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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

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

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

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

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

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.
Journal Article

Analyzing the Energy Consumption Variation during Chassis Dynamometer Testing of Conventional, Hybrid Electric, and Battery Electric Vehicles

Production vehicles are commonly characterized and compared using fuel consumption (FC) and electric energy consumption (EC) metrics. Chassis dynamometer testing is a tool used to establish these metrics, and to benchmark the effectiveness of a vehicle's powertrain under numerous testing conditions and environments. Whether the vehicle is undergoing EPA Five-Cycle Fuel Economy (FE), component lifecycle, thermal, or benchmark testing, it is important to identify the vehicle and testing based variations of energy consumption results from these tests to establish the accuracy of the test's results. Traditionally, the uncertainty in vehicle test results is communicated using the variation. With the increasing complexity of vehicle powertrain technology and operation, a fixed energy consumption variation may no longer be a correct assumption.
Journal Article

Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles.