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Although designed for the purpose of reducing engine-out Particulate Matter (PM) emissions to meet or exceed mandated emissions regulations, the particulate filter also incurs a fuel economy penalty. This fuel penalty is due to the increased exhaust flow restriction attributed to the PM accumulated in the filter, in addition to fuel consumed for active regeneration. Unlike the soot which may be oxidized through the regeneration process, incombustible material or ash continues to build-up in the filter following each regeneration event. Currently pressure- and model-based controls are used to provide an indirect estimate of the loading state of the particulate filter, in order to manage the filter operation and determine when to regenerate the filter. The challenges associated with pressure- and model-based particulate filter control over real-world operating conditions are well-known.

The U.S. Environmental Protection Agency (EPA) contracted with FEV, Inc. to estimate the per-vehicle cost of employing selected advanced efficiency-improving technologies in light-duty motor vehicles. The development of transparent, reliable cost analyses that are accessible to all interested stakeholders has played a crucial role in establishing feasible and cost effective standards to improve fuel economy and reduce greenhouse gas (GHG) emissions. The FEV team, together with engineering staff from EPA's National Vehicle and Fuel Emissions Laboratory, and FEV's subcontractor, Munro & Associates, developed a robust costing methodology based on tearing down, to the piece part level, relevant systems, sub-systems, and assemblies from vehicles “with and without” the technologies being evaluated.

Stringent global emissions legislation demands effective NOx reduction strategies, particularly for the aftertreatment, and current typical liquid urea SCR systems achieve efficiencies greater than 90% [1]. However, with such high-performing systems comes the trade-off of requiring a tank of reductant (urea water solution) to be filled regularly, usually as soon as the fuel fillings or as far as oil changes. Advantages of solid reductants, particularly ammonium carbamate, include greater ammonia densities, enabling the reductant refill interval to be extended several multiples versus a given reductant volume of urea, or diesel exhaust fluid (DEF) [2]. An additional advantage is direct gaseous ammonia dosing, enabling reductant injection at lower exhaust temperatures to widen its operational coverage achieving greater emissions reduction potential [3], as well as eliminating deposits, reducing mixing lengths, and avoiding freeze/thaw risks and investments.

Beginning in 2010, implementation of on-board diagnostics (OBD) is mandatory for all the heavy-duty engine applications in the United States. The task of developing OBD strategies and calibrating them is a challenging one. The process involves a strong interdependency on base engine emissions, controls and regulations. On top of that the strategies developed as a result of the regulatory requirements need to go through a stringent and time-intensive process of software implementation and integration. The recent increasing demands to minimize the development process have been pushing the envelope on the methodologies used in developing the strategies and the calibration for robust monitoring. The goal of this paper is to provide a concise overview of a process utilized to help the development, testing and calibration of the OBD strategies on a 2010 model year heavy-duty diesel engine.

The use of biodiesel and its blends with ultra low sulfur diesel (ULSD) is gaining significant importance due to its ability to burn in conventional diesel engines with minor modifications. However the chemical and physical properties of biodiesel are different compared to the conventional ULSD. These differences directly impact the injection, spray formation, auto ignition and combustion processes which in turn affect the engine-out emissions. To understand the effect of fueling with B-20, tests were conducted on a single cylinder 0.42L direct injection research diesel engine. The engine is equipped with a common rail injection system, variable EGR and swirl control systems and was operated at a constant engine speed of 1500 rpm and 3 bar IMEP to simulated turbocharged conditions. Injection timing and duration were adjusted with B-20 at different locations of peak premixed combustions (LPPC) and two different swirl ratios to achieve 3 bar IMEP.

With the advent of advanced diesel after-treatment technologies, sophisticated sensors are becoming a critical cost challenge to OEMs. This paper describes an approach for replacing the engine out NOx sensor with an artificial neural network (ANN) based virtual sensor. The technique centers around inferring NOx concentration from readily available engine operating parameters, eliminating the need for physical sensing and the cost associated with it. A multi-layer perceptron network was trained to estimate NOx concentration from engine speed, load, exhaust gas recirculation, and air-fuel ratio information. This supervised learning was conducted with measured engine data. The network was validated against measured data that was excluded from the training data set. The paper details application of this technique to both a heavy duty and light duty diesel engine. Results show good agreement between predictions and measured data under the steady state conditions studied.