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Prediction based optimal energy management systems are a topic of high interest in the automotive industry as an effective, low-cost option for improving vehicle fuel efficiency. With the continuing development of connected and autonomous vehicle (CAV) technology there are many data streams which may be leveraged by transportation stakeholders. The Suite of CAVs-derived data streams includes advanced driver-assistance (ADAS) derived information about surrounding vehicles, vehicle-to-vehicle (V2V) communications for real time and historical data, and vehicle-to-infrastructure (V2I) communications. The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy.

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.

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.

There are many design parameters in designing multi-element racecar wings even after the airfoil to be used has been determined. To choose the best parameter values for the wings of a Formula SAE car, Computational Fluid Dynamics combined with highly fractional factorial design of experiments was used. The CFD results were analyzed for the effectiveness of each parameter in increasing the down force, and effective parameters were used for the next CFD analyses with a fractional factorial design for choosing the best parameter values. The designed wings satisfied the target performance criteria.

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.

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.

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.

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.

A technology path has been identified for development of a high efficiency, durable, gasoline engine, targeted at achieving performance and emissions levels necessary to meet heavy-duty, on-road standards of the foreseeable future. Initial experimental and numerical results for the proposed technology concept are presented. This work summarizes internal research efforts conducted at Southwest Research Institute. An alternative combustion system has been numerically and experimentally examined. The engine utilizes gasoline as the fuel, with a combination of enabling technologies to provide high efficiency operation at ultra-low emissions levels. The concept is based upon very highly-dilute combustion of gasoline at high compression ratio and boost levels. Results from the experimental program have demonstrated engine-out NOx emissions of 0.06 g/hp/hr, at single-cylinder brake thermal efficiencies (BTE) above thirty-four percent.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.