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Advancement and validation of a plug...
~
Snyder, Kevin.
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Advancement and validation of a plug-in hybrid electric vehicle model utilizing experimental data from vehicle testing.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Advancement and validation of a plug-in hybrid electric vehicle model utilizing experimental data from vehicle testing./
Author:
Snyder, Kevin.
Description:
156 p.
Notes:
Source: Masters Abstracts International, Volume: 54-04.
Contained By:
Masters Abstracts International54-04(E).
Subject:
Automotive engineering. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1588144
ISBN:
9781321730838
Advancement and validation of a plug-in hybrid electric vehicle model utilizing experimental data from vehicle testing.
Snyder, Kevin.
Advancement and validation of a plug-in hybrid electric vehicle model utilizing experimental data from vehicle testing.
- 156 p.
Source: Masters Abstracts International, Volume: 54-04.
Thesis (M.S.)--Wayne State University, 2015.
The objective of the research into modeling and simulation was to provide an iterative improvement to the Wayne State EcoCAR 2 team's math-based design tools for use in evaluating different outcomes based on hybrid powertrain architecture tweaks, controls code development and testing. This thesis includes the results of the team's work in the EcoCAR 2 competition for university student teams to create and test a plug-in hybrid electric vehicle for reducing petroleum oil consumption, pollutant emissions, and Green House Gas (GHG) emissions.
ISBN: 9781321730838Subjects--Topical Terms:
2181195
Automotive engineering.
Advancement and validation of a plug-in hybrid electric vehicle model utilizing experimental data from vehicle testing.
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156 p.
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Source: Masters Abstracts International, Volume: 54-04.
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Adviser: Jerry Ku.
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Thesis (M.S.)--Wayne State University, 2015.
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The objective of the research into modeling and simulation was to provide an iterative improvement to the Wayne State EcoCAR 2 team's math-based design tools for use in evaluating different outcomes based on hybrid powertrain architecture tweaks, controls code development and testing. This thesis includes the results of the team's work in the EcoCAR 2 competition for university student teams to create and test a plug-in hybrid electric vehicle for reducing petroleum oil consumption, pollutant emissions, and Green House Gas (GHG) emissions.
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Plant model validations and advancements brought the vehicle plant model directionally closer to the actual vehicle's experimental data and achieved a significant error reduction in 10 of 11 metrics detailed in the research. The EcoCAR 2 competition events provided the opportunity for the team to get experimental data of the vehicle's behavior on the vehicle chassis dyno and the vehicle on road testing from General Motors proving ground test tracks. Experimental data was used from 5 sources to validate and advance the vehicle plant model: 1. Component Test Benches; 2. HIL Test Bench; 3. Component on Dynamometer (Dyno); 4. Vehicle on Chassis Dyno; 5. Vehicle On Road.
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The advancement of the electric motor powertrain and the vehicle chassis portions of the vehicle plant model provided significant error reduction (at least a 10% reduction) in: Dynamic Performance metrics and Emissions & Energy Consumption metrics (8 of 8 had more than 10% error reduction).
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However, significant error (more than 10%) still exists and more work is needed in: 1 of 3 Dynamic Performance metrics and 6 of 8 Emissions & Energy Consumption metrics.
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Future work includes adding a torque converter plant model between the engine plant model and the transmission plant model on the front wheel drive powertrain, implementing identified advancements into the engine and transmission plant models, and additional analysis for validation of the engine and transmission plant models.
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The vehicle plant model now provides higher confidence and higher accuracy (in most cases) for the simulation results, making the vehicle plant model significantly more useful for evaluating fuel economy, dynamic performance, and emissions improvement results when testing the team's controls code changes for optimization. (Abstract shortened by UMI.).
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School code: 0254.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1588144
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