An Agent-Based Model to Study Market Penetration of Plug-In

Hybrid Electric Vehicles

Margaret (Maggie) J. EppsteinDepartment of Computer Science

Director, UVM Complex Systems Center

Complex Systems CenterAchieving insight, innovative design, and informed decision making through systems thinking

MOTIVATION

Duvall, M., Knipping, E., Alexander, M., Tonachel, L., Clark, C. (2007) Joint NRDC/EPRI Report.

Jaramillo, P., Samaras, C., Wakeley, H., Meisterling, K., (2009). Energy Policy, 37, 2689-2695.

Smith, W.J., (2010). Energy Policy 38, 1485–1499.

The Promise of Plug-in Hybrid Electric Vehicles (PHEVs)

U.S. DOT2006 data

Transportation accounts for about 1/3 of GHG emissions in U.S. and is the fastest growing source.

PHEVs have potential to significantly reduce GHG emissions:

The Promise of Plug-in Hybrid Electric Vehicles (PHEVs)

It will be easier to green up the grid and use exiting electricity infrastructure ….

vs.

…than to create a whole new vehicle refueling infrastructure to accommodate greener liquid fuels for transportation.

?

??

The Promise of Plug-in Hybrid Electric Vehicles (PHEVs)

Anderson, P.H., Mathews, J.A., Rask, M., 2009. Energy Policy, 37, 2481 – 2486.

Andersson, S.-L., Elofsson, A.K., Galus, M.D., Gransson, L., Karlsson, S., Johnsson, F., Andersson, G., 2010. Energy Policy, 38, 2751-2762.

In proposed Vehicle-To-Grid (V2G) systems, electric vehicles would be used to store and release energy for the electrical power grid that would serve to even out the peaks and valleys inherent in electrical energy usage and the fluctuating supply typical of renewable energy sources (such as wind).

The Promise of Plug-in Hybrid Electric Vehicles (PHEVs)

A projected lifecycle analysis for the year 2030 finds that the PHEV and EVs will offer much lower lifecycle costs than either hydrogen fuel-cell or internal combustion vehicle options.

Offer, G.J., Howey, D., Contestabile, M., Clague, R. and Brandon, N.P., 2010. Energy Policy 38, 24–29.

The Promise of Plug-in Hybrid Electric Vehicles (PHEVs)

From the consumer’s perspective, PHEVs offer both:•economic and environmental benefits in the all-electric range•convenience and flexibility of traditional fuels for longer trips

Average U.S. commuter drives < 30 miles/day.

All-electric range of most 1st generation PHEVs projected to be 30-60 mile, so even if recharging is only done at home most commuters will stay in all-electric mode most of the time.

The Challenge of Plug-in Hybrid Electric Vehicles (PHEVs)

•In a recent survey of U.S. consumers, 69% of respondents reported little or no familiarity with PHEV technology (Axsen and Kurani, 2008)

•Many consumers are hesitant to adopt unfamiliar technologies; e.g., for PHEV there may be concerns about battery life & replacement costs, and recharging time and convenience (Sovacool and Hirsh, 2009)

•Sticker price of PHEVs is projected to be significantly higher than otherwise comparable HEVs or GVs

•Most consumers do not attempt to estimate fuel costs when making vehicle purchasing decisions, while the few that do try use varying degrees of rationality (Turrentine and Kurani, 2007).

•Even if they were to try, uncertainties in future petroleum prices, electricity prices, energy source for electricity, amount of miles driven (and proportion in all-electric range) will all make it difficult for consumers to accurately estimate the relative financial and/or environmental trade-offs of PHEVs relative to other vehicles.

Important potential leverage points factors that will affect PHEV market penetration and that can potentially be affected by

manufacturers and/or policy makers:

•Purchase price of PHEV

•Fuel prices

•Battery range of PHEV

•Ability of vehicle consumers to accurately assess lifetime costs and

environmental impacts for GVs, HEVs, and PHEVs

•Comfort level of vehicle consumers in adopting the new PHEV

technology

•“Greenness”: Relative weight that consumers place environmental

and/or energy concerns or image vs. financial costs

The goal of this project is to develop an agent-based model that can serve as a tool to begin to address these questions.

How should vehicle manufacturers and policy-makers prioritize investments toward these various potential leverage points?

What combinations of influences are the most effective and the most cost-effective?

How to encourage PHEV market penetration?

THE MODEL

Vehicle consumers weigh the costs and benefits of many vehicle characteristics in determining which vehicle to purchase…

Seating capacity

Cargo capacity

Brand loyalty

Styling Safety

Reliability

Fuel efficiency

Towing capacity

2wd/4wd

Etc!

Cost

We assume that, in the future, many comparable vehicles will be available with and with-out a plug-in option, so ultimately the choice will come down to whether or not to purchase the PHEV option.

“Greenness”

Feelings about new technology

In order to avoid having to:•make up specifications for a wide range of hypothetical PHEVs, and •make a host of other assumptions about vehicle model selection

Our initial experiments focus on modeling the subset of potential new-car buyers (agents) who have narrowed their choice to a representative:

•gas vehicle (GV)•hybrid-electric vehicle (HEV), •plug-in hybrid electric vehicle (PHEV)

that are otherwise similar in all characteristics except fuel type, fuel efficiency, and purchase price.

GV ~ Aveo HEV ~ Prius PHEV ~ Volt

Social and Media influences in our Agent Based Model (ABM):

1) Homophily (motivated by e.g., McPherson et al., 2001)

2) Conformity (motivated by e.g., Axelrod, 1997; Bednar & Page, 2007)

3) Threshold effects (motivated by e.g., Granovetter 1978; Watts 2002)

4) Media influences (motivated by e.g, Yin, 1999; Pew Research, 2009)

Agents are updated, in random order, on uniformly distributed dates throughout each year:

MMG SG

Media influence on Greenness:

Susceptibility to media influence

Change in average Media coverage inspiring need to save gas over past year

Social influence on Greenness:

With prob

if median in social network

pick a random friend above median and adopt its G if higher

else

pick a random friend below median and adopt its G if lower

SS

G G

Susceptibility to social influence

Agent’s preferred years of ownership (heterogeneous)

Probability of buying a vehicle this year

Vehicle

Type

MPG when

not within all-

Electric

Range

All-electric

Range

(ElecRange)

Energy

Capacity

of PHEV

battery

Sticker

Price

Rebate # years of

Rebate

GV 31 mpg NA NA $15,000 NA NA

HEV 50 mpg NA NA $25,000 NA NA

PHEV 50 mpg One of:

20 mi

40 mi

60 mi

One of:

8 kWh

16 kWh

24 kWh

One of:

$30,000

$32,500

$35,000

$37,500

$40,000

One of:

$0

$7500

One of:

0

5

25

GV ~ Aveo HEV ~ Prius PHEV ~ Volt

If yes, agents consider purchasing one of 3 vehicle types

Purchase Financing Gasoline ElectricRechargeC C C C C

ij j i jRC C C CEstimate pairwise relative costs of vehicles:

Estimate lifetime costs of each vehicle:

GasPerMile is based on daily commute, battery range, mpg when not on electricVehicleMilesTraveled per year is agent-specificDollarsPerGallon is a time-series based on various gas price scenarios, summed over expected agent-specific number of years of ownership

(Optional: Rational estimation of Fuel)

Sticker price less rebate

Finance remainder after subtracting

(depreciated) trade-in;

6% 4-yr loan

Based on energy capacity of battery,

nightly state of charge, electricity price projections

1

( )Gasoli

Y

e

y

n V DPGC MTGP yM

ij i j iRB GPM GPM GPM

Used as an estimate of non-financial benefits regarding concerns related to gasoline usage (e.g., GHG emissions, oil spills, and dependence on foreign oil)

Estimate the relative difference in gasoline used per mile for each pair of vehicles:

(1 )ij ji ijD G RCB GR

Compute pairwise relative desireability of vehicle, using Greenness to weight environmental benefits vs. cost:

Non-financial concerns Financial concerns

Compute proportion of PHEVs in agents social network + geographical neighborhood (based on commute distance).If vehicle j is a PHEV and agent threshold not met, set Dij to -

If any vehicle j is not affordable (maximum annual estimated cost of a vehicle j exceeds 20% of the agent’s salary), set Dij to -

If Dij 0, then vehicle j is considered the more desirable, otherwise vehicle is considered more desirable.

•National Household Travel Survey, Fed. Highway Admin., 2004.

•Annual Energy Outlook, 2009. http://www.eia.gov, 2009.

•Plug‐in Hybrid Electric Vehicles, Reuters / University of Michigan Surveys of Consumers, The University of Michigan, 2009

•EPRI/NRDC joint report on PHEVs, 2007

•RITA Bur. Trans. Stats. 2009

•Edmunds.com, Hybridcar.com, Dealer.com

•US Census data

•And a slew of other websites, journal articles, etc.

Used data where possible to initialize simulations,tried to make reasonable assumptions where not.

Extensive search for data, including:

SalaryPseudo- Transform:Min = $30KMax = $250KMedian ~ $65K

GreennessPseudo- Transform:Min = 0Max = 1e.g., Median ~ 0.17

15 mi2

The Turning-Bands method was used to create 2 spatially-correlated fields

Salary and Greenness are thus spatially-correlated, -distributed, and mildly cross-correlated.

Other agent attributes were then randomly generated using multivariate normal distributions with loose correlations to salary, then transformed to appropriate distributions as needed.

r=0.65

X,Y agent locations using Gaussian kernels for towns

Annual VMT is log-normally distributed with median of

~12K miles (2001 NHTS)

Average number of years consumers own a car is

normally distributed with mean of ~9 years (2001 NHTS)

People with higher VMT buy cars more often. Richer people tend to buy cars more often.

•Radii of spatial neighborhoods are linear functions of VMT (median spatial radius ~4 miles)

•Radii of social neighborhoods uniformly distributed (0 to 5 mi)•Social network: in social radius, w/similar salary and age

Poorer people have a reduced tendency to be early adopters.

•Normally distributed thresholds T with various means.

•A mean threshold T of 0 is consistent with the 2009 UM/Reuters Survey of Consumers indicating that about 1/2 of consumers interviewed would consider a PHEV if the price premium were low enough.

SOME RESULTS

Unless:a) Gas prices rise significantly ANDb) Consumers learn how to account for fuel costsPHEVs are not likely to penetrate the market significantly

Sensitivity to Gas Prices and Rationality of Fuel Cost Projections:25 yr simulation, 40 mi PHEV battery range, no rebate, mean T = 0

Sensitivity to PHEV battery range:25 year simulation, no rebate, mean T = 0

As the range of the PHEV battery increases:•Relative PHEV cost goes down so more rational buyers purchase PHEVs•Purchased PHEVs use less gasoline so are more efficient•increasing sensitivity to PHEV purchase price

Sensitivity to threshold T and rebates:$40,000 w/5-yr $7500 rebate; 40 mi range; high gas scenario

•Manufacturers need better assessment of thresholds to determine whether to focus on lowering this (e.g., better battery warranty) vs. other incentives (e.g., lower sticker price)•PHEVs are competing with HEVs, not with GVs•Temporary rebates have no long-term impacts

Sensitivity to Greenness G:25 year simulation, no rebate, mean T = 0, G held constant

•Even when all agents’ G = 0, PHEVs are still viewed as cost-effective and are thus purchased by many rational drivers.•Raising G (e.g., by increasing public awareness of importance of reducing gasoline consumption) can greatly increase PHEV purchase and reduce sensitivity to gas prices, but only if buyers are rational.

All Agents’ G = 0

0 0.5 10

0.1

0.2

Mean G = 0.32Median G = 0.30

0 0.5 10

0.1

0.2

0.3

0.4

mean G = 0.32median G = 0.30

frequency

0 0.5 10

0.1

0.2

0.3

0.4

mean G = 0.12median G = 0.09

Greenness G

frequency

Sensitivity to greenness G:$40,000 w/5-yr $7500 rebate; 40 mi range; high gas scenario0 0.5 1

0

0.1

0.2

0.3

0.4

mean G = 0.32median G = 0.30

frequency

0 0.5 10

0.1

0.2

0.3

0.4

mean G = 0.12median G = 0.09

Greenness G

frequency

Increasing greenness G helps PHEVs cut into market share of both GVs and HEVs

Spatially-explicit results:

•Spatial correlations in demographics and spatially-localized social influences result in spatial correlations in vehicle type (we’re exploring such correlations now with VT title database).•May be useful for spatially targeting marketing strategies (we’re exploring the effect of this in the ABM now).•May be useful for regionally-variant policies (ditto).•May be useful for assessing regionally-variant infrastructure needs.

Summary:We have developed a spatially explicit agent-based model of vehicle consumers that can be used to:• explore potential nonlinear interactions between various influences

that will impact PHEV market penetration, • provide insight into what combinations of policies and procedures will

best encourage PHEV adoption, and • inform us as to what additional data may be most useful to gather.

Based on preliminary simulations we infer that for PHEVs to compete:• Consumers must be helped to assess lifetime fuel costs (stickers?)• Gas prices must rise (gas tax?)• Temporary rebate programs will likely only have short-term impacts,

(unless manufacturers can lower prices after rebates end?)• Increasing PHEV battery range amplifies impacts of PHEV sticker price

(possible synergy with gas tax used to fund battery research?)• More data needed on consumer fear of PHEVs to focus investments

Donna M. RizzoSchool of Engineering, UVM

In collaboration with:

David K. GroverSchool of Engineering, UVM

Jeffrey S. MarshallSchool of Engineering, UVM

With support from: