DISSERTATION
TECHNO-ECONOMIC ANALYSIS AND
DECISION MAKING FOR PHEV BENEFITS TO SOCIETY, CONSUMERS, POLICYMAKERS AND
AUTOMAKERS
Submitted
by
Baha
Mohammed Al-Alawi
Department
of Mechanical Engineering
In
partial fulfillment of the requirements
For
the Degree of Doctor of Philosophy
Colorado
State University
Fort
Collins, Colorado
Summer
2012
Doctoral Committee:
Advisor: Thomas Bradley
Co-Advisor: William Duff
Daniel
Olsen
Dan
Zimmerle
John
Labadie
ABSTRACT
TECHNO-ECONOMIC ANALYSIS AND
DECISION MAKING FOR PHEV BENEFITS
TO
SOCIETY, CONSUMERS, POLICYMAKERS AND AUTOMAKERS
Plug-in hybrid electric vehicles (PHEVs) are an
emerging automotive technology that has the capability to reduce transportation
environmental impacts, but at an increased production cost. PHEVs can draw and store energy from an
electric grid and consequently show reductions in petroleum consumption, air
emissions, ownership costs, and regulation compliance costs, and various other
externalities. Decision makers in the
policy, consumer, and industry spheres would like to understand the impact of
HEV and PHEV technologies on the U.S. vehicle fleets, but to date, only the
disciplinary characteristics of PHEVs been considered. The multidisciplinary tradeoffs between
vehicle energy sources, policy requirements, market conditions, consumer
preferences and technology improvements are not well understood.
For example, the results of recent studies
have posited the importance of PHEVs to the future US vehicle fleet. No studies have considered the value of PHEVs
to automakers and policy makers as a tool for achieving US corporate average
fuel economy (CAFE) standards which are planned to double by 2030. Previous studies have demonstrated the cost and
benefit of PHEVs but there is no study that comprehensively accounts for the
cost and benefits of PHEV to consumers.
The diffusion rate of hybrid electric vehicle (HEV) and PHEV technology
into the marketplace has been estimated by existing studies using various tools
and scenarios, but results show wide variations between studies. There is no comprehensive modeling study that
combines policy, consumers, society and automakers in the U.S. new vehicle
sales cost and benefits analysis.
The aim of this research is to build a potential
framework that can simulate and optimize the benefits of PHEVs for a
multiplicity of stakeholders. This
dissertation describes the results of modeling that integrates the effects of
PHEV market penetration on policy, consumer and economic spheres. A model of fleet fuel economy and CAFE
compliance for a large US automaker will be developed. A comprehensive total cost of ownership model
will be constructed to calculate and compare the cost and benefits of PHEVs,
conventional vehicles (CVs) and HEVs.
Then a comprehensive literature review of PHEVs penetration rate studies
will be developed to review and analyze the primary purposes, methods, and
results of studies of PHEV market penetration.
Finally a multi-criteria modeling system will incorporate results of the
support model results.
In this project, the models, analysis and
results will provide a broader understanding of the benefits and costs of PHEV
technology and the parties to whom those benefits accrue. The findings will provide important
information for consumers, automakers and policy makers to understand and
define HEVs and PHEVs costs, benefits, expected penetration rate and the
preferred vehicle design and technology scenario to meet the requirements of policy,
society, industry and consumers.
ACKNOWLEDGMENTS
I would like to
express my gratitude to Dr. Thomas Bradley, my advisor, for his extremely
helpful guidance, support and encouragement.
His countless hours spent for guiding both my research and writing has
been extremely helpful for the successful completion of the dissertation. My appreciation also extends to his wife Dr.
Kimberly
Catton.
I gratefully
acknowledge my committee members, including Dr. William S. Duff, Dr. Daniel B.
Olsen, Mr. Dan Zimmerle and Dr. John W Labadie, for their insightful
suggestions, which were helpful for improving the thesis work. Also, thanks to Ms. Karen Mueller from
Colorado State University and Ms. May Abdul Sattar from Saudi Arabia Cultural
Mission for all of her assistance and support.
I would like to thank Saudi Arabia Ministry of Higher
Education for the scholarship, which helped me continue my
studies.
I would like to
thank my wife Reem for being very understanding and supportive. My thanks go to my parents for their
encouragement and continuous support.
Finally, I would
like to give a special thanks to my dad, Mohammed Al-Alawi, for his unending
support and friendship without which this would not be possible.
Chapter 1- Introduction
1.1 Background
The first
automobile fuel efficiency standards were passed in 1975 by the US Congress as
part of the Energy Policy Conservation Act (EPCA). In 1978, this legislation set the minimum
acceptable corporate average fuel economy (CAFE) standard at 18.0 mi gal-1
(mpg) for passenger cars.
EPCA sets a penalty of $5 per vehicle for every 0.1 mpg that the
CAFE is below the standard, and
sets up credits that are available when a corporation’s CAFE exceeds the
standards [1]. The CAFE requirements
have been incrementally increased to 26.0 mpg in 1985, to 27.5 mpg in 1989, and
to 36.5 mpg by 2016 [2]. Automakers have
developed vehicles to meet these increasing CAFE standards by continuously
developing and incorporating a suite of technologies including light-weighting,
improved aerodynamics and hybrid-electric vehicles.
Plug-in hybrid
electric vehicles (PHEVs) are hybrid electric vehicles which can draw and store
energy from an electric grid. The
benefits of plug-in hybrid vehicles are that they displace petroleum energy
with multi-source electrical energy.
PHEVs are generally characterized by lower petroleum consumption, lower
criteria emissions output, and lower carbon dioxide emissions [3].
1.2 Project Overview
The Venn diagram
shown at Figure 1 presents the interaction between decision makers in
quantifying the cost and benefits of PHEVs.
Figure
1. Venn Diagram of Decision Makers’ Interactions as Framework for this Study
Decision makers “DM” in Figure 1
are represented by
•
Automakers: Vehicle manufacturers who sell
vehicles in the US
•
Policymakers: US individuals with power to
influence or determine public
policy at state or federal
level
•
Consumers: individuals who buy and operate the
vehicle in the US
The regions of interaction among
these decision makers are labeled in Figure 1 as:
A.
Regulation of Automakers interaction: CAFE standards, Low Carbon Fuel standards,
EPACT and other legislation influence the market under which automakers design
and build vehicles.
B.
Vehicle demand and supply interaction: Automakers and consumers interact through the
automobile market to determine the types of vehicles that are manufactured and
sold in the US.
C.
Incentives and Taxes interaction: Consumers and
policy interact in a variety of ways.
Changes in fuel prices and taxes, government subsidies for advanced
vehicles, tax incentives and other means of financial support can influence the
consumer automobile decision making process.
D.
System level interactions: All decision makers interact to determine the
actual characteristics and evolution of the vehicle fleet. All decision makers must be able to meet
their individual and collective requirements for an economic, environmental,
and sustainable transportation system.
The goals of
this study are to calculate the economic value of PHEVs in allowing an
automobile manufacturer to meet increasing CAFE standards, to calculate and
study the total cost of ownership of the purchase and operation of PHEV to
consumers, to review and analyze the primary purposes, methods, and results of
studies of PHEV market penetration and to develop a multi-criteria modeling
system to help decision makers in evaluating different scenario of vehicle
technology that meet their needs and preferences.
1.3 Outline of this Document
This chapter
(Chapter 1) provides an introduction to the PHEV system modeling research
project and presents the outline of this dissertation.
Chapter 2
presents a literature survey of the state of the field of PHEV modeling and design. Chapter 2 also presents the research
questions and tasks required to complete the research project.
Chapter 3
presents a model for CAFE compliance for a major US automaker for the model
year 2008. Novel models of HEV and PHEV
fuel economy and incremental costs are used to quantify the relative costs and
benefits of these vehicle technologies.
Results and discussion sections compare the costs of CAFE compliance
among HEV technologies, vehicle types, and fuel economy quantification policy
options. The results are put into a
policy context through comparison of the effectiveness of PHEV CAFE compliance
to that of other alternative fuel vehicles for the period 2012-2016. The results of this work can inform
automakers, policy makers and technical analysts about the value of PHEVs in
allowing automakers to meet CAFE requirements.
Chapter 4
presents a total cost-of-ownership model that will allow consumers to compare
and understand CV, HEV and PHEV5-60 technology cost and benefits. The model also educates consumers about the
benefits of PHEV technologies and provides an optimized comparison of vehicle
technology based on their needs and preferences.
Chapter 5
presents an analysis and evaluation of PHEV market penetration rate
studies. This research synthesizes the
current understanding of the modeling needs for market penetration studies and
the economic feasibility of HEV and PHEV technologies. This research provides information for
researchers, automakers, and policymakers to understand and define the modeling
components and parameters that need to be integrated into estimation of HEV and
PHEV adoption rates.
Chapter 6
presents a multi-criteria modeling system that integrates and interacts with
each of the previous models to synthesize an overall understanding of the
tradeoffs among all of the decision maker and decision spheres presented in
Figure 1.
Chapter
7 provides conclusions to the study and a summary of future work.
Appendix A presents supporting
materials for Chapter 3.
Appendix B presents supporting
materials for Chapter 4.
Appendix C presents supporting
materials for Chapter 6
Appendix D presents a multi-criteria decision support
system for informing decision making at vehicle level and at scenario
level. The goal of the vehicle decision
support system is to determine the most preferred vehicle for a particular
consumer, automaker and policy maker.
The goal of the scenario level decision support system is to determine
the most preferred vehicle penetration scenario including the tradeoffs among
the preferences automakers and policy makers under various consumer preference
scenarios.
Chapter 2- State of the Field and Research Challenges
This chapter
reviews the results of studies of PHEV technology total cost of ownership,
penetration rate and multi-criteria decision support system. This is followed by a set of research questions
and tasks that are responsive.
2.1 The Integration of PHEV Technology in Automakers Fleets
The benefits of
PHEV technology can be understood only if PHEV types (range of vehicle, e.g.
0-60 miles), PHEV class (EPA classification e.g. compact car, mid-size car or
mid-size SUV) and fuel economy methods are integrated in an automakers vehicle
fleet model in order to increase fleet average fuel economy (CAFE).
To the
identified research needs, there are no studies specifically addressing the use
of PHEV technology in meeting CAFE standards for an automaker vehicle
fleet. Current studies mainly focus on
PHEV design and performance. The basis
of much of the research in PHEV field is the work by the Hybrid Electric
Vehicle Working Group (WG), assembled by the Electric Power Research Institute
(EPRI). Two technical reports (EPRI
2001, EPRI 2002) have been completed to provide technical specifications for
several vehicle classes, including compact car, mid-size car, mid-size SUV and
large-size SUV PHEVs [4,5]. Technical
parameters from the EPRI reports are used in this study and updated for new
fuel economy methods, utility factors and annual electricity consumption. The EPRI reports present fuel economy methods
used to calculate the mpg rating for HEVs.
The utility factor (UF) from SAE J1711 and other FE methods can be used
if updated with new values provided by the J2841 report [6]. New fuel economy (FE) methods need to be
studied and applied. The new FE methods
should consider the updated UF and modified conversion factor which has been
changed from 33.44 to 82.049kW/g (petroleum-equivalency factor
(PEF)). The
conversion factor has decreased as the Department of Energy (DOE) has revised
its regulation on electric vehicles to provide a petroleum-equivalency factor
(PEF) and procedures for calculating the petroleum-equivalent fuel economy of
electric vehicles [7]. Another method
should consider the weighted gasoline-only fuel economy for a fully charged
vehicle.
A U.S. Department of
Transportation National Highway Traffic Safety
Administration study has examined
the costs and benefits of improving passenger car and light truck fleet fuel
economy for vehicles models 2011-2016 [2].
The study includes a discussion of technologies that can improve fuel
economy and an analysis of the potential impact on retail prices, safety,
lifetime fuel savings and their value to consumers, and other societal
benefits. NHTSA uses the Volpe
Simulation model for their analysis and optimization of fuel economy
technologies to meet the proposed CAFE standards. The Volpe model consists of several
spreadsheet files that have information about automakers vehicle sales, fuel
cost, fuel efficiency, CAFE standards, technology penetration rate,
specifications and vehicle fuel improvement.
The Volpe model is not intended to be used to test the effect of
specific technologies like PHEV, rather is intended to test different
technologies using a decision tree method whereby PHEV technology can be
selected only after other technologies have been exhausted.
Modeling the
integration of PHEV technology into an automaker fleets is computationally
demanding. Fuel economy methods and
system incremental costs have to be studied to be used in the modeling
process. Costs saving to automakers,
consumers and society benefits have to be calculated. In order to enroll PHEVs technology in US
automakers fleets a new model must be developed and validated.
2.2 PHEV Total Cost of Ownership Modeling and Economic Cost/Benefits Analysis
Studies have
examined the potential of technological advances in improving vehicle fuel
economy in the United States.
Cost/Benefit analysis of fuel economy technologies using analytical
economics and automotive engineering methods have been developed and used. Fuel economy improvement could be
accomplished either through using more efficient but expensive technologies or
by re-designing internal combustion engine (ICE) vehicles.
Automakers have
introduced grid-independent HEVs and grid dependent PHEVs to the market
[3–5,8]. Some manufacturers have
announced plans to develop PHEVs; GM
Chevrolet Volt came out in 2010; FORD PHEV Escape in 2012;
Toyota PHEV Prius in
2012; NISSAN PHEV in 2012; VOLVO PHEV in 2012; Chrysler PHEV
in 2012;
Volkswagen PHEV Golf Twin E-Drive
in 2011; Saturn PHEV VUE in 2010; Audi PHEV A1 Sport-back in 2011; and Hyundai
PHEV Sonata in 2013 [8]. Studies have
attempted to assess market potential of PHEVs through an economic
analysis. A variety of studies have quantified
PHEV fuel efficiency and incremental costs in order to understand their value
to consumers [4,5,9–13]. Most of the
studies concluded that in order for the PHEVs to be cost effective, their
incremental cost has to come down and the gasoline price has to increase above
$5.00/gallon [10,14–17]. No studies have
considered all of the ownership cost parameters that may affect the
cost/benefits value of PHEVs or have included consumers’ preferences toward
PHEVs. Most of studies cited have
included only fuel consumption costs model in their PHEV economic model.
The modeling of
fuel economy technologies need to be implemented using a variety of vehicle
types, market conditions, driving and policy attributes and parameters. Costs and benefits of PHEV technology should
be linked to the consumer market preference surveys. A recent study that compares HEV (Toyota
Prius 2001) and ICE (Toyota Corolla 2001) concluded that the HEV Prius is not
cost-effective in improving fuel economy or lowering emission. To be attractive to the US consumers the
price of gasoline has to be three times more than $1.5/gal and Prius tailpipe
emission benefits to regulators and society have to be 14 times greater than
2001 CVs [16]. Based on the PHEV
economic model tested in this study, with current fuel costs PHEV technology
have more fuel economy benefits than both CVs and HEVs and the consumer payback
would be 3-10 years for most vehicle classes.
A model of PHEV economic benefits needs to consider options like
incentives and tax cuts which reduce the payback period.
Simpson [10]
compared the cost/benefits of PHEV to HEV and CV. Battery costs, fuel costs, vehicle
performance attributes and driving habits were considered in the valuation of
PHEV. Near-term and long-term scenarios
were considered. The economic analysis
showed that higher gasoline prices and lower PHEV incremental cost would be
required to have PHEV favorable over other technologies [10]. Similar but expanded analysis needs to be
conducted. For example, the economic
analysis needs updated fuel costs and the model should consider more
parameters. PHEV fuel efficiency and the
utility factor need to be updated. A
study by Kammen [15] compared a CV, HEV, PHEV20 and PHEV60 in compact passenger
car and full-size SUV classes. PHEVs
were found to reduce GHG emissions and oil consumptions and improve oil
security. For the PHEV to be economical
cost effective under current market conditions battery cost must decline to
below $500/kWh or U.S. gasoline must remain at $5/gallon [15]. A comparison between advanced electrical
technologies and advanced conventional technologies from 1997 to 2002 studies
were discussed in a paper by Santini [18].
Diesel engine, fuel cell, gasoline engine, HEV and hydrogen technologies
were compared in terms of fuel economy, incremental cost, and cost
effectiveness [18]. A paper by Diamond
[19] has examined the impact of government incentives policies in promoting
HEVs. For incentives to be effective, the payment had to be upfront and a strong
relationship existed between gasoline prices and HEVs adoption [19]. A study by Ogden et al [20] performed a
societal lifecycle cost analysis for a variety of alternative automotive
engine/fuel options. The study include
the vehicle first cost, fuel costs, oil supply security costs, GHG and other
emission costs [20].
Relevant
economic analysis simulations of PHEV technology to date have not included all
of the cost/benefits parameters. Fuel
economy of PHEVs used in existing studies needs to be updated with the new
ratings tested on a variety of HEV types and classes. New HEV incremental costs that include
lithium ion batteries have to be considered.
Different scenarios of vehicle purchase have also been overlooked. The benefits of HEVs have to be studied in
greater detail, including different scenarios for fuel savings, GHG emission
reductions, payback period and consumers preferences. Demand curves of market preferences toward
the purchase of PHEVs needs to be included and compared with PHEVs cost/benefits
supply curves. A sensitivity analysis of
the parameters needs to be included in the economic analysis.
2.3 Market Penetration Rate Modeling
There is a need
to forecast the market adoption to HEVs, PHEVs and EVs technology for society,
vehicle manufacturers, power companies and policy makers. Society will benefit from more economical and
environmental friendly vehicles. Vehicle
manufacturers need to meet the CAFE standards and understand the market
potential. Power companies need to model
future power demands. Policy makers need
to adjust CAFE standards, assign new environmental rules and, understand
various domestic power demand and foreign oil needs.
Studies have
developed models to estimate the penetration rate of the currently available
HEV technology and the new PHEV and EV technology in the US market. Four different major modeling techniques used
in the literature are agent based model, consumer choice model, diffusion model
and time series model. Agent-based
modeling (ABM) is a computer based simulation method that creates a virtual
environment to simulate the action and interaction of each agent. Agents are entities or individuals with
specific characteristics that have control over their interaction behavior with
other agents in the system model. It is
composed of mathematical models that simulate the actions and interactions of
agents within a specified environment.
It considers consumer’s social behavior and can includes other decision
makers interacting in the market such as policy makers, automakers, car
dealers, and fuel suppliers [21–23]. The
agent based model was applied to new vehicle technology adoption field
[18,24–28].
The consumer
choice model links consumers demand to a product with their preferences at
different market conditions and product criteria [29,30]. Discrete choice models or Logit models have
been used in the literature to describe individual’s decisions in choosing
among alternative products. Discrete
choice models calculate the probability of individual choosing a specific
alternative by incorporating their behavior and alternative characteristics
[29]. The two different logit models
used are multinomial logit model (MNL) which is the probability of choosing an
alternative over all alternatives [31–39] and nested logit model (NMNL) which
is the probability of choosing an alternative over the nest alternative
[38,40–44]. The discrete choice model
was used to estimate the penetration rate of HEV [19,24,29,44–53].
Finally the
diffusion and time series models estimates the adoption rate of a new product
based on the interaction of buyers and new buyers [54–59]. Diffusion is defined as the process of
accepting a new invention or product by the market. The new-product diffusion model developed to capture
the life cycle of new products over time.
The speed of the spreads of the new product is called the rate of
diffusion. The most widely used models
applied to model innovation diffusion are the Bass model, Gompertz model, and
Logistic model. These models were used
in the literature to model innovation diffusion [37,55,60– 80].
The modeling of
any new technology is a complex problem especially when no historical sales
data exist. PHEV is a new technology
without market data and differ from HEVs, though both share fuel savings and
lowered GHG emission relative to CV.
Modeling consumer actions and behavior in the market needs to include
the supplier behavior under varying conditions.
The market model needs to use the historical U.S. sales data since it
has consumer’s preference in regard to vehicle fleet, class, automaker and
brand. Additional information could be
extracted from existing sales data, such as vehicles MSRP and fuel economy,
which could be used to cluster consumer’s preferences and economic levels. Consumer’s preferences towards different
technologies at varying fuel and vehicle MSRP need to be linked in the market
model. An estimation of any new technology
division rate could be established using similar technology rate such as HEV
per each vehicle class and brand. The
model needs to support the diffusion of each technology by incorporating the
new carline technology to be available in the market with its manufacturer’s
class share in the market.
2.4 Multi-Criteria Decision Support System and Negotiation Process System
The literature
contains a long history of government and academic studies of the
transportation energy sector and the ways to reduce its greenhouse gas (GHG)
emissions, increase the use renewable energy, and decrease the quantity of
imported oil. In general, these goals
can only be achieved through cooperation of government, industry and
consumers. In this chapter a
multi-criteria modeling system will be developed which can allow for modeling
of the requirements and interaction of these agents. The purpose of the multi-criteria modeling
system is to evaluate the quantitative and qualitative costs and benefits of
different technology penetration scenarios.
This model investigates different available technology penetration
scenarios costs and impacts on US fleets fuel economy, air emissions, energy
consumption, and regulatory compliance.
The following sections review the state of the art in the field of
transportation and energy system modeling.
Transportation
system models have been developed to simulate, analyze or forecast vehicles’
air emission, economy, fuel economy, energy use and technology
penetration. Table 1 provides a summary
of the characteristics of some relevant transportation energy system models.
A number of
transportation system models have been developed to estimate and simulate the
air emissions of vehicles. MOBILE6 is a
vehicle emission modeling software used by Environmental Protection Agency
(EPA) to generate on-road motor vehicle emissions factors[1]. Motor Vehicle Emission Simulator (MOVES)
Model is developed by EPA's Office of Transportation and Air Quality (OTAQ) to
estimate emissions from cars, trucks &
motorcycles[2]. The Emission FACtors (EMFAC) model is
developed by the Air
Resources Board as the California version of MOBILE6. Climate Leadership in Parks
(CLIP) tool developed by the US
National Park Service for the EPA to measure for park’s GHG criteria pollutant
emissions resulting from solid waste, wastewater treatment, park vehicles,
electricity use, visitors and other sources at local level[3]. COMMUTER model developed by EPA to Analyzes
the impacts of transportation control measures (TCMs) on vehicle miles traveled
(VMT), criteria pollutant emissions, and GHG[4]. National Mobile Inventory Model (NMIM)
developed by EPA to estimates the current and future emission inventories for
on-road motor vehicles and non-road equipment[5].
Some models
included energy analysis in addition to the air emission analysis. Greenhouse gases, Regulated Emissions, and
Energy use in Transportation (GREET) is a lifecycle model developed by Argonne
National Laboratory (ANL) to evaluate advanced vehicles technology energy use
and wells-to-wheels and the vehicle cycle emissions impacts[6].
Lifecycle Emission Model (LEM)
developed by Mark Delucchi at University of California, Davis to estimate
energy use, criteria pollutant emissions, and GHG emissions from transportation
and energy sources[7]. Long range Energy Alternatives Planning
(LEAP) System tool developed in SEI’s U.S. center for energy policy analysis
and climate change mitigation assessment[8]. World Energy Protection System (WEPS)
Transportation Energy Model (TEM) developed by U.S. Department of Energy (DOE),
to generates forecasts of transportation sector energy use by transport mode at
a national and multi-national region level[9]. VISION Model developed by ANL to estimate the
potential energy use, oil use and carbon emission impacts of advanced light and
heavy-duty vehicle technologies and alternative fuels through the year 2100[10].
Other models
have considered the transportation and energy sectors with an emphasis on
economic analysis. National Energy
Modeling System (NEMS) developed by the U.S. Department of Energy (DOE), Energy
Information Administration (EIA) to estimate energy market behavior and their
economic interaction[11]. Intelligent Transportation Systems
Deployment Analysis System (IDAS) tool is developed by
Federal Highway Administration
(FHWA) to estimate the impacts benefits and costs resulting
from the deployment of
Intelligent Transportation Systems
ITS components[12]. It is used to estimates on-road lightduty
passenger vehicles to heavy-duty trucks emission rates in California. IDAS can evaluate impacts due to changes in
user mobility, travel time/speed, travel time reliability, fuel costs,
operating costs, accident costs, emissions, and noise. The MARKAL-MACRO Model developed by the U.S.
Department of Energy to link the use of energy and environmental resources to the
economy[13].
Other models
have been developed to simulate vehicles’ energy, economics and technological
evolution . ObjECTS GCAM is an economy,
energy and land-use model developed by Joint Global Change Research Institute
(PNNL)[14]. The National Energy
Modeling System (NEMS) developed by Energy Information Administration
(EIA) of the U.S. Department of Energy (DOE)[15]. It is a computer-based, energy-economy
modeling system of U.S. through 2030.
NEMS projects the production, imports, conversion, consumption, and
prices of energy. The Volpe model has
been developed by DOT’s National Transportation Systems Center to support
NHTSA’s CAFE rulemakings. The model is
used by NHTSA to estimates vehicle manufacturers costs, effects, and benefits
of technologies that could be added in response to a given CAFE standard[16]. Systems for the Analysis of Global Energy
Markets (SAGE) was developed by the U.S. DOE to replace WEPS[17]. It provides a projection of energy
consumption to meet energy demand following region’s existing energy use
patterns and the existing stock of energy.
Transitional Alternative Fuels and Vehicle Model (TAFV) developed by
University of Maine to evaluate economic decisions among auto manufacturers,
vehicle purchasers, and fuel suppliers and to predict the choice of alternative
fuel technologies for light-duty motor vehicles[18]. Overall, these modeling efforts recognize the
multidisciplinary system modeling scope that is required to model the
transportation and energy sectors with fidelity. Still, few of these models consider the role
of regulation in determining technological changes, and fewer still consider
the overarching role of the automotive consumer in enabling a change in the
transportation sector
Table 1. Transportation Models
Available in the Literature
|
Model name
|
Source
|
Function
|
Area
|
|
Climate Leadership in Parks (CLIP)
|
U.S. Environmental Protection Agency
|
Calculates air emission based of fuel consumption and/or
vehicle miles traveled
|
1. Air
Emission
|
|
COMMUTER Model
|
U.S. Environmental
Protection Agency
|
Analyzes the impacts of transportation control measures
(TCMs) on vehicle miles traveled (VMT), criteria pollutant emissions, and CO2.
|
2. Air
Emission
|
|
EMFAC Model
|
California Air Resources Board
|
Calculate emission rates from all motor
vehicles, operating on highways, freeways and local roads in
California
|
3. Air
Emission
|
|
MOBILE6
|
U.S. Environmental Protection Agency
|
Produce motor vehicle emission factors for use in
transportation analysis and can be used at any geographic level within the
U.S.
|
4. Air
Emission
|
|
Motor Vehicle Emission Simulator (MOVES) Model
|
U.S. Environmental Protection Agency
|
Estimates emissions for on-road and non-road sources for a
broad range of pollutants and allow multiple scale analysis.
|
5. Air
Emission
|
|
National Mobile Inventory Model (NMIM)
|
U.S. Environmental
Protection Agency
|
NMIM uses MOBILE6 and NONROAD to calculate emission
inventories, to calculate national or individual state or county inventories.
|
6. Air
Emission
|
|
Long Range Energy Alternatives Planning
(LEAP) System
|
Community for Energy,
Environment and
Development
|
Energy policy analysis and climate change mitigation
assessment tool for energy consumption, production, and resource extraction
in all sectors of an economy.
|
7. Air
Emission and energy use.
|
|
The Greenhouse Gases, Regulated
Emissions, and Energy Use in
Transportation (GREET) model
|
Argonne National
Laboratory
|
Full
life-cycle model to evaluate energy and emission impacts of advanced vehicle
technologies and new transportation fuel combinations.
|
8. Air
Emission and energy use.
|
|
Lifecycle Emissions Model (LEM)
|
University of California, Davis
|
Estimates energy use, criteria pollutant emissions, and CO2-equivalent
GHG emissions from transportation and energy sources.
|
9. Air
Emission and energy use.
|
|
National Energy Modeling System (NEMS)
|
Energy Information
Administration (EIA),
U.S. DOE
|
Simulates
the behavior of energy markets and their interactions with the U.S. economy
with transportation demand module (TRAN).
|
10. Air Emission, energy use and economy
|
|
Intelligent Transportation Systems
Deployment Analysis System (IDAS)
|
Federal Highway
Administration
|
Predict relative costs and benefits for more than 60 types
of ITS investments. Evaluated impacts relative to changes in user mobility,
travel time/speed, travel time reliability, fuel costs, operating costs,
accident costs, emissions, and noise.
|
11. Air Emission, energy and economy
|
|
The MARKAL-MACRO Model
|
U.S. Department of
Energy
|
Link the use of energy and environmental resources to the
economy.
|
12. Air Emission, energy and economy
|
|
World Energy Protection System (WEPS)
Transportation Energy Model (TEM)
|
U.S. Department of
Energy
|
Model for transportation energy use generates mid-term
forecasts of the transportation sector's
|
13. Air emission, energy use, and fuel economy
|
|
VISION Model
|
Argonne National
Laboratory
|
Forecasts energy use until 2050
|
14. Air emission, energy, and vehicle technology penetration
|
|
System for the Analysis of Global Energy Markets (SAGE)
|
U.S. Department of Energy
|
Integrated set of regional models that provides a
technology-rich basis for estimating regional energy supply and demand.
|
15. Energy and
economy
|
|
Transitional Alternative Fuels and Vehicle
Model (TAFV)
|
University of Maine
|
Economic decisions among auto manufacturers, vehicle
purchasers, and fuel suppliers and can predict the choice of alternative fuel
technologies for light-duty motor vehicles.
|
16. Economy,
and DSS
|
|
Volpe Model
|
DOT National
Transportation Systems
Center
|
Support NHTSA’s CAFE rulemakings. Estimates vehicle manufacturer’s costs,
effects, and benefits of technologies that could be added in response to a
given CAFE standard.
|
17. Technology, policy, economy and Energy
|
17
A multi-criteria modeling system for the U.S. new vehicles sales under
different HEV and PHEV5-60 technology penetration rate scenarios is developed
to simulate and evaluate the achieved fleet CAFE, total cost of ownership, air
damages and oil displacement. The first
stage starts when each DM revise and change the modeling components for the
base case (CVs, HEV and PHEVs incremental costs, fuel economy, fleet volume,
fuel price, and discount rate) and then assigning different set of policy and
standards to be achieved. The second
stage is to set different vehicle technology penetration rate. The modeling system will present the result
for each penetration rate scenario and the DMs will compare each scenario based
on the costs, benefits and policy standards met.
DMs can negotiate and revise the penetration rate scenario or revise the
modeling components within an agreeable components and policy value
limits. The model will give a new set of
results within the negotiation space.
The process will continue as DMs revise each model components and
technology penetration rate scenarios.
The process will stop when there is a common scenario or DMs agrees on
one scenario. Further analysis will be
carried and more technologies could be added to the model. Appendix D presents two multi-criteria
decision support systems models, vehicle technology level and vehicle
technology penetration scenario level.
2.5 Research Questions and Tasks
Based on the challenges
identified in the previous section, a primary research question is:
Main
Research Question:
IS THERE COMMON GROUND IN BETWEEN THE INTERESTS THAT
GOVERN
PHEV MARKETPLACE SUCCESS? WHAT SET OF AUTOMAKER, GOVERNMENT AND CONSUMER POLICIES
WILL GENERATE A BENEFICIAL
MARKETPLACE ENVIRONMENT FOR PHEVS?
In this research effort the question can be answered by establishing
methods and a framework for parametric modeling of PHEV types and their
integration in US automakers fleets, regulatory compliance needs and consumer
acceptability needs. The validation of
the models is performed by comparing the results of the analysis with or
without PHEV technology to the parameter and analysis performed by EPRI and
NHTSA [2,4,5]. The validation of the
total cost of ownership model will be performed by comparing the model
parameters, assumptions and results to other studies work and tested by
performing a sensitivity analysis. At
the last stage the results of the validated models will be integrated into a
multi-criteria decision support system and negotiation process model to define
the optimum technology that will meet and satisfy each decision maker goal.
2.5.1 Research
Question 1:
WHAT
IS THE VALUE OF INTEGRATING HEVS AND PHEVS INTO
AUTOMAKER’S
VEHICLE FLEETS TO MEET CAFE STANDARDS?
A variety of studies have quantified PHEV fuel efficiency and incremental
costs in order to understand their value to consumers [4,5,9–12]. To date, no studies have considered the value
of PHEVs to automakers and policy makers in achieving CAFE compliance [2].
2.5.1.1 Hypothesis 1.1
PHEVs and HEVs represent a net cost
of compliance saving to the US automotive industry over other available
technologies.
2.5.1.2 Task
1.1: Develop a model of a US automaker fleet and calculate the achievable CAFE.
2.5.1.3 Task 1.2: Update and modify PHEVs fuel economy methods and
calculate the hybridization incremental cost using lithium ion batteries for
different PHEV types and classes.
2.5.1.4 Task 1.3:
Integrate the PHEV technology in the model and calculate the achievable CAFE
and the total incremental cost of the technology.
2.5.1.5 Task 1.4: Scenario analysis to calculate the saving/benefits to
automakers, consumers and society associated with using PHEV technology of
meeting the CAFE standards proposed for 2012-2016.
2.5.2 Research
Question 2:
WHAT
ARE THE COMPONENTS OF A COMPREHENSIVE PHEV
CONSUMER’S
TOTAL COST OF OWNERSHIP MODEL, SO THAT WE CAN DEFINE
PHEV
COST/BENEFIT AND CONSUMER ACCEPTABILITY FOR THE PHEV
TECHNOLOGY?
A number of studies have demonstrated the cost and benefit of PHEVs but
there is no study that accounts for all of the variables that may affect the
cost and benefits of PHEV to consumers [4,5,9–13]. The problem is that the
benefits of PHEV are not well defined. In order understand the costs and
benefits of PHEVs purchase and use, this study constructs a comprehensive
ownership cost model that has the parameters and assumptions needed.
2.5.2.1 Hypothesis 2.1
The
payback period of PHEV purchase compared to CV or HEV purchase is not a robust
model for consumer acceptability. By incorporating a survey-based, more
detailed model of consumer acceptability, we can gain a richer understanding of
PHEV consumer preference.
2.5.2.2 Task 2.1: Develop a total cost of ownership model for a purchase
with loan of CVs and HEV 0-60 miles of range.
The model should account for down payment on vehicle MSRP, a loan,
vehicle salvage value, maintenance cost, title and registration cost, insurance
cost, fuel costs, annual vehicle miles traveled, utility factor and adjusted
fuel economy. The model should calculate
the annual costs of each vehicle and the payback period of HEV 0-60 and compare
it to CV or HEV 0.
2.5.2.3 Task 2.2: Construct a sensitivity analysis to measure the
effects of the model parameters and assumptions on each vehicle payback period
and cost/benefits.
2.5.2.4 Task 2.3: Develop a
supply demand curves of the market preferences towered
HEV, PHEV20 and PHEV60
cost/benefits.
2.5.2.5 Task 2.4: Develop a
user friendly total cost of ownership (TCO) model.
2.5.3 Research
Question 3:
WHAT
ARE THE COMPONENTS OF A COMPREHENSIVE PHEV PENETRATION RATE MODEL, SO THAT WE
CAN IMPROVE AND MINIMIZE THE UNCERTAINTY IN ESTIMATING PHEVS ADOPTION
RATE?
Results of recent studies have examined the importance
of PHEVs in the near future. The diffusion
rate of hybrid electric vehicle (HEV) and PHEV technology into the marketplace
has been estimated by existing studies using various tools and scenarios with
wide variations of the results between the studies.
2.5.3.1 Hypothesis 3.1
The
penetration rate forecasts of HEV, PHEV and EV are invalid because they do not
consider the role of government and automakers in the marketplace.
2.5.3.2 Task 3.1: Provide a comprehensive literature review of HEVs
penetration rate studies.
2.5.3.3 Task 3.2: Present
the result of each HEVs penetration rate model study.
2.5.3.4 Task 3.3: Provide a set of recommendations and conclusions to
improve the HEVs penetration rate modeling and minimize uncertainty and
variability among studies.
2.5.4 Research
Question 4:
WHAT
SET OF AUTOMAKER, GOVERNMENT AND CONSUMER POLICIES WILL GENERATE THE MOST
BENEFICIAL MARKETPLACE ENVIRONMENT FOR
PHEVS?
2.5.4.1 Hypothesis 4.1
A multi-criteria modeling system can be used to experiment and discover
the preferred policy, vehicle technology, and consumer marketplace conditions
for PHEV market success.
2.5.4.2 Task 4.1: Update
and upgrade the CAFE model to calculate the achieved U.S.
new vehicle
sales CAFE using CV, HEV and PHEV5-60 vehicle technology over the period
2010-2030.
2.5.4.3 Task
4.2: Update and upgrade the TCO model to calculate the total cost of ownership
of the U.S. new vehicle sales using CV, HEV, and PHEV5-60 technology over the
period 2010-2030.
2.5.4.4 Task 4.3: Develop air emission and oil displacement model to
calculate the U.S. new vehicle sales air emission and oil displacement
quantities and value over the period 2010-2030.
2.5.4.5 Task 4.4: Develop a multi-criteria
modeling system that interact with Task 4.1-4.3 models and calculates the U.S.
new vehicle CAFE, TCO, air damages, oil displacement and gasoline tax lost
under different criteria and vehicle technology scenarios.
2.6 Research Plan
A four phase research plan is proposed to address the problems defined.
Each phase is independent but indirectly builds on each other. Each phase of this research will be presented
in an individual research paper.
2.6.1 Phase 1:
Involves the
development of a PHEVs model for US automakers with new fuel economy methods to
quantify the benefits and calculates the saving of the integration of PHEV
technology in automaker vehicles fleet.
2.6.2 Phase 2:
Involves an
economic cost/benefits analysis to the consumers accounting for different
scenario and including a sensitivity analysis of the parameters.
2.6.3 Phase 3:
Involves
constructing a comprehensive literature review of PHEV penetration rate model
studies.
2.6.4 Phase 4:
Involves the developing the
multi-criteria modeling system.
Chapter 3- Analysis of Corporate Average Fuel Economy Regulation Compliance Scenarios Inclusive of Plug in Hybrid Vehicles
3. Chapter Summary
The US corporate average fuel economy (CAFE) standards dictate the fleet
fuel economy that must be achieved by automakers that manufacture and sell
automobiles in the US. CAFE standards
have increased by 24% (for the passenger car fleet) – 35% (for the light-truck
fleet) over the period 2012-2016. This
study compares the effects of 3 designs of plug in hybrid electric (PHEV) and
hybrid electric vehicles to estimate the cost of CAFE compliance with PHEVs as
a component of the domestic passenger car fleet and as a component of the
domestic light truck fleet. Results show
that in many vehicle classes, PHEVs with 20 miles of electric vehicle range
have a lower cost of CAFE compliance than both grid-independent HEVs and PHEVs
with 60 miles of electric vehicle range.
Passenger car PHEVs are shown to provide reduced costs of CAFE
compliance than the suite of conventional technologies used to benchmark CAFE
compliance costs. Overall, results show
that PHEVs can contribute to a reduction in the costs of CAFE compliance for
domestic automakers and should be considered in near-term regulatory and
industrial analyses of
CAFE compliance strategies.
+Fuel+Economy/Volpe+Model+for+Model+Years+2011+and+prior
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