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Due to raising interest in diesel powered passenger cars in the U.S. in combination with a desire to reduce dependency on imported petroleum, there has been increased attention to the operation of diesel vehicles on fuels blended with biodiesel. One of several factors to be considered when operating a vehicle on biodiesel blends is understanding the impact and performance of the fuel on the emission control system. This paper documents the impact of the biodiesel blends on engine-out emissions as well as the overall system performance in terms of emission control system calibration and the overall system efficiency. The testing platform is a light-duty HSDI diesel engine with a Euro 4 base calibration in a 1700 kg sedan vehicle. It employs 2nd generation common-rail injection system with peak pressure of 1600 bar as well as cooled high-pressure EGR. The study includes 3 different fuels (U.S.

Raising interest in Diesel powered passenger cars in the United States in combination with the government mandated policy to reduce dependency of foreign oil, leads to the desire of operating Diesel vehicles with Biodiesel fuel blends. There is only limited information related to the impact of Biodiesel fuels on the performance of advanced emission control systems. In this project the implementation of a NOx storage and a SCR emission control system and the development for optimal performance are evaluated. The main focus remains on the discussion of the differences between the fuels which is done for the development as well as useful life aged components. From emission control standpoint only marginal effects could be observed as a result of the Biodiesel operation. The NOx storage catalyst results showed lower tailpipe emissions which were attributed to the lower exhaust temperature profile during the test cycle. The SCR catalyst tailpipe results were fuel neutral.

In order to allow continued production of the AM General Optimizer 6500 during MY 2007 through 2010 this IDI engine (Indirect Injection - swirl chamber) requires sophisticated aftertreatment controls while maintaining its fuel economy and durability. The main purpose of the development program was to retain the relatively inexpensive and simple base engine with distributor pump and waste-gated turbocharger, while adding hardware and software components that allow achievement of the phase-in emission standards for 2007 through 2010. The aftertreatment system consists of Diesel Oxidation Catalyst (DOC), NOx Adsorber Catalyst (or DeNOx Trap - DNT) and Diesel Particle Filter (DPF). In addition to the base hardware, an intake air throttle valve and an in-exhaust fuel injector were installed. The presented work will document the development process for a 2004 certified 6.5 l IDI heavy-duty diesel engine to comply with the 2007 heavy-duty emission standards.

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.

Increasing fuel costs, the need to reduce dependence on foreign oil as well as the high efficiency and the desire for superior durability have caused the diesel engine to again become a prime target for light-duty vehicle applications in the United States. In support of this the U.S. Department of Energy (DOE) has engaged in a test project under the Advanced Petroleum Based Fuels-Diesel Emission Control (APBF-DEC) activity to develop a passenger car with the capability to demonstrate compliance with Tier 2 Bin 5 emission targets with a fresh emission control catalyst system. In order to achieve this goal, a prototype engine was installed in a passenger car and optimized to provide the lowest practical level of engine-out emissions.

Since the 1990's regulatory requirements for On-Board-Diagnostics (OBD) have continuously evolved with an increasing application of advanced electronics and control systems that have been adopted for automotive applications. The current and future demands on emissions and performance requirements are pushing the envelope with respect to management of complex control software strategies, hardware components and their interactions. This further challenges the implementation of OBD. In order to build a robust monitor for a complex system which has minimum risk of false detection, a thorough understanding of both system and components is required. In this paper, several methods will be presented that can be utilized to achieve a successful and robust diagnostic system implementation. Implementation begins with a discussion of the major challenges to achieve consistent performance in the base system control.

The growing complexity of powertrain control strategies, software, and hardware is proving to be a significant challenge to the engineering community with regard to managing effective optimization to meet the desired performance. With an increased emphasis on shorter development time and the use of additional sensors and actuators becoming common, the increased dependence on physical models and use of complex interdependent control systems demands a thorough system understanding. This also encourages the use of process improvement tools to assist in an effective engineering process. In this paper, such a tool is discussed in its second phase of development. The Micro-HiL system will be discussed over a wide scope that focuses on the interests of the calibration and development community. The purpose of this paper is to provide an update on the Phase 2 activity of Micro-HiL development; Phase 1 was discussed in-depth at the 2011 SAE World Congress [2011-01-0703].

The U.S. Tier 2 emission regulations require sophisticated exhaust aftertreatment technologies for diesel engines. One of the projects under the U.S. Department of Energy's (DOE's) Advanced Petroleum Based Fuels - Diesel Emission Controls (APBF-DEC) activity focused on the development of a light-duty passenger car with an integrated NOx (oxides of nitrogen) adsorber catalyst (NAC) and diesel particle filter (DPF) technology. Vehicle emissions tests on this platform showed the great potential of the system, achieving the Tier 2 Bin 5 emission standards with new, but degreened emission control systems. The platform development and control strategies for this project were presented in 2004-01-0581 [1]. The main disadvantage of the NOx adsorber technology is its susceptibility to sulfur poisoning. The fuel- and lubrication oil-borne sulfur is converted into sulfur dioxide (SO2) in the combustion process and is adsorbed by the active sites of the NAC.

The problem with traditional drive cycle fuel economy analysis is that kinematic (backward looking) models do not account for transient differences in charge air handling systems. Therefore, dynamic (forward looking) 1D performance simulation models were created to predict drive cycle fuel economy which encompass all the transient elements of fully detailed engine and vehicle models. The transient-capable technology of primary interest was mechanical supercharging which has the benefit of improved boost response and "time to torque." The benefits of a supercharger clutch have also been evaluated. The current US class 6-8 commercial vehicle market exclusively uses turbocharged diesel engines. Three vehicles and baseline powertrains were selected based on a high-level review of vehicle sales and the used truck marketplace. Fuel economy over drive cycles was the principal output of the simulation work. All powertrains are based on EPA 2010 emission regulations.

Stochastic Preignition (SPI) is an abnormal combustion event that occurs in a turbocharged engine and can lead to the loss in fuel economy and engine hardware damage, and in turn result in customer dissatisfaction. It is a significant limiting factor on the use and continued downsizing of turbocharged spark ignited direct injection (SIDI) gasoline engines. Understanding and mitigating all the factors that cause and influence the rate and severity of SPI occurrence are of critical importance to the engine’s continued use and fuel economy improvements for future designs. Previous studies have shown that the heavy molecular weight components of the fuel formulations are one factor that influences the rate of SPI from a turbocharged SIDI gasoline engine. All the previous studies have involved analyzing the fuel’s petroleum hydrocarbon chemistry, but not specifically the additives that are put in the fuel to protect and clean the internal components over the life of the engine.

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost. With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication.