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A work-based window method has been developed to calculate in-use brake-specific oxides of nitrogen (NOx) emissions for all engine speeds and engine loads. During an in-use test, engine speed and engine torque are read from the engine's electronic control unit, and along with time, are used to determine instantaneous engine power. Instantaneous work is calculated using this power and the time differential in the data collection. Work is then summed until the target amount of work is accumulated. The emissions levels are then calculated for that window of work. It was determined that a work window equal to the theoretical Federal Test Procedure (FTP) cycle work best provides a means of comparison to the FTP certification standard. Also, a failure criterion has been established based on the average amount of power generated in the work window and the amount of time required to achieve the target work window to determine if a particular work window is valid.

Tube Launched-Unmanned Air Vehicles (TL-UAV) are munitions that alter their trajectories during flight to enhance the capabilities by possibly extending range, increasing loiter time through gliding, and/or having guided targeting capabilities. Traditional munition systems, specifically the tube-launched mortar rounds, are not guided. Performance of these "dumb" munitions could be enhanced by updating to TL-UAV and still utilize existing launch platforms with standard propellant detonation firing methods. The ability to actively control the flight path and extend range of a TL-UAV requires multiple onboard systems which need to be identified, integrated, assembled, and tested to meet cooperative function requirements. The main systems, for a mortar-based TL-UAV being developed at West Virginia University (WVU), are considered to be a central hub to process information, aerodynamic control devices, flight sensors, a video camera system, power management, and a wireless transceiver.

West Virginia University has conducted research to characterize the emissions from medium-duty vehicles operating on Fischer-Tropsch synthetic gas-to-liquid compression ignition fuel. The West Virginia University Transportable Heavy Vehicle Emissions Testing Laboratory was used to collect data for gaseous emissions (carbon dioxide, carbon monoxide, oxides of nitrogen, and total hydrocarbon) while the vehicles were exercised through a representative driving schedule, the New York City Bus Cycle (NYCB). Artificial neural networks were used to model emissions to enhance the capabilities of computer-based vehicle operation simulators. This modeling process is presented in this paper. Vehicle velocity, acceleration, torque at rear axel, and exhaust temperature were used as inputs to the neural networks. For each of the four gaseous emissions considered, one set of training data and one set of validating data were used, both based on the New York City Bus Cycle.

To meet the ignition system needs of large bore high pressure lean burn natural gas engines a laser diode side pumped passively Q-switched laser igniter was designed and tested. The laser was designed to produce the optical intensities needed to initiate ignition in a lean burn high brake mean effective pressure (BMEP) engine. The experimentation explored a variety of optical and electrical input parameters that when combined produced a robust spark in air. The results show peak power levels exceeding 2 MW and peak focal intensities above 400 GW/cm2. Future research avenues and current progress with the initial prototype are presented and discussed.

Emissions tests for heavy -duty diesel-fueled engines and vehicles are normally performed using engine dynamometers and chassis dynamometers, respectively, with laboratory grade gaseous concentration measurement analyzers and supporting test equipment. However, a considerable effort has been recently expended on developing in-use, on-board tools to measure brake-specific emissions from heavy -duty vehicles with the highest degree of accuracy and precision. This alternative testing methodology would supplement the emissions data that is collected from engine and chassis dynamometer tests. The on-board emissions testing methodology entails actively recording emissions and vehicle operating parameters (engine speed and load, vehicle speed etc.) from vehicles while they are operating on the road. This paper focuses on in-use measurements of NOX with zirconium oxide sensors and other portable NOX detectors.

When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.

Today's computer-based vehicle operation simulators use engine speed, engine torque, and lookup tables to predict emissions during a driving simulation [1]. This approach is used primarily for light and medium-duty vehicles, with large discrepancies inherently due to the lack of transient engine emissions data and inaccurate emissions prediction methods [2]. West Virginia University (WVU) has developed an artificial neural network (ANN) based emissions model for incorporation into the ADvanced VehIcle SimulatOR (ADVISOR) software package developed by the National Renewable Energy Laboratory (NREL). Transient engine dynamometer tests were conducted to obtain training data for the ANN. The ANN was trained to predict carbon dioxide (CO2) and oxides of nitrogen (NOx) emissions based on engine speed, torque, and their representative first and second derivatives over various time ranges.

Emissions tests for heavy-duty diesel-fueled vehicles are normally performed using an engine dynamometer or a chassis dynamometer. Both of these methods generally entail the use of laboratory-grade emissions measurement instrumentation, a CVS system, an environment control system, a dynamometer, and associated data acquisition and control systems. The results obtained from such tests provide a means by which engines may be compared to the emissions standards, but may not be truly indicative of an engine's in-vehicle performance while operating on the road. An alternative to such a testing methodology would be to actively record the emissions from a vehicle while it was operating on-road. A considerable amount of discussion has been focused on the development of on-road emissions measurement systems (OREMS) that would provide for such in-use emissions data collection.

The use of Artificial Neural Networks (ANNs) as a predictive tool has been shown to have a broad range of applications. Earlier work by the authors using ANN models to predict carbon dioxide (CO2), carbon monoxide (CO), oxides of nitrogen (NOx), and particulate matter (PM) from heavy-duty diesel engines and vehicles yielded marginal to excellent results. These ANN models can be a useful tool in inventory prediction, hybrid vehicle design optimization, and incorporated into a feedback loop of an on-board, active fuel injection management system. In this research, the ANN models were trained on continuous engine and emissions data. The engine data were used as inputs to the ANN models and consisted of engine speed, torque, and their respective first and second derivatives over a one, five, and ten second time range. The continuous emissions data were the desired output that the ANN models learned to predict through an iterative training process.

The 1998 Consent Decrees between the United States Government and the settling heavy-duty diesel engine manufacturers require in-use emissions testing from post 2000 model year engines. The emissions gathered from these engines must be reported on a brake-specific mass basis. To report brake-specific mass emissions, three primary parameters must be measured. These are the concentration of each emission constituent, the exhaust mass flow rate, and the engine power output. The measurement of the concentration level and exhaust mass flow rate can be (and are generally) measured directly with instrumentation installed in the exhaust transfer tube. However, engine power cannot be measured directly for in-use emissions testing due to the direct coupling of the engine output shaft to the vehicle's transmission. Engine power can be inferred from the electronic control unit (ECU) broadcast of engine speed and engine torque.

Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption. Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors. One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.

The study was aimed at assessing in-use emissions of a USEPA 2010 emissions-compliant heavy-duty diesel vehicle powered by a model year (MY) 2011 engine using West Virginia University's Transportable Emissions Measurement System (TEMS). The TEMS houses full-scale CVS dilution tunnel and laboratory-grade emissions measurement systems, which are compliant with the Code of Federal Regulation (CFR), Title 40, Part 1065 [1] emissions measurement specifications. One of the specific objectives of the study, and the key topic of this paper, is the quantification of greenhouse gas (GHG) emissions (CO2, N2O and CH4) along with ammonia (NH3) and regulated emissions during real-world operation of a long-haul heavy-duty vehicle, equipped with a diesel particulate filter (DPF) and urea based selective catalytic reduction (SCR) aftertreatment system for PM and NOx reduction, respectively.

Beginning 2007, heavy-duty engine certification would require that in-use emissions from vehicles be measured under ‘real-world’ operating conditions using on-board measurement devices. An on-board portable emissions measurement system called Mobile Emissions Measurement System (MEMS) was developed at West Virginia University (WVU) to record in-use, continuous and brake-specific emissions from heavy-duty diesel-powered vehicles. The objective of this paper is to present a preliminary development of a test data quality assurance methodology for emissions measured using the any portable emissions measurement system (PEMS). The first stage of the methodology requires ensuring the proper operation of the different sensors and transducers during data collection. The second stage is data synchronization and pre-processing. The next stage is systematic checking of possible errors from transducers and sensors.

On-board emissions measurement for heavy-duty vehicles has taken on greater significance because new standards now address in-use emissions levels in the USA. Emissions compliance must be shown in a “Not-to-exceed” (NTE) zone that excludes engine operation at low power. An over-the-road 1996 Peterbilt tractor was instrumented with the West Virginia University Mobile Emissions Measurement System (MEMS). The researchers determined how often the truck entered the NTE, and the emissions from the vehicle, as it was driven over different routes and at different test weights (20,740 lb, 34,640 lb, 61,520 lb, and 79,700 lb) The MEMS interfaced with the truck ECU, while also measuring exhaust flowrate, and concentrations of carbon dioxide (CO2) and oxides of nitrogen (NOx) in the exhaust. The four test routes that were employed included varying terrain types in order to simulate a wide range of on-road driving conditions. One route (called the Bruceton route) included a sustained hill climb.

Hybrid diesel electric buses offer the advantage of superior fuel economy through use of regenerative braking and lowered transient emissions by reducing the need of the engine to follow load as closely as in a conventional bus. With the support of the Department of Energy (DOE), five Lockheed Martin-Orion hybrid diesel-electric buses were operated on the West Virginia University Transportable Laboratory in Brooklyn, New York. The buses were exercised through a new cycle, termed the Manhattan cycle, that was representative of today's bus use as well as the accepted Central Business District Cycle and New York Bus Cycle. Emissions data were corrected for the state of charge of the batteries. The emissions can be expressed in units of grams/mile, grams/axle hp-hr and grams/gallon fuel. The role of improved fuel economy in reducing oxides of nitrogen relative to conventional automatic buses is evident in the data.

Heavy duty tractor-trailers under freeway operations consume about 65% of the total engine shaft energy to overcome aerodynamic drag force. Vehicles are exposed to on-road crosswinds which cause change in pressure distribution with a relative wind speed and yaw angle. The objective of this study was to analyze the drag losses as a function of on-road wind conditions, on-road vehicle position and trajectory. Using coefficient of drag (CD) data available from a study conducted at NASA Ames, Geographical Information Systems model, time-varying weather data and road data, a generic model was built to identify the yaw angles and the relative magnitude of wind speed on a given route over a given time period. A region-based analysis was conducted for a study on interstate trucking operation by employing I-79 running through West Virginia as a case study by initiating a run starting at 12am, 03/03/2012 out to 12am, 03/05/2012.

Linear engine alternator (LEA) design optimization traditionally has been difficult because each independent variable alters the motion with respect to time, and therefore alters the engine and alternator response to other governing variables. An analogy is drawn to a conventional engine with a very light flywheel, where the rotational speed effectively is not constant. However, when springs are used in conjunction with an LEA, the motion becomes more consistent and more sinusoidal with increasing spring stiffness. This avoids some attractive features, such as variable compression ratio HCCI operation, but aids in reducing cycle-to-cycle variation for conventional combustion modes. To understand the cycle-to-cycle variations, we have developed a comprehensive model of an LEA with a 1kW target power in MATLAB®/Simulink, and an LEA corresponding to that model has been operated in the laboratory.

It is difficult to project the emissions performance of a vehicle on a route unless the test cycle used to gain the emissions data reasonably represents that route. A chassis dynamometer emissions measurement test schedule consisting of three modes (congested, non-congested and bus rapid transit (BRT) operation) was developed for use in a program to evaluate transit bus technologies in Mexico City. Existing buses were fitted with global positioning system (GPS) data loggers and, between September 2nd and 8th of 2004, 54 hours of speed-time data were collected while the buses were operated over several bus routes in Mexico City. The data set was then broken down into individual micro-trips, each consisting of an idle period followed by the bus traveling some distance, followed by a final deceleration to idle.