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

Colorado State University-Aaron Rabinowitz, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Farhang Motallebiaraghi, Zachary Asher, Alvis Fong, Rick Meyer
  • Technical Paper
  • 2020-01-0729
To be published on 2020-04-14 by SAE International in United States
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 the highest velocity prediction fidelity. We developed LSTM deep neural network which uses different groups of datasets collected in Fort Collins. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. Effect of different group of datasets on forward velocity prediction window of 10, 15, 20 and 30 seconds is studied. Developed algorithm is tested…
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Synchronous and Open, Real World, Vehicle, ADAS, and Infrastructure Data Streams for Automotive Machine Learning Algorithms Research

Colorado State University-Aaron I. Rabinowitz, Samantha White, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Zachary Asher
  • Technical Paper
  • 2020-01-0736
To be published on 2020-04-14 by SAE International in United States
The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy. This describes the gathering, processing, and use of on-road data collected from probe vehicles in Fort Collins, Colorado. Several synchronous datasets were gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. The datasets in both raw and data-fused formats is made available to the research community. The utility of these types of open data projects is briefly demonstrated by using them in the applications of vehicle velocity prediction, and real-time fuel economy modeling.
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Colorado State University EcoCAR 3 Final Technical Report

Colorado State University-Gabriel Christian DiDomenico, Jamison Bair, Vipin Kumar Kukkala, Jordan Tunnell, Marco Peyfuss, Michael Kraus, Joshua Ax, Jeremy Lazarri, Matthew Munin, Corey Cooke, Eric Christensen, Logan Peltz, Nathan Peterson, Logan Wolfe, Zach Vinski, Daniel Norris, Corrie Kaiser, Jacob Collier, Nick Schott, Yi Wang, Thomas Bradley
Published 2019-04-02 by SAE International in United States
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. The aim is to improve drivability and efficiency as measured by vehicle technical specifications (VTS) including acceleration, energy consumption, and emissions. In this interest, the team focused on the areas of mass reduction, efficient powertrain operation as well as optimal engine and motor use. While there is…
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V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy

Colorado State University-David Baker, Zachary D. Asher, Thomas Bradley
Published 2018-04-03 by SAE International in United States
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. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement.This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability. This Optimal Energy…
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Vehicle Electrification in Chile: A Life Cycle Assessment and Techno-Economic Analysis Using Data Generated by Autonomie Vehicle Modeling Software

Colorado State University-Carlos Quiroz-Arita, Zachary Asher, Nawa Baral, Thomas Bradley
Published 2018-04-03 by SAE International in United States
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. Autonomie was utilized to predict the fuel economy for the hybrid electric vehicle (HEV) and electric vehicle (EV) for each of the three vehicle types. The baseline fuel economy without vehicle electrification for each case was 44, 29, and…
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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

Colorado State University-David Baker, Zachary Asher, Thomas Bradley
Published 2017-03-28 by SAE International in United States
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. Various prediction windows are analyzed and compared to quantify tradeoffs between prediction window size and speed prediction error for a given drive cycle. To investigate the fuel economy (FE) improvement potential of this new control strategy, a high fidelity…
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The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management

Colorado State University-Zachary D. Asher, Thomas Bradley
Lockheed Martin-Anthony Navarro
Published 2017-01-10 by SAE International in United States
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. Since optimal control requires accurate prediction of the vehicle environment and drive cycle, prediction scope and error impact analysis is needed to understand the required accuracy of sensor and signal information received by the vehicle as well as the accuracy of…
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Weight Reduction through the Design and Manufacturing of Composite Half-Shafts for the EcoCAR 3

Colorado State University-Eric Jambor, Thomas Bradley
Published 2016-04-05 by SAE International in United States
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. The project is meant to explore composites manufacturing techniques and their use in the automotive industry. This paper will discuss the design and manufacturing of a composite half-shaft and the integration of…
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Reducing Effective Vehicle Emissions Through the Integration of a Carbon Capture and Sequestration System in the CSU EcoCAR Vehicle

Colorado State University-Thomas Bradley, Clinton Knackstedt, Eric jambor
Published 2016-04-05 by SAE International in United States
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. This paper will present a conceptual description of the system as applied to a conventional gasoline vehicle,…
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Validation and Analysis of the Fuel Cell Plug-in Hybrid Electric Vehicle Built by Colorado State University for the EcoCAR 2: Plugging into the Future Vehicle Competition

Colorado State Univ.-Thomas Bradley, Benjamin Geller, Jake Bucher, Shawn Salisbury
Published 2014-10-13 by SAE International in United States
EcoCAR 2 is the premiere North American collegiate automotive competition that challenges 15 North American universities to redesign a 2013 Chevrolet Malibu to decrease the environmental impact of the Malibu while maintaining its performance, safety, and consumer appeal. The EcoCAR 2 project is a three year competition headline sponsored by General Motors and U.S. Department of Energy. In Year 1 of the competition, extensive modeling guided the Colorado State University (CSU) Vehicle Innovation Team (VIT) to choose an all-electric vehicle powertrain architecture with range extending hydrogen fuel cells, to be called the Malibu H2eV. During this year, the CSU VIT followed the EcoCAR 2 Vehicle Design Process (VDP) to develop the H2eV's electric and hydrogen powertrain, energy storage system (ESS), control systems, and auxiliary systems. From the design developed in Year 1 of the EcoCAR 2 competition, a Malibu donated by General Motors was converted into a concept validating prototype during Year 2. Through extensive vehicle simulations and on-road testing, the FCPHEV architecture was optimized to meet the goals of the VTS in Year 3.…
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