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As vehicle emissions regulations become increasingly stringent, there is a growing need to accurately model aftertreatment systems to aid in the development of ultra-low NOx vehicles. Common solutions to this problem include the development of complex chemical models or expansive neural networks. This paper aims to present the development process of a simpler Selective Catalytic Reduction (SCR) conversion efficiency Simulink model for the purposes of modeling tail pipe NOx emission levels based on various inputs, temperature shifts and SCR locations, arrangements and/or sizes in the system. The main objective is to utilize this model to predict tail pipe NOx emissions of the EPA Federal Test Procedures for heavy-duty vehicles. The model presented within is focused exclusively on heavy-duty application compression ignition engines and their corresponding aftertreatment setups.

The reliable operation of power systems on the lunar surface is crucial for critical research activities and supporting life. These systems are standalone or interconnected grids that integrate intermittent power sources and distributed energy storage. Lunar microgrids must be highly reliable, reconfigurable, and efficient. To meet these requirements, we propose the flexible DC energy router (FeDER), a modular and scalable power management unit for interconnected lunar DC microgrids. The FeDER integrates local energy storage and addresses various microgrid power management needs such as fault management, stability enhancement, power flow regulation, and power quality improvement. The lunar DC microgrids' design, protection, and control are achieved using a three-layered approach: (1) graph theory, (2) energy management system, and (3) smart resistor control. The lunar power grid architecture is introduced and the FeDER stability enhancement is implemented in the OPAL-RT platform.

Future aircraft will demand a significant amount of electrical power to drive primary flight control surfaces. The electrical system architecture needed to source these flight critical loads will have to be resilient, autonomous, and fast. Designing and ensuring that a power system architecture can meet the load requirements and provide power to the flight critical buses at all times is fundamental. In this paper, formal methods and linear temporal logic are used to develop a contactor control strategy to meet the given specifications. The resulting strategy is able to manage multiple contactors during different types of generator failures. In order to verify the feasibility of the control strategy, a real-time simulation platform is developed to simulate the electrical power system. The platform has the capability to test an external controller through Hardware in the Loop (HIL).

The EcoCAR 2: Plugging into the Future team at The Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 44 miles of all-electric range. The vehicle features an 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

Physics-based electrochemical models and empirical Equivalent Circuit Models (ECMs) are well-established and widely used modeling techniques to predict the voltage behavior of lithium-ion cells. Electrochemical models are typically very accurate and require relatively little experimental data to calibrate, but present high mathematical and computational complexity. Conversely, ECMs are more computationally efficient and mathematically simpler, making them well-suited for applications in controls, diagnosis, and state estimation of lithium-ion battery packs. However, the calibration process requires extensive testing to calibrate the parameters of the model over a wide range of operating conditions. This paper bridges the gap between these two classes of models by developing a method to analytically define the ECM parameters starting from an already-calibrated Extended Single-Particle Model (ESPM).

Electrification is getting more important in the aviation industry with the increasing need for reducing emissions of carbon dioxide and fuel consumption. It is crucial to assess the behavior of Li-Ion batteries at high-altitude conditions to design safe and reliable battery packs. This paper aims at benchmarking the performance of different formats of battery cells (pouch cells and cylindrical cells) in low-pressure environments. A test setup was designed and fabricated to replicate the standard procedure defined by the RTCA DO-311 standard, such as the altitude test and rapid decompression test. During the test voltage, current, temperature, and pressure were monitored, and the evaluation criteria is based on the capacity retention, along with the structural integrity of the cell. From preliminary tests, it was observed that cylindrical cells do not show a significant change in performance at low-pressure conditions thanks to their steel casing.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

The occurrence of imbalance conditions within the cells of a battery pack can be reduced or mitigated by an active cell equalization circuit integrated in the Battery Management System (BMS), which transfers energy between the most charged cells and the least charged ones in the pack. However, incomplete knowledge on the performances and range of operability in real-world scenarios is still limiting the adoption of active equalizers for lithium-ion battery systems in different fields of application. In this paper, among the different architectures presented in literature for active cell equalizers, the multi-inductor configuration has been investigated. For the generalized category of inductor-based configuration, an analytical model has been developed by taking into account the static and dynamic parasitic parameters of the components of the equalization circuit as well as the operating conditions of the cells.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

NASA’s Watts on the Moon Challenge is seeking solutions to transfer at least 1.065 kW power from a 120 V dc source to a 24-32 V dc load over a 3-km distance under the same environmental conditions as the Lunar surface (i.e., 77 K temperature and 1 mTorr pressure). The selected solution from the author’s team proposed utilizing two modular multilevel Gallium Nitride (GaN) based isolated dc-dc converters to connect the 120 V dc source with the 24-32 V dc load bank via 1.5 kV rated dc transmission lines. The modular multilevel converters feature frequency multiplication, high step-down voltage ratio and low device voltage stress. In the converters, GaN gate injection transistor (GaN GIT) and GaN High-Electron-Mobility Transistor (GaN HEMT) devices are chosen as switching devices, due to the merits of lower power loss, radiation hardness and ability to work under cryogenic and vacuum conditions.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

Microgrids are a topic of interest in recent years, largely due to their compatibility with the integration of distributed renewable resources, capability for bidirectional power flow, and ability to reconfigure to mitigate the effects of faults. Fault diagnosis algorithms are a foundational technology for microgrids. These algorithms must have two primary capabilities. First, faults must be detectable; it is known when the fault occurs. Second, faults must be isolable; the type and location of detected faults can be determined. However, most fault handling research considering microgrids has focused on the protection algorithm. Protection algorithms seek to quickly extinguish dangerous faults which can damage components. However, these algorithms may not sufficiently capture less severe faults, or provide comprehensive monitoring for the microgrid. This is particularly relevant when considering applications involving fault tolerant control or dynamic grid reconfiguration.

The Ohio State University EcoCAR 3 team is designing a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of reducing the environmental impact of the vehicle, the Ohio State Camaro has been designed to have a 44-mile all-electric range. The vehicle is to consist of an 18.9 kWh Li-ion energy storage system, a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85) fuel, a 5-speed automated manual transmission, and a 150 kW peak-power electric machine. This report details the model and controls development process followed by the Ohio State team during Year 1 of the EcoCAR 3 competition. The focus of the paper will be on overall development of a vehicle model, initial simulation results, and supervisory controls development. Finally, initial energy consumption results from the model and future improvements will be discussed.

A significant number of formed parts constitute the components of an automobile or aircraft. The formed blanks for the components are produced at different temperatures ranging from room temperature to 2250 degrees Fahrenheit for steel. Forming progressions convert a basic shape or geometry (a cylindrical billet, for example) of metal into a more complex shape close to the required final component geometry. The progression steps, choice of temperatures and equipment significantly impact the cost of the blank. A ‘Discriminating Cost Model’ was developed to capture the cost effectiveness of a given choice of process or equipment, and an AI (Artificial Intelligence) search algorithm implemented to quickly search through the large number of process and equipment selection options to arrive at the most cost effective choice. Two applications of this methodology to existing plant processes to significantly reduce cost and implement ‘lean’ principles of manufacturing are discussed.

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

Second-life batteries (SLBs, automotive batteries that have lost their usefulness for vehicular applications) can provide low-cost environment-friendly solutions for grid-connected systems. The estimation of the remaining useful life (RUL) of SLBs is a fundamental step for the development of appropriate business models. This paper aims at unveiling correlations between the SLB's power profile and aging performance by defining appropriate metrics. A widely accepted empirical degradation model, that can predict calendar and cycling aging, is considered for this study. Several grid-connected power profiles are analyzed, such as peak shaving for DC-fast charge stations and frequency regulation. The results of this analysis show a correlation between the SLB's replacement rate with the minimum daily SoC.

The availability of DC Fast Charging Stations (DCFCs) is considered a fundamental step for the widespread adoption of electric vehicles (EVs). To mitigate the impact of high-power charging events on the grid, DCFCs are often equipped with stationary energy storage and renewable energy resources. In literature, many methods have been proposed to design, control, and optimize the performance of multi-sources DCFCs. Many of the research contributions use the averaged EV charging power consumption as input, not the real-time event-based power request. This paper aims at comparing the effects of average-based and event-based EV charging power profiles on the design and control of multi-sources DCFCs. An algorithm that generates event-based EV charging power profiles has been developed based on the data from the California Energy Commission (CEC) report and NREL's EVI-Pro I tool.

Vaporizing Foil Actuators (VFA) are based on the phenomenon of rapid vaporization of thin metallic foils and wires, caused by passage of a capacitor bank driven current on the order of 100 kA. The burst of the conductor is accompanied with a high-pressure pulse, which can be used for working metal at high strain rates. This paper focuses on the use of VFA for collision welding of dissimilar metals, in particular, aluminum and steel. Aluminum alloy 6061 sheets of 1 mm thickness were launched to velocities in excess of 650 m/s with input electrical energy of 8 kJ into 0.0762 mm thick, dog-bone shaped aluminum foil actuators. Target sheets made from dual phase steel (DP780) were impacted with the aluminum flyer sheet, and solid state impact welds were created. During mechanical testing, many samples failed outside the weld area, thereby indicating that the weld was stronger than the parent aluminum.

The heat rejection rates and skin temperatures of a liquid cooled exhaust manifold on a 3.5 L Gasoline Turbocharged Direct Injection (GTDI) engine are determined experimentally using an external cooling circuit, which is capable of controlling the manifold coolant inlet temperature, outlet pressure, and flow rate. The manifold is equipped with a jacket that surrounds the collector region and is cooled with an aqueous solution of ethylene-glycol-based antifreeze to reduce skin temperatures. Results were obtained by sweeping the manifold coolant flow rate from 2.0 to 0.2 gpm at 12 different engine operating points of increasing brake power up to 220 hp. The nominal coolant inlet temperature and outlet pressure were 85 °C and 13 psig, respectively. Data were collected under steady conditions and time averaged. For the majority of operating conditions, the manifold heat rejection rate is shown to be relatively insensitive to changes in manifold coolant flow rate.