Generic Methodology for Driving Range Estimation of Electric Vehicle with On-Road Charging Aditya Shekhar*, Venugopal Prasantht, Pavol Bauer+ and Mark Bolech§ Delft Institute of Technology & TNO, The Netherlands * Email:
[email protected] t Email:
[email protected] + Email:
[email protected] § Email:
[email protected]
Abstract-An vehicles
(EVs)
analytical estimation of driving range of electric with
contactIess
on-road
charging
system
is
presented in this paper. Inductive power transfer (IPT) systems with
different
configurations
(static,
dynamic),
power
levels
and road coverage have different (and non-linear) impact on the driving range.
A
generic methodology has been developed
to estimate the driving range of any EV by defining a set of formulae linearly dependant on vehicle mass, frontal area, IPT system configuration, power level and road coverage area. Driving cycle constants are defined to take into account the variation in the consumption pattern of the EV with the velocity profile. Keywords battery weight reduction, contactless charging, driving range, dynamic, electric vehicle, extension, inductive power transfer, on-road charging, state of charge, static
1. INTRODUCTION
Limited driving range has restricted the proliferation of electric vehicles in the market. Increasing the capacity of the expensive on-board battery not only increases the cost of ownership but also decreases the transport efficiency due to increase in battery weight. Further, with size constrains, it is not always possible to install adequate battery capacity to meet the driving range requirements. On-road charging of electric vehicles (EVs) by inductive power transfer (IPT) can aid in the driving range extension. The concept of inductively coupled roadways is not new [1] , [2] . Subsequent research has led to design advancements and more efficient power transfer [3] , [4]. The IPT system configuration (static, dynamic), charging power level and road coverage are important design considerations that influence the driving range [5], [6] . The required driving range can be achieved by a number of combinations of these parameters. However, a proper choice of parameters is essential from point of view of economic viability of the system. In this paper, a generic methodology is established by defining linear equations to estimate the driving range of any EV for different standardised velocity profiles and vehicle specifications. Quantification of driving range extension influenced by different IPT system parameters is presented. Section II describes the dynamic power consumption model
978-1-4673-6741-7/15/$31.00 ©2015 IEEE
of the EV, the reference vehicle parameters and the driving cycles which have been analysed. Section III establishes the mass and area constants for different driving cycles and sets up the equation to estimate the state of charge of the EV battery at the end of travelled distance for any vehicle parameters when no on-road charging is available. Section IV defines the equations to estimate the driving range of the electric vehicle with on-road charging options for different power levels of static and dynamic charging and different road coverage of dynamic IPT system. Section V discusses the impact of on-board battery weight on the transport efficiency. Equations to compute the battery capacity required to achieve the desired driving range without on-road charging and the battery weight dependant specific consumption of the EV is described. II.
DYNAMIC POWER CONSUMPTION OF
EV
In order to account for the energy inflow and outflow, it is essential to develop a model for the dynamic power consumption of the EV This consumption pattern is dependant on the vehicle's specifications and the velocity profile. A. Reference Vehicle Parameters The dynamic power consumption is simulated for a refer ence vehicle with parameters [7] listed in Table 1. TABLE I REFERENCE VEHICLE PARAMETERS
Empty Mass (kg)
13300
Gross Mass (kg)
19000
Frontal Area (m2)
8.568
Coefficient of Drag (assumed) Coefficient of Rolling resistance (assumed) Battery Capacity (Ah) Energy Capacity (kWh) Nominal Voltage (V ) Battery Type
0.7 0.0 1 600 (200x3) 324 540 Lithium Ion
Initial State of Charge (assumed)
95 %
Allowed Depth of Discharge (assumed)
80 %
TABLE II SORT 3 DRIVING CYCLE km/h
Rated Average Speed
25.3
Stop Time
20. 1
%
30/200
kmlh/m mls2
Trapezoid 1, Constant Speed/Length Acceleration 1
0.77
Trapezoid 2, Constant Speed/Length Acceleration 2
50/600 0.57
Trapezoid 3, Constant Speed/Length Acceleration 3 Stop Time
kmlh/m mls2
0.46
kmlh/m mls2
60/650
O,751r----�--� Simulated: SOC m -15' C, 19000 kg . .. Line;lT Regression: SoC == -O.0099(S)+0.95 0.7 R-Squar("n:O.99996 Simulated: SOC at 2 0 0 C, 16150 kg 0.65 Linc:u Regression: SoC == --O,0037(S)+0.95 R-Squared:O.99987 Simulated: SOC at 40' C, 19000 kg 0.6 . .. Linear Regression: SoC == -0.0071(S)+0.95 R-S ared:O 9 9 ----C O,55 !,,=� == � "� � �· ;;;'J 'J� ' C;=I O �===C -" --- � ---c030C---35� -�40 -o-52 15;C=== 0 02 Distance(S)inkrn
20/ 10110
seconds
Total Length
1450
m
Deceleration
0.8
mls2
Fig. 2.
•
60 -":::;::::;=::;:::;:=:::='-�--="���
1000_ oS
•
500 6
EV battery SoC without on-road charging.
Inertial load (PI M alvl) is the change in the stored energy of the vehicle due to dynamic motion (accelera tion/braking). It is important to consider here that some energy is recoverable through the regenerative braking. Gravitational load (Pg Mgsin(B)lvl) due to movement of the vehicle on the an inclined road. =
=
The model involves the following assumptions: • Time (seconds)
Fig. l.
Velocity and distance plot for Sort 3 driving cycle.
• • •
It must be noted here that while the chosen reference parameters are that of a heavy duty vehicle, the methodology is developed to estimate the driving range of any EV with different gross mass and frontal area. B. Driving Cycles Several standard driving cycles [8] , [9] like the Standardised On-Road Test Cycles (SORT 1, SORT 2 & SORT 3) for heavy duty vehicles, EPA Urban Dynamometer Driving Sched ule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET) have been simulated to establish the methodology. The simulation results for SORT 3 cycle are presented and the 'driving cycle constants' defined in the subsequent section are provided for all aforementioned driving cycles. The velocity profile of SORT 3 cycle is described in Table II and the plot is shown in Fig. 1. Vehicle Dynamics
e.
The physics of the vehicle power consumption [10] is governed by the following forces: •
•
Aerodynamic Drag (Pdrag 0.5pCdlvl3 Af) is the load due to resistance offered by the air. p is the density of air in kg/m-3, Cd is the coefficient of drag, v is the instantaneous velocity and Af is the frontal area of the vehicle. Rolling resistance (Prall CrgM cos(B) Ivl) is the fric tional resistance offered by the road due to the motion of wheels. C r is the coefficient of rolling resistance, M is the mass of the vehicle, g is the acceleration due to gravity and B is the angle of inclination.
The equations defined in the subsequent sections deal with estimation of state of charge (SoC) of the EV battery as the dependant variable. The importance of this choice is that unlike the driving range, the SoC is linearly dependant on the different independent variables with respect to the distance travelled by the EV III. STATE
OF CHAR GE ESTIMATION OF BAT T ERy-ALONE
S YSTEM The 324 kWh (200 Ah x 3) Li-ion reference battery model [11]- [14] is first simulated for a distance of 40 km without any on-road charging in place. A. Simulation Results The system is first simulated for SORT 3 driving cycle without any on-road charging for 3 different scenarios: •
=
=
The overall efficiency of the motor-drive system is assumed to be 80 % . 60 % energy is recovered during regenerative braking. The angle of inclination of the road is zero. Power demand of auxiliary system is assumed to be 50 kW at -15 ° C, 0 W at 20 ° C and 25 kW at 40 ° C ambient temperature.
•
•
-15 °C ambient temperature with 100 % occupancy of the EV In this case the vehicle mass is 19000 kg and the power demand of heating system is 50 kW. 20°C ambient temperature with 50 % occupancy of the EV In this case the vehicle mass is 16150 kg and the power demand of HVAC system is 0 kW. 40°C ambient temperature with 100 % occupancy of the EV In this case the vehicle mass is 19000 kg and the power demand of cooling system is 25 kW.
The SoC of the EV battery with respect to the distance travelled is shown in Fig. 2. The linear regression yields a coefficient of determination R2 > 0.99. An R2 value close to
0.96
'i
�
0.94
Simulated: SoC at 20 0 C. M = 16150 kg, A = 0 ,-,-, Linear Regression: SoC = -0.00314(S) +0.95 R-Squared:0.99982
0.92
Simulated: SoC at 20 0 C,O.lxM, A = 0 ,-,-, Linear Regression: SoC = -0.00031(S) +0.95 R-Squared:0.99981
0.9
Simulated: SoC at 20 0 C, 0.5xM, A = 0 ,-,-, Linear Regression: SoC = -0.00156(S) +0.95 R-Squared:0.99982 Simulated: SoC at 20 0 C, 0.6xM, A = 0 ,-,-, Linear Regression: SoC = -0.00188(S) +0.95 R-Squared:0.99982
" on
� 0.88 u
a 0.86
Simulated: SoC at 20 0 C, 0.7xM, A = 0 ,-,-, Linear Regression: SoC = -0.00219(S) +0.95 R-Squared:0.99982
0.84
Simulated: SoC at 20 0 C, 0.8xM, A = 0 Linear Regression: SoC = -0.00251(S) +0.95 R-Squared:0.99982
0.82
Simulated: SoC at 20 0 C, 0.9xM, A = 0 ,-,-, Linear Regression: SoC = -0.00282(S) +0.95 R-Squared:0.99982
"
' -0.8 0
----' ' --...L -1.5 IO
-
Fig. 3.
--
-' --" ---'- -" 30 25 20 35 Distance (S) in km
-----
-" 40
Simulated: SoC at 20 0 C, l.lxM, A = 0 ,-,-, Linear Regression: SoC = -0.00346(S) +0.95 R-Squared:0.99982
SoC versus distance for different gross masses of the vehicle.
3.5 X 10-
1 indicates a linear dependence of battery SoC on the distance travelled by the EV The slope is dependent on the velocity profile and vehicle parameters like gross mass, frontal area, coefficient of drag and power demand of the HVAC system. To derive a generic equation for SoC estimation, it is necessary to analyse these dependencies.
3
. .... ' Linear Regression: Y
B. Mass Constant (Km) of Driving Cycle Fig. 3. shows the plots for SoC versus distance for different gross masses with frontal area equal to zero and ambient temperature of 20°C (HVAC power demand is taken as 0 at this temperature). Auxiliary power demand of the EV is set to zero. The SoC, thus in this case, depends only on the mass of the vehicle, Fig. 4. shows the vanatlOn of the slope of the SoC versus Distance with respect to mass of the vehicle. Regression analysis yields R2 > 0.99 which indicates a linear dependence of final state of the charge of the battery on the vehicle mass. The slope, defined as the Mass Constant of Driving Cycle Km 1.9464e-07 for SORT 3 driving cycle, The mass constant of driving cycle is dependent only on the velocity profile and makes it possible to estimate the variation in SoC with different vehicle masses. =
=
1.9464e-07x
R-Squared:O.99999
2000
Fig. 4.
4000
6000
8000 10000 12000 Vehicle Mass (Kg)
14000
16000
18000
Slope of SoC versus distance W.r.t gross mass of the vehicle.
and drag coefficient of the vehicle. The linear regression yields R2 > 0.99 which indicates a linear dependence of the final battery SoC on the frontal area of the vehicle. The slope, defined as the Area Constant of Driving Cycle Ka 8.973ge-05 for SORT 3 driving cycle, Hence, using Ka, which only depends on the velocity profile of the driving cycle, it is possible to estimate the variation in SoC with different vehicle frontal areas and drag coefficient. =
D, Equation for Soc Estimation of Battery-Only System The SoC for the battery-alone electric vehicle system for any distance travelled can be estimated using (1).
C. Area Constant (Ka) of Driving Cycle Fig. 5. shows the plots for SoC versus distance for different frontal areas with gross mass equal to zero and ambient temperature of 20°C. Auxiliary power demand of the EV is set to zero. The SoC, thus in this case, depends only on the frontal area of the vehicle.
Where,
Fig. 6. shows the variation of the slope of the SoC versus Distance with respect to the product of frontal area
SoGb SoG(O) S
is the SoC of battery at end of travelled distance is the initial SoC of the battery is the total distance travelled by the vehicle in km
0.96
' 2 Simulated SoC: 20 C. A: 8.568 m , Cd: 0.7, M: 0
,-, -, Linear Regression: SoC R-Squared:0.99996
0.955
Simulated: SoC at 20
'
0.95
q Ul
r�i!;;:=
-----
Simulated: SoC at 20
� _______ __ _
=
C, 0.5xAxCd, M
=
C, 0.6xAxCd, M
=
C, 0.7xAxCd, M
=
C, 0.8xAxCd, M
=
C, 0.9xAxCd, M
=
,-, -, Linear Regression: SoC R-Squared:0.99996
';;" 0.945
Simulated: SoC at 20
f;"
o .!l ;;; Ul
'
,-, -, Linear Regression: SoC
..c: U �
'
R-Squared:0.99996 Simulated: SoC at 20
0.94
'
,-, -, Linear Regression: SoC R-Squared:0.99997 Simulated: SoC at 20
0.935
'
Linear Regression: SoC R-Squared:0.99996 Simulated: SoC at 20
0.93
'
-0.OO054(S) +0.95
1 C, 0. xAxCd, M
,-, -, Linear Regression: SoC R-Squared:0.99997
=
,-, -, Linear Regression: SoC
=
=
=
=
=
=
0
-5e-05(S) +0.95
0
-O.OO027(S) +0.95 0
-0.OO032(S) +0.95 0
-0.OO037(S) +0.95 0
-0.OO043(S) +0.95 0
-0.OO048(S) +0.95
R-Squared:0.99996 ' Simulated: SoC at 20 C, l .l xAxCd, M = 0 L---"----- '- --- '- --- '- --- '- --- '- --- '- ---" 0.925 ear = -0.OO059(S) +0.95 Regression: ,-, -, Lin SoC 1 1 40 20 25 0 5 0 5 30 35 R- s quared:0.99996 Distance (S) in km
Fig. 5.
SoC versus distance for different frontal area of vehicle.
TABLE III DRIVING CYCLE CONSTANTS Driving Cycle SORT I SORT 2 SORT 3 -0"
Linear Regression: Y
=
UDDS
8.973ge-05x
R-Squared:O.99982
HWFET
Km 2.09234e 07 1.9604e-07 1.9464e 07 1.8ge-07 1.3371e 07
Ka 2.4684e 05 4.9201e-05
Vave (kmlh)
8.973ge 05 9.3371e-05
2.2176e 04
12. 1 18 25.3 3 1.53 77.73
4 2 3 5 Frontal Area of Vehical x Drag Coefficient (A.Cd)
Fig. 6.
Slope of SoC versus distance w.r.t frontal area
x
Cd of vehicle.
IV.
DRI V IN G RANGE EXT ENSION WITH ON- ROAD CHAR GIN G
A. Design Considerations is the Mass Constant of the Driving Cycle is the Gross Mass of the Vehicle in kg is the Area Constant of the Driving Cycle is the Frontal Area of the Vehicle in m2 is the coefficient of drag is the power demand of the auxiliary system of the vehicle in kW is the average velocity is the discharge efficiency of the battery is the energy capacity of vehicle battery in kWh is the capacity factor defined as the ratio of the maximum energy that can be delivered by the EV battery to the maximum energy that can be delivered by the reference battery
(=
1/dis E/""
7Jd�i8 ,ref Ebat"reJ
•
Static IPT system has lower ratio of infrastructure cost to the utilization per vehicle as compared to dynamic system. As the average velocity of the transport cycle in creases, viability of dynamic charging system in creases. •
Power Level [6] Power Level of the static IPT system is constrained by the designed power level of the battery charging infrastructure and the costs involved therein. Power level of the dynamic system influences the percentage road coverage for meeting the same driv ing range requirement. However, constrains in terms of inverter switch rating, efficiency of transfer, length of single IPT segment and number of vehicles on single segment are involved .
)
Similar procedure was followed for different driving cycles to determine the velocity profile dependent mass and area coefficients. Table III presents the mass and area constants of different driving cycles.
IPT system Configuration (Static/Dynamic):
•
Percentage road coverage of dynamic system
970
, ..... ·15 C. 19000 kg
,
.. .. .. 20 C, 15150 kg
870
•
.. '40
C, 19000 kg
770
!
570
�
570
�
470
.i 'S
�
370 270
. ....... .. ... . .. . . .
...•..
170
..
..
.•
. ..
"
, . ,, -
. .
.
•.
!
. .. ... ..... . . . .. . .. .... . . . . . . .. . .. . . . ..... .... . ...... .. ...
.... ..... ... . �. .. .. ... . .. . ...
......
..
.
.
. •. . 0- .
..
•..
.
: : : : : : :: . : : . : ... : . -----.------r----� 70 �����-r o
50
Charging
100 Power Level
150
(kW)
200
Fig. 8. Driving range for different charging power levels of static IPT system.
SoC of battery-alone system that can be estimated using (1). Fig. 7.
SoC of the battery for different charging power of static IPT system.
These design considerations will influence the infrastructural costs involved. For making a proper choice of IPT configu ration, charging power level and percentage road coverage, it is essential to determine their contribution in driving range extension. Herein, the rated charging power level is considered to be the total power delivered to the EV, wherein the losses occurring in the system, corresponding to converter losses [16] and the losses due to misalignment [15] need to be taken into account for economic considerations. By assuming that the IPT system losses are not variable, it is possible to estimate the driving range directly from the rated power transferred from the charging system to EV B. Static IPT System Static IPT charging is employed at scheduled stoppages of the driving cycle. For the same energy transfer per vehicle, the infrastructure cost involved in static is less than the dynamic IPT system. The system is simulated with different static IPT charging power levels at scheduled stoppages of the SORT 3 driving cycle. Fig 7. shows the plots for the final battery SoC versus power level for different scenarios for travelled distance of 40 km and 20 km. The following can be observed from the simulation plot: •
•
•
Final SoC of the battery linearly varies with the charging power level. The regression analysis yields R-squared value of 1, which indicates that the relation is governed not by probability but by the physics of the system. The slope becomes half when the simulated distance is halved and is independent of vehicle parameters like mass and HVAC system power demand. The initial point (at zero static charging power) is the
Based on this information it is possible to define a generic equation for estimating the SoC of battery for different power levels using (2).
SoGstatic
=
( tstop ) Pstat *
Ebat
T)C
+
SoGb(Stot)
(2)
Where,
SoGstatic tstop T) c
Ebat Pstat SoGb(Stot)
is the SoC of battery at the end of travelled distance with a static charging system is the total scheduled stoppage time spent on IPT charging system in hours is the charging efficiency of the battery is the energy capacity of the battery is the static IPT charging power level is the SoC at the end of the total travelled distance without any on-road charging
The driving range of the vehicle can be calculated from the SoC of the battery using (3).
DR
=
(DoDmax x Stat) ( SoC(O) - SoGb)
(3)
Where,
DR DoDmax Stat SoC(O) SoGb
is is is is is
the the the the the
Driving Range in km maximum allowed depth of discharge total travelled distance in km initial SoC SoC at the end travelled distance Stat
Fig. 8. shows the driving range with respect to the charging power level of the static IPT system for different loading scenarios. The charging power should be such that during the heavy load condition, the desired driving range can be achieved. However, the downside of this is that during opti mum load conditions, the infrastructure is under utilized. For good utilization of the infrastructure, the driving range should approach infinity during average loading conditions, while still
310
84%
-+-10!tW -.-,20kW
260
79%
�30kW -t-40kw
Zi: 74%
210
...,
� li
_SOkW �60kw
'0
59%
160
� ;; ;I
54%
110
59% 0% 54%
0%
20%
40%
60%
Coverage Area of Dynamic IPT System (%)
80%
10%
20%
30%
40%
50%
60%
Coverage Area (%)
70%
80%
90%
10Cl%
Fig. 10. Driving range for different coverage areas and power levels of dynamic IPT system.
Fig. 9. Final SoC versus road coverage area for different power levels of dynamic IPT system.
maintaining the minimum driving range during worst load scenario. Dynamic IPT System
C.
Fig. 9. shows the simulation plots for the final battery SoC versus percentage road coverage area for different power levels of dynamic IPT charging for a travelled distance of 40 km with SORT 3 driving cycle at an ambient temperature of -15 °C and 100 % occupancy level. A static IPT system of 60 kW is considered to be installed in all cases. The following observations can be made: •
•
•
The final SoC linearly increases with road coverage area despite the randomness in the velocity profile. This is because the IPT system is also randomly distributed on the road track. Regression analysis yields R2 > 0.99, which indicates a strong correlation. The slope is directly proportional to the charging power level of the IPT system. SoC at zero coverage area can be computed using (1) & (2) corresponding to 'static-only' charging system.
The battery SoC of the EV with dynamic on-road charging can be estimated using (4).
SoCdyn
=
SOCstatic +
( (ttotal - tstoP) 'T}CPdyn ) (Croad(%)) Ebat , Slope v
"
100
V.
IMPACT OF BAT T ERY WEIGHT
In the previous section, the impact of IPT systems on driving range of the EV was studied. In order to achieve the same driving range with battery alone system, a greater energy capacity is needed, and hence, a greater battery weight and volume, which has implications on not only the transport efficiency [18], but also on the feasibility of installing such a system. This section describes the method to estimate the required energy capacity of the battery in order to achieve the desired driving range and the subsequent impact of increased weight on specific consumption of the electric bus. Rearranging (3), the required SoC ( SoCreq) of the battery for the desired driving range is described by (5). =
SoC(O) -
DO
Dr;;�
*
Stot
(5)
(4) Here, DR is the required driving range in km. Now, (1) can be modified to compute the battery weight dependant SoC at end of total travelled distance as shown in (6).
is the state of the charge of the battery at the end of travelled distance is the SoC of the battery at the end of travelled distance with only static charging
SOCstatic ttotal tstop 'T}c Ebat
Substituting SoCdyn in (3), the driving range for the EV can be estimated. The driving range of EV for different road coverage and power levels of dynamic IPT system with 60 kW static charging for SORT 3 driving cycle at an ambient temperature of -15°C and 100 % occupancy level is shown in Fig. 10.
SoCreq
Where,
SoCdyn
is the dynamic charging power level is the % road coverage of dynamic IPT
(-�) SLot
is the total travel time in hours is the total scheduled stoppage in hours is the charging efficiency of the battery is the energy capacity of the battery
Where Mre! is the gross mass of the reference vehicle being studied and Wbat,re! is the weight of the reference vehicle battery. The weight of the required battery is kb Wbat,re!'
-- .....
1"CIClt'�
r
!,
.... c....,....� ...... .,....
�
L.
L.
}.
!
f.
r j
J
..
...
•
Fig. 1 l. Capacity factor Kb versus driving range of vehicle for SORT 3 driving cycle.
��--�--�*.-�.��-�'--�---��.--.�--� -Fig. 12. Specific consumption versus driving range of vehicle for SORT 3 driving cycle. 3%
Rearranging,
kb =
,.'c .,occ-:, .. ·c oc..-r
km(Mref - Wbat, ref)
KaAfCd
(
P
)
+ + uav7Jd�s"E"'''bat-,re! �--------�------�----------�
--------
(SOC(O )-SOCreq ) S
_
km Wb at,ref (7)
Hence, we can obtain the value of kb for any SoC for the required driving range using (7). From the Ragone plot, the specific energy of a high power Li-ion battery used in the reference e-bus is about 100 Whlkg [l7]. Hence, the weight of reference vehicle (liVibat,ref) is
(lOOO*EEba1..ref) 100
=
3240
2% 1% 0%
_SOOTJ .UOOS
.2"'{' ·3%
.e-
0 6 -� en
kg .
Fig. 11. shows the increase in capacity factor with required driving range in different loading scenarios for SORT 3 driving cycle. The driving range of 80 km corresponding to the reference vehicle (kb 1) at -15 ° C, 100 % occupancy level is highlighted. As observed, in order to achieve 12 times this driving range, battery capacity must be increased by almost 40 times. This non-linearity in the relationship arises due to constrains put in terms of maximum allowed DoD and the battery weight dependent energy consumption of the vehicle.
_SORT 1 SORT 2
-1%
Fig. 13.
o
;
" i'0
0
I
" i'-
fl
.
I
" i'-
�
0 �
�
" i'-
�
0
;
" i'-
iil
. .
�
I
(j
g
R
" i'-
i'-
_Hv.!'ET
�
;
" i'-
g
0
go
I j
" i'-
a;
i'-
§
Percentage error in SoC estimation for different driving cycles.
=
As highlighted in the Fig. 11., to obtain a driving range equivalent to that of a 60 kW Static + 60 kW Dynamic System with 100 % road coverage, a battery of 4 times the original capacity is required. Consequently, increase in the battery weight has implications on the specific consumption of the vehicle (Esp ecific in kWh/km) as governed by (8).
Ebat *
(KaAfCd
Paux Uav'TJdisEbat,ref
---::'+ ::-::------"-:
)
(8)
Using this equation, the battery weight dependent increase in specific consumption of the electric vehicle in battery alone system with increase in required driving range for SORT 3 driving cycle is depicted in Fig. 12. V I.
CONCLUSION
An analytical methodology has been proposed to estimate the extension in driving range of any electric vehicle with on road contactless charging. The SoC of the battery is described
as a linear function of the distance travelled. The dynamics associated with the velocity profile are captured by the driving cycle constants defined for the mass and frontal area of the EV
The increase in the SoC of the battery depending on differ ent power levels and road coverage of the static and dynamic IPT system is described mathematically. Battery ageing and temperature dependence is not incorporated in the proposed equations. In Fig. 13., the error between the estimated value and the MATLAB simulation of battery SoC of the reference vehicle with -15°C ambient temperature and 100 % occupancy level for 40 km travelled distance with 60 kW static + 60 kW dynamic on-road charging is presented for different driving cycles. For a 30 % road coverage with 60 kW charging power level of both static and dynamic IPT system, the error in estimated SoC is 2 % with the corresponding error in estimated driving range is 6.69 % . Hence, the driving range of any EV in different scenarios can be estimated using simple linear equations with known parameters and vehicle specifications with reasonable accuracy. In order to further improve the accuracy of the driving range estimation, dependency of motor drive system and energy recovery due to regenerative braking on the velocity
profile of the EV can be incorporated. The road inclination angle is assumed to be zero which causes deviation from the real world application. The methodology is thus adequate for feasibility and economic viability analysis of on-road IPT charging systems with varying design parameters like static and dynamic charging power, road coverage and on-board battery size for an EV with any gross mass and frontal area. REFERENCES [I] Zell, CE.; Bolger, J.G., "Development of an engineering prototype of a roadway powered electric transit vehicle system: A public/private sector program," Vehicular Technology Conference, 1982. 32nd IEEE , vo1.32, no., pp.435,438, 23-26 May 1982 [2] Elliott, G. A J; Boys, J.T.; Green, A W., "Magnetically coupled systems for power transfer to electric vehicles," Power Electronics and Drive Systems, 1995., Proceedings of 1995 International Conference on , vol., no., pp.797,801 vol.2, 2 1-24 Feb 1995 [3] Sallan, J.; Villa, J.L.; Llombart, A; Sanz, J.E, "Optimal Design of ICPT Systems Applied to Electric Vehicle Battery Charge," Industrial Electronics, IEEE Transactions on , vo1.56, no.6, pp.2140,2149, June 2009 [4] Huh, J.; Lee, S.w.; Lee, w.Y.; Cho, G.H.; Rim, CT, "Narrow-Width Inductive Power Transfer System for Online Electrical Vehicles," Power Electronics, IEEE Transactions on , vo1.26, no. 12, pp.3666,3679, Dec. 20 1 1 [5] Chopra, Swagat, and Pavol Bauer. "Driving range extension o f E V with on-road contactless power transferA case study." Industrial Electronics, IEEE Transactions on 60. 1 (2013): 329-338. [6] Stamati, T-E.; Bauer, P., "On-road charging of electric vehicles," Trans portation Electrification Conference and Expo (ITEC), 20 13 IEEE , vol., no., pp.l,8, 16- 19 June 20 13 [7] Build Your Dreams (BYD) E-bus , 2014 [Online]. Available: http://www.byd.com/la/auto/ebus . html [Accessed 13 09 20 14] [8] International Association of Public Transport (UITP), "Standardised On Road Test Cycles ", 2009 [9] United States Environmental Protection Agency, "Dynamometer Drive Schedules, "[Online]. Available: http://www.epa.gov [Accessed 13 09 20 14] [ 10] Vincent van Assen, "Physics of city buses and their environment ", Master Thesis, University of Groningen, 20 13. [ 1 1] Tremblay, 0.; Dessaint, L-A; Dekkiche, A-I, "A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles," Vehicle Power and Propulsion Conference, 2007. V PPC 2007. IEEE , vol., no., pp.284,289, 9- 12 Sept. 2007 [ 12] Long Lam; Bauer, P, "Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles," Power Electronics, IEEE Transactions on , vo1.28, no. 12, pp.5910,5918, Dec. 20 13 [ 13] Minxin Zheng; Bojin Qi; Xiaowei Du, "Dynamic model for character istics of Li-ion battery on electric vehicle," Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on , vol., no., pp.2867,2871, 25-27 May 2009 [ 14] Low Wen Yao; Aziz, J.A; Pui Yee Kong; Idris, N.R.N., "Modeling of lithium-ion battery using MATLAB/simulink," Industrial Electronics Society, IECON 20 13 - 39th Annual Conference of the IEEE , vol., no., pp. l729, 1734, 10- 13 Nov. 20 13 [ 15] Prasanth, V; Bauer, P, "Distributed IPT Systems for Dynamic Powering: Misalignment Analysis," Industrial Electronics, IEEE Transactions on , vo1.61, no. l l, pp.6013,6021, Nov. 2014 [ 16] Voglitsis, Dionisios; Tsengenes, Georgios; Bauer, Pavol, "Inductive power transfer system with improved characteristics," Transportation Electrification Conference and Expo (lTEC), 2014 IEEE , vol., no., pp.l,8, 15- 18 June 20 14 [ 17] Klaus Jager, Olindo Isabella, Arno H.M. Smets, Rene A.CM.M. Van Swaaij, Miro Zeman "A Student Introduction to Solar Energy"[Online]. Available: https://courses.edx.org [Accessed 25-09-2014] [ 18] Wolterink, S.; Bauer, P., "High range on-line electric vehicles powered by Inductive Power Transfer, "Transportation Electrification Conference and Expo (ITEC), 20 14 IEEE , vol., no., pp.l,7, 15- 18 June 20 14
