a comparison of vehicle simulation software and ...

Proceedings of The Canadian Society for Mechanical Engineering International Congress 2016 CSME International Congress 2016 June 26-29, 2016, Kelowna, BC, Canada

A COMPARISON OF VEHICLE SIMULATION SOFTWARE AND DYNAMOMETER RESULTS FOR BATTERY ELECTRIC VEHICLES Kieran Humphries

Alexei Morozov

Department of Mechanical Engineering McGill University Montreal, Canada [email protected]

Centre for Intelligent Machines McGill University Montreal, Canada [email protected]

Abstract— The accuracy of simulation software when modeling electric vehicle performance is of great importance in the design and testing of such vehicles. In order to reduce energy consumption and improve range and performance, simulation software is often used prior to prototyping to save time and cost. However, the accuracy of simulation models may not be as good as that of prototypes and may depend on the software and accuracy of specifications used for analysis. In the presented research, a comparison of simulation results with published dynamometer data for currently available electric vehicles has been completed. ADVISOR 2003, a simulation software package, was used to simulate several passenger vehicles and the results were compared to those published by the United States Argonne National Laboratory. After considering the validity of the simulation data, an additional vehicle model of a Class 4 electric delivery truck was tested and the results are discussed.

simulation based on the basic forces on a moving vehicle. A particular vehicle driving cycle, a standard trace of velocity over time, is selected and vehicle specifications are input into the simulator, including component masses and efficiency tables. The simulator calculates the force required to move the vehicle at each time step of the selected driving cycle via the road load equation. Component efficiencies are used to calculate the amount of energy used at each of these steps. The total energy used is the sum of the energy used at each step. This way, vehicle models can be evaluated and component changes can be modeled quickly. The purpose of this study is to evaluate the accuracy of two simulation programs when compared with actual dynamometer testing performed on production vehicles by the Argonne National Laboratory (ANL) in the United States. This data is available to the public as the Downloadable Dynamometer Database1 .

Keywords- electric; vehicle; simulation; dynamometer; software

I.

I NTRODUCTION

Electric and hybrid vehicles are an important tool to reduce carbon dioxide emissions and mitigate climate change. Electric vehicles have reduced well-to-wheel emissions when compared with their conventional counterparts and they have no local tailpipe emissions. The emissions associated with such vehicles are due to electricity generation at power plants. This electricity can be generated using conventional combustion power plants, hydroelectric facilities, or renewable wind and solar collectors. Using renewable energy, it is possible to almost entirely eliminate the emissions associated with electric vehicle operation. Simulation is crucial in the design of modern vehicles and allows improvements in efficiency and performance at significantly lower cost and time than traditional prototyping. In the current paper, a comparison of the functionality and accuracy ADVISOR to dynamometer data is presented. The type of simulation used in this study is a vehicle efficiency

All vehicles tested by the ANL are passenger vehicles, therefore in the paper the analysis is presented with reference to these passenger vehicles in the database. Furthermore, a case study is presented with a Class 4 medium-duty electric delivery truck. The Class 4 vehicle weight class (14001-16000 lb or 6350-7260 kg) is an advantageous segment for fleet electrification because it is popular for delivery vehicles in and around urban centers which have a relatively short driving range. This limited range is an advantage because a smaller, lighter, and less expensive battery pack than otherwise needed can be used in these vehicles, thus improving their payback time when compared to the cost of conventional trucks. Low-speed cycles with many starts and stops can also lead to an advantage for electric vehicles due to their improved efficiency in these situations and the potential for regenerative braking [1]. II.

BACKGROUND

The vehicle architecture for this study is standard in terms of battery electric vehicles, with an electric motor powering the 1 http://www.transportation.anl.gov/D3/

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drive wheels through a single gear reduction and a differential. No shifting transmission is required in this case because of the large range of operation of the electric motor. Most electric vehicles on the market use this configuration, including the popular Nissan Leaf, the Mitsubishi i-MiEV, and the Ford Focus Electric. All vehicles considered in this study use lithium-ion battery packs to provide energy for the electric motor. The electric motors are permanent magnet synchronous motors that run on AC electricity produced from the DC battery using a power electronic inverter. Each of these components has an efficiency that must be included in simulation, although the inverter and motor efficiencies are bundled together. Electric vehicles are more efficient than their gasoline counterparts mostly because of the efficiency of their electric motors when compared with internal combustion engines. This means that they use less energy to travel the same distance as a conventional vehicle. However, fossil fuel is very energy dense and contains enough energy for a conventional vehicle to travel a great distance between refueling. Batteries are less energy dense and this means that the efficiency of electric vehicles is of great importance in increasing vehicle range. Vehicle testing is used to measure the vehicle energy consumption over standard driving cycles and predict their real-world efficiency. A. Vehicle Testing Most vehicle testing for efficiency and fuel consumption uses standard driving cycles, which are traces of speed versus time to be followed by the test vehicle, whether on a track or in a laboratory setting. These vehicle test cycles are also used in simulation. The automotive industry has developed various test cycles which simulate city and highway conditions and are used to produce fuel economy ratings for vehicles sold to consumers. The latest iteration of the United States Environmental Protection Agency’s fuel economy testing for passenger vehicle rating purposes uses four different driving cycles over five different tests which simulate city, highway, high speed/acceleration, cold temperature, and air conditioning conditions. The city test and cold temperature test both use the UDDS driving cycle, the highway test uses the HWFET cycle, the high speed/acceleration test uses the US06 cycle, and the air conditioning test uses the SC03 cycle. Table I lists several parameters of each of these driving cycles. TABLE I. Cycle Parameters Vehicle Driving Cycles Name

Time

Dist

Max Spd

Avg Spd

Max Accel

Avg Accel

-

s

km

km/h

km/h

m/s2

m/s2

UDDS

1369

11.99

91.3

31.5

1.48

0.50

HWFET US06

765 600

16.51 12.89

96.4 129.2

77.6 77.2

1.43 3.76

0.19 0.67

SC03

600

5.76

88.2

34.5

2.28

0.50

Tailpipe emissions can be collected when applicable during

these test procedures and analyzed in order to obtain fuel use and emissions data. However, in the case of electric vehicles there are no tailpipe emissions and the energy consumption is instead measured using voltage and current sensors. Using the voltage and current output of the battery, the battery power output can be calculated [2] Pbattery = Vbattery × Ibattery

(1)

where Pbattery is the battery output power in Watts, Vbattery is the battery voltage in Volts, and Ibattery is the battery current in Amps. The total energy use can be calculated from the battery power using [2] ∫ E = Pbattery · dt (2) where E is the energy used in Joules. Since test data is sampled at discrete time steps in both the dynamometer testing and in vehicle simulations, the integral in (2) becomes a summation in practice. Finally, the energy consumption in Wh/km can be calculated using EC =

E D · 3600

(3)

where EC is the energy consumption in Wh/km, and D is the distance traveled in km. B. Simulation Software Efficiency calculations in simulation software are based on the road load equation below [3] FR = FAD + FRR + Fgx

(4)

where FR is the road load force, FAD is the aerodynamic drag force, FRR is the tire rolling resistance, and Fgx is the force of gravity in the vehicle direction of travel. When each component is expanded to include its components, the equation becomes [3] FR =

ρ Af CD v 2 + mg cos β [Crr0 + Crr1 v] + mg sin β (5) 2

where ρ is the air density, Af is the vehicle frontal area, CD is the aerodynamic drag coefficient, v is the vehicle speed, m is the vehicle mass, g is the acceleration due to gravity, β is the angle of the road measured from horizontal, Crr0 is the first coefficient of rolling resistance, and Crr1 is the second coefficient of rolling resistance. This equation is used to estimate the forces on the vehicle which, along with the tractive force provided by the vehicle itself, determine the vehicle acceleration and speed throughout a simulated driving cycle. The driving cycle is broken up into discrete time steps of usually less than 1 second and the forces are calculated at each step in order for the vehicle to maintain the proper speed trace. The energy used by the vehicle is calculated using the required tractive force and the component efficiencies at each step and taking the summation of all these steps.

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for the Nissan Leaf and Ford Focus Electric but the validity of such data is unknown. Alternatively, motor maps were estimated using the Argonne data for wheel force and battery power. The wheel power output can be estimated using Pwheel = Fdyno · vdyno

where Pwheel is the power output at the wheel, Fdyno is the force applied by the dyno, and vdyno is the speed of the dyno. Combining equations (1) and (6) we get an equation for the motor/inverter efficiency: ηdrivetrain =

C. Regenerative Braking Regenerative braking is the use of methods to recapture energy while decelerating the vehicle. In a conventional vehicle, this energy would be lost as heat through the friction brakes. With electric or hybrid electric vehicles, this energy can be recaptured in the electric motor and sent to the battery for storage. During braking, the electric motor provides a negative torque on the wheels, slowing the vehicle while generating electricity. Many strategies exist for the control of regenerative braking and certain regulations and best practices must be followed in order to maintain vehicle stability. For example, the brake balance between the front and rear wheels of a vehicle is of importance for wheel lockup and stability. If the rear wheels are locked before the front wheels, this can cause an unstable condition and lead to a spin [6]. III.

M ETHOD

Pout Pwheel Fdyno · vdyno = = Pin Pbattery Vbattery · Ibattery

(7)

The gearbox and differential efficiencies were estimated as approximately 98% each, and removed from the efficiency via the following equation in order to obtain the motor/inverter efficiency only ηmotor =

ηdrivetrain ηgearbox · ηdif f

(8)

The resulting partial efficiency maps, an example of which is shown in Fig. 2, were calculated for various driving cycles and were used to estimate the overall efficiency map of each vehicle motor through interpolation. However, these maps showed a significantly lower efficiency than expected. Permanent magnet motors of this type in previous simulations show an overall efficiency of over 90% during some driving cycles. However, the motor maps created through this procedure did not attain overall efficiencies in this range during driving cycle testing.

A. Modeling and Simulation

Calculated Motor Efficiency

150

90

100

80 70

Motor Torque (Nm)

ADVISOR has a number of basic block diagrams available in the software including a battery electric model that was used for the vehicles in this study (Fig. 1) which did not need to be changed for the vehicle simulations in this test. Vehicle parameters including motor maps and regenerative braking strategy were modified. The vehicle parameters were mostly obtained from Argonne and DOE data, as mentioned in the previous section. Tests in ADVISOR were set up for each of the driving cycles in the standard testing procedure with appropriate accessory loads (higher loads for the SC03 air conditioning).

(6)

50 60 50

0

40

-50

Efficiency (%)

ADVISOR (ADvanced VehIcle SimulatOR) is a MatlabSimulink modeling and simulation plugin developed by the United States National Renewable Energy Laboratory (NREL), a US government department [4, 5]. The package is used for the analysis of performance, fuel economy, and emissions of conventional, electric, hybrid electric, and fuel cell vehicles. The latest ADVISOR version was released in 2004 though many researchers still use ADVISOR in their projects. The vehicle component specifications needed in the road-load calculation are input into ADVISOR by the user and the vehicle simulation is run over the prescribed driving cycle to determine the energy consumption.

30 20

B. Vehicle Specifications Vehicle specifications for all test vehicles were obtained through on-line manufacturer information or from the ANL data and the U.S. Department of Energy, Vehicle Technologies Office, Advanced Vehicle Testing Activity (AVTA) Data and Results. Table II shows a summary of the vehicle specifications for the simulations which were input into the two software packages as the vehicle setups. Unfortunately, motor maps and regenerative braking strategy are not usually published for vehicles as they are company secrets. Unverified motor maps were obtained on the Internet

-100 10

-150 -1000

0

0

1000

2000

3000

4000

5000

6000

7000

Motor Speed (RPM)

Figure 2. The estimated motor efficiency for the Nissan Leaf motor during several UDDS cycles

Since the motor maps created in this way were of such low quality (perhaps due to the fact that the dynamometer data included all four wheels in the tractive force calculation rather than just drive wheels and this may necessitate further

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Figure 1. ADVISOR battery electric vehicle block diagram TABLE II. Vehicle Specifications for all test vehicles Vehicle Specifications Make

-

Mitsubishi

Nissan

Ford

BMW

Workhorse

Model

-

i-MiEV RWD

Leaf FWD

Focus EV FWD

i3 RWD

P42 BEV RWD

kg kg

1510 1304

1902 1498

2085 1791

1620 1443

6373 5637

m2 -

2.23 0.35

2.27 0.29

2.26 0.295

2.38 0.29

7.02 0.70

m m

2.55 0.28

2.70 0.31

2.65 0.32

2.57 0.34

4.01 0.393

Crr0 Crr1

s/m

7.41e-3 4.64e-4

9.66e-3 7.56e-5

9.22e-3 2.94e-4

7.41e-3 4.66e-4

8.00e-3 1.20e-4

Motor Type

kW

PMSM 49

PMSM 80

PMSM 107

PMSM 125

PMSM 150

Nm RPM

196 2400

253 3000

245 4200

250 4775

575 2400

Final Drive Ratio

RPM -

9900 7.065:1

10000 7.9:1

8750 7.82:1

11400 9.7:1

12000 14.76:1

Battery Capacity

kWh

16

24

23

21.6

100

Configuration GVWR Test Mass Frontal Area Drag Coefficient Wheelbase Tire Rolling Radius

Motor Peak Power Motor Peak Torque Motor Base Speed Motor Max Speed

calculations), it was decided to use the available on-line maps for the Nissan Leaf and Ford Focus Electric and to scale these maps for the other two test vehicles. The less than ideal results of this solution are discussed later in this paper. A generic lithium-ion type battery model was used in ADVISOR but was modified to reflect the correct mass, battery architecture, and voltage from Table II. This is another area where detail is scarce in terms of actual battery performance charts, and the simulation accuracy could be improved if such data were available. C. Argonne Dynamometer Test Data Fig. 3 shows the measured force that was applied by the dynamometer during testing of the Nissan Leaf on a UDDS driving cycle compared with the simulated tractive effort that was calculated by ADVISOR. This graph shows that the simulation software is calculating a similar load to what the dynamometer provided, as expected. Based on the coast down test constants

used by Argonne to program the dynamometer, the simulation software is accurately estimating the required force for the vehicle to follow the cycle trace. Fig. 4 shows a comparison of the battery power output, the opposite end of the drivetrain from the force at the wheel. The power output of the battery on the dynamometer was measured by Argonne using voltage and current sensors at the battery. The power output of the battery in the simulation is calculated based on the efficiency specifications of each component block as well as the regenerative braking strategy and accessory load. The accessory load was estimated using the load on the vehicle when the wheels were stopped but the key was on. For example, the power use at standstill for the Leaf was approximately 180 Watts, which was used as the accessory load during this simulation. The close agreement in this example shows that the model has similar efficiency to the real vehicle in this case. When tested on other driving cycles, this vehicle simulation model similarly matched the dynamometer data. However, dur-

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Tractive Force Estimates

4000

Dyno Tractive Effort Simulation Tractive Effort

3000

High Voltage Battery Power

#10 4

Dyno HV Battery Power Simulation Battery Power

4

2000

3

1000

2

Power(W)

Force(N)

5

0

1

-1000

0

-2000

-1

-3000

-2

-4000

-3 0

200

400

600

800

1000

1200

0

200

400

Time(s)

ing cold weather SC03 testing, the accessory load on the vehicle was changed to match the base load visible in the dynamometer tests for this cycles using the same method of checking the average load when the vehicle was stopped. The amount of regenerative braking versus friction braking in simulation software can be tuned to match that of the actual road vehicles. For example, if the regenerative braking in the models is too low, the power returned to the battery during simulation will not match the power data from the dynamometer. In this way, the braking strategy can be reverse engineered and input into the simulated vehicles. Up to 90% of the braking force was found to come from regenerative braking in order to match this data. IV.

800

1000

1200

Time(s)

Figure 4. An example of the battery power calculated from Argonne test data compared to the calculated battery power in ADVISOR simulation for a Nissan Leaf during the UDDS cycle

Energy Consumption of Vehicles

220

Energy Consumption (Wh/km)

Figure 3. An example of the dynamometer force applied during Argonne testing compared to the calculated force required in ADVISOR simulation for a Nissan Leaf during the UDDS cycle

600

Mitsubishi i-MiEV Nissan Leaf Ford Focus Electric BMW i3

200 180 160 140 120 100 UDDS

HWFET

US06

SC03

Cycles

Figure 5. Results of energy efficiency tests in graphical format

R ESULTS

A. Energy Consumption Results Using equation (2), the overall energy use was calculated from the simulations and from the dynamometer results. This total energy divided by the distance traveled gives the energy consumption, shown in Table III and represented graphically in Fig. 5. This energy consumption calculated from the dynamometer data (also available in the ANL data sheets) as well as the energy consumption from the simulation program are shown in the table for comparison purposes. The percent difference between the dynamometer energy consumption and that from each simulation is shown beside each test row. The results in Table III show that the simulation model results can be very close to the dynamometer results, meaning the simulation models are accurately representing the vehicles, for example for most of the Nissan Leaf and Ford Focus Electric data. However, there are cases with significant discrepancy between the testing and simulation. For example, for the Mit-

subishi MiEV during the HWFET and US06 tests as well as the BMW i3 for all tests there are large percent differences between testing and simulation. For these two vehicles, no motor efficiency data was available on-line. Therefore the motor efficiency maps were created by stretching and scaling the efficiency table for the Nissan Leaf motor, and this may have led to significant error since the specific motor design can have a large influence on efficiency. B. Performance Results The performance results during certain dynamic tests were tested as well, namely the top speed and acceleration time from 0-60 mph (0-96.6 km/h). These are standard tests that are used to qualify the performance of road vehicles for driveability and marketing purposes. The simulation results in Table IV show good agreement with the published figures, as expected since it is significantly simpler to estimate these performance metrics. This is because the performance metrics do not depend on

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TABLE III. Dynamometer and Simulation Results for all test vehicles Mitsubishi i-MiEV

Make

Nissan Leaf

Ford Focus Electric

Class 4 BEV

Dyno

Sim

Diff

Dyno

Sim

Diff

Dyno

Sim

Diff

Dyno

Sim

Diff

Dyno

Sim

UDDS (Wh/km)

105

106

1%

108

109

1%

127

130

2%

98

114

16%

-

656

HWFET (Wh/km) US06 (Wh/km)

122 172

155 215

27% 25%

128 171

135 190

5% 11%

139 189

141 199

1% 5%

116 153

161 217

39% 42%

-

744 1194

SC03 (Wh/km)

130

132

2%

147

160

9%

148

159

7%

121

149

23%

-

738

Test Type

efficiency tables but on the maximum torque and power output of the motor as well as the overall gear ratio of the drivetrain. These are widely published figures found in vehicle literature and noted in Table II. TABLE IV. Published and Simulated Performance Data Vehicle

Top Speed (km/h)

Acceleration Time (s)

Published

Simulation

Published

Simulation

Mitshubishi i-MiEV Nissan Leaf

134 146

148 144

14.9 10.6

13.9 10.4

Ford Focus EV BMW i3

137 150

136 151

10.9 6.5

10.0 6.5

V.

BMW i3

C ASE S TUDY: E FFICIENCY A NALYSIS OF C LASS 4 E LECTRIC D ELIVERY T RUCK IN S IMULATION S OFTWARE

This section details the results of the new Class 4 mediumduty electric delivery truck simulations in ADVISOR. For practical purposes, a 2004 GM Workhorse P42 chassis with a body made by ENOVA Systems was selected. This truck is similar to those used by Purolator, DHL, UPS and other courier companies, and has a Gross Vehicle Weight Rating (GVWR) of 6350 kg (about 14000 lb). The motor used is an experimental motor designed at McGill which has a power rating of 200 kW, sufficient to move such a heavy vehicle at highway speeds even with only a single speed gearbox and an overall ratio of 6.4:1. The results of this vehicle’s simulation runs are available in Table III and show similar trends to those from the other vehicles, however with consumption values several times larger than those of the smaller vehicles. Energy consumption for other tested cycles is as follows: NYCC 821 Wh/km, OCC 660 Wh/km, HTUF4S 621 Wh/km. These cycles better represent actual daily driving for a delivery vehicle of this type. VI.

C ONCLUSION

Currently, some vehicle simulations showed good agreement with the dynamometer data, while others showed a large discrepancy between the dynamometer and the simulations. As discussed in the Results section, this may be due to the use of scaled motor maps for several vehicles instead of motor efficiency maps created for the vehicles in question. As previously mentioned, the motor maps created from dynamometer data

showed significantly lower efficiency than expected, leading to the belief that there was a missing factor in the calculations used to create these maps. Also, the dynamometer itself may have a resistance torque that needs to be included in the calculation. Further study of this phenomenon should be completed in order to attempt to create better quality motor maps for these vehicles, to be used in future simulation validation. Accessory loads should also be studied in more detail in order to improve accuracy. The simulation of the Class 4 delivery vehicle, however, used the motor map of an experimental electric motor design and the efficiency map was known exactly. This made the simulation task much easier and potentially more accurate, as shown by the results from the previous cases where the Nissan Leaf and Ford Focus Electric showed good agreement between testing and simulation due to the availability of their motor maps. ACKNOWLEDGMENT The research work reported here was supported by a grant under the Automotive Partnerships Canada Project APCPJ41890111. We also gratefully acknowledge the support of our industrial partners: Linamar, TM4 and Infolytica. R EFERENCES [1] M. O’Keefe, A. Simpson, K. Kelly, and D. Pedersen, “Duty cycle characterization and evaluation towards heavy hybrid vehicle applications,” SAE Technical Paper, 2007, pp. 01–0302. [2] H. Lohse-Busch, K. Stutenbeerg, M. Duoba, E. Rask, F. Jehlik, and G. Keller, “Chassis dynamometer testing reference document,” Tech. rep., Argonne National Laboratory, United States Department of Energy, 2013. [3] I. Husain, Electric and Hybrid Vehicles: Design Fundamentals, Second Edition, Taylor & Francis, 2011. [4] K. B. Wipke, M. R. Cuddy, and S. D. Burch, “ADVISOR 2.1: A user-friendly advanced powertrain simulation using a combined backward/forward approach,” Ieee Transactions on Vehicular Technology, vol. 48 (6), 1999, pp. 1751–1761. [5] T. Markel, A. Brooker, I. Hendricks, V. Johnson, K. Kelly, B. Kramer, M. O’Keefe, S. Sprik, and K. Wipke, “ADVISOR: a systems analysis tool for advanced vehicle modeling,” Journal of Power Sources, vol. 110 (2), 2002, pp. 255–266. [6] M. Ehsani, Y. Gao, and A. Emadi, Modern electric, hybrid electric, and fuel cell vehicles : fundamentals, theory, and design, Boca Raton: CRC Press, 2010.

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