newﬁndingsonpluginvehiclechargingdecisions€¦ · • predicting demand for charging and...

ZEV Ac'onable Science Webinar Series Presenta-on 2

May 28, 2015

New findings on plug-‐in vehicle charging decisions Don MacKenzie, Ph.D. Assistant Professor Department of Civil & Environmental Engineering University of Washington

Where should public charging stations be located to maximize the adoption of plug-in electric

vehicles (PEVs) and minimize gasoline-fueled vehicle travel?

•  These are actually two different questions •  The "best" design of a recharging system

depends on whether your goal is maximize new adopters or to maximize satisfaction of current PEV owners

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Understanding PEV owners' charging choices is important for several reasons

•  Predicting demand for charging and utilization of EVSE

•  Modeling electricity demand in time and space à emissions, grid congestion, energy security

•  Designing recharging networks to: – meet consumers' wants –  increase BEV use –  reduce gas-VMT by PHEVs

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Heterogeneous charging behavior contributes to variation in petroleum savings

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0.0 0.2 0.4 0.6 0.8 1.0

01

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4

Fraction of Total Distance

Density

Utility FactorPetroleum Displacement Factor

Mean = 0.28

Mean = 0.14

Data for 125 pre-production Prius PHEVs in the U.S. (3 kWh battery)

Prior work found ubiquitous charging is as effective as quadrupling PHEV battery size

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0 5 10 15 20 25 30

0.00

0.05

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0.35

Battery Capacity, kWh

Frac

tion

of D

ista

nce

Pow

ered

by

Ele

ctric

ity

0 2 4 6 80.00

0.05

0.10

0.15

0.20

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0.30

0.35

Charging at Every Stop ≥ X Hours

Frac

tion

of D

ista

nce

Pow

ered

by

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ctric

ity

Base battery capacity = 3 kWh

Electricity demand, generation mix, and price vary with time of day and day of week

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We model the probability that a driver will choose to charge, given an opportunity

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+ =

We model the probability that a driver will choose to charge, given an opportunity

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+ =

Price

Dwell time

Charging level

State of charge

Planned travel

We use statistical tools to model choices

•  Central idea: – A driver chooses to charge when the utility of

charging exceeds the utility of not charging – We want to know how the utility of charging

depends on the situation •  Price •  Charging level / power •  Dwell time •  State of charge •  Future travel plans

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First study

125 instrumented PHEVs Collaborators: Haixiao Yu, UW

Completed: Instrumented PHEV study

Vehicle Data •  125 instrumented PHEVs: 2010 Toyota

Prius-based prototype, 3 kWh battery •  Second-by-second data: GPS, speed,

energy use, state of charge (SOC), etc. •  A total of 59,287 drive cycles

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Charging locations identified from •  Vehicle dataset (observed events) •  Alternative Fuels Data Center •  PlugShare

We found three opportunities to improve prior analysis of Prius PHEV charging (Zoepf et al.)

1.  Crude representation of utility of charging –  State of Charge + Dwell Time

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2.  Modeled probability of charging – We really want to know probability of charging

given the opportunity

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given the opportunity 3.  Restrictive method of modeling heterogeneity

across users –  No correlation in preferences

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We hypothesize that utility depends on the amount of energy obtained during a charge

The smaller of:

Charge Energy

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Energy that be transferred during the stop

Available capacity in the battery

OR

http://www.freepik.com/free-icon/battery_692671.htm http://www.wired.com/2013/05/bosch-power-max/

We identify different types of charging events based on dwell time and energy obtained

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18

A: No charging

A


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A: No charging B: Partial charge at Level I power

A B


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A: No charging B: Partial charge at Level I power C: Full charge with time to spare

A B

C


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A: No charging B: Partial charge at Level I power C: Full charge with time to spare D: Partial charge w/ time to spare

A B

C

D


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A: No charging B: Partial charge at Level I power C: Full charge with time to spare D: Partial charge w/ time to spare E: Partial charge at Level II

power

A

E

B

C

D

Utility of charging is highly non-linear with Charge Energy

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0

1

2

3

4

U-lity of Charging (All else equal)

Charge Energy

Charge Energy provides an improved description of charging behavior

•  More theoretically sound •  Better model fit using standard measures

(Akaike information criterion, Bayesian information criterion, adjusted ρ2)

•  Relationship between charge energy and utility of charging: •  Highly non-linear •  Strongest below 20-30%

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We used various sources for EVSE locations, and various definitions of "nearby"

Locations 1.  Locations where we observed vehicles

charging in this study 2.  Alternative Fuels Data Center 3.  PlugShare

Proximity 1.  0 m (24,089 trips) 2.  100 m (29,387 trips) 3.  200 m (32,031 trips)

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Results sensitive to method of identifying EVSE locations

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0

0.2

0.4

0.6

0.8

1

Normalize

d part-‐w

orth of charge en

ergy

Charge energy

Effect of varying method of iden-fying EVSE loca-ons

Public EVSE loca-ons

Private EVSE loca-ons

AFDC dataset

Private + AFDC + Plugshare

A. Loca-ons where any vehicle was charged during this study B. Loca-ons where the same vehicle charged at some point in this study

C. Alterna-ve Fuels Data Center

D. [B + C + PlugShare loca-ons]

Results not sensitive to definition of "nearby" EVSE (up to 200 m)

0

0.2

0.4

0.6

0.8

1

Normalize

d u-

lity of charging

Charge energy

Fixed effects of charge energy with different distance threshold

0m

100m

200m

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Latent class logit is an alternative method of modeling heterogeneity

•  Divide individuals into classes (market segments), and assume that preferences are constant within each class.

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Latent class logit yields a better-fitting model, while capturing heterogeneity between drivers

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0

0.2

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1

1.2

1.4

Normalize

d part-‐w

orth of C

harge En

ergy

Charge energy

Differences in u'lity of charge energy by latent class

Class1 Class2 Class3 Class4 Class5 Class6

Conclusions from study 1

•  Specifying a utility function based on the charge that can be taken on (Charge Energy variable): •  Improves model fit •  Reveals non-linear relationship between Charge

Energy and utility, with effect strongest below 20-30%

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Energy and utility, with effect strongest below 20-30% •  Model estimation results in this work were:

•  Sensitive to how EVSE locations were identified •  Less sensitive to the distance threshold used to

define being "near" or "far" from an EVSE location

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Energy and utility, with effect strongest below 20-30% •  Model estimation results in this work were:

•  Sensitive to how EVSE locations were identified •  Less sensitive to the distance threshold used to

define being "near" or "far" from an EVSE location •  Latent class logit with six classes provided a better fit to

the data than mixed logit with independent normal parameter distributions

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Implications for future work

•  Future investigators may wish to consider latent class specifications that include a charge energy variable, and should exercise caution in inferring EVSE availability, when estimating models of charging choice

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Second study

Survey of BEV owners around the U.S.

Collaborators: Yuan Wen, UW David Keith, MIT

Data: Web-based stated preference survey

•  We presented existing PEV owners with eight hypothetical scenarios and asked whether they would charge or not

•  Scenarios were characterized by:

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Price $0.50, $1.00, $1.50, $2.00, $5.00/hr Charger power 1.9, 6.6, 50 kW Dwell -me 0.25, 0.5, 1, 2, 4, 8 hours Distance to home 2, 5, 10, 20, 30, 50 miles Current range remaining (SOC) 3 ~ 70 miles Distance to next charge opportunity 2, 5, 10, 20, 30, 50 miles

Respondents were recruited through Electric Auto Association (EAA) membership

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•  EAA members are highly interested in the technology and willing to participate in the study, even without tangible compensation

•  Many of the members have owned PEVs for longer than other users, so their decision making is "mature"

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30

40

50

-120 -110 -100 -90 -80 -70lon

lat

•  EAA chapters spread all over the U.S., providing geographic diversity

Sample skews male, wealthy, well-educated

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Age Gender 18-34 11% Female 12% 35-45 28% Male 88% 46-55 28% Household Size

55+ 33% 1 10% Education 2 41%

Less than Bachelor's Degree 29% 3 23% Bachelor's Degree 40% 4+ 26%

Master's Degree 19% Electricity price at home 　

Doctor's Degree 6% $0.06/kwh~$0.08/kwh 25% Professional Degree 6% $0.09/kwh~$0.11/kwh 46%

Income $0.12/kwh~$0.14/kwh 14% less than $59,999 12% $0.15/kwh~$0.17/kwh 6% $60,000-$99,999 19% $0.18/kwh~$0.20/kwh 3%

$100,000-$119,999 17% $0.21/kwh~$0.23/kwh 2% $120,000-$139,999 10% $0.24/kwh+ 4%

$140,000+ 42%

We started with what "should" matter to drivers, in a narrow economic sense

•  Cost – How much it costs to charge here – How much they will pay to charge at home later

•  Charge Energy – How much energy they can take on by charging

•  Necessity – Whether they need to charge to get to next EVSE

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Results were mostly consistent with expectations

•  Higher cost à lower utility •  More energy à higher utility •  Can reach next EVSE w/o charging à lower utility

•  But, we also found that the effect of a charging decision on (subsequent) home charging cost was not a significant predictor of utility – Respondents not considering how their away-from-

home charging affects their home electricity bill

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Next, we tested robustness by including some additional predictor variables

•  Some of the variables that had been presented to respondents: – Price ($/hour) – Dwell time – EVSE power – Distances to home and to next charging opportunity – Present state of charge

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Simpler variables yielded a better-fitting model than the variables that "should matter"

•  Cost, charge energy, and necessity were no longer statistically significant predictors – Respondents react to charger power and dwell time

more than to the charge energy (range charged). – They react to price more than to cost – They react to state of charge and distance to home

more than to the necessity of charging

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Simpler variables yielded a better-fitting model than the variables that "should matter"

•  Cost, charge energy, and necessity were no longer statistically significant predictors – Respondents react to charger power and dwell time

more than to the charge energy (range charged). – They react to price more than to cost – They react to state of charge and distance to home

more than to the necessity of charging •  Suggests that respondents may be applying rough

heuristics to salient information –  even though they "shouldn’t" if they were only trying

finish their travel day at minimal cost. 44

Next, we tested for heterogeneity in preferences

•  Latent class segmentation provided the best fit

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•  Latent class segmentation provided the best fit •  All classes responded to price

– Two responded to cost at stop, one to cost at home

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– Two responded to cost at stop, one to cost at home •  Most prefer Level 2 or DCFC

– One class strongly prefers DCFC

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– One class strongly prefers DCFC •  All more likely to charge when state of charge is low

– But amount of range gained is irrelevant

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– One class strongly prefers DCFC •  All more likely to charge when state of charge is low

– But amount of range gained is irrelevant •  All more likely to charge when further from home

– About half consider distance to next charging point

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Applying the model to estimate probability of charging in two illustrative scenarios

Variable CASE 1 CASE 2

Price ($/hour) $1.50 $5.00

Cost at this stop ($) $1.50 $20.00

Additional cost at home ($) ($0.56) ($1.85)

Dwell time: >30min Yes Yes

Charger power (kw) 50 6.6

Range charged (mi) 25 96.3

Range remaining in battery (mi) 50 32

Distance to home today (mi) 20 20

Enough to next charger Yes Yes

Distance to next charging opportunity (mi) 30 30

Average Probability 0.901 0.274

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Different classes respond differently to different cases

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Average

Charging Probability

CASE1 CASE 2

Reconciling results of the two studies

•  Both studies found that latent class segmentation provided the best-fitting models

•  Study 1 says "charge energy" provides better fit –  Instrumented, small-battery PHEVs

•  Study 2 says simpler variables provide better fit – Survey of BEV owners

•  We need to work out if this is due to differences in data collection, differences in vehicle type, or something else

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Future / ongoing work

Level 1, 6.6KW, $0.5/hour always available0

• Level 2, 6.6KW, $0.5/hour •  always available

• Level 2, 6.6KW, $0.5/hour • always available

stop 1

stop 2

stop 3

She will stay here for: 8 hours



Jane’s home Here is Jane’s travel day:

40 miles

20 miles

30 miles

10 miles

Dynamic vehicle use – charging survey

Here is Jane’s travel day:


Level 1, 6.6KW, $0.5/hour always available0

• Level 2, 6.6KW, $0.5/hour •  always available

• Level 2, 6.6KW, $0.5/hour • always available

stop 1

stop 2

stop 3




Jane’s home

40 miles

20 miles

30 miles

10 miles


Queue modeling & charge station economics

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$-

$10

$20

$30

$40

$50

$60

$70

$80

0 100 200 300 400 500 Vehicles Served per Month

Cost per Vehicle Served

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% UNlizaNon

Probability at least one EVSE is available

1 EVSE per bank

2

5

10

•  Fast chargers need high utilization to be cost effective

•  High utilization = less availability for users

•  Multi-unit banks improve this tradeoff

Preliminary conclusions for policymaking (1/2)

•  Sound research requires comprehensive data on EVSE locations & availability

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•  BEV owners report a strong preference for Level 2 and DCFC for away-from-home charging – Whether strictly necessary or not

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•  BEV owners report a strong preference for Level 2 and DCFC for away-from-home charging – Whether strictly necessary or not

•  Nothing in here says we should support discounted electricity at public charging stations – Need further study of PHEV owners' charging

decisions, and BEV owners' usage decisions

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•  EVSE to support current users may differ from EVSE to encourage future adopters

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•  EVSE to support current users may differ from EVSE to encourage future adopters

•  Future EVSE growth plans should consider benefits of more locations vs. more plugs per location –  "EV Everywhere" vs "EV Any Time"

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Thank you!

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