Anayzing the Capacity Utilization Rate of Traction Motor Drives in Electric Vehicles with Real World Driving Cycles Sadik Ozdemir, Onur Elma, Fatih Acar, Ugur S. Selamogullari Department of Electrical Engineering Yildiz Technical University Istanbul, Turkey {sadikoz, onurelma, selam,}@yildiz.edu.tr,
[email protected] Abstract—This study presents real word drive cycles in Istanbul, Turkey and analyzes the impact of these real road conditions on the performance of an electric vehicle. For this purpose, five different drive cycles has been obtained by collecting data for different traffic conditions in Istanbul. Both the velocity of the vehicle and the gradient of the road are considered. Also, a simplified dynamic vehicle model is developed to estimate the traction power requirement of drive-train of the vehicle during start up and when it is in motion. Keywords—Electric vehicles (EV); Driving Cycle; Energy Efficiency.
I.
INTRODUCTION
There is an increasing demand for electrical and hybrid electrical vehicles on the market. All over the world governments are giving great support for the electrical vehicles to launch them on the market rapidly since, most of the countries depend on the foreign sources for fuel and fuel sources seems to be under risk of diminishing. Besides, countries should reduce the fuel use as a main energy source in order to stop climate change. Electric vehicles give significant contribution for resolving these vital problems with zero emission. In [1] a real-world drive cycle is generated by collecting time series data. Average vehicle speed, number of stops during trip, average distance traveled between stops, vehicle acceleration, average drive cycle energy consumed per km parameters are presented and examined in three different drive cycles, highway, suburban and urban. The study provides an evidence that the certain drive cycle conditions have considerable effect on vehicle performance. Also, in [2], a novel methodology to generate stochastic drive cycle is proposed to design and control optimization of electric vehicles. The electrical motor, control unit, battery set and drive train are key system components of electric vehicle. The analytical and simulation models of electric vehicle are studied in [3]. A new dynamic vehicle kinematic model is developed in Lab-view in [4]. It consists inertial effects of different rotating components and real world drive cycle is generated by collecting data from different Indian roads. In [5], PSIM validity is discussed as an automotive simulator tool by creating electrical, mechanical, thermal and energy storage This work is supported by The Scientific and Technological Research Council of Turkey under Award number 113M072.
module boxes. The work in [6], reports a door to door 4 stops/km a new drive cycle and the new drive cycle is compared with standard one. In [7], a complete model is generated including inverter and motor loss and efficiency models. The switching and conduction losses in driver inverter and antiparallel diodes are modeled to investigate the efficiency. Also a medium-size induction motor (IM) is modeled in Advanced Vehicle Simulator (ADVISOR) considering the stator and rotor copper losses and core losses. A novel statistic driving cycle analysis is reported in [8]. The paper [9-10] presents the modeling, simulation and implementation of battery powered electric vehicles. Using future driving data to enhance the efficiency of the vehicle is presented in [11]. The studies [12-13] are compared forward dynamics, quasi-static backwards and inverse dynamics modeling and simulations models of electric vehicles. The main point of this study to generate real world drive cycles in real traffic conditions in Istanbul, Turkey. Then the power requirement of a commercially available conventional internal combustion engine car is calculated by using the developed kinematic model. The obtained drive cycles are used as input to the model. Then, the obtained data is evaluated to assess capacity usage of the traction system in an electric vehicle. This paper is organized as follows: In section 2, information about data collection and obtained drive cycles are given. In section 3, developed vehicle kinematic model is given. Analysis of obtained drive cycles for assessment of an electric vehicle tractive performance is summarized. In section 4, conclusions are given. II.
DATA COLLECTION OF REAL WORLD DRIVING CYCLES
Driving cycles, which consist of speed- time profiles, are crucial for anticipating the power consumption in vehicles. The importance of driving cycle is further increased for an electric vehicle which is powered from a battery bank. In order to make a realistic analysis of vehicle dynamics, driving profiles retrieved from real life driving experiences need to be examined. Global Positioning System (GPS) data acquisition devices have proven to be useful tools for gathering real-world driving data and statistics. Therefore, in this study, a GPS device is used to collect the data. The GPS data acquisition
device is sampled every 3 seconds while generating the drive cycles. The data for drive cycles is collected by using an available internal combustion engine vehicle throughout Istanbul traffic conditions. The routes of these driving cycles are depicted with Google Earth map is shown in Fig. 1. The data then used to evaluate electric vehicle performance.
Fig. 2.
Fig. 1.
Route of five different real world driving cycles in Istanbul
The specifications of the used vehicle are shown in Table I. TABLE I. TECHNICAL SPECIFICATIONS OF DRIVING TEST VEHICLE Conventional Car Specifications
Technical Overview
Unit
Brand
Opel
Model
Astra
Generation
Astra H
Engine
CDTI
Door
5
Power
90 / 67
HP / KW
172
km/h
1230
Kg
13.7
seconds
1248
cm3
200/1750
Nm / rpm
Maximum speed Mass Acceleration 0 - 100 km/h Volume engine
of
Torque Fuel System
Diesel – Common rail
Number gears
6
Gear ratio
of
Motor torque-speed characteristics after transmission
Details of selected routes and drive cycles are summarized in Table II. The study presents the result of this analysis to illustrate the importance of driving cycles. Some of the specific parameters are examined such as duration, range, average speed and number of stops. These drive cycles provide an opportunity to assess electric vehicle performance in detail. TABLE II. Technical Overview
SPECIFIC PARAMETERS OF THE DRIVE CYCLES
km
18
Conventional Car Ist 2 Ist 3 CityCity Highway 30 28
sec.
1310
1701
2010
1333
1342
km/h
49
63
51
53
51
km/h
103
125
108
121
124
30
70
40
50
50
Unit
Type
3.5-1.34-0.93-0.97-0.78-0.65
Fig. 2 shows the torque-speed characteristics of a motor and effect of the transmission system on it. As seen vehicles can produce high torque in a limited operating region. To provide the required torque at high speeds, a transmission system is used in vehicles. In the traction power requirement calculations within the developed kinematic model, gear ratio in Table I is taken into account.
Ist 1 City
Range Duration (seconds) Average speed Maximum speed Number of stops
III.
Ist 4
Ist 5
City
City
20
19
VEHICLE KINEMATIC CALCULATIONS
A. Vehicle Kinematic Model To estimate the power requirement of an electric vehicle while it is starting to move and while it is in motion, a simplified model can be used as seen in Fig. 3. The resultant force acting the vehicle can generally be considered as the combination of four main components: rolling force, friction, aerodynamic resistance and the force to accelerate or decelerate the vehicle.
Electric vehicle model parameters are selected as shown in Table III. TABLE III.
PARAMETERS OF ELECTRIC VEHICLE [14] Electric vehicle model parameters
Constant Fig. 3.
Value
Basic forces acting on a vehicle
The rolling force is defined as to handle the tire to road power loss. Also friction is specified with the road gradient, . The aerodynamic resistance and the force required are the part of the total accelerate or decelerate the vehicle linear force.
Unit
Definition
9.80665
n
(1)
3.72
Differential ratio
0.95
Total differential and gear efficiency
1 (2) 2 The equations (1) and (2) are added to gain the overall force needed at the vehicle tyres is shown (3)
0.164
.
inertia of tires
5.7 10
.
Rotor inertia of electric motor
(3)
0.274
After calculating the forces, the wheel torque can be calculated using equation of motion (4). (4)
.
By using the calculated tire torque, a general equation for electric motor torque is derived (5). 1
m
radius of tires
0.5
Torque distribution proportion factor on the rear axle (equally shared)
0.0267
The rolling resistance coefficient .
1.23
Air density
(5)
0.31
Drag force coefficient
Wheels and traction motor angular velocities are calculated as shown in equations (6) and (7).
1.75
Front area of vehicle
(6)
1100
.
(7)
The mechanical power of the electric machine is produced by multiplying torque with angular velocity of the motor:
Π
kg
Total mass of the vehicle and payload
rad.
Road gradient
3.14159
(8) B. Calculated Power of the Electric Vehicle Using Matlab / Simulink A simplified model for vehicles can be used to calculate the power requirements of the traction system as depicted in Fig. 4. With the help of all these calculations the electrical motor and the driver inverter specifications for an electric vehicle can be rated more correct.
TABLE V.
RATES OF SPEED RANGES Percentage (%)
Speed Range (m/s)
Fig. 4.
Vehicle kinematics Matlab/Simulink model
A series of collected velocity and altitude data from the drive cycles are used for calculation of the instant torque and power required for the vehicle. TABLE IV. Mechanical Power Range (KW) 0 - 10
CAPACITY UTILIZATION RATES Percentage (%)
IST 1
IST 2
IST 3
IST 4
IST 5
36,013
30,226
39,019
35,913
35,484
IST 1
IST 2
IST 3
IST 4
IST 5
0 – 10
33,461
21,869
31,608
38,935
34,501
10 – 20
46,142
31,158
40,319
26,482
40,387
20 – 30
20,397
46,972
28,074
31,583
25,112
As seen in Table IV, the vehicle does not require its full capacity in most of its working conditions. So power electronics for electric vehicle should be designed considering the light load operation duration. The efficiency characteristics of 2010 Toyota Prius inverter at 650Vdc is shown in Fig. 5 [15]. As seen, the light load operation means lower efficiency. It is an evidence of that the inverter efficiency must be increased in light load applications. Based on the presented study, the main purpose is to quantify how much energy can be saved with increased efficiency of driving inverter for an electric vehicle. 94
25,110
20,296
22,566
21,040
27,360
20 - 30
20,595
20,035
16,302
15,115
15,771
30 - 40
11,784
16,289
13,283
16,203
11,947
40 - 50
6,498
13,153
8,830
11,729
9,438
92 Efficiency (%)
10 - 20
90 88 86 84 82 0
The vehicle needs its maximum power ranges less than 10% of its working time and more than 50% of its working time the vehicles require less than 40% its capacity.
20 30 Output power (kW) Typical
Fig. 5. 650Vdc.
40
50
Improved
Efficiency curve of 2010 Toyota Prius inverter in 0-50 kW range at
2.5 2 1.5 Δη(%)
The drive cycles are examined as depicted in Table V, which shows that the all drive cycles have different regimes. In some of them the vehicle runs at higher speeds in most of the duty and conversely, some goes at lower speeds.
10
1 0.5 0 0
Fig. 6.
10
20 30 Output power (kW)
40
Light load efficiency improvement for the inverter circuit
50
30-40
It is assumed that the light load efficiency improvement of driving inverter starts with 2% at 10 kW and goes to zero as the power increases as shown in Fig. 6. Resulting energy savings with improved inverter efficiency for each drive cycle are summarized in Table VI. Once the energy dissipated for each power range is found, energy saving for each drive cycle is calculated as seen in Fig. 7.
0.901x106
5.184x106 Joule
3.431,75 IST 5
10-20
1.418x106
30.867,15
20-30
0.817x106
10.086,73 43.311,54
30-40
0.619x106
2.357,66
The inspection of the all drive cycles shows that the in order to pass 1 km of road, approximately 270 kJ energy is dissipated. With improved the inverter efficiency, the saved energy can be bring additional 150 m in IST 1, 400 m in IST 2, 220 m in IST3, 120 m in IST4 and 150m in IST5 drive cycles. All these calculations does not include the 0-10kW power range efficiency improvement. IV. Fig. 7.
Block diagram for calculating energy saving
TABLE VI.
SAVED ENERGY VALUES FOR EACH DRIVE CYCLE
Total dissipated energy: 4.99x106 Joule
IST 1 Dissipated energy (Joule)
Saved Energy (Joule)
10-20
1.253x106
27.275,42
20-30
1.027x106
12.679,40 42.194,41
30-40
0.588x106
2.239,59
Output power (kW)
8.288x106 Joule
Total dissipated energy (Joule)
IST 2
10-20
1.682x106
36.613,93
20-30
1.660x106
20.494,45 62.250,30
30-40
1.350x106
5.141,92
7.871x106 Joule
IST 3
10-20
1.77x106
38.529,52
20-30
1.283x106
15.802,95 58.293,65
30-40
1.04x106
3.961,18
CONCLUSION
The real world drive cycles are highly effective on assessing vehicle performance, so the generated knowledge about the power requirements of vehicles in daily life traffic conditions is highly informative to size the electric vehicles according to market needs. This study presents five different drive cycles which are gathered from Istanbul traffic conditions. The data is collected with GPS data acquisition device. All drive cycles are investigated in detail with a simplified vehicle kinematic model. The analyzed data is evaluated for an electric vehicle to examine the capacity utilization of the driver inverter throughout the real world drive cycles. The knowledge is crucial to design and size an electric vehicle traction system. Results show that the vehicle runs in light load conditions in its most of the working time and considerations must be given to improve the performance of power electronics at light load conditions for electric vehicles. An assumed inverter efficiency improvement is considered and corresponding energy savings are calculated for each driving cycle. Saved energy means not only less battery usage and thus improved battery lifetime but also extended driving range. This study shows the importance of real world driving cycles in assessing the performance of electric vehicles and highlights the benefit of improved inverter efficiency at light load conditions. Our future work will focus on hybrid IGBTMOSFET switch combination use and its unique control to achieve improved light load inverter efficiency.
ACKNOWLEDGMENT 5.566x106 Joule 10-20
This research has been supported by The Scientific and Technological Research Council of Turkey under Award number 113M072.
IST 4 1.171x106
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25.490,43 39.305,21
20-30
0.841x106
10.383,03
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