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As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

Today’s advanced vehicles have high degree of interaction due to numerous sensors, actuators and also with complex communication within the control units. In order to hack a vehicle, it has to be within a certain range of communication. Here, we discuss the On-Board Diagnostic (OBD) regulations for next generation BEV/HEV, its vulnerabilities and cybersecurity threats that come with hacking. We propose three cybersecurity attack detection and defense methods: Cyber-Attack detection algorithm, Time-Based CAN Intrusion Detection Method and, Feistel Cipher Block Method. These control methods autonomously diagnose a cybersecurity problem in a vehicle’s onboard system using an OBD interface, such as OBD-II when a fault caused by a cyberattack is detected, All of this is achieved in an internal communication network structure. The results discussed here focus on the first detection method that is Cyber-Attack detection algorithm.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

In this study a 1-dimensional computational model of a Fe-Zeolite catalyst, implementing conservation of mass, species and energy for both gas and catalyst surface phases has been developed to simulate emissions conversion performance. It is applied to both a fresh catalyst and one that has been aged through exposure to the exhaust system of a Heavy Duty Diesel engine performing in the field for 376K miles. Details of the chemical kinetics associated with the various NOx reduction reactions in the two Fe-Zeolite configurations have been examined and correlated with data from a synthetic gas rig test bench. It was found that the Standard reaction, (4NH3 + 4NO + O2 → 2N2 + 6H2O), which is one of the main reactions for NOx reduction, degraded significantly at the lower temperatures for the aged system.

Reynolds-averaged Navier-Stokes (RANS) turbulence model has been used extensively for diesel engine simulations due to its computational efficiency and is expected to remain the workhorse computational fluid dynamics (CFD) tool for industry in the near future. Alternatively, large eddy simulations (LES) can potentially deal with complex flows and cover a large disparity of turbulence length scales, which makes this technique more and more attractive in the engine community. An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a dynamic structure LES model to understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was closed using a delta probability density function (PDF) combustion model. A 103-species skeletal mechanism was used for n-dodecane chemical kinetic model.

This paper evaluates the potential of adding higher alcohols to gasoline blendstock in an attempt to improve overall fuel performance. The alcohols considered include ethanol, normal- and iso-structures of propanol, butanol and pentanol as well as normal-hexanol (C2-C6). Fuel performance is quantified based on energy content, knock resistance as well as petroleum displacement and promising multi-component blends are systematically identified based on property prediction methods. These promising multi-component blends, as well as their respective reference fuels, are subsequently tested for efficiency and emissions performance utilizing a gasoline direct injection, spark ignition engine. The engine test results confirm that combustion and efficiency of tailored multi-component blends closely match those of the reference fuels. Regulated emissions stemming from combustion of these blends are equal or lower compared to the reference fuels across the tested engine speed and load regime.

This paper introduces an intelligent energy distribution scheme for series plug-in hybrid electric vehicles (PHEVs) which incorporates the complexity of human driving behavior. Driving styles can have a significant impact on fuel consumption, but it is often unclear how one should drive to get the optimal fuel efficiency. Hybrid electric vehicles have been shown to improve fuel economy, reduce vehicle emissions and maintain drivability by incorporating electric motors into the drivetrain. Due to the highly complex system design and vehicle architecture, sophisticated energy management strategies (EMS) are required to optimize the vehicle performance. Currently, the power management system is based on static thresholds optimized on a fixed drive cycle for a given vehicle. This paper introduces an adaptive control method for EMS utilizing the complexity of human driving patterns for energy distribution in a series PHEV.

This paper develops a novel integrated navigational system for autonomous vehicle motion control. Vehicle control is defined in terms of the required vehicle steering angle, rate of steering change and speed. This paper proposes predictive control in the image plane. The proposed predictive control enables the navigation on the desired path, reduces the control complexity and increases the application space for multiple types of vehicles. The paper investigates vehicle control stability; especially in scenarios containing varying curvature turns and variable vehicle speeds. The primary emphasis of this paper is on vehicle control rather than scene analysis. To demonstrate the proposed vehicle control, a computer vision based multi-lane detection algorithm is introduced. The control strategy is applied such that the vehicle maintains position within the lane boundaries. Stability of the control algorithm is tested and demonstrated in multiple scenarios using 3-D simulation results.

An optically accessible single-cylinder high-speed direct-injection (HSDI) Diesel engine equipped with a Bosch common rail injection system was used to study the spray and combustion processes for European low sulfur diesel, bio-diesel, and their blends at different blending ratio. Influences of injection timing and fuel type on liquid fuel evolution and combustion characteristics were investigated under similar loads. The in-cylinder pressure was measured and the heat release rate was calculated. High-speed Mie-scattering technique was employed to visualize the liquid distribution and evolution. High-speed combustion video was also captured for all the studied cases using the same frame rate. NOx emissions were measured in the exhaust pipe. The experimental results indicated that for all of the conditions the heat release rate was dominated by a premixed combustion pattern and the heat release rate peak became smaller with injection timing retardation for all test fuels.

Computational fluid dynamic (CFD) simulations that include realistic combustion/emissions chemistry hold the promise of significantly shortening the development time for advanced high-efficiency, low-emission engines. However, significant challenges must be overcome to realize this potential. This paper discusses these challenges in the context of diesel combustion and outlines a technical program based on the use of surrogate fuels that sufficiently emulate the chemical complexity inherent in conventional diesel fuel.

The development of surrogate mixtures that represent gasoline combustion behavior is reviewed. Combustion chemistry behavioral targets that a surrogate should accurately reproduce, particularly for emulating homogeneous charge compression ignition (HCCI) operation, are carefully identified. Both short and long term research needs to support development of more robust surrogate fuel compositions are described. Candidate component species are identified and the status of present chemical kinetic models for these components and their interactions are discussed. Recommendations are made for the initial components to be included in gasoline surrogates for near term development. Components that can be added to refine predictions and to include additional behavioral targets are identified as well. Thermodynamic, thermochemical and transport properties that require further investigation are discussed.

In Homogeneous Charge Compression Ignition (HCCI) engines, fuel oxidation chemistry determines the auto-ignition timing, the heat release, the reaction intermediates, and the ultimate products of combustion. Therefore a model that correctly simulates fuel oxidation at these conditions would be a useful design tool. Detailed models of hydrocarbon fuel oxidation, consisting of hundreds of chemical species and thousands of reactions, when coupled with engine transport process models, require tremendous computational resources. A way to lessen the burden is to use a “skeletal” reaction model, containing only tens of species and reactions. This paper reports an initial effort to extend our skeletal chemical kinetic model of pre-ignition through the entire HCCI combustion process. The model was developed from our existing preignition model, which has 29 reactions and 20 active species, to yield a new model with 69 reactions and 45 active species.

A thermophoretic particulate sampling device was used to investigate the detailed morphology and microstructure of diesel particulates at various engine-operating conditions. A 75 HP Caterpillar single-cylinder direct-injection diesel engine was operated to sample particulate matter from the high-temperature exhaust stream. The morphology and microstructure of the collected diesel particulates were analyzed using a high-resolution transmission electron microscope and subsequent image processing/data acquisition system. The analysis revealed that spherical primary particles were agglomerated together to form large aggregate clusters for most of engine speed and load conditions. Measured primary particle sizes ranged from 34.4 to 28.5 nm at various engine-operating conditions. The smaller primary particles observed at high engine-operating conditions were believed to be caused by particle oxidation at the high combustion temperature.

Particulate and nitrogen oxide emissions from a 1.9-liter Volkswagen diesel engine were measured for three different fuels: neat diesel fuel, a blend of diesel fuel with 10% ethanol, and a blend of diesel fuel with 15% ethanol. Engine-out emissions were measured on an engine dynamometer for five different speeds and five different torques using the standard engine-control unit. Results show that particulate emissions can be significantly reduced over approximately two-thirds of the engine map by using a diesel-ethanol blend. Nitrogen oxide emissions can also be significantly reduced over a smaller portion of the engine map by using a diesel-ethanol blend. Moreover, there is an overlap between the regions where particulate emissions can be reduced by up to 75% and nitrogen oxide emissions are reduced by up to 84% compared with neat diesel fuel.

Homogeneous Charge Compression Ignition (HCCI) engines have the possibility of low NOx and particulate emissions and high fuel efficiencies. In HCCI the oxidation chemistry determines the auto-ignition timing, the heat release rate, the reaction intermediates, and the ultimate products of combustion. This paper reports an initial effort to apply our reduced chemical kinetic model to HCCI processes. The model was developed to study the pre-ignition characteristics (pre-ignition heat release and start of ignition) of primary reference fuels (PRF) and includes 29 reactions and 20 active species. The only modifications to the model were to make the proscribed adjustments to the fuel specific rate constants, and to enhance the H2O2 decomposition rate to agree with published data.

The role of post-combustion oxidation in influencing exhaust hydrocarbon emissions from spark ignition engines has been identified as one of the major uncertainties in hydrocarbon emissions research [l]*. While we know that post-combustion oxidation plays a significant role, the factors that control the oxidation are not well known. In order to address some of these issues a research program has been initiated at Drexel University. In preliminary studies, seven gaseous fuels: methane, ethane,ethene,propane,propene, n-butane, 1-butene and their blends were used to examine the effect of fuel structure on exhaust emissions. The results of the studies presented in an earlier paper [2] showed that the effect of fuel structure is manifested through its effect on the post-combustion environment and the associated oxidation process. A combination of factors like temperatures, fuel diffusion and reaction rates were used to examine and explain the exhaust hydrocarbon emission levels.

Time resolved exhaust port sampling results show that the gas mixture in the port at exhaust valve closing contains high concentrations of hydrocarbons. These hydrocarbons are mixed with hot in-cylinder gases during blowdown and can react either via gas phase kinetics in the exhaust port/runner system or subsequently on the exhaust catalyst before they are emitted. Studies were conducted on a single cylinder, four stroke engine in our laboratory to determine the interaction between the hot blowdown gases and the hydrocarbons which remain in the exhaust port. A preselected concentration and volume of hydrocarbon tracers (propane, propene, n-butane, and 1-butene) in either oxygen/nitrogen mixtures or pure nitrogen were injected into the exhaust port just behind the exhaust valve to control the initial conditions for any potential oxidation in the port.

Inevitably a fraction of the hydrocarbon fuel in spark ignition engines escapes in-cylinder combustion and flows out with the burned products. Post combustion oxidation in the cylinder and exhaust port may consume a part of this fuel and plays an important role in determining exhaust emission levels. This paper presents results from experiments designed to identify the factors that control post-combustion oxidation. Regulated exhaust components and detailed hydrocarbon species were measured using seven neat hydrocarbons and four blends as fuel. The fuels were selected to compare the relative rates of mixing and chemical kinetics. The results indicate that exhaust temperature, diffusion rates and fuel kinetics each play a complicated role in determining emission levels.

This paper presents the results of emission tests of a flexible fuel vehicle (FFV) powered by an SI engine, fueled by M85, and supplied with oxygen-enriched intake air containing nominal 21%, 23%, and 25% oxygen (by volume). Emission data were collected by following the standard federal test procedure (FTP) and U.S. Environmental Protection Agency's (EPA's) “off-cycle” test EPA-REP05. Engine-out total hydrocarbons (THCs) and unburned methanol were considerably reduced in the entire FTP cycle when the oxygen content of the intake air was either 23% or 25%. However, CO emissions did not vary appreciably, and NOx emissions were higher. Formaldehyde emissions were reduced by about 53% in bag 1, 84% in bag 2, and 59% in bag 3 of the FTP cycle when 25% oxygen-enriched intake air was used.