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Performance model

Optimal Design of Fuel Cell Vehicle and Impact of Public Policy on CO 2 Emission. Faculty Advisors : Dr. Shun Takai Dr. Ming C. Leu Department of Mechanical and Aerospace Engineering. Student : Swithin Samuel Razu Department of Mechanical and Aerospace Engineering.

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Performance model

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  1. Optimal Design of Fuel Cell Vehicle and Impact of Public Policy on CO2 Emission Faculty Advisors: Dr. Shun Takai Dr. Ming C. Leu Department of Mechanical and Aerospace Engineering Student: Swithin Samuel Razu Department of Mechanical and Aerospace Engineering Project Objectives Current Progress • Step 2: Vehicle performance model for fuel economy • Gasoline equivalent fuel economy ( • = • Energy content of gasoline = • Gasoline density = • Total distance covered in the NYCC cycle = • Acceleration energy • Total energy loss • Acceleration energy ( • is obtained from the vehicle model • Total energy loss () • FCV sub-system models • Market model • Data: Choice survey result comparing gasoline, hybrid, electric and fuel cell vehicle (GV, HV, EV, FCV) • Result: Choice probability (demand) of GV, HV, EV, FCV as a function of FCV price, FCV fuel economy, gasoline price, government subsidy, and availability of fuel stations • Create a method to optimize fuel cell vehicle (FCV) design incorporating uncertainty of exogenous variables such as public policy and gasoline price • Investigate the effects of public policy on FCV design and CO2 emission in transportation sector Background • Vehicle emissions account for up to 95% of city CO2 emissions creating smog, climate change, health risks and damage agricultural infrastructure • U.S government requires automobile manufacturers to meet Corporate Average Fuel Economy (CAFE) standards of 35.5 mpg by 2016 and 54.5 mpg by 2025 and emission targets of 250 g CO2/mile by 2016 and 163 g CO2/mile by 2025 Approach • Focus on two FCV performance variables that impact demand: Fuel economy and 0-60 mph acceleration • Decompose FCV to four sub-systems: Fuel cell, Battery, Motor, and Power demand • Step1: Create market model relating performance variables and profit • Step 2: Create FCV performance model and FCV sub-system models relating design variables and FCV performance variables • Step 3: Optimize design variables such that FCV profit is maximized • Step1: Market model • For lower gasoline prices ($2,3/gal), the FCV demand is sensitive to FCV price and for higher gasoline prices ($4,5/gal), the FCV demand is less sensitive to FCV price • Increased fuel economy (60mpg to 90mpg) can compensate for higher FCV price • At FCV price of $20,000 demand increases from 58.6% to 68.9% • At FCV price of $30,000 demand increases from 24% to 33% • Performance model • Mass →Power demand model → Acceleration energy and total energy loss → Gas-equivalent fuel economy • Sensitivity of gas-equivalent fuel economy with respect to vehicle mass • The FCV vehicle mass needs to be reduces by 26% to increase the FE from 60 to 90 gas-equivalent mpg Future Work Deceleration power demand [ • Complete linking performance variables to design variables • Optimize design variables to maximize profit • Incorporate vehicle cost and CO2 emissions into the market model • Model acceleration as a performance variable • Integrate dynamic competition between other alternative fuelled vehicles Profit = (Price – Unit Product Cost) x Units Sold = (Price – Unit Product Cost) x Market Size x Market Share Unit Product Cost Conditioned on FCV Design Market Share Conditioned on FCV Design, Price, Customer preference and Fuel Price Acceleration power demand [ Acknowledgements • This research is supported by the Intelligent System Center at the Missouri University of Science and Technology

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