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% Acceptance Price: $1,000 Premium Range: 100 miles

Stakeholders

Bayesian Network: Problem Statement: Determine the probability of success of EV’s for an existing car manufacturer.

Network Weights

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