ELECTRIC VEHICLES

•Will Barnard•Pam Becker•Troy “Hugin” Noble•Linda Sonne•Jonathan Weiss•Christian Wiest•Ted Yu

Coming soon to your everyday life!

Agenda

• EV Overview• EV Value Proposition• Bayesian Network Overview• Results and Sensitivity• Limitations of Model• Recommendations

What are EV’s?

Why should people like driving an EV?

• Quiet, Clean Driving Experience• High Performance• Lower Operating Costs • No Gas Stations - Refuel Where You Are!• Environmentally Friendly• Energy Security

But are they safe?

Where can I charge?How long will it take?

How far can I go?

What is the cost?

• Vehicle– Purchase– Lease– Subsidies

• Ownership– Battery Replacement– Wear and Tear

• Refueling

Consumer Sensitivity

Minimum Efficient Scale: 60% AcceptancePrice: $1,000 PremiumRange: 100 miles

Stackeholders Parties Interests

Consumers Individual, Rental, CorporateFleet, Public Transportation

Performance Total Cost of Ownership Convenience

Ecological EPA, Sierra Club, WorldPopulation

Environmental protection

Petroleum Stakeholders Gas Stations, ForeignGovernments

Continue world dependenceon fossil fuels

Electric Stakeholders Battery Manufacturers, PublicUtilities,

New sources of revenue Technological gains Efficient use of available

capacity

Political Stakeholders Local, National, and ForeignGovernments

Decrease dependence onforeign resources

Serve constituents

Car Manufacturers World Manufacturers, NewVentures

Profitable production Servicing consumer demand

Stakeholders

Bayesian Network:

Consum er Dem and Manuf. Investm entR&D and Capital

Governm entRequirem ents

Governm entAssistance

E lectic VehicleSupply

Problem Statement:

Determine the probability of success of EV’s for an existing car manufacturer.

Network Weights

E d u ca tion &In fo rm ation

V alu eP rop os it ion

S oc ie ta lA ccep tan ce

C on su m er D em an d

E con om ics

P artn e rs h ip s &A llian ces

S u c cess o fC om p etito rs

M an u f. In ves tm en tR & D an d C ap ita l

L ob b yin g

G lob a lR eg u la tion s

D om es ticR eg u la tion s

G overn m en tR eq u irem en ts

A n ti-tru s tL aws

P aten ts

S u b s id ies

G overn m en tA ss is tan ce

E lec tic V eh ic leS u p p ly

0.35 0.20 0.20 0.15

Results and Sensitivity Analysis

• ResultsResults: Probability (Supply = High) = 54.79%

• SensitivitySensitivity:– If Consumer Demand has 100% probability of being

high:Probability (Supply = High) = 69.94%

– If Consumer Demand has 100% probability of being low: Probability (Supply = Low) = 34.94%

Limitations of model

• Dilution of probabilities given high number of hierarchy levels

• Independence of probabilities• Definition of influence weights • Constraint of two states of nature per node• Lack of consideration of time shifts

Recommendations• Investment

– Prioritize according to influence of primary nodes– Create an implementation timeline

• Demand– Continue to monitor external influences

• Stakeholders– Partner for lobbying and product development