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Conventional crank-based engines are limited by mechanical, thermal, and combustion inefficiencies. The free piston of a linear engine generator reduces frictional losses by avoiding the rotational motion and crankshaft linkages. Instead, electrical power is generated by the oscillation of a translator through a linear stator. Because the free piston is not geometrically constrained, dead center positions are not specifically known. This results in a struggle against adverse events like misfire, stall, over-fueling, or rapid load changes. It is the belief that incorporating springs will have the dual benefit of increasing frequency and providing a restoring force to aid in greater cycle to cycle stability. For dual free piston linear engines the addition of springs has not been fully explored, despite growing interest and literature.

As emission regulations become increasingly strict, the need for more accurate sampling systems becomes essential. When calculating emissions from a dilution system, a correction is made to remove the effects of contaminants in the dilution air. The dilution air correction was explored to determine why this correction is needed, when this correction is important, and what methods are available for calculating the dilution factor (DF). An experimental and error analysis investigation into the standard and recently proposed methods for calculating the DF was conducted. Five steady state modes were run on a 1992 Detroit Diesel engine series 60 and the DF from eleven different equations were investigated. The effects of an inaccurate dilution air correction on calculated fuel flow from a carbon balance and the mass emissions was analyzed. The dilution air correction was shown to be important only for hydrocarbons, particulate matter (PM), and CO2.

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.

Biodiesel fuels benefit both from being a renewable energy source and from decreasing in carbon monoxide (CO), total hydrocarbons (THC), and particulate matter (PM) emissions relative to petroleum diesel. The oxides of nitrogen (NOx) emissions from biodiesel blended fuels reported in the literature vary relative to baseline diesel NOx, with no NOx change or a NOx decrease found by some to an increase in NOx found by others. To explore differences in NOx, two Cummins ISM engines (1999 and 2004) were operated on 20% biodiesel blends during the heavy-duty transient FTP cycle and the steady state Supplemental Emissions Test. For the 2004 Cummins ISM engine, in-cylinder pressure data were collected during the steady state and transient tests. Three types of biodiesel fuels were used in the blends: soy, tallow (animal fat), and cottonseed. The FTP integrated emissions of the B20 blends produced a 20-35% reduction in PM and no change or up to a 4.3% increase in NOx over the neat diesel.

Dual primary full-flow dilution tunnels represent an integral part of a heavy-duty transportable emissions measurement laboratory designed and constructed to comply with US Code of Federal Regulations (CFR) 40 Part 1065 requirements. Few data exist to characterize the evolution of particulate matter (PM) in full scale dilution tunnels, particularly at very low PM mass levels. Size distributions of ultra-fine particles in diesel exhaust from a naturally aspirated, 2.4 liter, 40 kW ISUZU C240 diesel engine equipped with a diesel particulate filter (DPF) were studied in one set of standard primary and secondary dilution tunnels with varied dilution ratios. Particle size distribution data, during steady-state engine operation, were collected using a Cambustion DMS500 Fast Particulate Spectrometer. Measurements were made at four positions that spanned the tunnel cross section after the mixing orifice plate for the primary dilution tunnel and at the outlet of the secondary dilution tunnel.

Heavy-duty trucks are an important sector to evaluate when seeking fuel consumption savings and emissions reductions. With fuel costs on the rise and emissions regulations becoming stringent, vehicle manufacturers find themselves spending large amounts of capital improving their products in order to be compliant with regulations. The Powertrain System Analysis Toolkits (PSAT), developed by the Argonne National Laboratory (ANL), is a simulation tool that helps mitigate costs associated with research and automotive system design. While PSAT has been widely used to predict the fuel consumption and exhaust emissions of conventional and hybrid light-duty vehicles, it also may be employed to test heavy-duty vehicles. The intent of this study was to develop an accurate model that predicts emissions and fuel economy for heavy-duty vehicles for use within PSAT.

The California Air Resources Board (ARB) developed a Medium Heavy-Duty Truck (MHDT) schedule by selecting and joining microtrips from real-world MHDT. The MHDT consisted of three modes; namely, a Lower Speed Transient, a Higher Speed Transient, and a Cruise mode. The maximum speeds of these modes were 28.9, 58.2 and 66.0 mph, respectively. Each mode represented statistically selected truck behavior patterns in California. The MHDT is intended to be applied to emissions characterization of trucks (14,001 to 33,000lb gross vehicle weight) exercised on a chassis dynamometer. This paper presents the creation of the MHDT and an examination of repeatability of emissions data from MHDT driven through this schedule. Two trucks were procured to acquire data using the MHDT schedule. The first, a GMC truck with an 8.2-liter Isuzu engine and a standard transmission, was tested at laden weight (90% GVW, 17,550lb) and at unladen weight (50% GVW, 9,750lb).

Research at West Virginia University has led to the development of a novel crankless reciprocating internal combustion engine. This paper presents a time-based model used to investigate the performance of two-stroke direct injection compression ignition linear engines. The two-stroke linear engine consists of two pistons, linked by a connecting rod, that are allowed to move freely in response to changes in the engine's fueling and load across the full operating cycle of the engine. The computer model uses a combination of a series of dynamic and thermodynamic numerical equations, which have been solved to provide a detailed analysis of the two-stroke direct injection linear engine operation. Parameters such as rate of combustion, convection heat transferred inside the cylinders, friction forces, external loads, acceleration, velocity profile, compression ratio, and in-cylinder pressures were modeled.

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.

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.

A study was performed in the spring of 2001 to chemically characterize exhaust emissions from trucks and buses fueled by various test fuels and operated with and without diesel particle filters. This study was part of a multi-year technology validation program designed to evaluate the emissions impact of ultra-low sulfur diesel fuels and passive diesel particle filters (DPF) in several different heavy-duty vehicle fleets operating in Southern California. The overall study of exhaust chemical composition included organic compounds, inorganic ions, individual elements, and particulate matter in various size-cuts. Detailed descriptions of the overall technology validation program and chemical speciation methodology have been provided in previous SAE publications (2002-01-0432 and 2002-01-0433).

Heavy duty diesel vehicle (HDDV) emissions are known to affect air quality, but few studies have quantified the real-world contribution to the inventory. The objective of this study was to provide data that may enable ambient emissions investigators to m,odel the air quality more accurately. The 25 vehicles reported in this paper are from the first phase of a program to determine representative regulated emissions from Heavy Heavy-Duty Diesel Trucks (HHDDT) operating in Southern California. Emissions data were gathered using a chassis dynamometer, full flow dilution tunnel, and research grade analyzers. The subject program employed two truck test weights and four new test modes (one was idle operation), in addition to the Urban Dynamometer Driving Schedule (UDDS), and the AC50/80 cycle. The reason for such a broad test cycle scope was to determine thoroughly how HHDDT emissions are influenced by operating cycle to improve accuracy of models.

Gaseous and particulate emissions from heavy-duty vehicles are affected by fuel types, vehicle/engine parameters, driving characteristics, and environmental conditions. Transient chassis tests were conducted on twenty-six heavy-duty vehicles fueled with methanol, compressed natural gas (CNG), #1 diesel, and #2 diesel, using West Virginia University (WVU) Transportable Heavy-Duty Vehicle Emissions Testing Laboratory. The vehicles were operated on the central business district (CBD) testing cycle, and regulated emissions of carbon monoxide (CO), total hydrocarbon (HC), nitrogen oxides (NOx), and particulate matter (PM) were measured. Comparisons of regulated emissions results revealed that the vehicles powered on methanol and CNG produced much lower particulate emissions than the conventionally fueled vehicles.

The West Virginia University (WVU) Transportable Heavy-Duty Vehicle Emissions Testing Laboratory was employed to conduct chassis dynamometer tests in the field to measure the exhaust emissions from heavy-duty buses and trucks. This laboratory began operation in the field in January, 1992. During the period January, 1992 through June, 1993, over 150 city buses, trucks, and tractors operated by 18 different authorities in 11 states were tested by the facility. The tested vehicles were powered by 14 different types of engines fueled with natural gas (CNG or LNG), methanol, ethanol, liquified petroleum gas (LPG), #2 diesel, and low sulfur diesel (#1 diesel or Jet A). Some of the tested vehicles were equipped with exhaust after-treatment systems. In this paper, a total of 12 CNG-fueled and #2 diesel-fueled transit buses equipped with Cummins L-10 engines, were chosen for investigation.

When heavy-duty truck emissions rates are expressed in distance-specific units (such as g/mile), average speed and the degree of transient behavior of the vehicle activity can affect the emissions rate. Previous one-dimensional studies have shown some correlation of distance-specific emissions rates between cycles. This paper reviews emissions data sets from the 5-mode CARB Heavy Heavy-Duty Diesel Truck (HHDDT) Schedule, the Heavy Duty Urban Dynamometer Driving Schedule (UDDS) and an inspection and maintenance cycle, known as the AC5080. A heavy-duty chassis dynamometer was used for emissions characterization along with a full-scale dilution tunnel. The vehicle test weights were simulated at 56,000 lbs. Two-dimensional correlations were used to predict the emissions rate on one mode or cycle from the rates of two other modes or cycles.

Increasingly stringent oxides of nitrogen (NOx) and particulate matter (PM) regulations worldwide have prompted considerable activity in developing emission control technology to reduce the emissions of these two constituents from heavy-duty diesel engines. NOx has come under particular scrutiny by regulators in the US and in Europe with the promulgation of very stringent regulation by both the US Environmental Protection Agency (EPA) and the European Union (EU). In response, heavy-duty engine manufacturers are considering Selective Catalytic Reduction (SCR) as a potential NOx reduction option. While SCR performance has been well established through engine dynamometer evaluation under laboratory conditions, there exists little data characterizing SCR performance under real-world operating conditions over time. This project evaluated the field performance of ten SCR units installed on heavy-duty Class 8 highway and refuse trucks.

Understanding the nitric oxide (NO) conversion process plays a major role in optimizing the Selective NOX Recirculation (SNR) technique. SNR has been proven in gasoline and diesel engines, with up to 90% NOX conversion rates being achieved. This technique involves adsorbing NOX from an exhaust stream, then selectively desorbing the NOX into a concentrated NOX stream, which is fed back into the engine's intake, thereby converting a percentage of the concentrated NOX stream into harmless gases. The emphasis of this paper is on the unique chemical kinetic modeling problem that occurs with high concentrations of NOX in the intake air of a spark ignited natural gas engine with SNR. CHEMKIN, a chemical kinetic solver software package, was used to perform the reaction modeling. A closed homogeneous batch reactor model was used to model the fraction of NOX versus time for varying initial conditions and constants.

Methods to quantify the energy losses within linear motion devices that included flexural springs as the main suspension component were investigated. The methods were applied to a two-stroke free-piston linear engine alternator (LEA) as a case study that incorporated flexure springs to add stiffness to the mass-spring system. Use of flexure springs is an enabling mechanism for improving the efficiency and lifespan in linear applications e.g. linear engines and generators, cryocoolers, and linear Stirling engines. The energy loss due to vibrations and windage effects of flexure springs in a free piston LEA was investigated to quantify possible energy losses. A transient finite element solver was used to determine the effects of higher modes of vibration frequencies of the flexure arms at an operational frequency of 65 Hz. Also, a computational fluid dynamics (CFD) solver was used to determine the effects of drag force on the moving surfaces of flexures at high frequencies.

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.