Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal Control Options for Plug-in Hybrid Vehicles
Context Sensitive Battery Management Strategies for Hybrid
Automobiles
J. Fox, N. Moehle, J. Platt, A. Ademola-Idowu, N. Redmon and B.
Dally

May 2 2017

J. D. Fox

AI-AT-SLAC Seminar1Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

US Energy Flow 2016 - efficiency, too

J. D. Fox

AI-AT-SLAC Seminar2

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

Efficiency of internal combustion engine
Why is this so inefficient?
How do Hybrid cars (e.g. a
Prius) get higher efficiency?
They still use internal
combustion engines.
How would electric drive
vehicles compare?
How do we make electricity fora vehicle fleet?
Need a sort of "Wellhead toWheel" efficiency to sort thisout
J. D. Fox

AI-AT-SLAC Seminar3

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasPlug-in Series Hybrid - Chevrolet Volt example
Plug-In Hybrid uses both electricaland petroleum energy sources
disadvantage - two drive trains,
weight, complexity
35 -50 mile range as plug-in (9.3 galtank, range 380 miles)
fast refuel from fuel distribution, slow
from gridConventional modern IC engine
Electric motor, regenerative braking
Series Hybrid allows either, or, bothdrive approach
1.4L 84 HP ( 63 kW)
149 HP (111kW) electric motor
16 kWh battery capacity ( 435 lbs,
200kg)EPA 98MPG equiv 35/40 MPG( 3781 lbs curb weight)

J. D. Fox

AI-AT-SLAC Seminar

4Motivation

Teasergrow
modestly overBattery
this time frame.
Overall,“vehicle electrifiNext
cation”(HEV, PHEV,
BEV, anFirst Optimal
Results
InitialConclusions
Steps
Extras
FCEV) progresses significantly to about 40% in 2050. Turbocharged engines, gasoline and d
are assumed to grow to almost half of the total internal combustion engine vehicle sales (currin the United States, these are mostly standard NA-SIs). There is the question as to whether t
automotive market would really evolve to this multi-technology state. Viewed as a decreasing
internal combustion engine (ICE) based component and an electrified component, it is not rea
seven different propulsion systems, but a bifurcation. We have assumed that fuel-cell technol
and hydrogen fuel, in the in-use fleet, grow slowly. Much will depend on the response that th
initial rollout of fuel-cell vehicles (FCV), expected over the next decade or so, evokes from t
vehicle-using
public.
An importantReductions
issue is developing refueling infrastructure for alternative
Potential
for
Substantial
Our judgment is that the evolution of more than one major new fuel infrastructure is unlikely
intheLight-Duty
VehicleUse is most likely (in the longer term) to be hyd
next few decades,
and thatEnergy
new infrastructureThus,Greenhouse
we do not view natural-gas-fueled
LDVs as a significant component of the alternativeandGas Emissions
vehicles and fuels mix. We have emphasized growth in PHEVs (developed from HEVs) as thReportlarger electricity-using component, with BEVs more modest in sales and on-road use. Throug
Massachusetts Institutemuch greater vehicle recharging flexibility, PHEVs and Extended Range Electric Vehicles (Eof Technology
moderate the impact of electricity use on the electrical supply and distribution system, signifi
Sloan Automotive Laboratory
Engineering Systems DivisionThis is intended to be a realistic scenario.

Progress

Estimates for PHEV Market ShareOn the Road toward 2050:

November 2015

New Vehicle Market Share100%

Is there going to be an impact
from PHEV vehicles?

90%

What fraction of the US fleet?

70%

MIT Sloan Study suggests PHEV
market roughly equal to BEV
marketPractical advantages to electricaland petroleum fuel, unlimited
range

Editors:
John Heywood
Don MacKenzie
Contributors:
Ingrid Bonde Akerlind
Parisa Bastani
Irene Berry
Kandarp Bhatt
Alice Chao
Eric Chow
Valerie Karplus
David Keith
Michael Khusid
Eriko Nishimura
Stephen Zoepf

80%

60%
50%
40%
30%

BEV
PHEV
HEV
DieselGasoline – Turbo
Gasoline – NA-SI

20%
10%
0%
2010

Figure 9.4

J. D. Fox

FCHEV

2020

2030

2050

Powertrain market share input values 2010–2050.

AI-AT-SLAC Seminar

5Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasContext Sensitive Hybrid Battery Management
Seed Grant, Precourt Institute for Energy Efficiency, Stanford
Original proposal - two research tracks
Task 1 - Investigation of Route Estimator methods
Task 2 - Vehicle Resource management techniques, based onroute knowledge

Research directed at improving grid-sourced energy use in plug-in
hybrids, minimizing fuel use

J. D. Fox

AI-AT-SLAC Seminar

6

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasContext Sensitive Hybrid Battery Management
Plug-In hybrid should maximally transfer energy from Grid, minimally use fuel resources
Battery State-Of-Charge ( SOC) - needs headroom to accept regenerative braking,downhill energy recovery
SOC minimum to allow acceleration torque, electric efficiency
But when do you use each resource?
How do you decide to torque split gas vs. electric drive?
Do you ever recharge the battery from the petroleum?
Can you plan resources to end a trip with low SOC?
Can you use knowledge of the trip energy profile (context) to optimally manage battery andfuel resources?

J. D. Fox

AI-AT-SLAC Seminar

7

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal beats CDCS Conventional by 10% to 20%

6

Med Urban First

 107

4.5

Med Urban Last

 107

4
5
3.5
3

Fuel

Fuel

4

3

2

2.5
2
1.5
1

1
OptimalDefault

OptimalDefault

0.5

0

0
0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

Time (s)

3000

4000

5000

6000

7000

Time (s)Compare Context-Sensitive Optimal Controller vs. Conventional(CDCS) controller fuel use
Energy-equal routes, with energy profile differences
Is this always true? How can we study this?
J. D. Fox

AI-AT-SLAC Seminar

8Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasResearch Directions
Literature Review
Previous Work in route estimation from Nissan USA, Microsoft
Research
Limited publications on optimal control using route information recent Argonne paper, Larsson Chalmers University Ph.D. Thesis

First decision - focus on optimal management with known route
Best match to graduate student interests
Defer Route Estimation for a later year, student with specific interest/expertiseFirst efforts - investigate vehicle simulation tools and options toquantify energy on a determined route
Selection of Autonomie simulation tools for initial studies
Investigate battery technology for vehicles , understand series and parallel hybrid
vehicle technologies
Investigate optimal control methods for hybrid engine vs battery tradeoffs, state ofcharge resource utilization

J. D. Fox

AI-AT-SLAC Seminar

9

Teaser

Progress

First Optimal Results

GB Torque Out, Nm

Motivation

Autonomie Vehicle Simulator

800

Battery

Initial Conclusions

Next Steps

Extras600
400

We build our simulation models, vehicle
technology around the200
Autonomie matlab/simulink framework

ZING

0
50
100
150
Developed at Argonne National Lab
Vehicle Speed, mph
Allows quantitative studies of vehicle performance on specified route
Figure8 – Mode shift map.
Outputsof primarysystemoking modeling
tool that cansimulate a broad
rangeparametersof powertrain configurations [6]. The drivermodelnd needed to meetthe vehicle
speed
The torquedemand is interpretedbyavarious
high-levelvehicle
controller that
Allowsstudy
of trace.
variouscontrollers,studiesoftechnology
choices
ENERGYMANAGEMENTSTRATEGYs torque demands while ensuring that thesystem operates
within its constraints.
Detailed transmission
Has including
significant
amountmeasuredvehicleusing SimDriveline,
specific
losses forof
gear
spin, as shownin Figuredatabase,
6. Such a levelmodels
of detail isof selected power plants
he benefits of new
PHEV models
powertrain configurations.
Accordingtemperature
to sizing results,effects
the average all-electric range of PHEVs is almost 40 mi. To achieve this ele
Battery
including

HICLE IN AUTONOMIE

management strategy must be developed.

BK1
CL2

CL1

MC1

MC2

ring

Charge Sustaining(CS)

Charge Depleting (CD)carrier
VehicleEnginesuncarrier

0.9

ring

0.8
0.7

SOC

PG_system
Planetary Gear
(Single Planet)

0.6
0.5
0.4
0.3

BK1

0.2
0.1
CL1

CL2

0

10

20

30

40

50

Distance

Figure 9 – Control strategy SOC behavior.
Figure 6 – Transmission model of the GM Voltec.

DURING ACCELERATION
J. D. Fox

The energy management strategy that we developed is shown in Figure 9. The controller drove the firs
the battery, which depleted
the battery
SOC from 90% SOC to 30% SOC. In the CD mode, the Voltec
AI-AT-SLAC
Seminar10

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasParallel Plug-In Hybrid Vehicle Model

Prius

2012 Similar to Toyota Prius plug-in, but bigger battery capacity
uses Automie hybrid controller models
includes parasitic losses, battery losses, thermal models
Autonomie IC engine
maps (Honda Insight)
Electric motors,
regenerative braking
Hybrid transmission(planetary gear)
allows either, or, bothdrive approach
Gas Engine 95 kW
Electric motor 1 55
kW, motor 2 66 kW
Prius Four shown in Blizzard Pearl with available Deluxe Solar Roof Package.1

Battery capacity 7
kWh( 4.4kWh Toyota)
Prius Four interior shown in Bisque SofTex t

The 2012 Toyota
Prius has demonstrated that there
can be Seminar
harmony between man, nature and machine. Prius has
J. D. Fox
AI-AT-SLAC
11

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasThottled Engines, Specific Fuel Efficiency vs Torque,
Power and RPM
Efficiency Curve

0.4

0.35

0.3

Efficiency

0.25

0.2

0.15

0.1

0.05

0
0

50

100

150

200

250

Torque (Nm)Throttled engines - are most efficient at wide open throttle
Efficiency vs. RPM a function of valve timing, induction effects
But can you use all the power? Use Battery Resources to store fuel-derived energy
Need Active Battery management -impacts from charge/discharge rates, heating, lifetime
J. D. Fox

AI-AT-SLAC Seminar

12

plot(dat2.time_simu,
xlabel('time(s)')
Progress
First Optimal
Resultsdat2.chas_plant_lin_spd_out_simu)
Battery
Initial Conclusions
Next Steps
Extrastitle('freeway(m/s)')first')
ylabel('speed
xlabel('time (s)')
ylabel('speed
dat1.t_split =(m/s)')
1800;
dat2.t_split = 1550;
dat1.t_split == 3000;1800;
dat1.t_split
dat2.t_split
dat2.t_split == 1550;4000;
We can evaluate various
controllers,on
two energy-equal routes
dat1.t_split=
3000;
dat1.t_split_ind= min(find(dat1.time_simu > dat1.t_split));dat2.t_split=
4000;
dat2.t_split_ind=ofmin(find(dat2.time_simu
This lets us quantify possible impacts
resource optimization > dat2.t_split));dat1.t_split_ind = min(find(dat1.time_simu > dat1.t_split));Conventional Controller(CDCS)- use=battery
first, when SOC is>30%,
become a hybrid
dat2.t_split_ind
min(find(dat2.time_simu
dat2.t_split));

Motivation

Teasercompare two drive cycles, with equal-energy demand

CruiseFirst

Urban
Stop and
Go First
J. D. Fox

AI-AT-SLAC Seminar

13

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

CDCS Controller - Route Engine Power, Torques
Urban component - 5 NYC taxi cycles ( lots of stop and go)
Suburban ( cruise) component - steady state cruise ( no stop and go)
Conventional CDCS Controller
4

urban firstx 10

urban first200

8Engine torque (Nm)Engine power (%)

10

6
4
2
0

0

1000

2000

4

4000

5000

6000

50
0

6
4

0

4000

4000

5000

6000

7000

5000

6000

7000

50

−50

3000

3000

100

0

2000

2000

150

2

1000

1000

200

8

0

0

freeway first

freeway first

x 10

100

−50

7000

Engine torque (Nm)Engine power(%)

10

3000

150

5000

6000

7000

0

1000

time (s)

2000

3000

4000
time (s)Very different torque and power profiles ( Gas engine)
Total Energy of the route is the same in both cases
J. D. Fox

AI-AT-SLAC Seminar

14

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasConventional CDCS Controller Fuel Use
Identical total energy demand both routes
Any variation in consumption is strictly from resource management
Can we do better than this? Fuel use 4.4 to 5.1 ×107 J
7

6

x 10

5

fuel (J)

4

3

2

1
urban firsturban last
0

0

1000

2000

3000

4000

5000

6000

7000

time (s)In this example, scheduling of battery use - roughly 10% difference
J. D. Fox

AI-AT-SLAC Seminar

15

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

CDCS Controller - Battery usage two routes
Identical energy both routes
Battery State of Charge ( SOC) reflects use during trip power,
torque profile
1

0.9

0.8

SOC

0.7

0.6

0.5

0.4

0.3
urban firsturban last
0.2

0

1000

2000

3000

4000

5000

6000

7000

time (s)J. D. Fox

AI-AT-SLAC Seminar

16

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal Control via Convex Optimization
We find the minimum-fuel control by (integer) convex optimization
Uses a vehicle/route model to predict and optimize energy usage
Considers
Gas Engine Specific Fuel vs Power, Torque and RPM
Efficiency and constraints of electric motors and engine planetary gearset
Battery losses (resistive-capacitive model, losses from internal series resistance)
Engine thermal minimum on-time efficiency costs

Solved on the fly (<1 sec.)enabled by fast algorithms and modern high throughput processors
can incrementally resolve problem with new information
Uses Convex Optimzation tools from Stephen Boyd

Many versions possible, e.g.:
Minimize combination of: fuel use, battery use, engine cycling, battery stress . . .
Assume known route, or probabilistic route (i.e., true route is one of several
candidates)

Allows study of various controllers, studies of various vehicle
technology choices (use as a tool to optimize vehicle
characteristics). Optimal solution applied to Autonomie model
vehicle via engine state parameters
J. D. Fox

AI-AT-SLAC Seminar

17

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal Controller fuel, battery SOC Urban First

Suburban (cruise)component medium
speed steadystate cruise
Data is Urban
First optimalbattery
enginedrive cycle

0

0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

3000

4000

5000

6000

7000

1
0.8

SOC

Urban
component 5 NYC taxi
cyclesMed Urban First

 104

-5

0.6
0.4
0.2

4

 107
Fuel
engine on

3

Fuel (J)convexoptimized
hybrid battery
controller,
mixed urbansuburban
routePower (W)

5

2
1
0
0

1000

2000

3000

4000

5000

6000

7000

Time

J. D. Fox

AI-AT-SLAC Seminar

18

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal Controller fuel, battery SOC Urban Last
4battery
enginedrive cycle

Suburban (cruise)component steady state
cruise ( nostop and go)
Data is Urban
Last optimal

Power (W)

-6
0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

3000

4000

5000

6000

7000

2000

3000

4000

5000

6000

7000

1
0.8

SOC

Urban
component 5 NYC taxi
cycles ( lotsof stop and
go)

0
-2
-4

0.6
0.4
0.2

4

 107
Fuel
engine on

3

Fuel (J)convexoptimized
hybrid battery
controller,
energy-equalroutesMed Urban Last

 104

2

2
1
0
0

1000

Time

J. D. Fox

AI-AT-SLAC Seminar

19

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOptimal Controller comparison, urban first/last
Med Urban First

 104

4
battery
enginedrive cycle

Med Urban Last

 104battery
enginedrive cycle

2Power (W)Power (W)

5

0

0
-2
-4
-6

-5
1000

2000

3000

4000

5000

6000

7000

1

1

0.8

0.8

SOC

SOC

0

0.6
0.4

0

 10

1000

2000

3000

4000

5000

6000

2000

3000

4000

5000

6000

7000

0

1000

2000

3000

4000

5000

6000

7000

2000

3000

4000

5000

6000

7000

0.6

7000

0.2

7

4
Fuel
engine on

 107
Fuel
engine on

3

Fuel (J)3

Fuel (J)

1000

0.4

0.2

4

0

2
1

2
1

0
0

1000

2000

3000

4000

Time

5000

6000

7000

0
0

1000

Time

Can we compare histograms of engine power output, engine run time,etc. to better understand how the optimal solution is different fromCDCS?

J. D. Fox

AI-AT-SLAC Seminar

20

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFuel for two routes, optimal-control vs. CDCS

6

Med Urban First

 107

Med Urban Last

 107

4.5
4

5
3.5
3

Fuel

Fuel

4

3

2

2.5
2
1.5
1

1
OptimalDefault

OptimalDefault

0.5

0

0
0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

Time (s)

3000

4000

5000

6000

7000

Time (s)convex-optimized hybrid battery controller, energy-equal routes
Compare with same routes using Conventional (CSCD) controller
in both cases - Optimal beats Conventional by > 10% to 20%
Optimal uses same fuel both routes
J. D. Fox

AI-AT-SLAC Seminar

21

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFuel, Battery SOC for two routes, Optimal vs. CDCS
Med Urban First

1

OptimalDefault

0.8

SOC

SOC

0.8
0.6
0.4

0.6
0.4

0.2

0.2
0

6

Med Urban Last

1
OptimalDefault

1000

 10

2000

3000

4000

5000

6000

7000

0

7

2000

3000

4000

5000

6000

7000

Fuel

4

Fuel

4

1000

 107

6

2

2

OptimalDefault

0

OptimalDefault

0
0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

Time (s)

3000

4000

5000

6000

7000

Time (s)Convex-optimized hybrid battery controller, energy-equal routes
Compare with same routes using Conventional (CDCS) controller
Both Optimal vs. Conventional Controller SOC end at 30%
Both controllers run the engine, but at what torque and RPM?

J. D. Fox

AI-AT-SLAC Seminar

22

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFast Cruise, optimal-control vs. Conventional CDCS

Fast Urban Last

1

SOC

SOC

0.4

0.6
0.4

0.2

0.2
0

1000

2000

3000

4000

5000

6000

7000

0

 108

4

1000

2000

3000

4000

5000

6000

7000

 108

3

Fuel

3

Fuel

OptimalDefault

0.8

0.6

4

Fast Urban First

1
OptimalDefault

0.8

2
OptimalDefault

1

2
OptimalDefault

1

0

0
0

1000

2000

3000

4000

5000

6000

7000

0

1000

2000

3000

Time (s)

4000

5000

6000

7000

Time (s)convex-optimized hybrid battery controller, energy-equal routes
Compare with same routes using Conventional (CDCS) controller
in both cases - Optimal beats Conventional by >10%
higher cruise speeds pushes engine into higher efficiency range - less to optimize

J. D. Fox

AI-AT-SLAC Seminar

23

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFuel Comparison for 4 cruise speeds
4

Optimal vs CDCS Control

 108

Fuel Efficiency Gain

35

Default
OptimalOptimal Urban Last
Optimal Urban First

3.5
30

3

Fuel (J) Percentage Gain

25

Fuel (J)

2.5

2

1.5

20

15

10

1

5

0.5

0
fast last/first0
fast last

fast first medfast lastmedfast first med last

med first

slow last

medfast last/first

med last/firstslow last/firstslow first

convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
Compared for 4 cruise speeds ( 85, 65, 39, 28 mi/hr ). Fixed 7000 sec cycle
in all cases - Optimal beats Conventional by >10% to >20%
higher cruise speeds pushes engine into higher efficiency range - less to optimize
J. D. Fox

AI-AT-SLAC Seminar

24

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasEngine On/Off cycles, comparisonsMedian On Time

18

Median Off Time

200

Optimal
CDCS

Optimal
CDCS

180

16

160
14

140
12

Seconds

Seconds

120
10

100

8

80
6

60

4

40

2

20

0

0
FUL

FUF

MFUL

MFUF

MUL

MUF

SUL

SUF

FUL

FUF

MFUL

MFUF

MUL

MUF

SUL

SUF

convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
Compared for 4 cruise speeds (85, 65, 39, 28 mi/hr ) Fixed 7000 sec cycle
Possible Impact on economy from engine temp
Optimal control cost on engine start
J. D. Fox

AI-AT-SLAC Seminar

25

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasEngine Power Histogram Insight
Optimal med urban first

1000
500

1500
1000
500

0
0

0.5

1

1.5

2

2.5

3

3.5

4

0

4.5

0

 104

0.5

1

Time (s)

500

2

2.5

3

3.5

4

4.5

CDCS med urban last

2000

1500
1000

1.5

 104

CDCS med urban first

2000

Time (s)Optimal med urban last

2000

1500

Time (s)Time (s)

2000

1500
1000
500

0
0

0.5

1

1.5

2

2.5

3

3.5

Engine Power (W)

4

4.5
 104

0
0

0.5

1

1.5

2

2.5

Engine Power (W)

3

3.5

4

4.5
 104

convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
Medium cruise speed, fixed 7000 sec cycle, urban first (left) last (right)
The optimal controller runs the engine at higher power, for shorter intervals
higher power - higher specific efficiency (throttled engine)Is there an impact on the battery?
J. D. Fox

AI-AT-SLAC Seminar

26

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasEngine Power Histogram Fast Routes
OptimalOptimal

3000

2000

Time (s)Time (s)

3000

1000
0

2000
1000
0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

0.5

1

1.5

2

2.5

3

3.5

4

 104

CDCS

3000

2000

Time (s)Time (s)

3000

1000
0

4.5
 104

CDCS

2000
1000
0

0

0.5

1

1.5

2

2.5

Engine Power (W)

3

3.5

4

4.5

0

0.5

1

1.5

 104

2

2.5

3

3.5

Engine Power (W)

4

4.5
 104

convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
fast cruise speed, fixed 7000 sec cycle, urban first (left) last (right)
The optimal controller runs the engine at lower average power
Still achieves a fuel economy advantage
This operating point minimizes combination of battery and fuel efficiency losses
J. D. Fox

AI-AT-SLAC Seminar

27

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras15

Fast Urban First Losses

 105

15

Optimal
CDCS

10

0

3

1000

 10

2000

3000

4000

5000

6000

7000

8

2

1

Optimal
CDCS

0
0

1000

2000

3000

4000

Time (s)

5000

6000

7000

Fast Urban Last Losses

 105
Optimal
CDCS

10

5

0

Cumulative Engine Losses (J) Cumulative Battery Losses (J)Cumulative Engine Losses (J) Cumulative Battery Losses (J)Fast Engine Power Histogram, mystery explained

5

0
0
3

1000

2000

3000

4000

5000

6000

7000

 108

2

1

Optimal
CDCS

0
0

1000

2000

3000

4000

5000

6000

7000

Time (s)This operating point minimizes combination of battery and fuel efficiency losses
Battery losses - from internal resistance and charge/discharge currents
Engine losses - efficiency of engine*fuel energy
convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
fast cruise speed, fixed 7000 sec cycle, urban first (left) last (right)
The optimal controller runs the engine at lower average power
Still achieves a fuel economy advantage
J. D. Fox

AI-AT-SLAC Seminar

28

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasCharge rates, Temperatures impact battery lifetime
4.5

Charge Limit Impact on Fuel Economy

 107

4

Operation
Temperature“15-year life capable of delivering atleast 55 kW for 18 seconds, 30kW
continuous at a system cost of$12/kW.”

Pulse
Power

Efficiency“60% peak energy-efficient, achieves
a 325W/kg and 220 W/L power
density.”

Li-ion Battery
3.5

Self
Discharge

Price

3

Fuel (J)

Freedom CAR goal

2.5

2

1.5

EnergyDensityLife

1Specific Power
0.5

Figure 1.4 Status of Li-ion battery compared to the energy storage goals set up by

0
12 kW

8 kW

4 kW

2 kW

FreedomCAR

To get good fuel efficiency, we run engine at high power output
Battery is charged with extra engine power
1.2 Overview of Li-ion battery and its application in EVs
Study impact of limiting battery charge rate in optimal controller for medium route
Compared for Autonomie (nominal), 2/3 , 1/3 and 1/6 nominal
Lithium element has low density and the lowest potential to the standard hydrogen
Thermal management of battery required at high charge rates, accessory power required
electrode. Its theoretical specific capacity is as high as 3861mAh/g [54-55]. Therefore,
Suggests balance of battery longevity and fuel economy possible
lithium based battery has been considered as an excellent energy storage solution forJ. D. Fox

AI-AT-SLAC Seminar

29

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFirst Results - Conclusions
The conventional CDCS Battery Controller is sensitive to route
profilesRoute variations for equal energy routes seen as 10% effect
Value of route knowledge
What sorts of optimal control methods work well?

First Optimal Tests - convex optimization
Fuel Economy increase over Conventional CDCS > 10 to 15% forsimple examples
A promising first result
Battery management might be part of an optimal solution
Recent publications looking at statistical route information - ours is
a complementary method

J. D. Fox

AI-AT-SLAC Seminar

30

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasFirst Results - are promising
The idea has value - demonstrated via simplified technology
models and hybrid route study
We have a technical framework to evaluate energy consumption ofvehicles, and test controllers
Autonomie models - complex, can include battery technical limits
Reduced ( simplified) controller - useful for Optimal control studies
and technique evaluation

We are just starting, now have an excellent framework forquantitive studies
We can use this approach with actual trip routes, estimate savingsin real-world trips
We can study particular vehicles, or optimize vehicle properties toa routeJ. D. Fox

AI-AT-SLAC Seminar

31Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasNext Steps
Concentrate on optimal methods, expansion of the convex
optimization technique, computational feasibility
Use database of actual routes, look at savings optimal vs.
conventional, impact on real-world routes
distribution of energy profiles
potential efficiency increases on real-world routes and vehicle use
patterns.

Include battery technology models, and battery thermal and stateof charge (SOC) limits in the route energy profile optimization.
Economic (lifetime) optimization of battery management
Investigate route estimation methods - the second central
research direction
Goals - Publications, submission of larger scale grantCollaborate with projects at ANL, new DOE-supported consortium
Collaborate and expand contacts with Industry research
Fully participate in Precourt, campus activity in Energy Efficiency
J. D. Fox

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Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasOur Interests
Continue to explore optimal control of Plug-In hybrids
Real-world routes and database of routes
Explore Voltec ( e.g. series) and Parallel Hybrid drivetrains,
understand optimal control options
Develop context sensitive vehicle designs ( e.g. optimize theengine and battery capacities for various types of use patterns)
We welcome collaborators with battery electrochemical expertise
Partner with a vehicle manufacturer who would like to try thesealgorithms on a physical car

Acknowledgements This work was supported by a seed grant from the Precourt Center
for Energy Efficiency. Additional Support was provided by SLAC under DOE Contract
DE-AC02-76SF00515. Nicholas Moehle and Jason Platt were partially supported by Professor
Stephen Boyd. Atinuke Ademola-Idowu was supported by an external fellowship. Several figuresin this talk are from the Autonomie group at ANL, we thank them for their use.
J. D. Fox

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Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasUseful Related References"Commuter Route Optimized Energy Management of Hybrid Electric Vehicles", Larsson, et al,IEEE Transactions on Intelligent Transportation Systems, Vol 15 No.3 June 2014
"Route Optimized Energy Management of Plug-In Hybrid Electric Vehicles", V. Larsson, Ph.D.
Thesis, Chalmers University of Technology, 2014
http://publications.lib.chalmers.se/records/fulltext/196399/196399.pdf"Route-Based Control of Hybrid Electric Vehicles", J. D. Gonder, presented at SAE World
Congress 2008, Detroit"Plug-In Hybrid Electric Vehicle Control Strategy: Comparison between EV and Charge
Depleting Options", Sharer, et al, SAE 2008 -01-0460 April 2008
"Vehicle Energy Management Optimization through Digital Maps and Connectivity" Karbowski, etal, ITS world Congress, Bordeaux October 2015
"Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip",
Karbowski, et al EVS26, Los Angeles CA 2012
"Optimal Control Strategy for PHEVs using Prediction of Future Driving Schedule", Lee, et al,
EVS26 Los Angeles CA 2012
"Instantaneously Optimized Controller for a Multi-Mode Hybrid Electric Vehicle", Karbowski, et al,
SAE 2010-01-0816"Dynamic Programming Applied to Investigate Energy Management Strategies for a Plug-In
HEV", O’Keefe and Markel, EVS-22 Yokohama, 2006
"Battery Management for Maximum Performance in Plug-In Electric and Hybrid Vehicles", P.
Krein IEEE - 7803-9761-4/07 2007
J. D. Fox

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Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasUseful Related References, II"Plug-In Hybrid Electric Vehicle Charge Pattern Optimization for Energy Cost and Battery
Longevity", Bashash, et al, Control Optimization laboratory, University of Michigan, submitted to
Journal of Power sources" A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Vehicles",Moura, et al, IEEE Transactions on Control Systems Technology, Vol 19 No.3,May 2011
"Bettery-Health Concoius Power Management in Plug-In Hybrid Electric Vehicles via
Electrochemical Modeling and Stochastic Control", Moura, et al, IEEE Transactions on Control
Systems Technology 1063-6536 2012
"Modeling of Lithium-Ion Battery Considering Temperature and Aging Uncertainties", X. Gong,
Ph.D. Thesis University of Michigan ( Dearborn) 2016
"Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains using Stochastic
Dynamic Programming", Johannesson, et al, IEEE Transactions on Intelligent Transportation
Systems, Vol. 8, Issue 1 March 2007

J. D. Fox

AI-AT-SLAC Seminar

35

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasU.S. Energy Flow, 2015
quadrillion Btu

1

10Includes lease condensate.
Includes 0.23 quadrillion Btu of electricity net imports.
11
Total energy consumption, which is the sum of primary energy consumption, electricity retail
Natural gas plant liquids.
sales, and electrical system energy losses. Losses are allocated to the end-use sectors inConventional hydroelectric power, biomass, geothermal, solar, and wind.
4
proportion
to each sector’s share of total electricity retail sales. See Note 1, “Electrical System
Crude oil and petroleum products. Includes imports into the Strategic Petroleum Reserve.
Energy Losses,” at the end of U.S. Energy Information Administration, Monthly Energy Review
5
Natural gas, coal, coal coke, biofuels, and electricity.
(April 2016), Section 2.
6
Adjustments, losses, and unaccounted for.
Notes: • Data are preliminary. • Values are derived from source data prior to rounding for
7
Natural gas only; excludes supplemental gaseous fuels.
publication. • Totals may not equal sum of components due to independent rounding.
8
Petroleum products,
fuel.
J. D. including
Fox natural gas plant liquids, and crude oil burned asAI-AT-SLAC
Seminar
Sources:
U.S. Energy Information Administration, Monthly Energy Review (April 2016), Tables36
2
3

9

!

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

The average driver makes three trips per day with an average of 9.7 miles for each trip.

What Plug-In electric Range is Useful?
Table 8.12
Household Vehicle Trips, 2009 NHTS

Number of daily
vehicle trips(per driver)
3.3
3.6
3.4
3.0

Averagevehicle trip
length (miles)
8.9
9.1
9.9
9.7

Daily vehicle
miles of travel(per driver)
28.5
32.1
32.7
29.0

Almost all trips are short - do you need 300 miles on a charge?
What benefits come froma hybrid vehicle approach?
8–14
1990
1995
2001
2009

Source:National Household Travel Survey, nhts.ornl.gov.

!

Figure 8.5. Average Daily Miles Driven (per Driver), 2009 NHTS

Figure 8.3. Share of Vehicle Trips by Trip Distance, 2009 NHTS

Source:National Household Travel Survey, nhts.ornl.gov.

Source:National Household Travel Survey, https://nhts.ornl.gov.
TRANSPORTATION ENERGY DATA BOOK: EDITION 30—2011!

J. D. Fox

Figure 8.4. Share of Vehicle Trips to Work by Trip Distance, 2009 NHTS

AI-AT-SLAC Seminar

37

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

effects are all considered in these models. Discrete form dynamic equations andNext Steps

ExtrasPossible
Batterysystemmathematicaldescriptionsof each modelmodels
are presented as follow:
Ro

Ro

+

OCV

OCV

Vt

Vt

Zero statehysteresis

(a)

OCV

+ U1 R1OCV
One statehysteresis

-

+
Vt

Ro

R1
C2
+U2 (e)-(c)+ U1 -

OCV

+
Vt

C1

(d)

2nd order
LPF

(b)

C1
Ro

Ro

+

C1
+
Vt

Ro

+ U1 R1

OCV

-

R2

One statehysteresis

+
Vt
-

(f)

Figure 2.2 Six typical equivalent circuit models
Simple model:
we are using
the simple type a) model in the optimal controller,
Autonomie has other more
complexbatterymodels (type d) (2.1)
𝑉 (𝑛)= 𝑂𝐶𝑉 𝑆𝑜𝐶(𝑛)− 𝐼(𝑛)𝑅J. D. Fox

AI-AT-SLAC Seminar

38

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

states estimation algorithm may not be able to predict the behavior and provide

Next Steps

ExtrasBattery
capacity vs. life cycles
estimation result correctly.
5.5
5
4.5

5.3Ah
100%

Capacity (Ah)4
3.5

4.24Ah80%

32.5

3.34Ah
63%

2
1.5
1
0.5
0
0

200

400

600

800

1000

Cycle Number

Figure 1.8 Li-ion battery capacity degradation as cycles increase

The rate of capacity loss is a strong function of depth of discharge andAs the EV mileage increasing, the battery performance gradually declines.
temperature of the battery during the charge cycle
Consequently,
the performance of EVs
is also influenced.
The main causes of the poor
J. D. Fox
AI-AT-SLAC
Seminar39

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasEngine On histograms
Comparison of Engine on times med urban first
OptimalCDCS

Comparison of Engine on times med urban last

40
Optimal
CDCS

120

35

100

80

25

Number engine On

Number engine On

30

20

60

15

40

10

20
5

0
0

0
0

50

100

150

200

250

300

350

400

time (s)

10

20

30

40

50

60

70

80

90

100

time (s)convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
Engine ON time histogram
Is tho short operating cycle realistic?
Optimal control cost on engine start
J. D. Fox

AI-AT-SLAC Seminar

40

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasEngine Off histograms
Comparison of Engine off times med urban lastComparison of Engine off times med urban first

New
Old

90

Optimal
CDCS

25

80

70

20

Number engine Off

Number engine Off

60

15

50

40

10

30

20

5

10

0
0

100

200

300

400

time (s)

500

600

700

0
0

50

100

150

200

250

time (s)convex-optimized hybrid battery controller vs. CDCS, energy-equal routes
Engine OFF time histogram
Possible Impact on economy from engine temp,cool down from long off
Optimal control cost on engine start
J. D. Fox

AI-AT-SLAC Seminar

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Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

Otto ( Spark Ignition) Cycle

Idealized cycle
V1 /V2 Compression Ratio
γ = Cp /Cv
Efficiency E = 1 − V 1γ−1
1V2

A throttled engine

J. D. Fox

AI-AT-SLAC Seminar

42

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasMore Real Otto ( Spark Ignition) Cycle

J. D. Fox

AI-AT-SLAC Seminar

43

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasDiesel ( Compression Ignition) Cycle
Idealized cycle
allows high V1 /V2Compression Ratio ( higher
efficiency)
What are Q1 and Q2 ?
un- throttled engine ( highefficiency)
Issues with particulates in
exhaust, NOx, noise
increasingly popular in
Europe, in US needs
aftertreatment foremissionsJ. D. Fox

AI-AT-SLAC Seminar

44

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

ExtrasMore Real Diesel Cycle

J. D. Fox

AI-AT-SLAC Seminar

45

Motivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions

Next Steps

Extras

Efficiency Advantage Diesel vs Otto ( throttled) cycles

J. D. Fox

AI-AT-SLAC Seminar46

Five minutes
doctor emerged
the house
Motivation
Teaserlater the
Progress
Firstfrom
OptimalResultsin
fur coat and with his gripsack in his hand. His faithful automobile was now his only thought. “How easily I can make thistrip,” he thought, “and how much quicker I can get to her than
I could with my team, and how much greater the chances willbe that I can save the life of this dear woman.” Imagine hishorror on going to the barn and seeing his automobile with
one tire flat. He calls his wife to hold the lamp, while he goesat it furiously to fix the puncture. One hour later, with his fingers stiff from the cold, and his wife shivering from head tofoot, he has the tire in order. Now he will be on the road in a
few minutes, but stay; the starting crank will not turn; the water pump is frozen. After another hour he has built a fire in
the kitchen stove and heated water and thawed out his pump.
Now he will soon be going. The motor runs beautifully
and as evenly as a clock. Out into the sharp, snow-driving
wind he points it and takes the road at a furious speed. It is
now 1 o’clock and he feels sure he will be there in an hour.
In a little while, however, he realizes that all is not well. The
engine is pounding, fuming and steaming. A little investigation shows him that his radiator is frozen up. Just what
to do he does not know. However, a happy idea strikes him
and he goes to a house by the roadside. After being almost
bitten by the dog and nearly breaking down the farmer’s door,
he finallyhas himAfter due
explanation,
Lettertoawake.
the Editor- Journalof the farmer,
in orderthe
to savea life, will Medical
accommodate
the suffering docAmerican
Association
tor and builds a fire in the kitchen stove and heats water toDr.
(Clevelandthaw out
theMetzenbaum
radiator.

beBatterya good thing.”
HisConclusions
faithful wife tells him
she has Extras
read
InitialNextthat
Steps
about an automobile made in the manner he describes. Shesays that she thinks it is called a “buggy-automobile.”
A record in Dr Smith’s diary will now show that he has abuggy-automobile and can go almost anywhere under any
conditions and at any time of year.
I had almost a duplicate of the above troubles until I purchased a high-wheeled, air-cooled, solid-tired automobile
four years ago. Since then I have run it and am still runningit with the greatest of success.
THE ELECTRIC IS EXCELLENT IN THE CITY.
Myron Metzenbaum, M.D., Cleveland, Ohio.
For most city physicians the electric is the preferable auto.
It is safe in the hands of the child and the aged; it starts quietly,
runs smoothly, responds easily, stops quickly; it will not strain,
but rest, the nerves. With ordinary care the electric will not skid
in rainy, sleety, or snowy days, when gasoline cars are but seldom seen. The life of an electric machine is longer than that ofthebesthand-madecarriage,sinceitisnotsubjectedtothebump
which a carriage undergoes, nor to the continuous throbbingof machinery which the gasoline car endures. The replacing ofworn-out parts and reassembling of a gasoline car will requireas many days as the same process on an electric car will requirein hours. At the end of the third or fourth season, the electricmachine will not have depreciated over 50 per cent., while thegasoline car will have but little value left.

Electric Cars - are the newest thing in transportation

JAMA. 1908;50:788-789
JAMA 100 Years Ago Section Editor: Jennifer Reiling, Assistant Editor.

Ohio) says the Electric Car is thebest choice for the city doctor©2008 American Medical Association. All rights reserved.

(Reprinted) JAMA, March 5, 2008—Vol 299, No. 9

1079

Downloaded from www.jama.com at Health Services Research Library, on March 5, 2008

J. D. Fox

AI-AT-SLAC Seminar47

from the drifting snow. “For God’s sake, Doctor, go to myenoughthe snow drifts, myautomobile
wouldBatteryto get throughInitial Conclusions
Next
Steps
ExtrasFive minutes later the doctor emerged from the house in be a good thing.” His faithful wife tells him that she has read
fur coat and with his gripsack in his hand. His faithful auto- about an automobile made in the manner he describes. She
mobile was now his only thought. “How easily I can make this says that she thinks it is called a “buggy-automobile.”
A record in Dr Smith’s diary will now show that he has a
trip,” he thought, “and how much quicker I can get to her than
I could with my team, and how much greater the chances will buggy-automobile and can go almost anywhere under any
conditions
and at any time of year.
be that I can save the life of this dear woman.” Imagine his
I had almost a duplicate of the above troubles until I purhorror on going to the barn and seeing his automobile with
JAMA 100 YEARS AGO
one tire flat. He calls his wife to hold the lamp, while he goes chased a high-wheeled, air-cooled, solid-tired automobile
MARCH
7, 1908 to fix the puncture. One hour later, with his fin- four years ago. Since then I have run it and am still runningat it furiously
it with the greatest of success.
gers stiff from the cold, and his wife shivering from head to
THE ELECTRIC IS EXCELLENT IN THE CITY.
foot, he has the tire in order. Now he will be on the road in a
Myron Metzenbaum, M.D., Cleveland, Ohio.
few minutes, but stay; the starting crank will not turn; the waFor most city physicians the electric is the preferable auto.
ter pump is frozen. After another hour he has built a fire in
It
is
safe
in
theandhands
child
the aged;
quietly,
the kitchen stove
andOF
heatedwaterDOCTOR.
and thawed out his pump. Two o’clock
DIARY
A BUSYthisofisthe
done,
theanddoctor
now itonstarts
the roadrunsandsmoothly,quickly;
will notstrain,Now he will C.
soon
be going.
TheChicago.
motor runs beautifully again
going atresponds
a furiouseasily,
speed.stops
Ten miles
areitbehindhim
H. Bryan,M.D.,
nerves.
With ordinarycare
andevenlya clock.
Out
sharp,
lessrest,thanthethirtyminutes.
Once moretothehiselectric
horrorwill
his not
mo-skidDr.asSmith
wasassittingby hisfireinto
onethenight
aboutsnow-driving
11 o’clock. in but
sleety,or snowydays,
when
gasolineare butwind
he points
and takes
road atarounda furious
It is torinisrainy,
pounding
andhot. Whatcanbe thecausecarsof this?
He selThe
frostywind itoutsidewas thewhistling
thespeed.
cornersdom
The life
an electricmachinelongerthan
nowdriving
1 o’clockheintofeelsdrifts
sureandhe willbe there
in anhour. stops
hisseen.
machineandofalights
just intime to issee
the last
bitthatof of
andthe andsnow
sifting
it underdoors.
thebesthand-madecarriage,sinceitisnotsubjectedtothebump
In adoctor
little while,
however,he realizes
that allis notwell.
The water
leak out of a crack in his radiator. Another farm houseThe
was telling
his faithful
wife about
how
much
time
which
a carriageThis
undergoes,to thecontinuous
throbbingbe invaded.
time he nor
obtainsa bucketof watertoenginepounding,
steaming.
littlehe
couldishave
saved infuming
the pastandtwentyyears ifAhe
hadinvestibeen mustmachinerywhich
therags
gasolinecartheendures.
radiator andsometo stopleak. The replacing ofgationshows
him that hisduring
radiatorfrozenwhat fillofhis
able
to usean automobile
thatistime.
Heup.
wasJusttelling
partsandandhe
reassemblingof afast
gasoline
car willwith
requireThree o’clock
is going again,
and furious,
to dohefromdoes now
not know.
a happyideaenjoyment
strikes him worn-out
herhow
on he However,expected toget more
manygettingdays asdeeper
the same
an electric
car will
andprocess
deeper.onTen
miles more
andrequireheand heto a hehouse
bysavethe roadside.
Afteralmost theassnow
fromlifegoesbecausecould
half his timeonbeing
the road.
in hours.
At theof thethirdfuriously
or fourthseason,the electric
there.
Theendmotor
is nowhot
again and
bebittenthe dogand nearly
farmer’sdoor, is almost“Justbythink,Mary,
I wentbreaking
20 milesdown
to seethe
FarmerJonesginning
topound.
Once
more
he
must
have
water.
Thoughtmachine
will
not
have
depreciated
over
50
per
cent.,
whilethe
he finallyhas himawake.
After
due explanation,
the farmer,
andback again
almostin twohours.
Doesn’t that seemimhe has carbrought
alongbutthe little
bucket.
A nearby
gasoline
will havevalue
left. creek will supin order tosaveI awill
life,bewill
accommodate
doc- fully
possible?
Now,able
to get home theto asuffering
warm mealit almost
JAMA. 1908;50:788-789
tor and builds
fire inup
thesomekitchen
stove
heats
water
occasionallyanda makeof thesleepandI havelost
dur- to ply the water. After breaking the ice and going throughto JAMA
his kneesandAgosoakingfeet,
he oncemore
getsEditor.
a buck100 Years
Sectionhis
Editor:Jennifer
Reiling,
Assistantthaw
radiator.
ing
theoutpastthetwentyyears.”
Just then the bell rang. “Do not go out to-night, my dear,” etful of water and cools his thirsty engine. At 4 o’clock he ar©2008 American Medical Association. All rights reserved.
(Reprinted)
JAMA,
March
5,
2008—Vol
299,
No. 9 1079
said his wife; “besides you know you have a cold.” A man rives and finds that a nearby physician has saved the life of
was at his door who had just come 25 miles on horseback. his patient. As daylight breaks he reaches home again with
His face showed the effects of his battle with the storm. His another tire flat and his radiator leaking badly.
“Mary, I think that if the water cooling and tire trouble could
fur overcoat and cap, well pulled up over his ears, were white
beSeminar
eliminatedfrom
my automobile,
Downloaded
fromDoctor,
www.jama.com
HealthServices
Research
Library,on March 5, and2008the wheels made highD. Fox snow. “For
AI-AT-SLAC
48
from theJ.drifting
God’s sake,
go
to at
my

Motivation
TeaserFirst you
OptimalResults
wife at once.
I doubt ifProgress
she will live until
get there.”

Electric Cars - are the newest thing in 1908

1908 Baker ElectricMotivation

Teaser

Progress

First Optimal Results

Battery

Initial Conclusions
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Interesting Directions in 2015

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Fewer Youths Jump Behind the Wheel at 16

Mor

Erik S. Lesser for The New York Times

Kelsey Sheffer, 16, of Bethlehem, Ga., says she lost the motivation to pursue a full license after she saw accident sites witha police officer. For now, her mother is happy to shuttle her around.
By MARY M. CHAPMAN and MICHELINE MAYNARD
Published: February 25, 2008

DETROIT — For generations, driver’s licenses have been tickets tofreedom for America’s 16-year-olds, prompting many to line up at
motor vehicle offices the day they were eligible to apply.

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Multimedia

Graphic
Behind the Wheel

No longer. In the last decade, theproportion of 16-year-olds nationwide
who hold driver’s licenses has dropped
from nearly half to less than one-third,
according to statistics from the Federal
Highway Administration.

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Today’s teens - less than
60% even have driver’s
licenses

Reasons vary, including tighter state laws governing when
teenagers can drive, higher insurance costs and a shift fromschool-run driver education to expensive private driving
academies.

They would rather text
than drive

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To that mix, experts also add parents who are willing to chauffeur their children to
activities, and pastimes like surfing the Web that keep them indoors and glued tocomputers.

(I prefer not text AND
drive)

Jaclyn Frederick, 17, of suburban Detroit, is a year past the age when she could get aMichigan license. She said she planned to apply for one eventually, but sees no rush.
“Oh, I guess I just haven’t done it yet, you know?” said Jaclyn, a senior at Ferndale HighSchool, in Ferndale, Mich.

Future demand for
personal vehicles?

“I get rides and stuff, so I’m not worried about it. I’ll get around to it, maybe this summer

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