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Technical Paper

Design of a Direct Injection Retrofit Kit for Small Two-Stroke Engines

Carbureted 2-stroke engines are a worldwide pandemic. There are over 50 million 2-stroke cycle engines in Asia alone, powering motorbikes, mopeds, “three-wheelers”, “auto-rickshaws”, “tuk-tuks”, and “tricycles”. These carbureted 2-stroke engines are characterized by high levels of hydrocarbon (HC), carbon monoxide (CO), and particulate matter (PM) emissions. Direct injection is a technology that has shown a great ability to reduce these emissions while at the same time improve fuel economy. A prototype kit has been designed for use in retrofitting existing carbureted two-stroke engines to direct injection. The kit was designed for use on a Kawasaki HDIII; a motorcycle from the Philippines that is commonly used as a taxi. It is however, a relatively common engine design and Kawasaki manufactures similar models for sale all over the world. The retrofit kit incorporates the Orbital air blast direct injection system.
Technical Paper

As-Assembled Suspension Geometry Measurement using Photogrammetry

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.
Technical Paper

Tailoring the Energy Absorption Profile of a Carbon Fiber Impact Attenuator

Predictive capabilities and understanding of how energy absorbing composite structures can be constructed, without additional size or weight, are necessary in order to ensure the deceleration of the driver is kept below human threshold levels. Often, the deceleration rate is modified by changes in the core or crushable material of the composite structure. In the current research, carbon fiber energy attenuators were designed, constructed, and tested to demonstrate the ability to tailor the energy absorbing profile through changes of the fiber orientation and ply stacking sequence. Reinforcing wraps of uni-directional carbon fiber were used to modify a baseline ply sequence and improved response was recorded in each case.
Technical Paper

State Transition Diagrams of Transient Roll and Pitch

In racing, understanding how the setup and setup changes affect the racecar's ability to produce optimum longitudinal and lateral acceleration is vital to producing a winning team. To better understand and characterize how setup and setup changes affect the racecar, the use of state transition diagrams to model the vehicle's transient roll and pitch while negotiating the track is being investigated. State transition diagrams are made up of statistically significant patterns or events, and show the interconnection or transition from one state to another.1 The basic application of a state transition diagram to the phenomena of a racecar's roll and pitch is to identify the locations on the race course of the major events that make up the vehicle braking into, maneuvering through, and accelerating out of a corner. Major events that are examined include the maximum roll and pitch displacements, velocities, and accelerations.
Technical Paper

A Liftless Electronic 100ms Shift System for Motorcycle-Engined Racecars

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.
Technical Paper

Optimization of a Direct-Injected 2-Stroke Cycle Snowmobile

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.
Technical Paper

The Effect of Hill Planning and Route Type Identification Prediction Signal Quality on Hybrid Vehicle Fuel Economy

Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements.
Technical Paper

Reducing Effective Vehicle Emissions Through the Integration of a Carbon Capture and Sequestration System in the CSU EcoCAR Vehicle

As the rigor of vehicle pollution regulations increase there is an increasing need to come up with unique and innovative ways of reducing the effective emissions of all vehicles. In this paper, we will describe our development of a carbon capture and sequestration system that can be used in-tandem with existing exhaust treatment used in convention vehicles or be used as a full replacement. This system is based on work done by researchers from NASA who were developing a next generation life support system and has been adapted here for use in a convention vehicle with minimal changes to the existing architecture. A prototype of this system was constructed and data will be presented showing the changes observed in the effective vehicle emissions to the atmosphere. This system has the potential to extract a significant portion of tailpipe emissions and convert them into a form that allows for safe, clean disposal without causing any harm to the environment.
Technical Paper

Weight Reduction through the Design and Manufacturing of Composite Half-Shafts for the EcoCAR 3

EcoCAR 3 is a university based competition with the goal of hybridizing a 2016 Chevrolet Camaro to increase fuel economy, decrease environmental impact, and maintain user acceptability. To achieve this goal, university teams across North America must design, test, and implement automotive systems. The Colorado State University (CSU) team has designed a parallel pretransmission plug in hybrid electric design. This design will add torque from the engine and motor onto a single shaft to drive the vehicle. Since both the torque generating devices are pre-transmission the torque will be multiplied by both the transmission and final drive. To handle the large amount of torque generated by the entire powertrain system the vehicle's rear half-shafts require a more robust design. Taking advantage of this, the CSU team has decided to pursue the use of composites to increase the shaft's robustness while decreasing component weight.
Technical Paper

Development of an Externally-Scavenged Direct-Injected Two-Stroke Cycle Engine

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.
Technical Paper

Colorado State University EcoCAR 3 Final Technical Report

Driven by consumer demand and environmental regulations, market share for plug-in hybrid electric vehicles (PHEVs) continues to increase. An opportunity remains to develop PHEVs that also meet consumer demand for performance. As a participant in the EcoCAR 3 competition, Colorado State University’s Vehicle Innovation Team (CSU VIT) has converted a 2016 Chevy Camaro to a PHEV architecture with the aim of improving efficiency and emissions while maintaining drivability and performance. To verify the vehicle and its capabilities, the CSU Camaro is rigorously tested by means of repeatable circumstances of physical operation while Controller Area Network (CAN) loggers record various measurements from several sensors. This data is analyzed to determine consistent output and coordination between components of the electrical charge and discharge system, as well as the traditional powertrain.
Technical Paper

Investigation of Vehicle Speed Prediction from Neural Network Fit of Real World Driving Data for Improved Engine On/Off Control of the EcoCAR3 Hybrid Camaro

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.
Technical Paper

Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network

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.
Technical Paper

The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.
Technical Paper

Vehicle Electrification in Chile: A Life Cycle Assessment and Techno-Economic Analysis Using Data Generated by Autonomie Vehicle Modeling Software

The environmental implications of converting vehicles powered by Internal Combustion Engines (ICE) to battery powered and hybrid battery/ICE powered are evaluated for the case of Chile, one of the worldwide leaders in the production of lithium (Li) required for manufacturing of Li-ion batteries. The economic and environmental metrics were evaluated by techno-economic analysis (TEA) and Life Cycle Assessment (LCA) tools - SuperPro Designer and Gabi®/GREET® models. The system boundary includes both the renewable and nonrenewable energy sources available in Chile and well-to-pump energy consumptions and GHG emissions due to Li mining and Li-ion battery manufacturing. All the major input data required for TEA and LCA were generated using Autonomie vehicle modeling software. This study compares economic and environmental indicators of three vehicle models for the case of Chile including compact, mid-size, and a light duty truck.
Technical Paper

Towards Improving Vehicle Fuel Economy with ADAS

Modern vehicles have incorporated numerous safety-focused Advanced Driver Assistance Systems (ADAS) in the last decade including smart cruise control and object avoidance. In this paper, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy. We combine ADAS sensor data with a previously developed prediction model, dynamic programming optimal energy management control, and a validated model of a 2010 Toyota Prius to explore fuel economy. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.
Technical Paper

V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius.
Technical Paper

Enabling Prediction for Optimal Fuel Economy Vehicle Control

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).
Technical Paper

Application of Pre-Computed Acceleration Event Control to Improve Fuel Economy in Hybrid Electric Vehicles

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.
Technical Paper

Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.