Search Results

A number of racing series have seen an influx of motorcycle engines as basic powerplants which incorporate a performance oriented sequential shift transmission. However, due to common placement of the engine behind the driver, shift actuation can often become a difficult design issue. Further, the time of one up-shift can be 500 ms or more when the clutch is used, and manually unloading the transmission to allow shifting does not substantially reduce the time lost. A lightweight, low cost electronic liftless shift system has been designed to overcome the problems of packaging and improve shift speed. The system uses a small 12v DC gearmotor, cam and follower to execute the up-shift and downshift, and a current sensor and programmable IC's are used to automatically unload the drivetrain for liftless up-shifts.

Liquefied petroleum gas (LPG), whose primary composition is propane, is a promising candidate for heavy-duty vehicle applications as a diesel fuel alternative due to its CO2 reduction potential and high knock resistance. To realize diesel-like efficiencies, spark-ignited LPG engines are proposed to operate near knock-limit over a wide range of operating conditions, which necessitates an investigation of fuel-engine interactions that leads to end-gas autoignition with propane combustion. This work presents both experimental and numerical studies of stoichiometric propane combustion in a spark-ignited (SI) cooperative fuel research (CFR) engine. Engine experiments are initially conducted at different compression ratio (CR) values, and the effects of CR on engine combustion are characterized.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

Application of predictive optimal energy management strategies to improve fuel economy in hybrid electric vehicles is an active subject of research. Acceleration events during a drive cycle provide particularly attractive opportunities for predictive optimal energy management because of their high energy cost and limited variability, which enables optimal control trajectories to be computed in advance. In this research, dynamic-programming derived optimal control matrices are implemented during a drive cycle on a validated model of a 2010 Toyota Prius to simulate application of pre-computed control to improve fuel economy over a baseline model. This article begins by describing the development of the vehicle model and the formulation of optimal control, both of which are simulated over the New York City drive cycle to establish baseline and upper-limit fuel economies. Then, optimal control strategies are computed for acceleration events in the drive cycle.

Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed.

This study presents experimental and numerical examination of directly injected (DI) propane and iso-octane, surrogates for liquified petroleum gas (LPG) and gasoline, respectively, at various engine like conditions with the overall objective to establish the baseline with regards to fuel delivery required for future high efficiency DI-LPG fueled heavy-duty engines. Sprays for both iso-octane and propane were characterized and the results from the optical diagnostic techniques including high-speed Schlieren and planar Mie scattering imaging were applied to differentiate the liquid-phase regions and the bulk spray phenomenon from single plume behaviors. The experimental results, coupled with high-fidelity internal nozzle-flow simulations were then used to define best practices in CFD Lagrangian spray models.

Crashes involving Cummins powered heavy vehicles can damage the electronic control module (ECM) containing heavy vehicle event data recorder (HVEDR) records. When ECMs are broken and data cannot be extracted using vehicle diagnostics tools, more invasive and low-level techniques are needed to forensically preserve and decode HVEDR data. A technique for extracting non-volatile memory contents using non-destructive board level techniques through the available in-circuit debugging port is presented. Additional chip level data extraction techniques can also provide access to the HVEDR data. Once the data is obtained and preserved in a forensically sound manner, the binary record is decoded to reveal typical HVDER data like engine speed, vehicle speed, accelerator pedal position, and other status data. The memory contents from the ECM can be written to a surrogate and decoded with traditional maintenance and diagnostic software.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

First responders and traffic crash investigators collect and secure evidence necessary to determine the cause of a crash. As vehicles with advanced autonomous features become more common on the road, inevitably they will be involved in such incidents. Thus, traditional data collection requirements may need to be augmented to accommodate autonomous technology and the connectivity associated with autonomous and semi-autonomous driving features. The objective of this paper is to understand the data from a fielded autonomous system and to motivate the development of requirements for autonomous vehicle data collection. The issue of data ownership and access will be discussed. Additional complicating factors, such as cybersecurity concerns combined with a first responder’s legal authority, may pose challenges for traditional data collection.

Natural Gas used in high-efficiency engines holds promise as a low-cost intermediate solution to reduce Greenhouse Gases and particulate matter. However, to achieve high engine efficiencies, engines need to be operated at increased Brake Mean Effective Pressures (BMEP), which is limited by destructive, engine damaging knock. Alternatively, if controlled, the same End-Gas Autoignition (EGAI) process responsible for knock can boost efficiencies and consume unburned methane while leveraging low-cost traditional exhaust aftertreatment technologies, such as a three-way catalyst, to minimize environmental impact. For this reason, this work has developed a method to detect the presence of EGAI and to determine its onset location (or crank angle).

Autonomous vehicle development has benefited from sanctioned competitions dating back to the original 2004 DARPA Grand Challenge. Since these competitions, fully autonomous vehicles have become much closer to significant real-world use with the majority of research focused on reliability, safety and cost reduction. Our research details the recent challenges experienced at the 2017 Self Racing Cars event where a team of international Udacity students worked together over a 6 week period, from team selection to race day. The team’s goal was to provide real-time vehicle control of steering, braking, and throttle through an end-to-end deep neural network. Multiple architectures were tested and used including convolutional neural networks (CNN) and recurrent neural networks (RNN). We began our work by modifying a Udacity driving simulator to collect data and develop training models which we implemented and trained on a laptop GPU.

Two-stroke cycle engines used for modern snowmobiles produce high-levels of carbon monoxide and unburned hydrocarbons. In order to address the emissions and noise issues resulting from the use of snowmobiles, the Clean Snowmobile Challenge 2000 was held under the auspices of the Society of Automotive Engineers. The CSC 2000 competition was intended to facilitate the development of high-risk concepts to address the negative impact of snowmobiles. Hydrocarbon emissions from two-stroke cycle snowmobile engines are primarily due to short-circuiting of the air/fuel mixture during the scavenging process. Carbon monoxide emissions are due to rich combustion mixtures and poor combustion produced by inefficient scavenging. A student research team at Colorado State University undertook an ambitious engine development project for the competition.

In some studies, a relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from vehicles equipped with spark ignition engines. The fundamental cause of the PM increase seen for moderate ethanol concentrations is not well understood. Ethanol features a greater heat of vaporization (HOV) than gasoline and also influences vaporization by altering the liquid and vapor composition throughout the distillation process. A droplet vaporization model was developed to explore ethanol’s effect on the evaporation of aromatic compounds known to be PM precursors. The evolving droplet composition is modeled as a distillation process, with non-ideal interactions between oxygenates and hydrocarbons accounted for using UNIFAC group contribution theory. Predicted composition and distillation curves were validated by experiments.

High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations.

From November 2010 until May of 2011, NASA's Icing Remote Sensing System was positioned at Platteville, Colorado between the National Science Foundation's S-Pol radar and Colorado State University's CHILL radar (collectively known as FRONT, or ‘Front Range Observational Network Testbed’). This location was also underneath the flight-path of aircraft arriving and departing from Denver's International Airport, which allowed for comparison to pilot reports of in-flight icing. This work outlines how the NASA Icing Remote Sensing System's derived liquid water content and in-flight icing hazard profiles can be used to provide in-flight icing verification and validation during icing and non-icing scenarios with the purpose of comparing these times to profiles of polarized moment data from the two nearby research radars.

A relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from direct injection spark ignition (DISI) vehicles. The fundamental cause of this observation is not well understood. One potential explanation is that increased evaporative cooling as a result of ethanol’s high HOV may slow evaporation and prevent sufficient reactant mixing resulting in the combustion of localized fuel rich regions within the cylinder. In addition, it is well known that ethanol when blended in gasoline forms positive azeotropes which can alter the liquid/vapor composition during the vaporization process. In fact, it was shown recently through a numerical study that these interactions can retain the aromatic species within the liquid phase impeding the in-cylinder mixing of these compounds, which would accentuate PM formation upon combustion.

When a two-element airfoil for the wing of a racecar has to be inside a rectangle space dictated by regulations or dictated by the available space, the ratio of the flap chord length to the main element chord length, the overlap and gap sizes between the main element and the flap are design parameters, besides the element shapes. To find the configuration for the high downforce-to-drag ratio, CFD simulations were performed in 2D using the FX63-137 airfoil for both the main element and the flap. Some important findings are that the flap chord length should be 50 to 70% of the main element chord length to achieve the high lift-to-drag ratio. This finding will help design better two-element wings.

A student design team at Colorado State University (CSU) has developed an innovative snowmobile to compete in the Clean Snowmobile Challenge 2003 competition. This engine concept was originally developed for the CSC 2002 competition and demonstrated the lowest emissions of any engine that competed that year. The team utilized a 3-cylinder, 594cc, loop-scavenged, two-stroke cycle engine (Arctic Cat ZRT600) and then modified the engine to operate with direct in-cylinder fuel injection using the Orbital OCP air-assisted fuel injection system. This conversion required that the team design and cast new heads for the engine. The direct-injection approach reduced carbon monoxide (CO) emissions by 70% and total hydrocarbon (THC) emissions by 90% from a representative stock snowmobile. An oxidation catalyst was then used to oxidize the remaining CO and THC.

Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the ability to respond to the system. A Pure Pursuit controller was developed and utilized as the path tracking control algorithm, and the Carla simulator was used to implement the algorithm and develop the testing environment for this methodology.

Liquefied petroleum gas (LPG), like many other alternative fuels, has witnessed increased adoption in the last decade, and its use is projected to rise as stricter emissions regulations continue to be applied. However, much of its use is limited to dual fuel applications, gaseous phase injection, light-duty passenger vehicle applications, or scenarios that require conversion from gasoline engines. Therefore, to address these limitations and discover the most efficient means of harnessing its full potential, more research is required in the development of optimized fuel injection equipment for liquid port and direct injection, along with the implementation of advanced combustion strategies that will improve its thermal efficiency to the levels of conventional fuels.