Enhanced Plug-in Hybrid Electric Vehicles
Alan Millner, Nicholas Judson, Bobby Ren, Ellen Johnson, William Ross
Massachusetts Institute of Technology Lincoln Laboratory. 244 Wood St. Lexington, MA 02420
{amillner, judson, bobby.ren, ejohnson, bross}@ll.mit.edu
Abstract—Plug-in hybrid electric vehicles (PHEVs) have
the potential to reduce fossil fuel use, decrease pollution,
and allow renewable energy sources for transportation, but
their lithium ion battery subsystems are presently too
expensive. Three enhancements to PHEVs are proposed
here that can improve the economics. First, the
incorporation of location information into the car’s energy
management algorithm allows predictive control to reduce
fuel consumption through prior knowledge of the upcoming
route and energy required. Second, the use of the vehicle
battery while parked, offsetting the short peaks in
commercial-scale facility electrical demand to reduce
demand charges, can provide additional revenue to pay for
the battery. Third, the battery cycle life must be maximized
to avoid high replacement costs; a model of battery wear
out for lithium ion batteries is presented and is used to
confirm that the above strategies are compatible with long
battery life.
1. BACKGROUND: A VISION FOR PHEVS
The US energy economy suffers from the use of too much
petroleum. This is a problem because the world’s oil
reserves are in places far from our national borders and
often governed by countries with different political agendas;
60% of the reserves are in the Mideast [1]. It is also a
problem because the long term supply of petroleum is
limited, with discoveries declining since 1964 [2]. And, it is
a problem because our burning of this non-renewable
resource produces too much greenhouse gas, with 25% of
CO2 production coming from the US, and 33% of US CO2
coming from oil. Transportation represents 28% of US
energy use and 70% of petroleum use [1].
Power obtained from gasoline burned in automobile engines
is extremely inefficient, with an average final efficiency of
only 15%, with the rest wasted in heat and drive train losses
[3]. By comparison, central electric power plants operate at
an efficiency of 35% [1].
___
This work was sponsored by the United States Government
under Air Force contract FA8721-05-C-0002. Opinions,
interpretations, conclusions, and recommendations are those
of the authors and not necessarily endorsed by the United
States Government.
By shifting our least efficient energy conversion step from
inefficient small engines to central power plants, the total
well to wheel efficiency of our transportation sector can be
greatly improved. Also, use of electric power transmission
and distribution allows the source of transport energy to be
renewable sources, nuclear, or natural gas, with far greater
long term supplies and lower carbon footprint. If half the
passenger vehicle fleet of the US were replaced by 100 mile
per gallon (mpg) plug-in hybrids, the petroleum use of the
US would be reduced by approximately the amount now
imported from the Mideast. Our CO2 emissions would be
reduced by 0.5 billion tons per year, or 8%. Use of plug-in
hybrid electric vehicles (PHEVs) allows the limited use of
liquid fuels to extend range when that is needed for
convenience, taking advantage of their very high energy
density, while addressing the need for fuel conservation
during the typical shorter commuting trips that dominate
usage.
PHEVs are becoming available. Conversions such as the
A123 Systems, Inc. Hymotion L5 converted Toyota Prius
hybrid have been sold in quantities of hundreds for several
years now. The General Motors Chevrolet Volt is due late
this year, and the Toyota Prius PHEV is scheduled for
release in 2011. Other sources are under development,
including some suitable for trucks and military vehicles.
The problem holding back this technology now is the high
cost of the battery subsystem. Lithium ion batteries, the
technology of choice today, are expensive and will probably
remain so for some years to come. For example, the
aforementioned Toyota Prius conversion by A123 Systems
in 2008 cost about $32,000: $22,000 for the hybrid car, and
$10,000 for the retrofit module with a 5 kWh battery pack.
Vehicles at this cost level can be leased for approximately
$400 per month. The larger the battery capacity, the more
expensive the PHEV becomes.
The solution envisioned here is to first make use of an
efficient vehicle with the battery sized to address common
commuting distances electrically. Then, make best use of
intelligent controls to minimize fuel consumption by
making predictive use of knowledge of the route to be
traveled. Third, use the battery to provide grid regulation
services through peak power shaving while parked at work,
a version of vehicle to grid (V2G) power transfer that makes
use of the economic relationship between a commuter and
employer and that we call vehicle to building (V2B),
generating revenue to offset the battery cost. And finally,
make use of good battery life models to minimize
degradation of the battery while it is used for both
transportation and regulation. By enhancing the PHEV in
these ways, the high initial cost of battery subsystems can
be addressed and moderated. By allowing economies of
scale to be reached through the manufacture of the large
production quantities of PHEVs, this may bootstrap the
technology into a viable production range.
Gas
Engine
Gas
Tank
Transmission
Shaft
Electric
Motor
Generator
Inverter
Charger
Road
Friction
Acceleration
Elevation
Wind
2. PREDICTIVE CONTROL OF VEHICLE ENERGY
Battery
Figure 1. Vehicle energy model; each boxed element has an
efficiency dependent on energy flow through that element.
Energy flow directions are indicated by arrows and external
sources of energy and driving conditions that affect energy
expenditure are indicated.
A simple power flow model of a PHEV vehicle was created
using MATLAB [7] with vehicle weight, rolling resistance,
and aerodynamic parameters modeled on the 2010 Toyota
Prius. This included a model of each major component
(Figure 1), with energy efficiency as a function of power
level through that component. A state diagram control
strategy was implemented for this vehicle diagram (Figure
2), in which the state variables are the power required of the
system at that time and the state of charge (SOC) of the
battery.
Full
Mechanical
Braking
Variable Electric
Only
Battery SOC
Max
Target
Regen
Charging
Maximum Electric
Max
+ Variable Gas
Electric
+
Max
Gas
Optimum
Variable Electric
Gas
+ Optimum Gas
Charging
Min
Variable Gas Only
Charging
Maximum Gas
Only
tri
c
+ Ma
G x
as El
O ect
pt ric
im
um
M
ax
+
M Ele
ax c
G tric
M
as
ax
D
em
an
d
ec
um
El
im
ax
M
O
as
G
Ze
ro
pt
Po
ak
w
in
er
g
Empty
Br
To test this concept, a model was needed of the vehicle, the
vehicle energy control system, and the driving cycle or route
including speed vs. time. This allows evaluation of
predictive control strategies and comparison with
conventional strategies, to determine quantitative benefits in
terms of improved fuel consumption.
V2G
Charger
Utility
ax
The present state of the art is to operate a PHEV with as
much energy as possible coming from the battery until it
reaches a low state of charge [6]. Then energy is obtained
from the liquid fuel engine to sustain this minimum battery
charge for the rest of the trip. The problem with this strategy
is that over that last part of the trip, the engine is often
required to operate at a power level that is not its optimum
operating point. If more battery energy is saved until later in
the trip, however, this optimization can be extended and fuel
saved.
V2B
M
The concept of improving fuel economy by foreknowledge
of the route to be traveled, using global positioning satellite
(GPS) and other available sources of information, has been
considered for some years now [4, 5]. The creation of
simple control algorithms making use of specific
information for a parallel or series-parallel hybrid vehicle
has not been clear. Presented here is an approach that makes
use of at least the distance to be traveled, and potentially the
elevation changes to be encountered, speed changes
including expected traffic congestion, road surfaces, and
other factors affecting dynamic vehicle power demand, to
predict the requirements on the vehicle power plant over its
trip, and schedule the use of electrical or liquid fuel energy
to optimize engine efficiency and minimize fuel use.
Power Demand by Car
Figure 2. Vehicle controller state machine; states are
dependent on power demand and battery SOC.
As part of this control strategy, there is a parameter
representing the target SOC of the battery. If this is taken to
be the minimum SOC, then this strategy reduces to the
conventional one of charge depletion followed by charge
sustaining mode [6]. However, if this parameter is allowed
to vary over the trip, gradually reducing and reaching the
minimum only at the end of the trip, then a more flexible
strategy emerges.
A comparison of control strategies was done for the
modeled vehicle with a 5 kWh battery capacity (the same as
the A123 Systems, Inc. Hymotion L5 PHEV conversion)
over a route made up of three US06 driving cycles [8], to
make a 24 mile trip typical of the length of daily commute
of US drivers. The US06 driving cycle is one of the
published test cycles by the US Environmental Protection
Agency and is the most aggressive available standard test
cycle, although it does not capture the full range of driving
styles of all US drivers [9]. The conventional control
strategy resulted in a trip averaging 66 mpg, whereas the
same vehicle parameters simulated using a gradual
depletion resulted in a trip of 71 mpg (Figure 3). For trips of
this length and similar parameters, the fuel savings using
this simple control improvement, knowing the length of the
trip, was between 7 and 10%.
1
3. VEHICLE TO BUILDING
Electric power customers with high power demand service,
such as large commercial and institutional ones, in addition
to charges per kWh as residential customers are accustomed
to, also pay demand charges for the maximum number of
kilowatts drawn during the billing period. These demand
charges generally start for customers exceeding 100-500 kW
of peak power demand, depending on the utility provider.
Often these charges are then the largest portion of the bill.
For MIT Lincoln Laboratory in the Boston area, the demand
charge is most of the electric bill.
If electric vehicles are used in V2G mode, they can level the
peaks in demand and reduce these charges. As commuters
will be at work during peak demand hours, and since
employees have a ready means of exchanging money with
their employer, this is a compelling model for realizing the
specific mode of V2G that we refer to as V2B. This is also a
way of aggregating the potentially many small utility
interfaces of PHEV’s. Looking at the electrical usage of
MIT Lincoln Laboratory in 15 minute power demand
increments during the month of July 2008 (the time scale
over which demand charges are calculated in this case) there
were three narrow peaks well above the power drawn for the
rest of the days (Figure 4). Baseline night load in July was
about 8 MW.
12
0.7
SOC
Power Demand (MW)
Gradual Depletion
0.4
$15 - $20 / kW /
month
11
Conventional Control
0.1
0
600
1200
1800
Time (seconds)
10
12AM
6AM
12PM
6PM
12AM
Time of Day
Figure 3. Vehicle control strategies with and without
knowledge of trip length: battery SOC vs. time.
Use of more information about the trip in advance, available
at the vehicle via GPS, the web, real-time traffic updates, or
vehicle to vehicle communication, might include elevation
changes, speed variation over the trip, traffic patterns, road
surfaces and conditions, weather, air temperature, and
individual driver driving habits. It remains to be seen which
of these are worthwhile to include in the energy
management algorithms.
Figure 4. Electric power demand at MIT Lincoln
Laboratory for days in July 2008. Each day is shown as a
different series of 15 minute power consumption points (the
graph is truncated below demands of 10 MW). The
difference in power demand for three peaks that could be
shaved with a V2B approach is shown.
These peaks are due to heating, ventilation, and air
conditioning system transients, and are typical for many
months of the year. Their duration of fewer than 30 minutes
is a good match to the capabilities of PHEV batteries. As
demand charges in the summer and winter are on the order
of $20 and $14 per kW respectively, reduction in these
peaks offers significant savings. Initial calculations showed
that if a V2B system could identify and reduce these peaks
in real-time, the resulting savings were over $100 per month
per car. This could go far to offset the high cost of the
battery subsystem in the vehicle.
Subsequent analyses varying the number of vehicles
available for V2B, the power capability of their bidirectional chargers, and the size of their batteries led to the
emergence of several patterns (Figure 5).
600
13.3kWh battery, 3C discharge rate (40kW charger)
5kWh battery, 4C rate (20kW charger)
13.3kWh battery, 1.13C (15kW charger)
Monthly Savings ($)
5kWh battery, 3C (15kW charger)
control safe charging. This charger can serve as a building
control interface to the vehicle and request V2B services
when it senses the building demand is rising.
The algorithm for controlling such a charger would make
use of rapidly sampled (1 or 5 minute) building demand.
Given the building load, the number of cars, the available
energy storage and V2B power level per car, there are two
parameters to adjust. ΔP represents the rate of change of
building power demand representing a peak worth leveling.
represents the difference in power level below the
P
current monthly maximum below which peaks will be
ignored. The algorithm would turn on the V2B when the
building demand is rapidly ramping up (successive samples
of demand differ by more than ΔP), and is near the
maximum power demand level to date in the billing period
). The algorithm
(demand greater than maximum - P
must also turn off the peak shaving pulses if they continue
to the maximum time (Pulse # •0) or if the vehicles are
below the desired depth of discharge (Energy •0)(Figure 6).
THRESHOLD
THRESHOLD
400
200
Test: power,
power ramp
rate
0
0
5
10
15
20
Read in
next meter
data
Not pulsing
Peak power level:
P > max-PTHRESHOLD
AND ramp rate:
Change in power > ΔP
Start peak
shave V2B
pulse,
Pulse # =
maximum
Otherwise
Continue...
not pulsing
Pulse # > 0 AND
P > max-PTHRESHOLD
Cont. peak
shave pulse,
Decrement
Pulse #
Pulse # == 0 OR
P < max-PTHRESHOLD OR
Energy <=0
Stop
pulsing
Car Number
Figure 5. V2B cost savings for analysis of all peaks at MIT
Lincoln Laboratory during 2009, a cooler than normal year.
The maximum possible savings ($25,068) can be divided
among different numbers of cars, depending on battery size
and discharge rate.
Each billing period was analyzed and all peaks in power
demand that lasted <45 minutes were reduced to the nonpeak maximum power draw in that billing period. The
number of cars, the size of their batteries, and the rate of
discharge of those batteries were varied to calculate cost
savings per additional car added to the system. It is clear
that as more vehicles are added, the incremental value of
each subsequent vehicle goes down as the peaks requiring
the fewest cars have already been addressed. From this
analysis, approximately 150 kW of chargers and 50 kWh of
car batteries are good numbers. This would be 10 cars at
5 kWh and 15 kW each, or could be divided among more
cars with smaller depth of discharge and smaller chargers.
Time t
Power p
Peak shaving
pulse
Test: power,
Pulse #,
Energy
Figure 6. Algorithm for peak selection for V2B peak
shaving.
The algorithm was implemented in MATLAB [7] and then
applied to data on power consumption from the main power
meter at MIT Lincoln Laboratory for the first five months of
2010. The performance of the algorithm was tested by
, and
changing the values of the parameters of ΔP, P
Pulse #. The results for a day in May 2010 are shown in
Figure 7. The algorithm was optimized with a total of 20
cars having 9 kWh batteries discharged to up to 80% depth
of discharge (DOD) per peak shaved. The algorithm
required an average of 7 peak shaving pulses per month per
car of 15 kW for 20 minutes. This provided 300 kW of peak
power capacity; for these months in 2010 this produced a
THRESHOLD
One challenge is that bidirection chargers appropriate for a 3
phase power interface in the 10 to 30 kW range do not at
present exist as commercial products, but it is well within
technical possibility. It could be beneficial to have the
chargers in the building rather to avoid the weight penalty in
the vehicle. The charger might have a DC interface to the
vehicle and allow the vehicle battery management system to
mean value of peak shaving per month of $3160 total or
$158 per car.
Actual maximum
Shaved maximum
Power Demand (MW)
13
Power Demand
12
11
Shaved power demand
V2B Pulse
Ramp(t)
Peak power(t)
6AM
12PM
6PM
12AM
Time of Day
Figure 7. Peak shaving simulation for power demand at
MIT Lincoln Laboratory on a day in May 2010. Note when
power demand approaches the maximum, when ramps
occur, and when the vehicle provides shaving pulses.
600
4. BATTERY MODELING
In order to assess the total value of using a PHEV battery to
reduce peak power loads, it is important to correctly
evaluate the added cost of cycling the battery in this way.
For this, as well as to calculate cost to the battery for
general vehicle driving and storage conditions, a battery
cycle life model is needed. Such a model is described in
[10] and is not repeated here. The development of a new
model is motivated by the observation that physical models
in the literature predict battery life to be linearly
proportional to the amount of charge removed from it
(“constant throughput”) [11] and therefore to the depth of
discharge. However, empirical data shows an exponential
relationship of life to depth of discharge, not a linear
relationship. This has been approximated over limited
ranges of DOD by power law models, without a theoretical
justification [12]. However, a truly exponential model is a
better fit to data on modern lithium ion battery cells [13, 14]
(Figures 9 and 10).
Rosenkranz original data
Rosenkranz linear model
Peterson data
Peterson linear model
Peterson data adjusted
to 50% SOC
1000000
400
Cycles (log #)
Total Power Shaved (kW)
convenience would probably dictate more like an 80% DOD
to keep this from reducing battery life significantly more
than the pure driving cycle effect. It was noted that 2009, an
unusually cool summer, worked well with lower values of
ΔP. These parameters will need to be adjusted to the
particular building and climate for best results.
100000
200
0
10000
25
50
75
PTHRESHOLD
100
125
(kW)
200
25
50
75
100
150
200
300
1000
1
10
100
Depth of Discharge (log %)
ΔP (kW)
Figure 8. Performance of the peak detection algorithm with
power demand data from MIT Lincoln Laboratory for
January - May 2010. The total demand power shaved from
the peak of the electric bill is shown as it varies with a
and ΔP.
changing P
THRESHOLD
The parameters in the algorithm showed a sharp optimum at
=
ΔP = 150 kW (Figure 8), and a weak optimum at P
50 kW. This compares with a battery aging cost of
approximately $7 per car per month for a battery
replacement cost of $5000, or $1 per Wh. Therefore, over a
wide range of assumed parameters, the peak shaving of the
building looks worthwhile. Depth of discharge for the V2B
function is financially optimized at full discharge, but owner
THRESHOLD
Figure 9. Mismatch of linear models to battery aging data.
Data shown in the figure comes from Rosenkranz [12],
which refers to a battery produced by Varta, Inc., and from
Peterson [13], which refers to a battery produced by A123
Systems, Inc.
In searching for a theoretical basis for an exponential model,
the physical nature of cell degradation, as previously
described in the literature [11], is based on crack
propagation phenomena. Battery cells degrade as lithium
ions form a precipitate in cracks in the carbon anode
electrode. This low conductivity insoluble precipitate
removes lithium ions from active electrolyte chemistry,
reducing the cell capacity and increasing cell resistance.
Another crack propagation mechanism, less important in
low resistance cells, interrupts the electrical connection
between the electrode materials and the metal current
collectors.
Crack propagation is a thermal mechanism activated by
stress [15], which suggests an exponential dependence for
battery degradation. The model therefore uses exponential
dependence of aging rate for all stress mechanisms. The
resulting life prediction, when applied to lithium ion battery
life, fits well to test data [12-14].
Rosenkranz original data
Rosenkranz exponential
Peterson original data
Peterson exp. model
Peterson data adjusted
to 50% SOC
Peterson adjusted to
50% SOC exp. model
100000
As shown in Figures 11, 12, and 13, the effect of climate
and local electric rates causes only slight differences from
one US city to another. Los Angeles and Dallas have higher
summer temperatures than Boston, increasing the general
degradation of the battery in a PHEV, but both cities have
lower electricity rates, decreasing costs to recharge the
battery. There effects tend to cancel each other. Each effect
individually tends to tip the curves so that only the most
extreme battery prices give a curve with an optimum DOD
between the extremes. High temperatures encourage
gasoline use over accelerated battery aging; high electricity
prices encourage gasoline use over utility charging.
10000
900
1000
1
10
100
Depth of Discharge (log %)
Figure 10. Exponential model match to battery aging data.
Data shown in the figure comes from Rosenkranz [12],
which refers to a battery produced by Varta, Inc., and from
Peterson [13], which refers to a battery produced by A123
Systems, Inc.
Total Annual Cost ($)
Cycles (log #)
1000000
Los Angeles, and $0.11/ kWh for Dallas). Total costs were
calculated for a baseline condition of 80% discharge of a
5 kWh vehicle battery, full recharge each evening at 8 PM
at a 5 hour rate, and keeping the battery at 100% charge
before driving the same 24 mile commute as described
above. Average daytime battery SOC was set at 80%.
Battery cost
$5000
$4000
$3000
$2000
800
700
0
20
40
60
80
100
DOD Centered on a 60% mean SOC (%)
5. OVERALL EFFECTS ON BATTERY LIFETIME
The battery life model was then used to compute the cost of
use over the lifetime of the battery including the effect of
temperatures in different locations in the country, the effect
of variation of the DOD of the battery, the effect of varying
mean value at which the battery SOC is maintained and
used, the sensitivity to cost of gasoline, and the sensitivity to
replacement cost of the battery, ranging from a typical cost
today of $5000 for a 5 kWh battery [17] to $2000. (A $2500
battery ($500/ kWh) would be an aggressive US Advanced
Battery Consortium (USABC) target for future cost
reductions [18].)
The model was applied to three locations, Boston, MA,
Dallas, TX, and Los Angeles, CA to evaluate the effects of
temperature [19] on battery use and storage conditions. This
analysis also used local EIA residential electricity pricing
for each area ($0.163/ kWh for Boston, $0.125/ kWh for
Figure 11. Annual cost due to degradation of vehicle
batteries using Boston climate and electric rates ($0.163/
kWh) and $3 per gallon gasoline, as it varies with battery
replacement cost.
For each cost level of a 5 kWh battery, the effect on battery
lifetime was tested and monetized with a mean battery state
of charge centered on 60%. For example, with a 60% depth
of discharge, the SOC varied from 30% to 90%. Gasoline
was fixed at $3 per gallon, electricity at $0.163/ kWh, and
historical temperatures for Boston were used to calculate
aging to the battery.
900
Total Annual Cost ($)
This aging model is combined with the equivalent circuit
model of a lithium ion battery [16] to give terminal
characteristics at any rate and temperature; the resulting loss
of capacity at a standard rate (1C) is then calculated for the
vehicle battery.
Battery cost
$5000
$4000
$3000
$2000
800
700
0
20
40
60
80
DOD Centered on a 60% mean SOC (%)
100
Figure 12. Annual cost due to degradation of vehicle
batteries using Los Angeles climate and electric rates
($0.125/ kWh) and $3 per gallon gasoline, as it varies with
battery replacement cost.
Battery cost
$5000
$4000
$3000
$2000
800
800
700
0
20
40
60
80
100
DOD Centered on a 60% mean SOC (%)
Figure 13. Annual cost due to degradation of vehicle
batteries using Dallas climate and electric rates
($0.11/ kWh) and $3 per gallon gasoline, as it varies with
battery replacement cost.
Total Annual Cost ($)
As the top curves ($5000 battery replacement cost) in each
of the three city graphs do not reach a minimum, we can see
that with $3 per gallon gasoline and today’s battery prices,
the replacement cost of the battery is so high that it is
cheaper to burn gasoline and not use the battery to even a
40% DOD (assuming the cost of CO2 production is not
included). At target future battery costs, the battery would
be most cost effective used at a deep discharge. At
intermediate battery costs, a broad shallow optimum occurs
at 60% DOD, reducing battery aging cost while displacing
some gasoline.
night
both
750
40
60
80
100
Mean State of Charge (%)
Figure 15. The effect of mean SOC on total annual cost,
using temperature and electricity prices in Boston.
This result leads to the observation that if the battery is to be
recharged at work, perhaps under a V2B scenario, it appears
that an optimum strategy might be to charge the battery
fully, including equalization, and then bring it down to 80%
charge for most of the day – to leave room for peak shaving
in to the building’s electricity grid, but also to optimize
battery lifetime. An evening charging strategy for at home
use might be to charge the car to 80%, then wait until early
morning a few hours before normal commuting time to
charge it the rest of the way and equalize it.
It may also be useful, but has not been analyzed, to
thermally condition the vehicle (heating or air conditioning
the space) just before the commute in either direction, using
the inexpensive electric utility energy without giving up
battery capacity. Depending on climate and driver
preferences, this would be easy to implement with the same
smart controller used for battery charging.
Battery cost
$5000
$4000
$3000
$2000
1200
day
Total Annual Cost ($)
Total Annual Cost ($)
900
If the battery is not fully charged immediately, but is
maintained at a constant SOC until just before it is needed,
the mean SOC has an effect on battery lifetime, with
maintenance of the battery at full charge having a negative
effect on battery lifetime (Figure 15). This effect is
exacerbated by temperature, with the rate of degradation at
night being half as much as during the day. The effects are
additive.
1100
6. CONCLUSIONS
1000
0
20
40
60
80
100
DOD Centered on a 60% mean SOC (%)
Figure 14. Annual cost due to degradation of vehicle
batteries using Boston climate and electric rates ($0.163/
kWh) and $5 per gallon gasoline, as it varies with battery
replacement cost.
However, the effect of the price of gasoline is dramatic. If
gasoline increases from $3 per gallon to $5 per gallon, the
Boston graph changes to the one shown in Figure 14. In this
case it is most cost effective to discharge the battery fully
while driving.
Models were developed for plug-in hybrid electric vehicle
operation, both on the road, and used while parked as a
vehicle to building peak leveling system. A battery life
model was used to evaluate the cost of battery aging during
these activities. Total subsystem costs including battery,
electricity, and gasoline were compared for a baseline
vehicle in Boston, Los Angeles, and Dallas using the
weather and electric rate data for each location.
If the battery replacement cost is close to the USABC goal
levels, the economics of driving the PHEV show an
optimum for all locations with a depth of discharge of 60%.
Economics are enhanced if the battery SOC during long
parked periods, such as during the work day and overnight,
are maintained at 80% rather than full charge. Battery
thermal management to keep batteries close to ambient air
temperature is important.
Vehicle to building peak leveling has attractive economics
and can be realized with a simple practical algorithm for
peak selection. The aging model predicts that the cost of up
to 7 V2B cycles a month on the vehicle battery will be less
than $7 per month of added battery replacement cost;
comfortably less than the projected $158 per month of
potential revenue from V2B operations. Therefore, V2B has
the potential to provide far more value than the battery
aging will cost.
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