Measuring & Mitigating Electric Vehicle Adoption Barriers

214 Pages·2015·20.93 MB·English

by  Tommy Carpenter

Measuring & Mitigating Electric Vehicle
Adoption Barriers
by
Tommy Carpenter
A thesis
presented to the University of Waterloo
in fulfillment of the
thesis requirement for the degree of
Doctor of Philosophy
in
Computer Science
Waterloo, Ontario, Canada, 2015
(cid:13)c Tommy Carpenter 2015
Author’s Declaration
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis,
including any required final revisions, as accepted by my examiners.
I understand that my thesis may be made electronically available to the public.
ii
Abstract
Transitioningourcarstorunonrenewablesourcesofenergyiscrucialtoaddressingconcerns
over energy security and climate change. Electric vehicles (EVs), vehicles that are fully or
partially powered by batteries charged from the electrical grid, allow for such a transition.
Specifically, if hydro, solar, and wind generation continues to be integrated into the global
power system, we can power an EV-based transportation network cleanly and sustainably.
To this end, major car manufacturers are now producing and marketing EVs. Unfortunately,
at the time of this writing, drivers are slow to adopt EVs due to a number of concerns. The
two greatest concerns are range anxiety—the fear of being stranded without power and
the fear that necessary charging infrastructure does not exist—and the unknown return on
investment of EVs over their lifetime.
This thesis presents computational approaches for measuring and mitigating EV adoption
barriers. Towards measuring the barriers to adoption, we build a sentiment analysis system
for programmatically mining detailed perceptions towards EVs from ownership forums. In
addition, we design the most comprehensive electric bike trial to date, which allows us to
study several aspects of electric vehicles, including range anxiety, at a much lower cost.
Towards mitigation, we develop algorithms for managing a network of gasoline vehicles to
be used by EV owners when a planned trip exceeds the range of their EV. Further, we design
a model for taxi companies to compute whether it is profitable to transition a fraction of
their fleet to EVs.
To summarize our findings, we find that sentiments towards EVs are very positive, especially
regarding performance and maintenance, but there are concerns over range anxiety and the
higher initial price of EVs. There is a delicate balance between these two adoption barriers.
Larger batteries cost more, so alleviating range anxiety with larger batteries leads to pricier
vehicles. Conversely, EVs with low range capabilities can also induce costs, because drivers
and fleets that own EVs may have to often acquire (or own as an additional vehicle) a
gasoline vehicle to fully meet their mobility demands. As a result, EVs are best suited for
drivers and fleets that are able to make long-term return on investment calculations, and
whose mobility patterns do not include many very long trips. Fleets can greatly reduce their
operating costs by adopting EVs because they have the capital to make upfront investments
that are profitable long-term. We show that even under conservative assumptions about
revenue loss due to battery depletion, EVs are already profitable (the company saves more
than enough money to recoup all initial investments) for a large taxi company in San
Francisco. Similarly, EVs can be profitable for two-car families (those who already have a
gasoline car) and for those who can easily acquire a gasoline vehicle when needed, hence
our work on sizing networks of gasoline-vehicle pools for EV owners. Finally, we find that
not only are electric bikes and EVs operationally similar, the sentiments towards the two
technologies are as well. Advancements made in the battery sector, especially those that
reduce costs or weight, are likely to accelerate sales in both markets.
The results presented in this thesis, as well as in prior work, suggest that EVs are suitable
for many drivers and will hence serve a role in our eventual transition away from fossil fuels.
iii
Acknowledgements
First and foremost, I would like to thank Prof S. Keshav, my advisor, for this dissertation
would not exist without his guidance. I started working with Prof Keshav in August 2010,
and in just shy of five years, he has shaped the way I think about research, engineering, the
world, and my place in it. Many methods in this thesis are a result of his vast knowledge of
systems and networking, and his endless supply of red pens. Many positive changes/events
in my life are a result of his teachings. I am very fortunate to have been supervised by him.
I thank my PhD committee: Prof Hussein Mouftah, Prof Lukasz Golab, Prof Johnny Wong,
and Prof Yuying Li for all of their help and suggestions on this thesis. It is greatly improved
due to their guidance.
My wife, Lisa Patel Carpenter, I cannot imagine life—past, present, or future—without.
She has provided infinite love and support throughout my life and journey. Additionally, she
has meticulously read my work and provided countless clarifications and fixes.
I thank my parents for their support and motivation. Each has helped me greatly in a variety
of ways.
Next, I thank the co-authors and collaborators on all projects presented in this thesis. In
order by chapter, they are Prof Lukasz Golab and Sohail Javaad Syed (Chapter 4), Prof
Wong (Chapters 5 and 6), Pavlo Kystmey (Chapter 6), Dr. Andrew Curtis (Chapter 7),
Costin Ograda-Bratu, Rayman Preet, Milad Khaki, and Tobias Schroeder (Chapter 8).
Chapters 5 and 6 would not have been possible without data from Community Carshare.
Similarly, much of my knowledge of queueing theory, which is essential to these chapters,
is inherited from Prof Wong.
I thank Dr. Andrew Curtis and Dr. Earl Oliver, the two senior PhD students under Prof
Keshav’s supervision at the time of my arrival, for their help early in my graduate career.
Jason Baek, Prof Augustin Chaintreau, Prof Catherine Rosenberg, Parsiad Azimzadeh, and
Anthony Schmieder all made contributions to the work presented in this thesis.
To all members of the Tetherless Computing/ISS4E research lab present from August
2010—January 2015: thank you for making DC3562 a second home.
Finally, I would not have applied to Waterloo if it were not for Prof Frédéric Latour, my
undergraduate mathematics professor, a Waterloo alumni. I also thank all professors in
CCSU’s Computer Science department for pushing me as an undergraduate student.
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Table of Contents
List of Tables ix
List of Figures xi
List of Symbols xv
List of Acronyms xix
1 Introduction 1
2 Technology Overview 4
2.1 Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 Battery Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 Plug In Hybrid Electric Vehicles . . . . . . . . . . . . . . . . . . . . 5
2.1.3 Hybrid Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.4 Price Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Electric Bikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Charging (“Refeuling") . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Related Work 10
3.1 Gauging Driver Perceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.1 Field Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.2 Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Range Anxiety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Calculating the Return On Investment . . . . . . . . . . . . . . . . . . . . . 21
3.4 Using GPS to Infer Mobility Demands . . . . . . . . . . . . . . . . . . . . . 26
4 Mining Electric Vehicle Opinions 29
4.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4 Phase 1: Data Collection and Preprocessing . . . . . . . . . . . . . . . . . . 32
4.5 Phase 2: Building The Feature Space . . . . . . . . . . . . . . . . . . . . . 32
v
4.6 Phase 3: Chunking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.6.1 Part of Speech Tagging . . . . . . . . . . . . . . . . . . . . . . . . 33
4.6.2 NLTK Chunking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.6.3 Advantages Of Chunking . . . . . . . . . . . . . . . . . . . . . . . . 35
4.7 Phase 4: Sentiment Querying . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.7.1 Handling Context-Dependent Opinions . . . . . . . . . . . . . . . . 36
4.7.2 Sentiment Querying Pipeline . . . . . . . . . . . . . . . . . . . . . . 37
4.8 Phase 5: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.8.1 Evaluation Methodology . . . . . . . . . . . . . . . . . . . . . . . . 38
4.8.2 Distribution of Sentiments . . . . . . . . . . . . . . . . . . . . . . . 40
4.8.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.9 Prior Feature-Based Opinion Mining Methodologies . . . . . . . . . . . . . . 44
4.9.1 Feature-Based Opinion Mining . . . . . . . . . . . . . . . . . . . . . 45
4.9.2 Word and Sentence Polarity Classification . . . . . . . . . . . . . . . 46
4.10 Conclusions & Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5 A Vehicle Pool Model For Reducing Range Anxiety 49
5.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.2 Sizing A Pool Subject To A QoS Target . . . . . . . . . . . . . . . . . . . . 50
5.2.1 Binomial Based Sizing . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2.2 Sizing Via The Engset Loss Model . . . . . . . . . . . . . . . . . . . 52
5.2.3 Dataset-based sizing . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2.4 Optimizing The Pool Size . . . . . . . . . . . . . . . . . . . . . . . 58
5.3 Evaluation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.3 Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3.4 Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.3.5 Performance Metric Computation . . . . . . . . . . . . . . . . . . . 62
5.4 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.4.1 Arrivals are Non-Markovian and Non-Stationary . . . . . . . . . . . . 63
5.4.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.5 Conclusions & Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6 A Carsharing Model For Reducing Range Anxiety 70
6.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2.3 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.3 Pool Sizing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3.1 Engset Loss Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3.2 Additive Decrease Multiplicative Increase (ADMI) . . . . . . . . . . . 75
6.3.3 Offline Optimal Baseline . . . . . . . . . . . . . . . . . . . . . . . . 76
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6.4 Fleet Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.5 Evaluation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.5.1 Bootstrapping and Confidence Intervals . . . . . . . . . . . . . . . . 78
6.5.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.5.3 Performance Metric Computation . . . . . . . . . . . . . . . . . . . 79
6.5.4 Parameter Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.6.1 Single Pool Dynamic Sizing . . . . . . . . . . . . . . . . . . . . . . 81
6.6.2 Mutli-Pool Dynamic Sizing . . . . . . . . . . . . . . . . . . . . . . . 83
6.7 Modeling Removals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.8 Prior Fleet Sizing and Management Models . . . . . . . . . . . . . . . . . . 89
6.9 Conclusions And Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.9.1 Supporting One Way Trips . . . . . . . . . . . . . . . . . . . . . . . 91
6.9.2 Subscribers Borrowing From Multiple Locations . . . . . . . . . . . . 92
6.9.3 Reservations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.9.4 Budget Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.9.5 Multi Period Future Optimization . . . . . . . . . . . . . . . . . . . 93
6.9.6 Redirecting Blocked Arrivals . . . . . . . . . . . . . . . . . . . . . . 93
7 A Profitability Model for Fleet Transition 94
7.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.2 Taxi Operation Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.3 Data-Oriented Process to Estimating ROI . . . . . . . . . . . . . . . . . . . 97
7.3.1 Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.3.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
7.3.3 Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.3.4 Estimating Charge Levels . . . . . . . . . . . . . . . . . . . . . . . . 99
7.3.5 Using The Model To Infer Costs . . . . . . . . . . . . . . . . . . . . 102
7.4 Switching Station Optimization Algorithm . . . . . . . . . . . . . . . . . . . 105
7.5 San Francisco Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.5.1 Dataset and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 107
7.5.2 Clustering Locations . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.5.3 Assumptions For BEV/PHEV Revenue Analysis . . . . . . . . . . . . 108
7.5.4 Case Study EV Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 109
7.5.5 Existing Taxi Revenue . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.5.6 Revenue Analysis for BEVs . . . . . . . . . . . . . . . . . . . . . . . 110
7.5.7 Revenue Analysis for PHEVs . . . . . . . . . . . . . . . . . . . . . . 114
7.5.8 PHEV vs. BEV Comparison . . . . . . . . . . . . . . . . . . . . . . 115
7.5.9 ICEVs With Increased Efficiency, HEVs . . . . . . . . . . . . . . . . 115
7.5.10 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.6 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 118
8 Designing A Trial To Explore the Barriers to Electric Bike Adoption 120
8.1 Motivating Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . 121
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8.1.1 Coordinating Usage Data With Preconceived Barriers . . . . . . . . . 122
8.1.2 Questions Relevant to EV Researchers/Manufacturers . . . . . . . . 123
8.1.3 HCI Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8.2 Field Trial Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8.2.1 The eBikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8.2.2 The Sensor Kit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
8.2.3 Data Transmission and Analysis . . . . . . . . . . . . . . . . . . . . 125
8.2.4 Participant Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 127
8.2.5 Incentivizing Participation & Care Of Bikes . . . . . . . . . . . . . . 128
8.3 Mapping Battery Voltage to SOC . . . . . . . . . . . . . . . . . . . . . . . 128
8.3.1 Estimating SOC Based On Voltage . . . . . . . . . . . . . . . . . . 129
8.3.2 Voltage Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8.3.3 Biking Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
8.4 Trip Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
8.5 Survey Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.5.1 Common Phrases In Survey . . . . . . . . . . . . . . . . . . . . . . 135
8.5.2 Sentiments In Survey . . . . . . . . . . . . . . . . . . . . . . . . . . 137
8.6 WeBike Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.7 Prior Bicycle Telematic Systems . . . . . . . . . . . . . . . . . . . . . . . . 144
8.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
9 Conclusions 147
9.1 Summary Of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
9.2 Directions for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
9.3 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
References 153
Appendix A Sentiment Analysis Chunking Grammar 173
Appendix B Metric Over Time Figures For Chapter 6 174
Appendix C Proof Of Convergence For Algorithm 1 178
C.1 Blocking Probability As A Fixed Point . . . . . . . . . . . . . . . . . . . . . 178
C.2 Existence and Uniqueness of a Fixed Point . . . . . . . . . . . . . . . . . . . 179
C.3 Fixed Point Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Appendix D WeBike Field Trial Participation Survey 181
Appendix E WeBike To EV Mapping 193
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List of Tables
4.1 Examples of solvable sentiment analysis errors. “FSD" denotes “feature spe-
cific dictionary" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Examples of sentiment analysis errors that are “better left unsolved". . . . 44
4.3 Examples of ambiguous sentiment analysis classifications. . . . . . . . . . . 44
6.1 Comparison of two ELM and two ADMI sizing configurations (corresponds
to Figure 6.4a). P denotes the pool number. ADMI does not size for a QoS
target. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Sensitivity of ELM to the QoS parameter (see Figure 6.4b). P denotes the
pool number. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.3 Comparison of four sizing configurations (corresponding to Figure B.2a). P
denotes the pool number. ADMI does not size for a QoS target. . . . . . . 84
6.4 SensitivityanalysistoQoStarget(correspondingtoFigureB.2b).P denotes
pool number. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.5 Comparison of metrics across the two pools (corresponding to Figure B.3a).
P denotes the pool number. ADMI does not size for a QoS target. . . . . . 84
6.6 The averages of each metric achieved over all periods across pools and for
twodifferentADMIconfigurations(alsoseeFigureB.1).P denotesthepool
number. ADMI does not size for a QoS target. R is a boolean indicating
whether removals are implemented. . . . . . . . . . . . . . . . . . . . . . 88
6.7 The averages of each metric achieved over all periods for pool 0 and four
different ELM configurations with and without removals (also see Figure
B.3b). P denotes the pool number. R is a boolean indicating whether re-
movals are implemented. Note the offline optimal baseline always removes
cars when beneficial with respect to performance metrics. . . . . . . . . . 88
7.1 Table of variables in the Bayesian network. O = Observeable, H = Helper,
Hd = Hidden, D = Discrete, C = Continuous . . . . . . . . . . . . . . . . 102
7.2 Nissan Leaf Specifications [Nis14b] . . . . . . . . . . . . . . . . . . . . . . 111
7.3 Percentage of fares lost in different BEV scenarios. BSS: battery switching
stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
ix
7.4 Example showing how τ affects the number of battery swaps performed and
the remaining SOC of swapped batteries at the airport switching station for
one taxi. q (τ) (Eq(7.15) is computed assuming the 24kWh can charge in
b
four hours from depleted using level 2 charging, i.e., B = 4/24. With
Fx
respect to the last column, the operator cannot buy a fraction of a battery,
but the fractional batteries needed in the system per taxi per station are
summed to compute c . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
exbt
7.5 Chevrolet Volt Specifications [Che14a] . . . . . . . . . . . . . . . . . . . . 116
8.1 Sensor hardware and functionality. †: this is not the exhaustive list of sensors
in the Galaxy S III, which, for example, also includes a barometer for sensing
atmosphericpressureandasensorformeasuringwhetherthereisamagnetic
field being applied to the X,Y,Z axes. . . . . . . . . . . . . . . . . . . . . 126
x