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The passive oxidation of particulate matter (PM) in a diesel catalyzed particulate filter (CPF) was investigated in a series of experiments performed on two engines. A total of ten tests were completed on a 2002 Cummins 246 kW (330 hp) ISM and a 2007 Cummins 272 kW (365 hp) ISL. Five tests were performed on each engine to determine if using engine technologies certified to different emissions regulations has an impact on the passive oxidation characteristics of the PM. A new experimental procedure for passive oxidation testing was developed and implemented for the experiments. In order to investigate the parameters of interest, the engines were initially operated at a steady state loading condition where the PM concentrations, flow rates, and temperatures were such that the accumulation of PM within the CPF was obtained in a controlled manner. This engine operating condition was maintained until a CPF PM loading of 2.2 ±0.2 g/L was obtained.

The objective of this study is the development of a computationally efficient CFD-based tool for finding optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses, the duration of each pulse, the duration of the dwell, the exhaust gas recirculation rate and the boost pressure.

Higher carbon number alcohols offer an opportunity to meet the Renewable Fuel Standard (RFS2) and improve the energy content, petroleum displacement, and/or knock resistance of gasoline-alcohol blends from traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part II of this paper builds upon the alcohol selection, fuel implementation scenarios, criteria target values, and property prediction methodologies detailed in Part I. For each scenario, optimization schemes include maximizing energy content, knock resistance, or petroleum displacement. Optimum blend composition is very sensitive to energy content, knock resistance, vapor pressure, and oxygen content criteria target values. Iso-propanol is favored in both scenarios' suitable blends because of its high RON value.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

A gradient-based optimization tool has been developed and, in conjunction with a CFD code, utilized in the search of optimal fuel injection strategies. The approach taken uses a steepest descent method with an adaptive cost function, where the line search is performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for a non-road version of the Sulzer S20 DI diesel engine which, for these simulations, is equipped with a multi-orifice, asynchronous injection system. This system permits an independent timing of the fuel pulses, and each orifice has its own diameter and injection direction.

Recently, outboard engine technology has advanced significantly. With these new technologies comes a substantial improvement in emissions compared to traditional carbureted two-stroke engines. Some two-stroke gasoline direct injection (GDI) marine outboard engines are now capable of meeting California Air Resources Board 2008 Ultra-Low emissions standards. With improvement of gaseous emissions, studies are now being conducted to assess particulate matter (PM) emissions from all new technology marine outboard engines which include both four-stroke and two-stroke designs. Methods are currently being developed to determine the best way to measure PM from outboard engines. This study assesses gaseous and PM emissions, mutagenic activity of PM and oil consumption of two different technologies over the useful life of the engines.

To meet future NO, heavy-duty diesel emissions standards, exhaust gas recirculation (EGR) technology is likely to be used. To improve fuel economy and further lower emissions, the recirculated exhaust gas needs to be cooled, with the possibility that cooling of the exhaust gas may form sulfuric acid condensate in the EGR cooler. This corrosive condensate can cause EGR cooler failure and consequentially result in severe damage to the engine. Both a literature review and a preliminary experimental study were conducted. In this study, a manually controlled EGR system was installed on a 1995 Cummins Ml l-330E engine which was operated at EPA mode 9* (1800 rpm and 75% load). The Goksoyr-Ross method (1)** was used to measure the particle-phase sulfate and vapor-phase H2SO4 and SO2 at the inlet and outlet locations of the EGR cooler, obtaining H2SO4 and SO2 concentrations. About 0.5% of fuel sulfur in the EGR cooler was in the particle-phase.

The projected frontal area of a vehicle has a significant impact on aerodynamic drag, and thus is an important parameter, for vehicle development, benchmarking, and modeling. However, determining vehicle frontal area can be tedious, time consuming, expensive, or inaccurate. Existing methods include analysis of engineering drawings, vehicle projections, 3D scanners, planimeter measurements from photographs, and estimations using vehicle dimensions. Currently accepted approximation methods can be somewhat unreliable. This study focuses on introducing a method to find vehicle frontal area using digital images and subtraction functions via MATLABs' Image Processing Toolbox. In addition to an overview of the method, this paper describes several variables that were examined to optimize and improve the process such as camera position, surface glare, and vehicle shadow effects.

The Environmental Protection Agency (EPA) has published new emissions standards for snowmobiles, Federal Register 40 CFR, “Control of Emissions from Non-road Large Spark Ignition Engines and Recreational Engines (Marine and Land Based)”; Final Rule, Volume 67., No.217, November 8, 2002. These rules require a phase in of lower snowmobile emissions over the period of 2006 to 2012. In addition, the International Snowmobile Manufacturers' Association (ISMA) is developing new pass-by noise standards to replace the current wide-open throttle noise standard SAE J - 192 and J 1161. These new requirements set the stage for improvements in snowmobiles and form the basis for the Society of Automotive Engineers (SAE) Clean Snowmobile Challenge (CSC). SAE and Michigan Technological University (MTU) worked together, along with many other volunteers, to continue the SAE CSC, moving it from its original venue in Wyoming to Michigan.

For the 2003 and 2004 SAE Clean Snowmobile Challenges, the successful implementation of a clean, quiet, high-performance four-stroke motorcycle engine into an existing snowmobile chassis was achieved. For the 2005 Challenge, a new motor and chassis were selected to continue the development of a four cylinder, four stroke powered snowmobile. The snowmobile is as powerful as today's production performance models, as nimble as production touring sleds, easy to start, and environmentally friendly. This report describes the conversion process in detail with actual dynamometer, emissions, noise, and field test data, and also provides analysis of the development processes and data. The vehicle meets the proposed 2012 EPA snowmobile emissions regulations and is significantly quieter than a stock snowmobile.

The objective of this study is the development of a computationally efficient CFD-based tool with the capability of finding optimal engine operating conditions with respect to emissions and fuel consumption. The approach taken uses a conjugate gradient method, where the line search is performed with a backtracking algorithm. The initial backtracking step employs an adaptive step size mechanism which depends on the steepness of the search direction. The engine simulations are performed with a KIVA-3-based code which is equipped with well-established spray, combustion and emission models. The cost function is based on the idea of the penalty method and is minimized over the unit cube in n-dimensional space, which represents the set of normalized injection parameters under investigation. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine.

This paper presents and discusses the results of an integrated 4-Bed Molecular Sieve (4BMS), mechanical compressor, and Sabatier Engineering Development Unit (EDU) test. Testing was required to evaluate the integrated performance of these components of a closed loop atmosphere revitalization system together with a proposed compressor control algorithm. A theoretical model and numerical simulation had been used to develop the control algorithm; however, testing was necessary to verify the simulation results and further refine the model. Hardware testing of a fully integrated system also provided a better understanding of the practical inefficiencies and control issues, which are unavailable from a theoretical model.

Monitoring of the wear rate of a diesel engine will yield valuable information regarding the wear mechanism within a diesel engine and ultimately will improve the predictions of failing engines and/or their components to allow preventive maintenance which will prolong the life of the engine. A mathematical model was developed that describes the wear particle concentration as a function of time in a diesel engine. This model contains engine and lubrication system parameters that determine the concentration of wear particles in the engine sump. These variables are the oil system volume, oil flow rate, particle generation rate, filtering efficiency and the initial particle concentration. The model has been employed to study the wear particle concentrations in the sump and the mass of particles in the filter for the Cummins VT-903 diesel engine.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

This paper presents a Short Time Fourier Transform based algorithm to identify unknown parameters in fuel dynamics system during engine cold start and warm-up. A first order system is used to model the fuel dynamics in a port fuel injection engine. The feed forward transient fuel compensation controller is designed based on the identified model. Experiments are designed and implemented to verify the proposed algorithm. Different experiment settings are compared.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

The Vehicle Engine Cooling System Simulation (VECSS) computer code has been developed at the Michigan Technological University to simulate the thermal response of the cooling system of an on-highway heavy duty diesel powered truck under steady and transient operation. This code includes an engine cycle analysis program along with various components for the four main fluid circuits for cooling air, cooling water, cooling oil, and intake air, all evaluated simultaneously. The code predicts the operation of the response of the cooling circuit, oil circuit, and the engine compartment air flow when the VECSS is operated using driving cycle data of vehicle speed, engine speed, and fuel flow rate for a given ambient temperature, pressure and relative humidity.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

This paper presents a methodology that optimizes the periods of engine operation on a selected route for a Plug-in Hybrid Electric Vehicle (PHEV) or Hybrid Electric Vehicle (HEV) using Connected Vehicle data to minimize energy consumption. The study was conducted using a Reduced-Order Powertrain model of second-generation Chevrolet Volt. The method utilizes the Backward Induction Dynamic Programming algorithm to come up with an optimal control mode matrix of engine operation along the selected route for various battery states of charge. The objective of this method is to make use of Vehicle Connectivity to minimize the energy utilization of an HEV by using the speed and elevation profile of a selected route transmitted to the vehicle via V2X communication systems.