up to date introduction to fuel cells for use in automotive engineering applications. It is intended for engineers and particularly engineering managers who want to jump‐start their understanding of this emerging technology and to enable them to engage in its development. Following a brief description of fuel cells and how they work, how they integrate and add value, and how hydrogen is produced, stored and distributed, the course will provide the status of the technology from fundamentals through to practical implementation.
In this joint AIAA / SAE course, participants will learn about Electro-chemical Energy Systems (EES), with an emphasis on electrified aircraft propulsion and power applications. The course will present the fundamentals in chemistry, materials science, electrical, and mechanical engineering for various EESs including high voltage battery systems (Li-ion and beyond) and fuel cells (PEM, solid oxide fuel cells, and others).
The secular trend of automotive body structure light-weighting for internal combustion engine (ICE) vehicles is constrained by simultaneous and increasingly challenging vehicle cost, fuel economy and passenger safety standards. Mass optimization via materials selection in ICE vehicles, therefore, is ultimately dependent on the normalized cost of mass reduction solutions and the associated implications on passenger safety and vehicle performance metrics. These constraints have resulted in development and implementation of increasingly high specific-strength solutions for metallic components in the body structure and chassis. In contrast, mass optimization in battery electric vehicles is subject to alternative performance metrics to fuel efficiency, although considerations for vehicle safety and cost naturally remain directionally similar.
The transition from traditional gasoline-powered automobiles to electric vehicles (EVs) has taken time, two major challenges of engine- powered vehicles are greenhouse gas emissions and fuel economy. Electric cars require less maintenance. A lot of money can be saved while also helping the environment. In today's world, working with lightweight materials have emerged as a key area for improvement in the automotive industry. The most efficient method for increasing power output is to reduce the weight of vehicle components. Composite materials have benefited greatly from research and development because they are stronger, more recyclable, and easier to integrate into vehicles. The primary goal of this research is to design the body and chassis frame of a two-seater electric car.
Connected and automated vehicles (CAVs) can improve traffic efficiency and reduce fuel consumption. This paper proposes a cooperative game approach to merging sequence and optimal trajectory planning of CAVs at unsignalized intersections. The trajectory of the vehicles in the control zone is optimized by the Pontryagin minimum principle. The vehicle's transit time, fuel consumption, and passenger comfort are considered to construct the joint cost function, completing the optimal trajectory planning to minimize the joint cost function. Analyzing the different states between the two CAVs at the intersection to calculate the minimum safety interval. The cooperative game approach to merging sequence aims to minimize the global cost and the merging sequence of CAVs is dynamically adjusted according to the gaming result. The multi-player games are decomposed into two-player games, to realize the goal of the minimal global cost, and improve the calculation efficiency.
Decreasing fuel consumption in Internal Combustion Engines (ICE) is a key target for engine developers in order to achieve the CO2 emissions limits during a standard cycle. In this context, reduction of engine friction can help meet those targets. The use of Low Viscosity Engine Oils (LVEOs), which is currently one of the avenues to achieve such reductions, is studied in this manuscript through a validated numerical simulation model that predicts the friction of the engine’s piston-cylinder unit, journal bearings and camshaft. These frictional power losses are obtained for four different lubricant formulations which differ in their viscosity grades and design. Results show a maximum friction savings of up to 6% depending on the engine operating condition, where the major reductions come from hydrodynamic-dominated components such as journal bearings, despite an increase in friction in boundary-dominated components such as the piston-ring assembly.
As the global automotive market shifts towards electric vehicles, the United States Army must naturally consider this alternative for its combat vehicles. Indeed, electric vehicles offer numerous tactical advantages over traditional diesel engines, including higher torque at lower speeds and lower signature. Unfortunately, full electrification of most military vehicles is not feasible due to the weight of the requisite battery pack. However, the Army can take advantage of electric vehicles through hybrid power trains. Hybrid options allow for quiet, resilient, and powerful vehicles that are less constrained by battery technology. This study looks at the feasibility of hybrid power systems for military vehicles including the Infantry Squad Vehicle, the High Mobility Multipurpose Wheeled Vehicle, and the Joint Light Tactical Vehicle.
Downsized turbocharged engines have been increasingly popular in modern light-duty vehicles due to their fuel efficiency benefits. However, high power density in such engines is achieved thanks to high in-cylinder pressure-temperature conditions that increase knock propensity. Control strategies could be used to extend the knock limit if an accurate prediction of knock events were possible. Although knock modeling has been investigated with 3D computational fluid dynamics (CFD) simulations, such models are computationally expensive and cannot be executed in real-time for cycle-to-cycle control purposes. Advances in data analytics and machine learning, however, have enabled the development of real-time executable computer models with different levels of complexity. In this study, artificial neural networks (ANN) were used to develop a predictive model for knock events using the in-cylinder pressure data recorded before knock onset.
Fuel chemistry plays a crucial role in the continued reduction of particulate emission (PE) and cleaner air quality while using internal combustion engines (ICE). Over the past ten years, there has been great improvements in the measurements of particulate formation indices. Examples of these indices would be the Honda Particulate Matter Index (PMI) equation and the General Motors Particulate Evaluation Index (PEI), among others. Even though there have been improvements in particulate index (PI) measurement tools, the method analysis within these tools are still very time-consuming. These methods can include the use of chromatography separation techniques such as detailed hydrocarbon analysis (DHA), which have become very popular in the petrochemical industry. A review of historical PI methods will be discussed, along with a PE comparison to a less time-consuming simulated distillation method analysis.
Gasoline Direct-Injection Spark-Ignition (DISI) injector performance 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, seem to 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. 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? Field testing was used to develop high-mileage injectors from DISI vehicles.
The fuel spray process is of utmost importance to internal combustion engine design as it determines engine performance and emissions characteristics. While designers rely on CFD 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. Utilizing these detailed experimental results, we have explored a machine learning approach to prediction of fuel sprays. The ML approach for spray prediction is promising because (1) it does not require phenomenological spray models, (2) it can provide time-resolved spray data without time-stepping simulation, and (3) it is computationally faster than CFD. In this study, a pixel-regression model has been developed and applied for gasoline spray prediction.
In previous work we have shown that fuel power consumption of Internal Combustion Engine Vehicles (ICEV) is proportional to vehicle traction power, across vehicle and engine sizes and across different drive cycles, for a given technology. This results from the linear transfer function between powertrain input (fuel) power and output (mechanical) power at moderate power levels. In this paper we extend the analysis to battery electric vehicles (BEV), which differ from ICEV in several respects in terms of energy consumption. ICE engines have unidirectional power flow, while BEV electric motors have bidirectional power flow capability, and the battery provides reversible energy storage. This enables the recuperation of vehicle kinetic energy during braking. As a result, the net energy needed to drive through a cycle is reduced. Additionally, the BEV accessories are driven directly from the battery energy source and not by the powertrain as in ICEV.
Indrio Technologies has developed a novel on-board sensor, named Ignis, for detecting oxides of nitrogen (NOx) and ammonia (NH3) in diesel exhaust streams with sensitivities and molecular specificity unmet by existing technologies. This is a key technological need for diesel engine manufacturers, who face difficulty in precisely controlling their exhaust aftertreatment systems due to the lack of widely deployable sensors capable of differentiating between NOx, NH3 and other species in the exhaust stream. The successful incorporation of the proposed sensor can result in greater fuel efficiency improvements while matching new stringent 2027 California and 2030 EPA NOx emissions standards. Once the product has reached deep market penetration, the fleet-wide fuel economy improvements and NOx emissions reductions enabled by this product will lead to reduced carbon emissions and healthier air with lower amounts of NOx-induced smog, ground-level ozone, and acid rain.