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Particulate matter emissions from internal combustion engines have become an increasingly important area of focus for development teams in recent years. This is due to greater regulatory scrutiny on vehicles globally, and especially on particulate emissions. The chemical composition and bulk physical properties of the fuel have been shown to influence the particulate number emissions characteristics. Although some predictive models have been proposed, the causality of specific properties or constituents has not been demonstrated due to the co-linearity of the variables considered in previous studies. In this work, fuels were formulated to capture the expected variation in three key properties of United States (US) market gasoline fuels. Specifically, total aromatics, volatility, and particulate matter index (PMI) were varied across market extremes within regulatory limits--while holding other properties constant.

Studies have shown that fuel quality plays an important role in engine-out emissions. The wide variation in composition and properties of gasoline fuels available in the market can lead to discrepancies between the expected emission levels as per set regulations and actual on-road measurements. This study compares engine-out gaseous and particulate emission results between 5 US market fuels, 5 certification fuels and one street-legal race fuel. The market fuels were acquired from different terminals in Michigan. Tests were performed on a 4-cylinder 2.3 L turbocharged direct injection spark-ignited engine. The tests covered a wide range of steady-state operating conditions including load, injection timing and engine speed sweeps. Transient load steps were also performed under warm and cold engine conditions.

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control aimed at optimizing energy consumption with associated benefits. In this paper, we implement an optimal control framework, developed previously, in an Audi A3 etron plug-in hybrid electric vehicle, and demonstrate that we can improve the vehicle’s efficiency and travel time in a corridor including an on-ramp merging, a speed reduction zone, and a roundabout. Our exposition includes the development, integration, implementation and validation of the proposed framework in (1) simulation, (2) hardware-in-the-loop (HIL) testing, (3) connectivity enabled virtual reality based bench-test, and (4) field test in Mcity. We show that by adopting such inexpensive, yet effective process, we can efficiently integrate and test the control framework, establish proper connectivity and data transmission between different modules of the system, and reduce uncertainty.

With the inception of model-based design and automatic code generation, many organizations are developing controls and diagnostics algorithms in model-based development tools to meet customer and regulatory requirements. Advances in model-based design have made it easier to generate C code from models and help software engineers streamline their workflow. Typically, after the software has been developed, the models are handed over to a calibration team responsible for calibrating the features to meet specified customer and regulatory requirements. However, once the models are handed over to the calibration team, the calibration engineers are unaware of how to calibrate the features because documentation is not available. Typically, model documentation trails behind the software process because it is created manually, most of this time is spent on formatting. As a result, lack of model documentation or up-to date documentation causes a lot of pain for OEM’s and Tier 1 suppliers.

Non-reacting spray behaviors of the Engine Combustion Network “Spray G” gasoline fuel injector were investigated at flash and non-flash boiling conditions in an optically accessible single cylinder engine and a constant volume spray chamber. High-speed Mie-scattering imaging was used to determine transient liquid-phase spray penetration distances and observe general spray behaviors. The standardized “G2” and “G3” test conditions recommended by the Engine Combustion Network were matched in this work and the fuel was pure iso-octane. Results from the constant volume chamber represented the zero (stationary piston) engine speed condition and single cylinder engine speeds ranged from 300 to 2,000 RPM. As expected, the present results indicated the general spray behaviors differed significantly between the spray chamber and engine. The differences must be thoughtfully considered when applying spray chamber results to guide spray model development for engine applications.

This paper studies the use of active rear steering (4-wheel steering) to change the transient lateral dynamics and body motion of passenger cars in the stable or linear region of the tires. Rear steering systems have been used for several decades to improve low speed turning maneuverability and high speed stability, and various control strategies have been previously published. With a model-based, feed-forward rear steer control strategy, the lateral transient can be influenced separately from the steady-state steering gain. This lateral transient is influenced by many vehicle parameters, but we will look at the influence of active rear steer and various tire types such as all-season, snow, and summer. This study will explore the ability for a rear steering system to change the lateral transient to a step steer input, compared to the effect of changing tire types.

High-pressure gasoline fuel injection is a means to improve combustion efficiency and lower engine-out emissions. The objective of this study was to quantify the effects of fuel injection pressure on transient gasoline fuel spray development for a wide range of injection pressures, including over 1000 bar, using a constant volume chamber and high-speed imaging. Reference grade gasoline was injected at fuel pressures of 300, 600, 900, 1200, and 1500 bar into the chamber, which was pressurized with nitrogen at 1, 5, 10, and 20 bar at room temperature (298 K). Bulk spray imaging data were used to quantify spray tip penetration distance, rate of spray tip penetration and spray cone angle. Near-nozzle data were used to evaluate the early spray development.

Today, 99% of the two wheelers in India operate with carburetor based fuel delivery system. But with implementation of Bharath Stage VI emission norms, compliance to emission limits along with monitoring of components in the system that contributes towards tail pipe emissions would be challenging. With the introduction of the OBD II (On-Board Diagnostics) and emission durability, mass migration to electronically controlled fuel delivery system is very much expected. The new emission norms also call for precise metering of the injected fuel and therefore demands extended calibration effort. The calibration of engine management system starts with the generation of pre-calibration dataset capable of operating the engine at all operating points followed by base calibration of the main parameters such as air charge estimation, fuel injection quantity, injection timing and ignition angles relative to the piston position.

Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

Low pressure (LP) and cooled EGR systems are capable of increasing fuel efficiency of turbocharged gasoline engines, however they introduce control challenges. Accurate exhaust pressure modeling is of particular importance for real-time feedforward control of these EGR systems since they operate under low pressure differentials. To provide a solution that does not depend on physical sensors in the exhaust and also does not require extensive calibration, a coupled temperature and pressure physics-based model is proposed. The exhaust pipe is split into two different lumped sections based on flow conditions in order to calculate turbine-outlet pressure, which is the driving force for LP-EGR. The temperature model uses the turbine-outlet temperature as an input, which is known through existing engine control models, to determine heat transfer losses through the exhaust.

Automotive systems can generate un-intentional radio frequency energy. The levels of these emissions must be below maximum values set by the Original Equipment Manufacturer (OEM) for customer satisfaction and/or in order to meet governmental requirements. Due to the complexity of electromagnetic coupling mechanisms that can occur on a vehicle, many times it is difficult to measure and identify the noise source(s) without the use of an electromagnetic interference (EMI) receiver or spectrum analyzer (SA). An efficient and effective diagnostic solution can be to use a low-cost portable, battery powered RF detector with wide dynamic range as an alternative for automotive electromagnetic compatibility (EMC) and design engineers to identify, locate, and resolve radio frequency (RF) noise problems. A practical circuit described here can be implemented easily with little RF design knowledge, or experience.

Spark Assisted Compression Ignition (SACI) aims to increase the load limit of homogeneous charge compression ignition (HCCI) engines, enabling the benefits of dilute combustion over a larger engine operation range. Compared to HCCI, SACI exhibits higher cyclic variation of several combustion features. Due to the necessity of control of the timing of the auto-ignition event during SACI operation, a suitable characterization of the combustion at a given set of actuator inputs is required to enable robust model-based controls of combustion. This paper investigates statistical approaches to analyze in-cylinder pressure data of SACI in order to find a real or reconstructed cycle that will represent the important characteristics of combustion. To determine the representativeness of such a cycle, several combustion characteristics were compared that could serve as operational limits.

This paper focuses on the combustion development portion of the Advanced Combustion Controls Enabling Systems and Solutions (ACCESS) project, a joint research project partially funded by a Department of Energy grant. The main goal of the project is to improve fuel economy in a gasoline fueled light-duty vehicle by 30% while maintaining similar performance and meeting SULEV emission standards for the Federal Test Procedure (FTP) cycle. In this study, several combustion modes Spark Ignited (SI), Homogeneous Charge Compression Ignition (HCCI), Spark- Assisted Compression Ignition (SACI)) were compared under various conditions (naturally aspirated, boosted, lean, and stoichiometric) to compare the methods of controlled auto-ignition on a downsized, boosted multi-cylinder engine with an advanced valvetrain system capable of operating under wide negative valve overlap (NVO) conditions.

This paper focuses on the engine design portion of the Advanced Combustion Controls Enabling Systems and Solutions (ACCESS) project, a joint research project partially funded by a Department of Energy grant. The main goal of the project is to improve fuel economy in a gasoline fueled light-duty vehicle by 25% while maintaining similar performance and meeting SULEV emission standards. A Cadillac CTS with a high-feature naturally-aspirated 3.6L V6 engine was chosen as the baseline vehicle. To achieve the target fuel economy improvement over the baseline engine configuration, gasoline turbocharged direct-injection (GTDI) technology was utilized for engine downsizing in combination with part-load lean homogeneous charge compression ignition (HCCI) operation for further fuel economy gains. The GM 2.0L I4 GTDI Ecotec engine was used as the platform for the basis of this design.

The electrification of the powertrain, the diversity and the complexity of the more or less individual technical solutions which are preferred by different car manufacturers, create a steadily increasing challenge for the whole automotive industry. Missing standards and sales volumes still below the market expectations on the one hand, and the increasing interaction of the main powertrain domains (engine, transmission, e-drive) caused by upcoming cross domain functions on the other hand, lead to increasing development costs and non-optimal solutions concerning fuel economy improvement. Within the domain of engine management systems Bosch established in the mid-nineties the so called torque structure as the solution to a similar situation addressing the coordination of air management, fuel injection and ignition.

The primary function of an airbag control module is to detect crashes, discriminate and predict if a deployment is necessary, then deploy the restraint systems including airbags and where applicable, pretensioners. At General Motors (GM), the internal term for airbag control module is Sensing and Diagnostic Module (SDM). In the 1994 model year, GM introduced its SDM on some of its North American airbag-equipped vehicles. A secondary function of that SDM and all subsequent SDMs is to record crash related data. This data can include data regarding impact severity from internal accelerometers and pre-crash vehicle data from various chassis and powertrain modules. Previous researchers have addressed the accuracy of both the velocity change data, recorded by the SDM, and the pre-crash data, but the assessment of the timing of the pre-crash data has been limited to a single family of modules (Delphi SDM-G).

High technology, spark ignition direct injection (SIDI), engines are currently capable of achieving optimum horsepower and ULEV emissions levels. However, to meet the requirements of modern automotive powertrains, the task of increasing power density, improving fuel economy and reaching SULEV2 emissions is much more challenging. To achieve this, direct injection (DI) fuel systems offer the greatest precision and flexibility for engine fuel control. Features like high pressure start and improved catalyst heating, through multiple injections per combustion cycle, produce low engine-out emissions without the need for a secondary air injection system. This paper describes the analytical and experimental work done to achieve SULEV emissions levels for a twin-turbocharged derivative of General Motors (GM) high feature V6 engine.

This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software, with a focus on a closed-loop vehicle drive-train model incorporating a detailed automatic transmission plant dynamics model developed for certain applications. Specifically, this paper presents the closed-loop integration of a 6-speed automatic transmission model developed for our HIL transmission controller and algorithm test bench (Opal-RT TestDrive based). The model validation, integration and its application in an HIL test environment are described in details.

In recent years, gasoline direct injection (GDi) engines have been popular due to their inherent potential for reduction of exhaust emissions and fuel consumption to meet stringent EPA standards. These engines require high-pressure fuel injection in order to improve the atomization process and accelerate mixture preparation. The high-pressure fuel pump is an essential component in the GDi system. Therefore, understanding the flow characteristics of this device and its associated behavior is critical for improving the performance of this category of engines. In this paper, the fluid flow characteristics in a high-pressure single-piston pump for use in GDi engines are analyzed using 1-D LMS Imagine.Lab AMESim system and 3-D Ansys Fluent computational fluid dynamics (CFD) models. The flow rate of the fuel pump under various cam speeds has been examined along with characteristics of the pump's control valve.

This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and CFD modeling results were used separately to develop regression models for spray patternation. These regressions predicted the influence of a number of injector operating and design parameters, including injection system operating pressure, valve lift, injector hole length-to-diameter ratio (L/d) and the orientation of the injector hole. The regression correlations provided a good fit with both experimental and CFD spray simulation results. Thus CFD offers a good complement to experimental validation during development efforts to meet a desired injector spray pattern.