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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.

A measurement system based on photogrammetry has been developed and used to measure the “as-assembled” geometry of a variety of racecar suspensions. A standard methodology for photographing a suspension, and special targets have been developed to use with commercially available photogrammetry software. Several types of targets are discussed; these included targets to identify the center of rotation of the linkages and the orientation of the wheel mounting surface. The system is used with a 5.1 mega-pixel camera to measure the 3D geometry of a suspension in space. Physical camber and toe variation in bump is then measured and correlated with the numerical computation of camber and toe variation using a suspension kinematics package and the geometry generated using the technique.

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.

Concerns with stripping and soldering copper magnet wire in ignition coils and other related products have led to the investigation of an alternative product and process design, microjoining. This paper describes the initial development occurring during the improvement phase of a Six Sigma project. The use of microjoining with a folded over welding tab terminal design coupled with a parallel gap welding process is developed as a suitable method for joining a tin plated brass terminals to the 0.65mm magnet wire without prior removal of the polyesterimide over-coated polyamideimide insulation.

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.

Internal combustion engine poppet valves operate in extreme conditions. These extreme conditions are a result of the high temperatures in the combustion chamber. Especially in Motorsport applications, the high temperatures have led to the development of exotic metallic alloys that can operate in this environment. One key problem in developing materials for poppet valves is that it is necessary to know the temperature at which they operate. This is increasingly important when developing valves from alternative materials such as fiber reinforced composites. Composite engine valves have the potential to produce substantial increases in engine performance, through substantial weight reductions, if they can be designed to withstand the environment. Research to-date has demonstrated the functionality of fiber reinforced composite intake valves that are significantly lighter than metallic valves; however, composite valve surface temperatures seem higher than expected.

This paper outlines the development of a diesel fuel burner for Diesel Particulate Filter (DPF) regeneration. The burner utilizes the application of a dual featured ignition system that may enable a burner system to be more cost effective, reliable, and efficient than other burners or Diesel Oxidation Catalysts (DOC). The ignition system incorporates high-energy ignition and ion sensing into a single controller. These two features provide many benefits for burner applications. The high-energy ignition provides enhanced light-off characteristics while simultaneously cleaning the electrode surfaces. Ion sensing allows precise flame control through high-speed ignition and flameout feedback. Initial data has already confirmed many of these anticipated benefits.

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.

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.

Vehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX).

The transportation sector contributes significantly to emissions and air pollution globally. Emission models of modern vehicles are important tools to estimate the impact of technologies or controls on vehicle emission reductions, but developing a simple and high-fidelity model is challenging due to the variety of vehicle classes, driving conditions, driver behaviors, and other physical and operational constraints. Recent literature indicates that neural network-based models may be able to address these concerns due to their high computation speed and high-accuracy of predicted emissions. In this study, we seek to expand upon this initial research by utilizing several deep neural networks (DNN) architectures such as a recurrent neural network (RNN) and a convolutional neural network (CNN). These DNN algorithms are developed specific to the vehicle-out emissions prediction application, and a comprehensive assessment of their performances is done.

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.

The EcoCAR3 competition challenges student teams to redesign a 2016 Chevrolet Camaro to reduce environmental impacts and increase energy efficiency while maintaining performance and safety that consumers expect from a Camaro. Energy management of the new hybrid powertrain is an integral component of the overall efficiency of the car and is a prime focus of Colorado State University’s (CSU) Vehicle Innovation Team. Previous research has shown that error-less predictions about future driving characteristics can be used to more efficiently manage hybrid powertrains. In this study, a novel, real-world implementable energy management strategy is investigated for use in the EcoCAR3 Hybrid Camaro. This strategy uses a Nonlinear Autoregressive Artificial Neural Network with Exogenous inputs (NARX Artificial Neural Network) trained with real-world driving data from a selected drive cycle to predict future vehicle speeds along that drive cycle.

A study is presented in which light-weight composite materials are used for an engine intake valve. This paper is an interim progress report and documents the successful demonstration of a net-shape, resin transfer molded intake valve in a running engine. A short review of a previous dynamic model is presented showing the advantages in engine performance by using the composite valves. It is shown that the use of reduced mass composite valves allows for increased engine speed and/or more aggressive cam profiles without sacrificing valve strength or stiffness while at the same time maintaining reliable operation. The use of composite materials allows for a significant weight reduction compared to more conventional materials such as steel and titanium. A brief review of the use of composite materials is presented. The development and design process for carbon fiber reinforced valves is discussed.

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.

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles.

Even when charging at the highest rates currently available, Electric Vehicles (EVs) add range at substantially lower rates than Internal Combustion Engine Vehicles (ICVs) do while fueling. In addition, DC charging comes at a cost premium and leads to accelerated battery degradation. EV users able to rely on AC charging during long dwells at home or work may experience cost and time savings relative to ICV users with similar driving patterns. However, EV users unable to charge during long dwells will face higher charging costs and higher dedicated charging time. An important question is how occupants of Multi-Unit Dwellings (MUDs), which provide some AC Electric Vehicle Supply Infrastructure (EVSE) but not enough for all cars to charge at once, will be effected. In this paper the authors’ previously published method for quantifying EV user inconvenience due to charging is extended to deal with stochastic charger availability.

Practical applications of recently developed sensor fusion algorithms perform poorly in the real world due to a lack of proper evaluation during development. Existing evaluation metrics do not properly address a wide variety of testing scenarios. This issue can be addressed using proactive performance measurements such as the tools of resilience engineering theory rather than reactive performance measurements such as root mean square error. Resilience engineering is an established discipline for evaluating proactive performance on complex socio-technical systems which has been underutilized for automated vehicle development and evaluation. In this study, we use resilience engineering metrics to assess the performance of a sensor fusion algorithm for vehicle localization. A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator.