-
fEnvironmental Energy Technologies Division, Lawrence
h i g h l i g h t s
and 4-or scoond 123ditioninass an
tions. This study uses detailed vehicle powertrain models to
estimate per kilo-
vehicles is rapidly growing in India, with 2020 annual
projectedsales of 10 million passenger vehicles, 2.7 million
commercial vehi-cles, and 34 million two-wheelers. India currently
imports about85% of its oil and is projected to reach 92% by 2020,
creating a signif-icant challenge for the balance of payments and
the energy securityof the country [1]. Based on the pressing
challenges with growth invehicle sales and energy security facing
the country, the Central
Electric Mobilitypathwayrids, andment of 5
lion EVs (hybrids and full EVs) on the road by 2020, and the
Gment of India has committed Rs 22,500 cr (approximateBillion USD)
to this initiative. Similar goals for widespread electricvehicle
adoption have been set by other governments around theworld
[24].
The rapid deployment of plug-in hybrid and fully electric
vehi-cles (collectively called plug-in vehicles, PEVs, in this
paper) calledfor in the NEMMP places signicant demands on an
alreadystrained electricity grid in India [5]. However, since range
anxietyis a signicant consumer perception barrier to EV
deployment
Corresponding author.
Applied Energy 115 (2014) 582590
Contents lists availab
lseE-mail address: [email protected] (S. Saxena).1.
Introduction
India is one of the worlds most rapidly growing economies, andis
the third largest vehicle market in the world. Annual demand of
Government of India has released the NationalMission Plan
(NEMMP) [1] which establishes awidespread deployment of hybrids,
plug-in hybvehicles in India. The NEMMP calls for the
deploy0306-2619/$ - see front matter 2013 Elsevier Ltd. All rights
reserved.http://dx.doi.org/10.1016/j.apenergy.2013.10.043for
theelectric7 mil-overn-ly $4.1PowertrainTransportationVehicle to
gridIndia
meter electrical consumption for electric scooters, 3-wheelers
and different types of 4-wheelers in India.The powertrain modeling
methodology is validated against experimental measurements of
electrical
consumption for a Nissan Leaf. The model is then used to predict
electrical consumption for several typesof vehicles in different
driving conditions. The results show that in city driving
conditions, the averageelectrical consumption is: 33 Wh/km for the
scooter, 61 Wh/km for the 3-wheeler, 84 Wh/km for thelow power
4-wheeler, and 123 Wh/km for the high power 4-wheeler. For highway
driving conditions,the average electrical consumption is: 133 Wh/km
for the low power 4-wheeler, and 165 Wh/km forthe high power
4-wheeler. The impact of variations in several parameters are
modeled, including theimpact of different driving conditions,
different levels of loading by air conditions and other
ancillarycomponents, different total vehicle masses, and different
levels of motor operating efciency.
2013 Elsevier Ltd. All rights reserved.Keywords:Electric
vehicles
EVs in Indian driving condiModel electrical consumption of 2-,
3- Average city energy use is 33 Wh/km f Average city energy use is
84 Wh/km a The increased energy use from air con Energy use from
variations in vehicle m
a r t i c l e i n f o
Article history:Received 9 August 2013Received in revised form
18 October 2013Accepted 27 October 2013Available online 20 November
2013Berkeley National Laboratory, United States
wheelers in India.ters, 61 Wh/km for 3-wheelers.Wh/km for low
and high power 4-wheelers.g is quantied.d motor efciency are
quantied.
a b s t r a c t
The Government of India has recently announced the National
Electric Mobility Mission Plan, which setsambitious targets for
electric vehicle deployment in India. One important barrier to
substantial marketpenetration of EVs in India is the impact that
large numbers of EVs will have on an already strained elec-tricity
grid. Properly predicting the impact of EVs on the Indian grid will
allow better planning of newgeneration and distribution
infrastructure as the EV mission is rolled out. Properly predicting
the gridimpacts from EVs requires information about the electrical
energy consumption of different types ofSamveg Saxena , Anand
Gopal, Amol PhadkeElectrical consumption of two-, three-
andvehicles in India
Applied
journal homepage: www.eour-wheel light-duty electric
le at ScienceDirect
Energy
vier .com/ locate/apenergy
-
In support of the India National Electric Mobility Mission
Plan,
Ener[6], the absence of reliable charging points (which require
a stableelectricity grid) in India will make it difcult to achieve
the tar-geted levels of EV market penetration. Additionally, if the
electric-ity grid is unable to accommodate PEV charging, it is
possible thatdiesel generators will be used to provide the unmet
electricity de-mand. Although this local distributed generation
solution mayaccommodate PEV charging demand in the interim, it is
not aneffective way to decouple the Indian transportation sector
fromoil and can still lead to urban air quality problems.
The Government of India has recently joined the Electric
VehicleInitiative (EVI) of the Clean Energy Ministerial, which
seeks to facil-itate the deployment of 20 million EVs by 2020.
Under this initia-tive, Lawrence Berkeley National Laboratory is
supporting theNEMMP in assessing the real-world costs, benets and
environ-mental impacts of EV uptake in India; this publication is
the rstin a series of studies in this effort.
To properly plan for the rapid deployment of PEVs in India,there
is a need for nely resolved temporal and spatial predictionsof PEV
charging load on the electricity grid. The ability to
properlyforecast PEV charging load is essential for utility grid
operators toensure that adequate generation capacity is available
at the correcttimes, and ensure that distribution infrastructure
can accommo-date substantial PEV charging. Several studies [713]
have devel-oped methods to estimate PEV charging load for the
USelectricity grid. The most rigorous of these studies [1416]
followa three-step methodology (listed below) to predict temporally
re-solved PEV charging load proles. A modeling tool, called
V2G-Sim, has been developed at Lawrence Berkeley National
Laboratoryto streamline the simulation of vehicle-grid interactions
and thistool is available for use in potential research
collaborations [17].
1. Estimating the time when vehicles are plugged in: Survey data
isused to provide information on how drivers use their
vehicles,including number of vehicle trips per day, time of
departureof each trip, trip travel length, arrival time of each
trip, typeof vehicle, etc. In the United States, a common data
source forthis information is the National Household Travel
Survey(NHTS) [18], however other data sources have also been
used.
2. Estimating the amount of energy required to charge the
vehiclebattery: Typically, a simplied vehicle model is used to
esti-mate: (a) how much of the vehicle battery is depleted
duringeach trip, and (b) howmuch energy is required during
charging.A standard approach in prior studies [1416] is to assume
aconstant value for electrical consumption (kWh/km or kWh/mile)
depending on the type of vehicle (i.e. car, van, SUV, truck,etc.)
that is being modeled. More accurate estimates of batterydepletion
while driving can be obtained with detailed vehiclephysics models
(such as models used in other papers), howeverthis approach may be
prohibitively computationally expensivewhen attempting to model
hundreds, thousands, or millionsof PEVs on an electricity grid.
3. Estimating charging rates while a vehicle is plugged in:
Using esti-mates of when different vehicles will plug in for
charging fromstep 1, how much charging is required from step 2, and
infor-mation about the charging rate (i.e. level 1, level 2, or DC
fastcharger), number of PEVs and any smart charging
strategies,aggregate charging load proles are estimated for a large
num-ber of vehicles within a given region (i.e. utility service
territory,state, or country).
Successful implementation of the NEMMPwithin the
prescribedtimeline requires immediate planning and infrastructure
deploy-ment to ensure that the Indian electricity grid can cope
with the
S. Saxena et al. / Appliedadded charging load from large numbers
of PEVs. Thus, the 3-stepanalysis methodology described above must
be applied to the In-dian context, however much of the required
data for India is notthis study provides critical data to enable
detailed predictions ofPEV temporal charging load proles for the
Indian electricity grid.Detailed vehicle powertrain modeling is
used for:
1. Providing estimates of average electrical consumption(Wh/km)
for vehicles that are representative of typicalIndian two-, three-
and four-wheel vehicles over drivecycles that are representative of
Indian driving conditions.
2. Providing correlations for the Wh/km results that accountfor
variations in vehicle use, such as variability in vehiclemass, the
use of air conditioners, and variations in power-train component
efciency.
3. Vehicle models
3.1. Vehicle powertrain models
A detailed vehicle powertrain model is used to estimate
electri-cal consumption for four types of vehicles, with
specications foreach vehicle listed in Table 1. The powertrain
models are createdin the industry standard Autonomie powertrain
modeling platform.
3.2. Drive cycles
Given that energy consumption of a vehicle depends signi-cantly
on driving patterns [1925], several different drive cyclesare
chosen. Five drive cycles are chosen based on Indian
drivingconditions, including a New Delhi cycle [26], Pune cycle
[27], themodied Indian drive cycle (MIDC) [28], and an Indian urban
andavailable in published studies. For instance, for step 1 better
datais required to characterize vehicle usage patterns in India.
For step2, average electrical consumption (Wh/km) numbers are
requiredfor vehicles specically in the Indian context (i.e. for
vehicle sizesrepresentative of typical Indian vehicles driving in
Indian trafcconditions). The use of prior published electrical
consumption val-ues does not adequately account for typical Indian
vehicles or forthe inuence of driving and usage factors (i.e. from
dense trafc,or the use of power-consuming devices like an air
conditioner).Electrical consumption data for scooters, 3-wheelers,
and small4-wheelers has previously been unavailable in the
literature, par-ticularly for the Indian context where driving
conditions will bedifferent than in developed countries and air
conditioning load willbe a signicant factor. For the Indian context
in particular, it maybe inappropriate to use prior published Wh/km
values becausetwo-wheelers and ultra-compact four-wheelers that are
typicalin India are signicantly smaller and lighter than the US
market,and typical driving conditions are different in India with
more fre-quent stopping, lower average speeds and potentially more
suddenacceleration and deceleration [19].
In support of the NEMMP and as a step towards predicting
thecharging load of PEVs on the Indian electricity grid, the
results pre-sented in this study will enable better estimates of
PEV chargingload on the Indian electricity grid. Specically, the
results of thisstudy provide Wh/km values that are representative
of typicalvehicles in India, driving in conditions representative
of Indianroads. The results of this study can then be used in Step
2 of the3-step methodology above to estimate temporally resolved
PEVcharging loads on the Indian electricity grid.
2. Specic objectives
gy 115 (2014) 582590 583Indian highway cycle. Additionally three
US certication cyclesare also included for comparison purposes, the
EPA UDDS, HWFETand US06 cycles [29]. Figs. 14 compare the
characteristics of each
-
3-W
5005.466.384.251000
0.352.40
584 S. Saxena et al. / Applied EnerTable 1Vehicle specications
used in powertrain models.
Scooter
Base vehicle mass (kg) 150Motor max power output (kW) 1.5Final
drive ratio 6.3805Usable battery capacity (kWh) 2.16Tire size 1000
300Drag coefcient 0.60Frontal area (m2) 1.25drive cycle in terms of
velocity, stopping/idling, acceleration anddeceleration
characteristics. The values in these gures are nor-malized by the
average values across all driving cycles to alloweasier
comparisons.
Fig. 1 compares the velocity characteristics of the US and
Indiandrive cycles. The plot shows that driving conditions on the
Indiancycles involve lower maximum speed, lower mean speed, and
low-er mean driving speed1. Even the speeds on the Indian highway
cy-cle are considerably lower than the speeds on the US highway
cycles.
Baseline electrical accessory & AC load (W) 50 100Estimated
range in City (km) 6471 608Estimated range on Highway (km) N/A
N/ATop speed (km/h) 50 73
Fig. 1. Velocity characteristics of US and Indian drive
cycles.
Fig. 2. Stopping/idling characteristics of US and Indian drive
cycles.
1 Mean speed is dened as the average of all velocities over the
drive cycle. Meandriving speed is dened as the average of all
non-zero velocities.heeler Low power 4-wheeler High power
4-wheeler
898 149319 80
05 6.8737 7.93776.54 16.7
4.500 P155/70R13 P205/55 R160.335 0.282.0 2.50
gy 115 (2014) 582590Fig. 2 compares the stopping and idling
characteristics of the USand Indian drive cycles. As expected, the
results show that stop fre-quency and fraction of total time
stopped are much higher on thecity cycles as compared with the
highway cycles. Of particularimportance, Fig. 2 shows that stop
frequency is much higher inthe Indian city cycles than the US city
cycle. The total fraction oftime stopped is highest in the Pune
cycle, followed by the US citycycle.
Fig. 3 compares the acceleration characteristics of the US
andIndian drive cycles. The highest acceleration values are seen
inthe high speed US highway cycle (US06). Comparing the US and
In-dian city cycles, it is seen that greater maximum acceleration
and
200 2000 7095 123138
3476 73136117 120
Fig. 3. Acceleration characteristics of US and Indian drive
cycles.
Fig. 4. Deceleration characteristics of US and Indian drive
cycles.
-
maximum acceleration from stop values are encountered in the
In-dian city cycles, however the average acceleration is higher in
theUS city cycle.
Fig. 4 compares the deceleration characteristics of the US
andIndian drive cycles. The results show that maximum
deceleration
signicant impact on energy consumption [3031]. Loading the
mates were compared against published measurement data [32]
4. Results
values denote that the vehicle was unable to perform on the
drivecycle, either because the drive cycle requests speeds which
arehigher than the maximum speed capability of the vehicle,
orbecause acceleration proles are demanded which exceed
thecapabilities of the powertrain components. Thus, these
crossed
on.
eeler Low power 4-wheeler High power 4-wheeler
.50 0.203.0 0.204.0
Table 3Electrical consumption range of each vehicle.
Electrical consumption (Wh/km)
Avg city Avg hwy Range
Scooter 33 38 31403-Wheeler 61 85 5397Low power EV 84 133
70192High power EV 123 164 101224
S. Saxena et al. / Applied Energy 115 (2014) 582590 585for the
EPA UDDS, Highway, and US06 drive cycles over a rangeof total
vehicle mass. Fig. 5 shows a comparison of the modeledand measured
electrical consumption values for a Nissan Leaf.
The modeled and experimentally measured electrical consump-tion
values plotted in Fig. 5 show that the vehicle powertrain mod-el
reasonably predicts both the trends and absolute values
ofelectrical consumption for a range of different vehicle masses
forall three drive cycles. The largest difference in absolute
valuesbetween the model and the experimental measurements is11.50%,
which occurs for the lowest vehicle mass on the highwaycycle. It is
typically the case that increased vehicle mass leads toincreased
energy consumption, however the experimental
Table 2Range of parameter variations explored for their impact
on vehicle energy consumpti
Scooter 3-Wh
Ancillary loading (i.e. A/C) (kW) 0.00.30 0.00vehicle with more
passengers or cargo will also impact energyconsumption.
Additionally, variations in powertrain componentefciency will also
impact energy consumption. Table 2 lists therange of parameter
variations that were explored using the vehiclepowertrain models
for their impact on vehicle energyconsumption.
3.4. Model validation
To ensure that the electrical consumption estimates presentedin
the results section of this paper are reasonable, the same
mod-eling methodology is followed to create a powertrain model for
aNissan Leaf electric vehicle, for which there are well
documentedvalues of electrical consumption under various driving
conditions.
A vehicle powertrain model was constructed with
specicationsresembling a Nissan Leaf, and electrical consumption
model esti-and maximum deceleration to stop are higher in Indian
city condi-tions than US city conditions, however higher levels of
averagedeceleration are seen in the US city cycle.
Summarizing the results in this section, Figs. 14 compared
thedrive cycle characteristics for the US and Indian drive cycles.
It wasgenerally observed that the Indian drive cycles involve lower
driv-ing speeds, greater frequency of stopping, and higher levels
ofmaximum acceleration and deceleration. These results suggest
thatdriving in India may involve more severe stop-and-go
conditions,and previous studies [19,25] have found that these types
of drivingconditions create unique opportunities for achieving
greater levelsof fuel savings with vehicle electrication.
3.3. Parametric variations
In addition to the vehicle speed proles while driving,
otherparameters will signicantly inuence vehicle energy
consump-tion as well. The vehicle modeling that is discussed in
this papercaptures the impact on vehicle energy usage from several
parame-ters that will change with different vehicle designs, usage
patterns,and driving conditions. For warm climates like India,
ancillarycomponents such as vehicle air conditioning load will have
aVehicle mass (kg) 150300 500800Motor efciency (%) 5590 55904.1.
Baseline electrical consumption estimates
Table 1 lists the vehicle specications that were used in
thepowertrain models for an electric scooter, electric 3-wheeler,
lowpower EV 4-wheeler and high power EV 4-wheeler. These
power-train models provide the electrical consumption per kilometer
esti-mates over several different drive cycles in Fig. 6 and Table
3. Thereare several numerical values in Fig. 6 which are crossed
out (partic-ularly for the electric scooter and 3-wheeler). These
crossed outmeasurements on the highway cycle do not display this
expectedtrend. This may be due to experimental error because
obviouslythe vehicle mass will have a signicant inuence on the
electricityconsumption of a vehicle. This expected trend is indeed
seen forthe UDDS and US06 experimental measurements, thus the
datapoints at the lowest mass values for the highway cycle seem
higherthan expected. As a result of the overall agreement of trends
andabsolute values shown in Fig. 5, the modeling methodology is
con-sidered accurate enough for the purposes of this study.
Fig. 5. Model validation: comparison of modeled and measured
electrical con-sumption for a Nissan Leaf.8981200 149318005590
5590
-
586 S. Saxena et al. / Applied Energy 115 (2014) 582590Fig. 6.
Electrical energy consumption rate for different types of EVs on
differentdrive cycles.out values should not be given much weight
but instead simplyconsidered for reference.
The results in Fig. 6 show that electrical consumption per
kilo-meter is highest for the 4-wheelers and lowest for the
electricalscooter, which comes as no surprise given the differences
in vehi-cle mass. For the vehicles which are capable of sustaining
highwayspeeds (i.e. only the 4-wheelers), electrical consumption is
signi-cantly higher for high speed highway driving.
Fig. 7. Variation of vehicle electrical consumption with
different ancillary compo-nent loading.
Table 4Coefcients for equation of t for impact of ancillary
component loading (kW) on vehicle
UDDS HWFET US06
2 Wheeler m 42.35b 32.83R2 1.00
3 Wheeler m 36.47b 65.70R2 1.00
4 Wheeler low power m 35.30 15.43 15.73b 87.86 117.48 189.49R2
1.00 1.00 1.00
4 Wheeler high power m 34.22 14.21 14.70b 128.27 142.42 220.64R2
1.00 1.00 1.004.2. Impact of parameter variations on vehicle
electricity consumption
The electrical consumption was calculated for several vehicleson
several US and Indian drive cycles in Section 4.1, with
specica-tions dened in Table 1. Vehicles on the road, however, will
rarelyhave exactly the same specications as those dened in Table
1,thus this section explores how different parameters will
impactelectrical consumption of each vehicle.
4.2.1. Ancillary component and air conditioning loadsFor hot
climates like India, energy use by air conditioners will
have a signicant impact on the electricity consumption of a
vehi-cle. Additionally, other ancillary components (like vehicle
controlelectronics, radio, and lights) will consume energy. Fig. 7
presentsthe impact on vehicle electricity consumption from
different levelsof loading by ancillary components. As two- and
three-wheelerstypically do not have an enclosed cabin they will not
have air con-ditioners, and thus their maximum loading from
ancillary compo-nents will be lower. Thus, in Fig. 7 the modeled
range of energyconsumption from ancillary components for the two-
and three-wheel vehicles is much lower than for the
four-wheelers.
Fig. 8. Variation of vehicle electrical consumption with
different total vehiclemasses.Fig. 7 shows that for each vehicle on
all the different drive cy-cles, vehicle electricity consumption
(Wh/km) increases linearlywith increasing loading from ancillary
components. It is particu-larly important to note that the slope of
this linear increase is dif-ferent across the different drive
cycles. The equation of t for therelationship between ancillary
component loading and vehicleelectricity consumption follows the
form of Eq. (1), where x is
electricity consumption (Wh/km).
India urban India highway Delhi Pune MIDC
48.13 61.62 57.59 40.5928.85 30.40 28.84 33.971.00 1.00 1.00
1.00
45.85 22.75 59.28 55.40 34.4250.62 67.15 46.94 50.46 69.801.00
1.00 1.00 1.00 1.00
47.11 23.72 60.64 56.86 34.4967.81 80.78 57.03 69.33 89.801.00
1.00 1.00 1.00 1.00
46.14 22.88 59.57 55.73 33.38112.28 118.40 88.84 113.99
124.401.00 1.00 1.00 1.00 1.00
-
Table 5Coefcients for equation of t for impact of vehicle mass
(kg) on vehicle electricity consumption (Wh/km).
UDDS HWFET US06 India urban India highway Delhi Pune MIDC
2 Wheeler m 0.03 0.02 0.02 0.03 0.02b 29.94 28.12 30.88 27.64
32.73R2 1.00 0.95 0.95 0.98 0.96
3 Wheeler m 0.07 0.07 0.05 0.04 0.07 0.04b 34.43 22.27 45.54
32.00 23.64 51.03R2 1.00 1.00 1.00 1.00 1.00 1.00
4 Wheeler low power m 0.07 0.05 0.07 0.07 0.06 0.04 0.07 0.05b
30.87 77.49 127.74 18.62 31.96 32.06 22.60 48.43R2 1.00 1.00 1.00
1.00 1.00 1.00 1.00 1.00
4 Wheeler high power m 0.06 0.04 0.07 0.06 0.05 0.04 0.06 0.05b
44.05 83.86 114.44 31.90 42.89 39.43 37.38 60.272
S. Saxena et al. / Applied Energy 115 (2014) 582590 587R 1.00
1.00 1.00the electrical loading in kW, m is the slope, and b is the
y-intercept(in this case, the electrical consumption if there was
no loadingfrom ancillary components). Values for m and b for each
vehicleon each drive cycle are listed in Table 4.
y mx b 1
The tting equation parameters in Table 4 shows that the slopeof
each tting equation for a given drive cycle is not sensitive
tovehicle type. The slopes are generally higher for lower speed
driv-ing conditions. These results suggest that ancillary
component
Fig. 9. Variation of vehicle electrical consumption with
different average motoroperating efciency.
Table 6Coefcients for equation of t for impact of motor efciency
(%) on vehicle electricity con
UDDS HWFET US06
2 Wheeler m 54.11b 75.97R2 0.99
3 Wheeler m 125.9b 165.3R2 0.99
4 Wheeler low power m 198.8 189.0 319.0b 248.5 271.7 441.8R2
0.99 0.99 1.00
4 Wheeler high power m 304.2 231.1 438.0b 361.7 326.1 568.6R2
0.99 0.99 0.99loading has a greater impact on vehicle electricity
consumptionat lower speed driving conditions (i.e. city driving),
and is not sen-sitive to vehicle type.
1.00 1.00 1.00 1.00 1.00
Fig. 10. Powertrain architecture for electric vehicle
models.4.2.2. Variations in vehicle, passenger and cargo
massIndividual vehicles are bound to be loaded with different
mass
due to variations in the number of passengers or cargo being
car-ried. Fig. 8 shows the impact of variations in total vehicle
massfor each vehicle on each drive cycle. The 3- and 4-wheelers
willhave greater carrying capacity and thus the range of vehicle
massesmodeled is larger for these vehicles.
Fig. 8 shows that vehicle electricity consumption is also
linearlydependent on vehicle mass, with increased electricity
consumptionfor greater total vehicle mass. The equation of t
relating changesin vehicle electrical consumption with changes in
vehicle mass fol-lows the form of Eq. (1) as well, with x being
vehicle mass in kg.Table 5 lists the coefcients for the equations
of t for variationsin vehicle mass. The tting coefcients in Table 5
suggest thatthe impact of vehicle mass on vehicle electricity
consumption is
sumption (Wh/km).
India urban India highway Delhi Pune MIDC
50.93 49.53 51.55 54.3469.54 70.89 69.97 77.540.99 0.99 0.99
0.99
106.2 118.2 74.5 99.9 118.0135.6 161.4 110.2 131.3 164.60.99
0.99 0.99 0.99 0.99
286.7 266.0 167.1 274.9 251.1328.7 322.9 219.4 321.1 318.90.99
0.99 0.99 0.99 0.99
268.7 266.0 167.1 274.9 251.1328.7 322.9 219.4 321.1 318.90.99
0.99 0.99 0.99 0.99
-
good t.
Ener5. Conclusions
Given the ambitious targets for electric vehicle deployment
inIndia under the National Electric Mobility Mission Plan whichwas
announced by the Government of India, there are signicantconcerns
with the impact that EV charging will have on an alreadystrained
Indian electricity grid. This study is part of a larger
efforttowards estimating the impact on the Indian electricity grid
fromsubstantial deployment of EVs on the Indian grid to
subsequentlyplan the deployment of new generation and
distributioninfrastructure.
This study used detailed vehicle powertrain models to
estimatethe per kilometer electrical consumption of several types
of EVs,including a scooter, a 3-wheeler, a low power 4-wheeler, and
ahigh power 4-wheeler. Electrical consumption data for
scooters,3-wheelers, and small 4-wheelers has previously been
unavailablein the literature, particularly for the Indian context
where drivingconditions will be different than in developed
countries and airconditioning load will be a signicant factor. The
powertrain modelmethodology was validated against experimental
measurementsfor a Nissan Leaf. The main conclusions from this study
are asfollows:
1. Average electrical consumption: Vehicle size has the
greatestimpact on per km electrical consumption, followed by
thedriving characteristics (i.e. city vs. highway driving). In
citydriving conditions average electrical consumption results
were:33 Wh/km for the scooter, 61 Wh/km for the 3-wheeler, 84 Wh/km
for the low power 4-wheeler, and 123Wh/km for the highpower
4-wheeler. For highway driving conditions average elec-fairly
consistent across the different drive cycles and across the
dif-ferent vehicles (especially the 3-wheeler and both
4-wheelers).
4.2.3. Variations in average motor efciencyThe values chosen for
the motor efciency maps used for the
baseline vehicle simulations (in Section 4.1) were established
tot the Nissan Leaf model validation results in Section 3.4.
Electricvehicles released in the Indian market, however, may use
differenttypes of motors with different efciency operating proles,
thusthis section explores the impact of changes is motor operating
ef-ciency. Fig. 9 shows the variation of vehicle electricity
consump-tion with average motor operating efciency for the
differentvehicles driving on the different drive cycles.
As expected, the results in Fig. 9 show that vehicle
electricityconsumption decreases as a more efcient motor is used. A
partic-ularly interesting result, however, is that changes in motor
ef-ciency have very little impact on electricity consumption for
thesmaller vehicles, especially the two-wheeler. This result is of
sig-nicant importance as it suggests that the use of less
expensivemotors, which may be less efcient, can be used to lower
the costof electric scooters while having minimal impact on vehicle
elec-tricity consumption (and thus vehicle range). For the larger
vehi-cles and for higher speed driving conditions (i.e. on
highways),however, motor efciency impacts electrical consumption
signi-cantly and thus better motors must be used. The results for
the lar-ger vehicles in Fig. 9 show that the relationship between
vehicleelectricity consumption and motor efciency is not perfectly
linear(i.e. a slight curvature can be seen on the plots), however
the R2 t-ting parameters in Table 6 show that a linear equation of
the formof Eq. (1), with x being the average motor efciency (%),
produces a
588 S. Saxena et al. / Appliedtrical consumption results were:
133Wh/km for the low power4-wheeler, and 165Wh/km for the high
power 4-wheeler. Theelectrical consumption and thus on EV range.
For larger vehi-cles, however, motor efciency has a signicant
impact, withmore efcient motors allowing signicantly reduced
electricalconsumption. For larger vehicles there is also an impact
of driv-ing characteristics, with higher speed driving conditions
show-ing greater variation of electrical consumption with changes
inmotor operating efciency.
Acknowledgements
This work was supported by the Assistant Secretary of Policyand
International Affairs, Ofce of Policy and International Affairs,of
the US Department of Energy and the Regulatory AssistanceProject
through the US Department of Energy under Contract
No.DE-AC02-05CH11231.
Appendix A.
This Appendix presents a brief description of the powertrainand
component models that are used to model the four types ofelectric
vehicles considered in this study. For a detailed descriptionof
each model, readers are referred to the documentation associ-ated
with the commercially available powertrain modeling soft-ware
Autonomie, which was used in this study.
A.1. Overall powertrain architecture
The electric vehicle models in Autonomie include the compo-nent
models shown in Fig. 10, as well as an overarching propulsionand
brake control model.
The propulsion control model translates driver
accelerationcommands, which are governed by the specied drive
cycle, intomotor torque demands while simultaneously considering
vehicleand motor speed, battery state of charge, maximum torque
outputbefore wheel slip at a given speed, and loading from
ancillarycomponents.
The braking control model performs a similar function of
trans-lating driver braking commands, which are governed by the
spec-scooter and 3-wheeler were incapable of sustaining
highwayspeeds. Readers are referred to Section 4.1 for a detailed
break-down of electrical consumption for different driving
conditions.
2. Impact of air conditioners and ancillary component loads on
elec-trical consumption: Ancillary components have a
signicantimpact on electrical consumption, with per km electrical
con-sumption increasing linearly with greater ancillary
componentloads. The slope of increasing electrical consumption is
largerfor lower speed driving conditions (i.e. in cities), but is
not sen-sitive to vehicle type.
3. Impact of variations in vehicle mass on electrical
consumption: Perkm electrical consumption also increases linearly
with increas-ing vehicle mass (i.e. for more passengers or cargo).
The slope ofincrease is fairly consistent across different driving
conditionsand vehicle types.
4. Impact on variations in motor efciency on electrical
consumption:Per km electrical consumption decreases linearly with
greatermotor operating efciency, however the slope of this
decreaseis highly sensitive to vehicle size. An important nding is
thatfor smaller vehicles, like scooters, increasing motor
efciencyhas little impact on electrical consumption. As a result,
theuse of inexpensive and less efcient motors to minimize thecost
of electrical scooters will only have minimal impact on
gy 115 (2014) 582590ied drive cycle, into braking torque demands
while consideringseveral factors and constraints. One further
function of the braking
-
le for a full range of motor speeds according to experimental
data.
ear. Losses from mechanical braking and tire friction are
calculated
Enermodel is to specify the braking torque provided by the
tractionmotor and the mechanical brakes. In general, braking torque
isprovided entirely by the traction motor until the motor orbattery
power limits are encountered. Beyond these limits,mechanical
braking is used to absorb the remaining required brak-ing
torque.
A.2. Battery model
The battery model calculates the state of an individual cell
andassumes that all cells operate identically. Cell state of charge
(SOC)is calculated according to the coulomb counting approach in
Eq.(1):
SOC R
I3600dt AhinitAhmax
1
In Eq. (1), I is the charging or discharging current requested
fromthe battery, Ahinit is the amount of energy stored in the
batterywhen the model is initialized, and Ahmax is the maximum
energystorage capacity of the battery as a function of cell
operating tem-perature, as shown in Eq. (2):
Ahmax f Tcell 2
The values for Ahmax are specied in an initialization le using
mea-surement data for the maximum capacity of a cell that is
dischargedat a C/5 rate.
The open circuit voltage and the internal resistances of the
cellon charging or discharging are determined as a function of SOC
andcell temperature, as shown in Eq. (3) through Eq. (5)
respectively:
VOC f SOC;Tcell 3
Rint;chg f SOC;Tcell 4
Rint;dis f SOC;Tcell 5Open circuit voltage and internal
resistance data on charging anddischarging is specied in an
initialization le based on experimen-tally measured data. For
lithium ion batteries, open circuit cell volt-age typically spans a
range from 3.5 to 4.2 V.
The cell output voltage at any given operating condition (i.e.
atthe battery terminals) is calculated using Eq. (6) for charging
andEq. (7) for discharging:
Vout;chg VOC gcoulIoutRint;chg 6
Vout;dis VOC IoutRint;dis 7gcoul is the coulombic efciency,
which for these models is simplyset to 1.0.
A simple thermal model is included as part of the battery
modelto estimate the cell operating temperature. The rate of heat
gener-ation in a cell is calculated according to Eq. (8) while
charging andEq. (9) while discharging:
_Qgen;chg I2outRint;chg VoutIout1 gcoul 8
_Qgen;dis I2outRint;dis 9Heat dissipation is calculated by
assuming a fan ows cooling airacross the cells within a pack. Eq.
(10) is activated when the celltemperature rises above a specied
threshold to cause the batterymanagement system to turn the cooling
fan on.
_Qcooling Tmodule air TmoduleThermal resistance 10
S. Saxena et al. / AppliedIn situations where the cooling fan
remains off, Eq. (10) is simply setto zero. The module air
temperature is calculated using Eq. (11):within this model. Linear
force exerted or absorbed by the tires iscalculated using Eq.
(13):
F T=rwheels 13In Eq. (13), T is the total input or output torque
to the tires, and
rwheels is the wheel radius. Torque input or output is
calculatedusing Eq. (14):
T Tin Tbraking Tres 14
In Eq. (14), Tin is the torque input from the vehicle
powertrain,The absolute maximum torque output is specied according
toa predened value for continuous to peak torque ratio. Themotor
efciency map is specied for a full range of torque andspeed points
in the initialization le using experimentallymeasured data. The
motor model inputs are the command to themotor (i.e. required
propulsion torque), the input voltage, andthe motor speed.
The maximum propulsion and regenerative torque capabilitiesof
the motor are determined as a function of motor speed. Themaximum
torque map (as a function of speed) is specied in thevehicle
initialization le. The specied torque map enables maxi-mum torque
at low speeds (up to roughly 2000 RPM) and subse-quently decaying
maximum torque up to the high speed limits ofthe motor.
Section 4.2.3 of this paper examines the impacts of
differentlevels of motor efciency for the vehicles that are
modeled. Motorefciency is scaled by multiplying the efciency map
specied inthe initialization le by a scaling factor. The scaling
factor is de-ned as the ratio of desired maximum motor efciency
over themaximum efciency specied in the map dened in the
initializa-tion le.
A.4. Torque coupling, nal drive and wheel model
The nal drive and torque coupling models are
functionallysimilar, and serve to apply a xed gear reduction ratio
to both tor-que and speed by taking into account the losses. The
torque cou-pling and nal drive are assumed to be 97% efcient across
theentire torque/speed range.
The wheel model serves to transform rotational energy into
lin-Tmodule air Tair 1=2_Qcooling
_mcooling airCp;module11
Finally, the module temperature is calculated through the
bal-ance of heat generation and heat dissipation rates in Eq.
(12),and it is assumed that each cell within the module will have
thesame temperature.
Tcell Tmodule R _Qgen _Qcooling
dt
mmoduleCp;module12
A.3. Motor model
The motor model provides the torque demanded by the propul-sion
controller, while taking into account the effects of losses
androtor inertia. Motor temperature is used to determine the time
thatthe motor can spend above the maximum continuous rated
torquelevels.
The maximum continuous torque is specied in an
initialization
gy 115 (2014) 582590 589Tbraking is the braking torque exerted
by the mechanical brakes,and Tres is the resistive torque from tire
rolling resistance which iscalculated using a third-order
polynomial function of speed. The
-
coefcients for the polynomial are specied in an initialization
le [8] Rahman S, Shrestha GB. An investigation into the impact of
electric vehicleload on the electric utility distribution system.
IEEE Trans Power Deliv1993;8(2):5917.
http://dx.doi.org/10.1109/61.216865.
590 S. Saxena et al. / Applied Energy 115 (2014) 582590based on
experimentally measured data.
A.5. Chassis model
By balancing the total powertrain output against the
totalopposing forces, the linear acceleration and vehicle speed is
nallycalculated at the chassis model. Powertrain output (or
regenerativeinput) is calculated at earlier sub-models based on the
specieddrive cycle and the specied component parameters.
Opposingforces include factors such as hill climbing, aerodynamic
losses,and tire rolling resistance.
Aerodynamic losses are calculated within the chassis modelusing
Eq. (15):
Floss;aero 1=2qCdAV2 15In Eq. (15), q is the density of air, Cd
is the vehicle drag coefcient, Ais the frontal area of the vehicle,
and V is the vehicle speed.
Opposing force from hill climbing is calculated using Eq.
(16):
Floss;hill mg sinh 16In Eq. (16), m is the vehicle mass, g is
the acceleration from gravity,and h is the hill grade.
The acceleration of the vehicle is subsequently calculated
usingEq. (17):
a Fin Flossmstatic mdynamic 17
In Eq. (17), Fin is the input from the vehicle powertrain, Floss
is thesum of all opposing forces, mstatic is the static mass of the
vehicleand mdynamic is the dynamic mass of the vehicle from
rotating com-ponents. Vehicle speed is calculated by integrating
Eq. (17) overtime.
A.6. Ancillary components models
Power losses from ancillary components (such as air
condition-ing and electronic in-vehicle equipment) are calculated
as a spec-ied continuous power draw. The power that is owed
toancillary components is assumed to travel through a power
con-verter which maintains its output voltage at the required
voltageinput for ancillary components (i.e. 12 V). The power
converter isassumed to have 95% conversion efciency.
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Electrical consumption of two-, three- and four-wheel light-duty
electric vehicles in India1 Introduction2 Specific objectives3
Vehicle models3.1 Vehicle powertrain models3.2 Drive cycles3.3
Parametric variations3.4 Model validation
4 Results4.1 Baseline electrical consumption estimates4.2 Impact
of parameter variations on vehicle electricity consumption4.2.1
Ancillary component and air conditioning loads4.2.2 Variations in
vehicle, passenger and cargo mass4.2.3 Variations in average motor
efficiency
5 ConclusionsAcknowledgementsAppendix A.A.1 Overall powertrain
architectureA.2 Battery modelA.3 Motor modelA.4 Torque coupling,
final drive and wheel modelA.5 Chassis modelA.6 Ancillary
components models
References