Autonomie Vehicle Validation Summary

Aymeric Rousseau, Neeraj Shidore, Dominik Karbowski, Phil Sharer Argonne National Laboratory

This presentation does not contain any proprietary, confidential, or otherwise restricted information Outline

. Validation Process . Component Model Development and Validation . Vehicle Validation Examples – Conventional Vehicles – Mild Hybrids – Full Hybrids – Plug-in Hybrids (Blended) – E-REV PHEV – BEV . Thermal Model Validation Overview

2 Generic Process to Validate Models Developed over the Past 15 Years

Vehicle Instrumentation, Test Selection Test data analysis using ‘Import Test Data’ function in Autonomie

- Evaluate individual sensors (QC) - Estimate additional signals for each component - Component performance data estimation - Find key parameter values and control scheme

Calibration and validation of the vehicle model with test data Vehicle model development - Dynamic performance validation - Energy consumption validation - Develop models - Instantiate models To quickly and accurately predict or evaluate the energy - Develop low and high level control strategies consumption and dynamic performance of the vehicle under various driving conditions.

3 Advanced Vehicle Laboratory Capabilities

4 In-depth Approach to Vehicle Instrumentation

. Significant instrumentation contributes to detailed vehicle/component understanding (120+ signals collected)

Many Signal Sources . Torque sensors (axles) . Components Speeds . Coolant flow sensors . Coolant / Component temperatures . Exhaust temperatures, emissions . Fast CAN data . Scan tool data . Power analyzer on many nodes . Dynamometer loads, speeds . Direct fuel measurement  All integrated into one DAQ system

5 Large Number of Test Should be Performed Example of some of the cycles for the Prius MY10

Cycle Engine starting temp Cycle Engine starting temp Cycle Cycle length(s) (°C) length(s) (°C)

1 Accels_merge 258.1 72.5 - 92 14 UDDS_prep_merge 1372.9 70 - 87.5

2 Cycle_505_2_merge 1019.5 75 - 88.5 15 UDDScs_merge 1372.9 22.5 - 78.5

3 Hwy_01_merge 764.9 88.5 - 88.5 16 UDDShs_01_merge 1372.9 66 - 85.5

4 Hwy_02_merge 764.9 88.5 - 88.5 17 UDDShs_02_merge 1372.9 69 - 86

5 JC08_01_merge 1204.9 55 - 85 18 UDDShs_03_merge 1372.9 74.5 - 87.5

6 JC08_02_merge 1204.9 68 - 88 19 US06_01_merge 599 78.5 - 90

7 LA92_01_merge 1431.5 46.5 - 90.5 20 US06_02_merge 598.9 92.5 - 89

8 LA92_02_merge 1433.9 74.5 - 90.5 21 SS_0pct_Grade 549.6 80 - 89

9 NEDC_01_merge 1164.9 70 - 88.5 22 SS_0p5pct_Grade 549.6 84.5 - 89.5

10 NEDC_02_merge 1164.9 77 - 88.5 23 SS_1pct_Grade 549.6 89.5 - 89.5

11 NYCC_merge 607.6 23 - 57.5 24 SS_2pct_Grade 549.6 88 - 90

12 SC03_01_merge 599.9 45 - 77 25 SS_4pct_Grade 549.6 90.5 - 89.5

13 SC03_02_merge 599.9 63 - 84.5 26 Long_SS_Warmups 1800 24 - 89.5

6 Importing Test Data into Same Environment as

Models to Speed up Validation.Change signal names and convert unit type for Autonomie Autonomie GUI for ‘Import Test Data’ function format

• All post-process files • Format conversion file • Calculating missing signals file

Raw test data set

Signals used in the calculation of missing parameters.

7 Additional Signals Estimated Based on Measured Data Prius PHEV Battery Example Generic Processes to define performance maps have been developed for the main powertrain components

8 New Powertrain Might Need to be Developed Example - Honda PHEV System Structure

- Tow Motor Hybrid System: “i-MMD” (Intelligent multi- mode drive powertrain)

Operation Mode

BAT MC1 EV Drive (One-Motor EV) Hybrid Drive (Series ER) MC2 Engine Drive (Parallel ER) Out

ENG Change three modes according to system efficiency. Clutch

9 New Plant Models Might Be Necessary Example - DCT Plant Model Development

BAT Dual-clutch Gear-train Final drive

CL1 CL2 shaft2 shaft1

Jc1 Jc2 J2 J4 J6 J5 J3 J1 Tout ENG MC N1 N2 N3

N4 N5 ClutchClutch N6 Nf Clutch Dual-clutch Gear-train

Jout

e1 e e e e2 f1 f f f2

- vpa_par_2wd_p2_dct_cpl1_1mc : “Parallel PreTrans HEV Dual Clutch e Trans 2wd Midsize” f

E-machine 10 Thermal Models Required Based on Current Standards Battery Thermal Management System(TMS) . Liquid cooling system – Components : pump, radiator, radiator fan, chiller, heater – Cooling mode : Active cooling, Passive cooling, Bypass, Heating

. Liquid cooled thermal Heat generation system model from voltage loss

Heat transfer between Battery & Coolant

Heat transfer between TMS & Coolant Bypass & No current But temp. increases Heat transfer between Coolant & Ambient Air

11 Each Individual Model Needs to Be Independently Validated

Process available for all the powertrain component models

12 Normalized Cross Correlation Power (NCCP) Used to Quantify the Validation

. Definition of NCCP (SAE 2011-01-0881 Test Correlation Framework for HEV System Model – Ford Motor Co.)

max[ ] = max[ , ] 𝑅𝑅𝑥𝑥𝑥𝑥 𝜏𝜏 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑅𝑅𝑥𝑥𝑥𝑥 𝜏𝜏 𝑅𝑅𝑦𝑦𝑦𝑦 𝜏𝜏 = lim 𝑇𝑇 𝑥𝑥𝑦𝑦 𝑅𝑅 𝜏𝜏 𝑇𝑇→∞ �0 𝑥𝑥 𝑡𝑡 ∘ 𝑦𝑦 𝑡𝑡 − 𝜏𝜏 𝑑𝑑𝑑𝑑 This metric estimates the correlation between two signals by considering their magnitude and linearity.

Here, x and y represent individual signals. When applied to a test signal and a simulation signal of the same quantity, a value of NCCP equal to or greater than 0.9 indicates a high level of correlation. Conversely, a value less than this indicates a relatively poor correlation.

13 NCCP Fully Integrated into Autonomie

. Loading signals to compare

Test data result from ‘Import Test Data’, and Simulation data result from Autonomie model

A user can apply the NCCP function to the various comparisons, for example, ‘simulation vs. simulation’ or ‘test result vs. test result’ as well as ‘test vs. simulation’.

14 Outline

15 Generic Processes Used to Generate Component Performance Maps from Test Data

. Engine map example (2010 Prius) – From 25 tests, the engine map is generated.

Engine Hot Efficiency Map (Torque) - Points - Overall 160

38 140

41 120

100

Max Trq 80 Min Trq Max Eff (Speed based) 60 35 Eff. Map Torque (N.m) 33 40 35 30 27 33 20 24 22 30 1916 27 7.65 1310 241922 35333038 0 2.14.87 7.654.87 131610 7.65 27241619221013

-20 100 150 200 250 300 350 400 450 500 550 600 Speed (rad/s)

16 Some Component Performance Maps Also Provided by Nat Lab System

. Motor map (2010 Prius example) – Motor map is obtained from Oak Ridge National Laboratory (ORNL).

Motor Efficiency Map (Torque) 250 Propeling Max Torque Curve 200 86 81 91 76 Regen Max Torque Curve 56 71 86 71 61 81

76 91 150 Eff. Map 61 66

100 91

91 86 86 91 8176 71 7681 71 46513156364161 7681 71 7681 0 766671 6166 76 6666 2126 76 6166 716676 81 8681 8186 91 81 91 71

Torque (N.m) -50

-100 91

-150 61 76 91

71 61 81

86 91 76 71 81 56 -200 86

-250 -1500 -1000 -500 0 500 1000 1500 Mitch Olszewski, EVALUATION OF THE 2010 TOYOTA PRIUS HYBRID SYNERGY DRIVE SYSTEM Speed (rad/s)

Without current for each motor, it is not possible to obtain the motor efficiency.

17 Accurate Low Level Controls Are Critical DCT Shifting Events Example

CL1 declutching . st  nd Acceleration - U.S. performance process : 1 2 CL2 clutching Shaft1 neutral Par HEV Dual Clutch Trans

Pre-selection synchronizing

18 Accurate Low Level Controls Are Critical Using the Motor for a Pre-Trans HEV during Shifting

BAT E-machine torque Par HEV Dual Clutch Trans (speed increase phase)

ENG MC Simulation Vehicle mode 3 : driving Clutch

The process of a tractive restart ANL Test

. The controller reacts by setting a torque-increasing intervention that is added to the torque of the electric machine (speed increase phase)

19 Outline

20 Advanced Vehicle Benchmark Testing

• Advanced Powertrain Research Facility (APRF) - Benchmark advanced technology - Disseminate data, analysis to U.S. OEM’s, national labs, and universities.

4WD 2WD chassis chassis dyno dyno opened 2000, upgraded 2011 opened 2009

• Argonne has been conducting very extensive testing of advanced vehicles for technology assessment: In-depth Benchmarking Instrumentation Expertise

Ford Fusion ··· (6ATX) Mercedes S400H Scan-tool ··· (7ATX) OCR Module Direct Fuel Engine Torque HMC Sonata Measurement (6ATX) Sensor 21 Calculation of Additional Signals Gear Ratio Calculation Algorithm Developed

• 2013 HMC Sonata Conv. I4 6ATX Sonata 6ATX Gear Ratio : 4.212 2.637 1.800 1.386 1.000 0.772

Speed In (Measured from Turbine Speed ) Speed Out (Calculated from Vehicle Speed)

UDDS HS

400 GB Speed In GB Speed Out SR (2) x 100 300 SR (4) x 100 Gear Ratio Calculation 0) Signals are aligned based on vehicle speed 200 Speed, rad/s Speed, 1) SR = Speed In (Measured from Turbine Speed ) / 100 Speed Out (Calculated from Vehicle Speed) UDDS HS 0 0 50 100 150 200 250 300 GB Speed In 400 Time, sec 2) SR = 1st Gear Ratio GB Speed Out when Vehicle Speed =0 & Engine speed < idle speed SR (2) x 100 300 SR (4) x 100 3) Rounds the elements of SR to the nearest value of gear ratio that we know. 200 Speed, rad/s Speed,

4) Filter out SR to keep current gear ratio for 0.8 sec 100 (minimum)

0 160 170 180 190 200 210 220 Time, sec 22 Calculation of Additional Signals Gear Ratio Calculation Validation

• 2013 HMC Sonata Conv. I4 6ATX

UDDS HS Gear Ratio Verification 400 GB Speed In GB Speed Out x SR SR x 100 300

UDDS HS 200

Speed, rad/s Speed, GB Speed In 400 100 GB Speed Out x SR SR x 100 300 0 0 200 400 600 800 1000 1200 1400 Time, sec 200 Speed, rad/s Speed,

100 . The number of up shifts : 62

0 . The number of down shifts : 44 340 360 380 400 420 440 460 480 500 Time, sec . The number of total shifts : 106

From ANL DOT NHTSA VOLPE Report : Numerous conventional vehicles were tested on the FTP cycle at Argonne’s APRF. including the Fusion (6-speed); the Toyota Echo (5-speed); the Mercedes Benz S400 (7-speed); and the Volkswagen Jetta (7- speed DCT) . 1) 5-speed automatic: 110 to 120 2) 6-speed automatic: 110 to 120 3) 7-speed automatic: 130 to 140 4) 7-speed DCT: 130 to 140

23 Calculation of Additional Signals Status of Torque Converter Lockup

• 2013 HMC Sonata Conv. I4 6ATX When we do not know the status of the torque converter lockup for ATX

Speed In Speed Out 0) Signals are aligned based on vehicle speed = Engine Speed (CAN) = From Vehicle Speed 1) cpl_cmd = 1 (Locked) when the gap between speed in and out < 5 rad/s 2) cpl_cmd = 0 (unlocked) Used torque ratio map when 1st Gear Ratio as a function of speed ratio 3) cpl_cmd = 0 (unlocked) when the accel of speed_in > 1 rad/s^2 Step 3), 4) 4) Filter out cpl_cmd to keep current status for 1 sec (minimum)

Percentage Time Torque Converter is Locked (UDDS) : 10.1%

24 Test Data Analysis for Automatic Transmission Impact of Powertrain Technology

• Sonata Conv. 6ATX vs. Sonata HEV 6ATX

Sonata Conv 6ATX

1st : 4.21 2nd : 2.64 3rd : 1.80 4th : 1.39 5th : 1.00 6th : 0.77 FD : 2.89

Sonata HEV 6ATX

1st : 4.21 2nd : 2.64 Load leveling 3rd : 1.80 4th : 1.39 5th : 1.00 6th : 0.77 FD : 3.32

By shifting maps

25 Test Data Analysis for Automatic Transmission Dedicated Analysis Functions Developed

• 2013 HMC Sonata Conv. 6ATX Example

26 Test Data Analysis for Automatic Transmission Functions to Develop Shifting Curves

• 2013 HMC Sonata Conv. 6ATX Example

Upshifting Downshifting

Integrated and analyzed more than a dozen set of vehicle test data into Autonomie

27 Developing Shifting Maps Calibrations Refined Shifting Algorithm / Calibration

• The new values of the parameters of shifting controller are added in the shifting initializer to defines the shifting maps created from test data.

Economical Performance Shifting Speeds At very low pedal position Shifting Speeds At high pedal position

66→→55 𝜔𝜔𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

5 →4 5 →6 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 4 →3 4 →5 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 2 →1 3 →4 𝑒𝑒𝑒𝑒𝑒𝑒3 →2 𝜔𝜔𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 CurrentRefined Shifting Shifting 2 →3 2 →1 𝑒𝑒𝑒𝑒𝑒𝑒 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 AlgorithmAlgorithm 𝜔𝜔 1 →2 1 →2 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒 𝜔𝜔𝑒𝑒𝑒𝑒𝑒𝑒

1 →2 2 →3 3 →4 4 →5 5 →6 Example of engine speed range in Maximum𝑉𝑉𝑝𝑝𝑝𝑝 𝑝𝑝𝑝𝑝engine𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 torque𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 at𝑉𝑉𝑝𝑝 𝑝𝑝wheels𝑝𝑝𝑝𝑝 and𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 economical driving, and economical shift performance upshift speeds

28 Developing Shifting Maps Calibrations Refined Shifting Algorithm / Calibration

• Final shifting curves RefinedCurrent Shifting Algorithm Upshifting ∆𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 Downshifting 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 Economical 𝛼𝛼 Shifting Speeds 𝑑𝑑𝑑𝑑 𝛼𝛼𝑒𝑒𝑒𝑒𝑒𝑒

𝑢𝑢𝑢𝑢 𝑑𝑑𝑑𝑑 𝑢𝑢𝑢𝑢 𝑒𝑒𝑒𝑒𝑒𝑒 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝛼𝛼 𝑉𝑉 𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑢𝑢𝑢𝑢 Performance ∆𝜔𝜔 Design of upshifting and Shifting Speeds 𝑑𝑑𝑑𝑑 𝑢𝑢𝑢𝑢 ∆𝑉𝑉 ∆𝑉𝑉 𝑑𝑑𝑑𝑑 𝑢𝑢𝑢𝑢 𝑢𝑢𝑢𝑢 downshifting speed curves 𝑒𝑒𝑒𝑒𝑒𝑒 𝑑𝑑𝑑𝑑 𝑉𝑉 𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑉𝑉𝑒𝑒𝑒𝑒𝑒𝑒 𝑉𝑉𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 for two adjacent gears 𝑑𝑑𝑑𝑑 𝑢𝑢𝑢𝑢 𝑉𝑉𝑒𝑒𝑒𝑒𝑒𝑒 𝑉𝑉𝑒𝑒𝑒𝑒𝑒𝑒 RefinedCurrent Shifting Shifting AlgorithmAlgorithm

New shifting speed curves for a default light-duty vehicle in Autonomie

29 Developing Shifting Maps Calibrations Refined Shifting Algorithm / Calibration

• Example : 2013 HMC Sonata Conv. I4 6ATX

“gb_ctrl_dmd_n_gen_eng_mot_not_included_detail. m” is created to match the shifting map based on test data.

30 Vehicle Model in Autonomie Conv. ATX Configuration

• The ATX vehicles were simulated with two different shifting logic - Shifting schedule analyzed from test data - Shifting schedule automatically generated by current and new shifting Initializer • The Lockup/Release used was the same for both vehicles • The initialization files of transmission are created using public specification and test data. • The vehicle specification including dyno losses, vehicle mass, wheel radius… are used from test data • Rest of the vehicles were identical

2013 Hyundai Sonata Conv I4 6ATX

2012 Ford Fusion V6 6ATX

2012 Chrysler300 V6 8ATX

31 Model Validation in Autonomie 2013 Hyundai Sonata Conv. 6ATX

• UDDS – wheel power, engine fuel rate Simulation 1 – current shifting Initializer Simulation 2 – new shifting Initializer UDDS - Vehicle Speed 60 Vehicle Speed (Simulation1) 50 Vehicle Speed (Simulation2) 40 Vehicle Speed (ANL Test)

Speed, mph Speed, 20

0 0 200 400 600UDDS - Wheel Power800 1000 1200 1400 60 Time, sec Wheel Power (Simulation1) 40 *NCCP = 0.926 NCCP = 0.952 Wheel Power (Simulation2) Wheel Power (ANL Test) 20

0 Power, kW -20

-40 0 200 400 600UDDS - Fuel Integrated800 1000 1200 1400 0.8 Time, sec

0.6

0.4

Fuel Integrated (Simulation1) 0.2 Fuel Integrated (Simulation2) Fuel Integrated, kg/s Integrated, Fuel Fuel Integrated (ANL Test) 0 0 200 400 600 800 1000 1200 1400 Time, sec 32 Model Validation in Autonomie 2013 Hyundai Sonata Conv. 6ATX

• UDDS – gear number Simulation 1 – current shifting Initializer UDDS - Vehicle Speed Simulation 2 – new shifting Initializer 60 Vehicle Speed (Simulation1) 50 Vehicle Speed (Simulation2) 40 Vehicle Speed (ANL Test)

Speed, mph Speed, 20

0 0 200 400 600UDDS - Gear number800 1000 1200 1400 Time, sec NCCP = 60.982 Gear number (Simulation1) 5 Gear number (ANL Test) 4

Gear number Gear 2 1

0 200 400 600UDDS - Gear number800 1000 1200 1400 Time, sec NCCP = 60.983 Gear number (Simulation2) 5 Gear number (ANL Test)

Gear number Gear 2 1

0 200 400 600 800 1000 1200 1400 Time, sec NCCP : Normalized Cross Correlation Power 33 Model Validation in Autonomie 2013 Hyundai Sonata Conv. 6ATX

• HWFET – gear number Simulation 1 – current shifting Initializer

HWFET - Vehicle Speed Simulation 2 – new shifting Initializer 80 Vehicle Speed (Simulation1) 60 Vehicle Speed (Simulation2) Vehicle Speed (ANL Test) 40

Speed, mph Speed, 20

0 0 100 200 300 HWFET - Gear400 number 500 600 700 Time, sec NCCP = 60.990 5

Gear number Gear 2 Gear number (Simulation1) 1 Gear number (ANL Test)

0 100 200 300 HWFET - Gear400 number 500 600 700 Time, sec NCCP = 0.9966

Gear number Gear 2 Gear number (Simulation2) Gear number (ANL Test) 1

0 100 200 300 400 500 600 700 Time, sec 34 Model Validation in Autonomie 2012 Chrysler300 V6 8ATX

• UDDS – gear number Simulation 1 – current shifting Initializer

UDDS - Vehicle Speed Simulation 2 – new shifting Initializer 60 Vehicle Speed (Simulation1) Vehicle Speed (Simulation2) 40 Vehicle Speed (ANL Test)

20 Speed, mph Speed,

0 0 200 400 600UDDS - Gear number800 1000 1200 1400 Time, sec 8 NCCP = 0.956 Gear number (Simulation1) Gear number (ANL Test) 6

4 Gear number Gear 2

0 200 400 600UDDS - Gear number800 1000 1200 1400 NCCP = 0.962 Time, sec 8 Gear number (Simulation2) Gear number (ANL Test) 6

4 Gear number Gear 2

0 200 400 600 800 1000 1200 1400 Time, sec NCCPNCCP : Normalized : Normalized Cross Cross Correlation Correlation Power Power 35 Model Validation in Autonomie 2012 Chrysler300 V6 8ATX

• NEDC – gear number Simulation 1 – current shifting Initializer

NEDC - Vehicle Speed Simulation 2 – new shifting Initializer 80 Vehicle Speed (Simulation1) 60 Vehicle Speed (Simulation2) Vehicle Speed (ANL Test) 40

Speed, mph Speed, 20

0 0 200 400 NEDC - Gear600 number 800 1000 Time, sec 8 NCCP = 0.970 Gear number (Simulation1) Gear number (ANL Test) 6

4 Gear number Gear 2

0 200 400 NEDC - Gear600 number 800 1000 Time, sec NCCP = 0.9738 Gear number (Simulation2) Gear number (ANL Test) 6

4 Gear number Gear 2

0 200 400 600 800 1000 Time, sec NCCPNCCP : Normalized : Normalized Cross Cross Correlation Correlation Power Power 36 Model Validation in Autonomie 2012 Chrysler300 V6 8ATX • Accel. performance – gear number Simulation 1 – current shifting Initializer Simulation 2 – new shifting Initializer

Accel - Vehicle Speed Accel - Engnie Speed 100 700 Vehicle Speed (Simulation1) 600 80 Vehicle Speed (Simulation2) Vehicle Speed (ANL Test) 500

60 400

40 300 Speed, mph Speed, Speed, rad/s Speed, 200 Engine Speed (Simulation1) 20 100 Engine Speed (Simulation2) Engine Speed (ANL Test) 0 0 20 22 24 26 28 30 32 20 22 24 26 28 30 32 Time, sec Time, sec

Accel - Gear number Accel - Gear number

6 Gear number (Simulation1) 6 Gear number (Simulation2) Gear number (ANL Test) Gear number (ANL Test) 5 5

4 4

3 3 Gear number Gear number Gear 2 2

1 1

20 22 24 26 28 30 32 20 22 24 26 28 30 32 Time, sec Time, sec

37 Shifting Algorithm Model Validation

. 2013 Hyundai Sonata Conv. (6ATX)

UDDS HWFET NEDC LA92 Simulation 1 – current shifting Initializer 0.982 0.990 0.980 0.986 Simulation 2 – new shifting Initializer 0.983 (0.10%) 0.996 (0.61%) 0.982 (0.20%) 0.991 (0.51%)

. 2012 Ford Fusion (6 ATX)

UDDS HWFET NEDC LA92 Simulation 1 – current shifting Initializer 0.968 0.998 0.951 0.994 Simulation 2 – new shifting Initializer 0.995 (2.79%) 0.998 (0%) 0.981 (3.15%) 0.988 (-0.61%)

. 2013 Chrysler 300 (8 ATX)

UDDS HWFET NEDC LA92 Simulation 1 – current shifting Initializer 0.956 0.984 0.970 0.938 Simulation 2 – new shifting Initializer 0.962 (0.63%) 0.993 (0.91%) 0.973 (0.31%) 0.957 (2.03%)

NCCP (the correlation for the gearbox) are above 0.93, that means we have a high level of correlation and that both simulations are very close to test data.

38 2013 VW Jetta HEV 7DCT Example

Outstanding differences due to the fact that the vehicle level . UDDS – vehicle speed, SOC and gear number energy management was only correlated, not validated NCCP = 0.984

NCCP = 0.9980.962

NCCPNCCP == 0.9920.937

39 Nissan Altima Conventional CVT Example UDDS cycle – vehicle speed, gear ratio and engine speed

NCCP : 0.993

NCCP : 0.980 Conventional Vehicle Summary

• Test data were imported for numerous vehicles: - We defined the input name and unit conversion data to import test data into Autonomie. - We calculated missing signals and developed a new process for analysis (gear ratio and input and output effort and flow for each component). • Dedicated analysis functions for shifting logic were developed. • Shifting algorithm for several vehicles was calibrated by using two approaches: calibration of initial algorithm and refined algorithm and calibration. • The simulation shows closed correlation with test data (NCCP > 0.9). • Future work will focus on: - Develop and validate algorithm to select optimum gear ratios for advanced transmissions (i.e. DCT, CVT) - Automating the shifting map calibration process from vehicle test data - Optimization of gear shift patterns to refine the gearshift patterns for immediate usage and thus the reduction of transmission calibration effort

41 Outline

42 Honda Insight Validation

80 vehicle speed (m/s) engine torque (N.m) 70 motor torque (N.m) 60 Motor Assist at high SOC 50

810 815 820 825 830 835 840 845 850 855 Time (s)

vehicle speed x10 600 motor electrical power 12V power in 500

400

300

200

100

Motor used to compensate 12V load 0

-100

-200

-300 70 72 74 76 78 80 82 84 86 88

43 43 Honda Insight Validation

Measured 30 Simulated

-10

-20

-30 Motor torque (N.m)

-40

-50

-60 100 200 300 400 500 600 Time (s) Measured 60 Simulated

40 2 ZOOM 20 1 Japan 10-15 0

Motor torque (N.m) Motor torque -20

-40

510 520 530 540 550 560 Time (s)

44 Honda Insight Validation

Japan 10-15 SOC Comparison

Measured Simulated 62

60 SOC (%) 59

57 100 200 300 400 500 600 Time (s)

45 Honda Insight Validation Results

Cycle Cons Cons Diff in SOC SOCf SOCf Diff in test simul % init test simul % mpg mpg Japan 57.9 58.8 1.5 0.596 0.610 0.611 0.4 10-15 NEDC 60.6 60.2 0.6 0.600 0.602 0.583 3.6 HWFET 74.2 75.3 1.4 0.590 0.588 0.589 0.2 UDDS 58.3 57.8 0.8 0.728 0.706 0.720 2.0

46 46 Outline

47 Toyota Prius 2010

48 2010 Toyota Prius Vehicle Configuration

Configuration in Autonomie

2010 Toyota Prius

Engine 1.8L, 73kW MOT S Battery Ni-MH, 6.5Ah, 27kW C Motor 60kW Net power 100kW R R VEH Final drive 3.268

50 MPG (comb.), ENG C FE (EPA) 51/48 MPG (city/hwy)

S MOT2 0-60mph 10.0 s

49 Supervisory Control Analysis Mode Decision Control (Engine On/Off)

Motor usually provides the propulsion power at low power demand

50 Supervisory Control Analysis

Mode Decision Control (Engine On/Off20 ) All points Selected points The engine is turned on if the demand power15 is higher than a given threshold power.

1600 10 All points 1400 Selected points 5 1200

1000 (kW) demand power Wheel 0

800

-5 40 45 50 55 60 65 70 75 600 SOC (%)

400

Wheel (Nm) torque demand 200

-200 0 10 20 30 40 50 60 Wheel speed (rad/s)

0 2 4 6 8 10 12 14 16 18 Vehcle speed (m/s) 51 Supervisory Control Analysis Energy Management Strategy (SOC Balancing)

The engine is turned on if the demand power is higher than a given threshold power.

56 Battery SOC (%) 54 0% Grade 0.5% Grade 52 1% Grade 2% Grade 50 4% Grade

48 0 100 200 300 400 500 52 Time (s) Supervisory Control Analysis Powertrain Component Control (Engine Operating)

The engine torque is controlled according to the engine speed.

120

100

Engine torque (Nm) 40 Generator speed and torque are determined by the engine speed 20 and torque

0 50 100 150 200 250 300 350 400 450 Engine speed (rad/s)

53 Impact of Thermal Condition on Control Engine On/Off Is Changed By the Thermal Condition

The engine is forced to be turned on if the coolant temperature is low.

(a) (b)

Vehicle speed (m/s) [x 10] Not turned off Engine speed (rad/s) 250 Engine torque (N.m) 70 250 70 Coolant temperature (C)

200 65 200 65 Turned on

150 60 150 60

100 55 100 55 Speed, Torque Temperature (C) Temperature

50 50 50 50

0 45 0 45

950 1000 1050 1100 300 350 400 450 Time (s) Time (s) The engine is not turned off if the coolant temperature is low.

54 Impact of Thermal Condition on Control Engine On/Off Is Changed By the Thermal Condition

The engine coolant temperature is maintained above 53ºC.

100 Normal abmient (21C) Cold ambient (-7C), Heater on 90 Cold ambient (-7C), Heater off

50 Engine turned on 40 Engine turned off Engine coolant temp (C)

30 Cold start 20

10 0 200 400 600 800 1000 1200 1400 Time (s)

55 Impact of Thermal Condition on Control Battery Power Is Constrained by the Battery Temperature

Maximum charging and discharging power are constrained by battery temperature.

Instant power

Rated power

Regenerative power

56 Impact of Thermal Condition on Control Engine Operating Target Changed

The engine tries to use higher torque if the coolant temperature is lower.

120 120

100 100

80 80

60 60

40 40 Engine torque (Nm) Engine torque (Nm) 20 20 <90ºC >92ºC 0 0 100 200 300 400 100 200 300 400 Engine speed (rad/s) Engine speed (rad/s) 57 Thermal Impact on Component Performance Engine Fuel Efficiency According to the Engine Temperature

More fuel is consumed if the coolant temperature is lower.

58 Thermal Impact on Component Performance Battery Efficiency According to the Battery Temperature

Internal resistance of the battery decreases as the temperature increases.

280 Battery temp. (52C) Battery temp. (7C) 260

240

220

200

Battery output voltage (A) 180

160 -50 0 50 100 Battery output current (A)

59 Thermal Impact on Component Performance Round Trip Efficiency of the Battery

The loss is mostly caused by the Internal resistance.

110 Efficiency by V base Efficiency by V output 105

100

io ut pu t Cb + Round-trip efficiency (%) 85 R + in te rna l + Rb Vb ase Vo ut pu t Vs _ 80

0 5 10 15 20 25 30 35 40 45 50 55 Battery pack temperature (C)

60 Vehicle Model Development In Autonomie Control Development for the Thermal Model Control concept based on the analyzed results

Control Target Target signals generating tracking

System Target generating feedback Target tracking

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off)

Energy Engine management power demand (SOC balancing) Engine torque demand Engine target Engine torque demand Engine Motor 2 Thermal conditions generating speed demand torque demand Motor 2: Motor 2 torque demand Engine speed tracking Motor: Battery power demand torque target Motor torque demand Driver power demand generation

61 Vehicle Model Development In Autonomie Control Development for the Thermal Model

1600 All points 1400 Selected points Engine off (PEV mode)

1200 Demand or Cold

1000

800 Hot 600 Cranking Col d Idling Fuel cut 400

Wheel (Nm) torque demand 200 Hot Hot

-200 0 10 20 30 40 50 60 Wheel speed (rad/s)

0 2 4 6 8 10 12 14 16 18 Engine on (HEV mode) Vehcle speed (m/s)

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off)

62 Vehicle Model Development In Autonomie Control Development for the Thermal Model

Instant power

Rating power

Regenerative power

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off)

63 Vehicle Model Development In Autonomie Control Development for the Thermal Model

Engine idling control

Under cold conditions

250 200 150 100 50

Engine speed (rad/s) 0 20 40 60 80 100 120

Coolant temperature (hot) 150 Coolant temperature (medium) Coolant temperature (cold) 100

Engine torque (Nm) -50 0 20 40 60 80 100 120 Time (s)

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off)

64 Vehicle Model Development In Autonomie Integration of Thermal Models

Generic component models are replaced by thermal component models

65 Simulation Results UDDS (Hot Ambient, 35ºC) UDDS (Hot) UDDS (Hot) 30 0.5 Test 0.4 20 Simu

0.3 Test 10 0.2 Simu 0 0.1

vehicle speed (m/s) vehicle -10 0 0 200 400 600 800 1000 1200 (kg) consumption fuel 0 200 400 600 800 1000 1200

600 60 Test Test 400 Simu Simu 55 200 50 0 SOC (%)

-200 45 engine speed (rad/s) engine 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200

200 100

100 80 Engine(Test) Engine(Simu) 0 60 Battery(Test) Battery(Simu) -100 Test 40

Simu (C) temperature

engine torque (Nm) torque engine -200 20 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 time (s) time (s)

66 Simulation Results HWFET (Cold Ambient, -7ºC) HWFET (Cold) HWFET (Cold) 30 1.5

20 1

10 0.5 0 Test Test Simu Simu

vehicle speed (m/s) vehicle -10 0 0 500 1000 1500 (kg) consumption fuel 0 500 1000 1500

400 68 Test 300 Simu 66

200 64

100 62 SOC (%) 0 60 Test Simu -100 58 engine speed (rad/s) engine 0 500 1000 1500 0 500 1000 1500

150 100

100 80 Engine(Test) 50 Engine(Simu) 60 0 Battery(Test) Battery(Simu) 40 -50 Test

Simu (C) temperature

engine torque (Nm) torque engine -100 20 0 500 1000 1500 0 500 1000 1500 time (s) time (s)

67 Prius HEV Thermal Model Validation under Different Ambient Temperature

Fuel Mass Consumed (kg) Final SOC

test simu Difference(%) test Simu ∆SOC

UDDS(Normal) 0.300 0.303 1.2 56.9 55.8 -1.1

UDDS(Cold) 0.523 0.543 3.8 65.7 68.4 2.7

UDDS(Hot) 0.478 0.462 -3.3 50.8 48.2 -2.6

HWFET(Normal) 0.914 0.915 0.1 65.8 66.6 0.8

HWFET(Cold) 1.035 1.012 -2.2 65.8 66.2 0.4

HWFET(Hot) 1.089 1.104 1.4 64.6 64.3 -0.3

68 Nissan HEV CVT

69 Metal V belt CVT

140

Throttle, 100%318.71887 341.09383 285.15644 329.90635 120 273.96896 262.78148 285.15644

262.78148

273.96896

352.28131

307.53139 100 296.34392

363.46879

296.34392 80 262.78148 273.96896 Engine273.96896 OOL 307.53139 60 285.15644 285.15644 341.09383 296.34392 296.34392 318.71887 307.53139 307.53139 329.90635 318.71887 352.28131 385.84375 329.90635 329.90635 341.09383 363.46879 397.03123 40 341.09383 374.65627 352.28131 352.28131 363.46879 363.46879 430.59366441.78114 374.65627 374.65627 385.84375 408.2187 452.96862 385.84375 397.03123 419.40618 408.2187419.40618 397.03123 408.2187 430.59366441.78114 464.1561 486.5310630% 430.59366 419.40618 452.96862 475.34358 452.96862441.78114 464.1561 486.53106 497.71854508.90601 542.46845 475.34358 464.1561 475.34358 531.28097520.09349 Engine torque, Nm 497.71854 486.53106 542.46845 553.65593 520.09349508.90601 497.71854520.09349508.90601 564.84341576.03089587.21837598.40585 609.59332 20 531.28097 542.46845 531.28097553.65593 620.7808654.34324 564.84341553.65593 564.84341576.03089587.21837 609.59332 643.15576631.96828 676.7182 576.03089587.21837 598.40585620.7808 665.53072 687.90568710.28063 598.40585620.7808 609.59332 654.34324676.7182 699.09315732.65559 643.15576631.96828 643.15576631.96828 687.90568 721.46811 755.03055 777.40551 665.53072 654.34324 665.53072 699.09315710.28063 743.84307 766.21803788.59299 799.78046 699.09315687.90568676.7182 732.65559 721.46811 710.28063 777.40551799.78046810.96794 866.90534 743.84307 732.65559 721.46811 766.21803755.03055 833.3429822.15542 844.53038855.71786 878.09282 0 777.40551 766.21803755.03055 743.84307 788.59299 900.46777889.2803 911.65525922.84273 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 ω e_desired Engine speed, rpm

. CVT can provide high engine thermal efficiency by continuous ratio control independent of the vehicle velocity . CVT has disadvantages such as low torque capacity, mechanical and hydraulic loss Overall CVT Structure Variator working principles and lay-out

Final reduction Primary pulley (engine side)

Output Secondary pulley Secondary (road side) pulley High ratio Low ratio (OD) Primary pulley • Pp : provide ratio adjustment of the belt. ∗ 𝑃𝑃𝑝𝑝 • Ps : provide clamping. Torque is transmitted by the friction between belt and pulley Engine 𝑃𝑃𝑠𝑠

Hydraulic 𝑃𝑃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 unit

CVT hydraulic control system

71 Hydraulic pump loss and mechanical loss (variator belt & pulley loss)

Vehicle energy loss with CVT CVT loss** according to driving cycles*

. Hydraulic pump loss : major part in the total loss at low vehicle speeds . Mechanical loss : major part in the total loss at high vehicle speeds

*V. Francis, “Fuel consumption potential of the pushbelt “CVT”, FISITA World Automotive Congress, 2006 **Tohru Ide, “Effect of Belt Loss and Oil Pump Loss on the Fuel Economy of a Vehicle with a Metal V-Belt CVT”, FISITA World Automotive Congress, 2000 72 Vehicle Configuration

. Parallel hybrid configuration < Vehicle specifications > Displacement 1500 cc 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆 𝝎𝝎 172 Nm @ 𝝎𝝎 Torque Engine 5500 rpm 𝒊𝒊𝒄𝒄𝒄𝒄𝒄𝒄 82 kW @ Power 5500 rpm 𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘 Max. power/ 𝝎𝝎 17 kW/106 Nm Motor torque Max. speed 9500 rpm Number of cells 40 The engine and motor speed can be Nominal cell Battery 3.6 V controlled independently of the vehicle voltage speed, owing to the CVT’s continuously Rated pack energy 0.675 kWh variable feature CVT ratio 3.172–0.529:1 TM = Final drive ratio 3.94:1 Weight 1295 kg 𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆= 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎× Vehicle 𝝎𝝎 𝝎𝝎 Tire radius 0.381 m 𝝎𝝎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒊𝒊𝒄𝒄𝒄𝒄𝒄𝒄 𝝎𝝎𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘𝒘

73 Oil pump efficiency depends on the line pressure and input speed

. Oil pump efficiency map from experiment is used

P = P D ω /η Oil pump efficiency* pump,loss line pump pump pump → = η Tpump,loss Pline Dpump / pump : line pressure : pump displacement 𝑃𝑃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 • η pump = f (icvt , Pline ) 𝐷𝐷𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 : pump speed i (= ω / ω ) : CVT ratio η : oil pump efficiency cvt p s 𝜔𝜔pump𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 Pump loss model

*W. Ryu “A Study on Metal Belt CVT Control for System Efficiency Improvement”, Dissertation of Sungkyunkwan University, 2006 74 Mechanical losses depend on the speed ratio, input torque and vehicle speed

. Mechanical loss model

CVT mechanical efficiency* • ηmech = f (icvt ,Tt ,Vv )

1 / icvt (= ωs / ω p ) : speed ratio : turbine torque Tt : vehicle speed Vv

• ηmech = (Tin −Tmech.loss ) /Tin

→ Tmech.loss = (1−ηmech )Tin

Tin = Tengine − Tpump.loss

* Bas Vroemen, “Component Control for The Zero Inertia Powertrain”, Dissertation of Technische Universiteit Eindhoven, 2001

75 Mechanical losses depend on the speed ratio, input torque and vehicle speed

. CVT shift dynamics model Ide’s formula using SKKU experiments

di * = β(i)⋅ω ⋅(P − P ) dt p p p Ratio changing speed is dependent on the deviation of (Pp-Pp*) and input speed.

FpFs map* Fp - The CVT ratio-torque ratio-thrust • FpFs = = f (icvt ,Tr ) Fs FpFs : thrust ratio ratio characteristic is shown by Fp : primary clamping force FpFs map* Fs : secondary clamping force

icv t : CVT ratio - The FpFs map* is obtained from T =T /T : torque ratio r in max SKKU experiments

*T. Ide, A. Udagawa and R. Kataoka, “Simulation Approach to the Effect of the Ratio Changing Speed of a Metal V-Belt CVT on the Vehicle Response”, Vehicle System Dynamics, 24: 4, 377 — 388, 1995 **J. Park “A Study on Shift Control Algorithm for a 2 stage CVT”, Dissertation of Sungkyunkwan University, 2012

76 Overall New CVT Model

. The each pressure are determined to control the demanded CVT ratio

CVT control Block CVT plant Block

_ Hydraulic 𝑠𝑠_𝑑𝑑𝑑𝑑𝑑𝑑 𝑃𝑃 actuator 𝑠𝑠 Shift Speed 𝑃𝑃 dynamics _ model calculate 𝑝𝑝 𝑑𝑑𝑑𝑑𝑑𝑑 𝑝𝑝 model 𝑃𝑃 𝑃𝑃 𝑖𝑖 model 𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑑𝑑𝑑𝑑𝑑𝑑 𝜔𝜔 𝑃𝑃 𝑃𝑃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝜔𝜔𝑜𝑜𝑜𝑜𝑜𝑜 ∗ 𝑖𝑖 𝑃𝑃𝑝𝑝

Pump loss 𝑖𝑖 Mechanical 𝑖𝑖 model 𝑇𝑇𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 loss model 𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜

𝑇𝑇𝑖𝑖𝑖𝑖

CVT system block diagram

77 Development of vehicle performance simulator . The component and models developed in this study are integrated into Autonomie

78 Driving Mode Control Analysis

. In the target HEV, the engine is a main power source and the motor assists the engine according to the vehicle operating conditions Propel Acceleration Cruising Regen Engine on Motor Engine off Motor Engine Engine

Transmission Transmission

Battery Motor assist or off Battery Motor operate (propel)

Deceleration(regenerative braking) Idling Engine off Motor Engine off Motor Engine Engine

Transmission Transmission

Battery Motor regenerate Battery Motor operate (starting)

79 Driving Mode Control Analysis

. Control analysis was performed for Mode Operating Conditions a hybrid electric vehicle (HEV) Low-speed cruise/ EV mode equipped with a continuously low power variable transmission (CVT) under Engine Slow acceleration/ various driving conditions mode high-speed cruise Start-up/ HEV mode aggressive acceleration Driving mode control analysis in EV mode Driving Mode Control Analysis

. The OOL_high and OOL_low curves were determined from the experimental results by considering the engine operation for each vehicle driving mode

Driving mode control analysis in HEV mode Results

UDDS cycle HWFET cycle Validation Results

. The normalized cross correlation power (NCCP) value, which shows the correlation level, was used to compare the simulation and experimental results quantitatively

max[Rxy (τ )] 1 τ = τ = ⋅ −τ NCCP Rxy ( ) lim ∫0 x(t) y(t )dt max[Rxx (τ ), R yy (τ )] τ →∞ T

NCCP values for UDDS and HWFET cycles

Vehicle Gear Engine Battery Cycle Speed Ratio Torque SOC

HWFET 0.99 0.98 0.90 0.97

UDDS 0.99 0.93 0.90 0.96 Outline

85 Mode Control: Engine On/Off Determined by Demand Power and Vehicle Speed . Vehicle operating points according to engine status – Wider range of motor only operation in PHEV due to CD mode

Prius PHEV Prius HEV

Engine off (PEV mode) Engine operation mode of Prius PHEV is the same as HEV Engine Speed Fueling 1 6 7 Engine off 0 Off Cranking Idling Fuel cut Cranking Accelerating Ready to fuel 3 8 Idling Idle speed Idling 2 4 5 9 Fuel cut Idle speed Off Engine on (HEV mode) Engine on Controlled Controlled

86 Mode Control: Engine Is Turned On Differently in CS & CD Modes . Engine turn on condition – CD mode: Turned on if the demand power > power threshold (35kW, battery maximum) – CS mode: Turned on mostly if the demand power > defined power threshold line

2000 2000 Prius PHEV Prius HEV 1800 1800

1600 1600

1400 1400

1200 1200

1000 CD mode 1000

800 800

600 600 Wheel (Nm) torque demand 400 Wheel (Nm) torque demand 400

200 200

0 0

-200 -200 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Wheel speed (rad/s) Wheel speed (rad/s)

0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 Vehcle speed (m/s) Vehcle speed (m/s) The on/off strategy in CS mode looks similar to the strategy of HEV - More EV driving in PHEV - Change of driving pattern (CD to CS)

87 Mode Control: CD or CS modes Can Be Determined by Engine On Points and SOC . Driving mode change – Analysis of driving mode according to engine turn on point change – When SOC is over 28% engine is turned on in higher power demand (30kW) than under 28% (under 15kW)

–  reasonable to select 28% for mode change between CD mode to CS mode ACC > Max. Power 40 Yes electric power? performance 35 Not enough power only with motor mode 30 No 25 Yes EV mode 20 SOC > 28% ? Enough energy for EV (CD mode) 15 No 10

Wheel power (kW) demand 5 CS mode Engine turns on according to CS mode 0 Engine on when Temp. of Eng. > 90C and SOC > 28% Eng. on map and proportional to SOC Engine on when Temp. of Eng. > 90C and SOC < 28% -5 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 State Of Charge (normalized)

88 Mode Control: Engine Is Turned On If Battery Cannot Provide All Power Required In CD Mode

0.9 CD mode only 0.8

0.7

0.6

0.5 SOC 0.4

0.3

0.2

0.1 0 500 1000 1500 Time (s)

Engine turn on when the vehicle requires the power exceed battery maximum output power (< 35kW)

89 Mode Control: Engine Is Turned ON If Demand Torque Exceeds a Threshold Line In CS Mode

0.9 CS mode only 0.8

0.7

0.6

0.5 SOC 0.4 High SOC 0.3

0.2

0.1 0 500 1000 1500 Time (s)

Low SOC

Threshold line is changed according to SOC We can say that the threshold power is lower if SOC is lower.

90 Energy Management Strategy: SOC Balancing Strategy Is The Same As the HEV Strategy In CS mode

. SOC Balancing Strategy – ACC pedal > 80%  engine and battery working together – CS mode: Battery power is proportional to SOC (the same as HEV) – CD mode: output power of the battery is 0kW to 10kW

Prius PHEV Prius HEV

Filtered points obtained when the engine is on

CS CD

91 Energy Management Strategy: The Battery Produces Constant Power On Highway Driving In CD Mode

. Strategy in CD mode – Normal speed driving (28m/s ≈ 100km/h): Engine provides all required power. – High speed driving (> 28m/s): Output power of the battery is about 10kW (Battery supporting mode)

20 80 Battery output power when ACC > 80% Engine output power when ACC > 80% Battery output power when ACC < 80% and V < 28m/s Engine output power when ACC < 80% and V < 28m/s 70 Battery output power when ACC < 80% and V > 28m/s Engine output power when ACC < 80% and V > 28m/s 15 60 Low speed 50 10

5 30 High speed Engine output power (kW) Battery output power (kW) 20 0 10

-5 0 20 30 40 50 60 70 80 90 20 30 40 50 60 70 80 90 Wheel power demand (kW) Wheel power demand (kW)

In CD mode, the engine is basically not used. However, the engine is mostly turned on for highway driving, so this special control concept is necessary for highway driving.

92 Component Operation Control: Engine Target Is The Same As The Engine Target of HEV . Engine is operated followed by a specific line . No difference between CD and CS mode

Prius PHEV Prius HEV

120 120

100 100

80 80

60 60 Engine torque (Nm) 40 Engine torque (Nm) 40

20 20

0 0 50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450 Engine speed (rad/s) Engine speed (rad/s)

93 PHEV Has Similar Regenerative Performance Than Prius HEV Regenerative power limit The battery in PHEV is constrained by SOC. (not observed in HEV due to SOC range)

Instead, constraint by temperature in PHEV is not observed because the temperature is really well controlled

Prius PHEV Prius HEV

PHEV uses slightly wider range due to benefit of the larger battery.

Prius PHEV Prius HEV

94 Summary of Operation

. Summary (at normal temperature)

CD mode Brake signal & Vehicle CS mode Brake signal & Vehicle SOC < 28% Engine off (EV) speed < 28m/s Engine off (EV) speed < 20m/s

Engine on Engine on Required power > Max. • Vehicle speed < 28m/s Engine turn on map: - Battery output power is electric power - Engine only mode vehicle speed and SOC proportional to SOC  (Pbat = 0)

• Vehicle speed > 28m/s - Battery supporting

mode  (Pbat = 10kW)

SOC > 30%

95 In CD Mode, Engine Is Forced To Be Turned On To Provide Heat To The Cabin Normal and hot amb. tests . Normal and hot ambient tests 100 90 CD mode .  Leave the engine off 80 70

. Cold ambient tests 60 .  50 On if the coolant temp. < 40°C 40

30 . (no electrical heater for the cabin) Engine coolant temp (C) 20

10 Eng. coolant Temp. in CD mode at normal Env. Temp. Eng. coolant Temp. in CD mode at hot Env. Temp. 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 100 Time (s) Cold amb. tests 90

80 CD mode

30 Engine is turned by the demand power Engine coolant temp (C) Turned on at 40 C and off at 65 C 20 or by the temperature condition.

10 Eng. coolant Temp. in CD mode at Env. Temp < 10C Eng. turn on points at ACC < 50% 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time (s)

96 In Cold Start Case, The Engine Is Turned On All The Time On UDDS Driving Cycle Even In CD Mode

. Engine is not able to reach to 65ºC for UDDS driving cycle under cold + cold case

Cold amb. (not cold start)

Cold amb. (Cold start) => cold + cold

97 In CS Mode, Engine Is Turned On At Launch If Coolant Temp. Is Below 65°C . In CD mode, the engine was allowed to be off even if the coolant temp < 65°C . No exception for CS mode at the launch of the vehicle, (the same as HEV) . The coolant temperature does not go below 40°C because of frequent engine operations (In HEV, the threshold for the engine on was about 53°C) 100

90 All CS mode 80

Engine coolant temp (C) temp coolant Engine 30 Engine on control at the launch of the vehicle

20 Cold ambient Normal ambient 10 Hot ambient Eng. turn on points not by CS mode condition 0 Points selected when the engine is turned on 0 200 400 600 800 1000 1200 1400 1600 1800 2000without demand torque. Time (s)

98 Cold Start Condition Keeps the Engine On For the First UDDS Cycle . The impacts of cold start under cold ambient tests (Three consecutive UDDS tests)

UDDS 7C 61303015 UDDS 7C 61303016 UDDS 7C 61303017 400 90 400 90 400 90 1st Vehicle speed (m/s) [x 10] 2nd Vehicle speed (m/s) [x 10] 3rd Vehicle speed (m/s) [x 10] 350 Engine speed (rad/s) 80 350 Engine speed (rad/s) 80 350 Engine speed (rad/s) 80 Engine coolant temperature (C) Engine coolant temperature (C) Engine coolant temperature (C) 300 70 300 70 300 70

250 60 250 60 250 60

200 50 200 50 200 50

150 40 150 40 150 40

100 30 100 30 100 30

50 20 50 20 50 20 Speed, and Power Temperature Speed, and Power Temperature 0 10 0 10 Speed, and Power Temperature 0 10

-50 0 -50 0 -50 0 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 1200 1400 Time (s) Time (s) Time (s) nd rd 80 CS mode, Hot start 2 and 3 look similar. CS mode, Cold start CD mode, Hot start 70 CD mode, Cold start

50 The coolant temperature cannot reach to 65°C under cold start 40

Engine coolant temperature (C) Whatever the mode is CS or CD, the first UDDS test of 20 each day keeps the engine on under the cold ambient.

10 0 200 400 600 800 1000 1200 1400 Time (s)

99 There Is No Significant Impact(s) On Energy Management Strategy For SOC Balancing

CD mode: The battery output power reduced under cold ambient tests

Only when the engine is on

CS mode

No special impact on the control observed in CS mode

100 The Impact On Engine Target Control Is The Same As The Change Observed In HEV Control . In low coolant temperature  engine torque is controlled in higher range than normal Prius PHEV Prius HEV

120 120

100 100

80 80

60 60 Engine torque (Nm) 40 Engine torque (Nm) 40

20 20 Density for blue points only Density for blue points only

0 0 50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450 Engine speed (rad/s) Engine speed (rad/s)

101 Summary of Operation with Thermal Condition

CD mode CS mode SOC < 28% Engine off (EV) Brake signal & Vehicle Engine off (EV) Brake signal & Vehicle speed < 28m/s speed < 20m/s AND Engine coolant Temp. > 65C (Heater on)

Engine on Engine on • Vehicle speed < 28m/s - Battery output power is Required power > Max. - Engine only mode Engine turn on map: proportional to SOC electric power (Pbat = 0) vehicle speed and SOC OR • Vehicle speed > 28m/s OR Heater on & - Battery supporting (Engine coolant Temp. Engine coolant Temp. < mode (Pbat = 10kW) < 40C) 40C • Cold condition OR OR - Battery supporting Cold start: Engine Cold start: Heater on & mode (Pabt = 5kW) coolant Temp. < 65C (Engine coolant Temp. < 65C

SOC > 30%

102 Model Development and Validation

. Control model development (Change from HEV model) – CD & CS mode decision – Engine turn on map(condition) both in CD and CS – Battery management strategy map(Output power of battery according to SOC) – Cold start condition and other behavior depending on temperature

. Comparison of simulation results and test data in PHEV – Difference in 5% between test and simulation results based on fuel economy or SOC difference – Different coefficient of rolling resistance and accessary power applied depending on the temperature and driving cycles CD: FE discrepancy (%) (SOC difference) CS: FE discrepancy (%) (SOC difference)

UDDS HWFET UDDS HWFET

22C - (0.7%)* - (-0.8%)* 0.25 (-0.6%) 0.37 (-0.5%)

35C - (-0.15%)* - (-1.95%)* 3.20 (0.4%) 1.17 (0.1%)

-7C 2.02 (-2%) 0.04 (1.25%) 3.92 (-0.6%) 3.76 (0.5%)

*Not FE but SOC discrepancy: no or very few fuel consumption 103 Model Validated Within Test to Test

Repeatability. Comparison of simulation results and test data in PHEV – Difference mostly in 5% between test and simulation results based on fuel consumption or SOC – Different coefficient of rolling resistance and accessary power applied depending on the temperature and driving cycles

Ambient Driving Fuel consumption (g) ∆SOC (%) Final SOC (%) Cycle name Temperature mode Test Simulation diff.(%) Test Simulation diff.(%) Test Sim. diff. CD 0 0 - -28.95 -29.64 2.4% 55.87 55.18 -0.69 22C CS 286.5 287.1 0.2% -0.58 +0.01 - 21.64 22.23 0.59

CD 67.1 52.4 - -35.90 -35.76 -0.4% 49.27 49.41 0.14 UDDS 35C CS 405.4 392.8 -3.1% -0.01 -0.46 - 20.88 20.43 -0.45 CD 450.3 459.2 2.0% -21.03 -19.06 - 64.16 66.13 1.97 -7C CS 357.1 371.4 4.0% -5.10 -4.46 - 20.27 20.91 0.64 CD 0 0 - -45.22 -44.41 -1.8% 39.99 40.8 0.81 22C CS 442.3 443.9 0.4% +0.05 +0.58 - 25.29 25.82 0.53 CD 0 0 - -54.68 -52.73 -3.6% 30.65 32.6 1.95 HWFET 35C CS 521.3 527.4 1.2% +0.51 +0.36 - 25.36 25.21 -0.15 CD 413.9 413.7 0.0% -22.2 -23.45 - 63.47 62.22 -1.25 -7C 2015-01-1157 CS 497.1 516.5 3.9% -1.77 -2.30 - 26.16 25.63 -0.53 104 Conclusion

. APRF test data analysis – Engine on/off condition depend of the battery SOC (CD/CS mode) – CD mode: Operates in EV unless the battery cannot provide the requested power • No blended mode operation: EV or engine only except high speed (battery supporting) – CS mode: Similar operation as for the Prius HEV (engine turns on followed by map) • Battery output power is proportional to SOC – Thermal management • Only keep engine coolant temperature high (40~65) when the heater is on in CD mode • Always maintain high engine coolant temperature in CS mode • Two types of cold start according to engine coolant temperature

. Model development and validation – Parameters for individual component models have been developed based on test data – Developed the vehicle energy management for the PHEV model based on the HEV – Validated the model on the UDDS and HWFET cycles for multiple ambient temperature (-7C, 22C and 35C) under different operating modes (CD and CS) – Difference between test and simulation is within test to test repeatability (5%) for both vehicle energy consumption and battery SOC.

105 Outline

106 GM Volt 2012

107 Test Data Analysis for Control Model - Energy management strategy analysis

80 Engine On Points . SOC balancing during charge sustaining 70

Strategy 1. 40

SOC is too low  Engine is turned on at low kW Power, Wheel 10  0 power ( HEV mode) -10

-20 60 -30 0.2 0.205 0.21 0.215 0.22 0.225 0.23 Battery SOC 40 Strategy 2. 20

0 Compute battery power demand as a function of -20 wheel power demand Battery Power, kW Power, Battery

-40 60

-60 -3 -2 -1 0 1 2 3 4 5 40 Wheel Power, W 4 x 10 20 Strategy 3. 0 -20 Battery Power, kW Power, Battery In normal level, compute battery power -40 HWY_CS (HEV Mode) -60 UDDS_CS (HEV Mode) demand for SOC regulation 0.216 0.218 0.22 0.222 0.224 0.226 0.228 0.23 0.232 0.234 0.236 Battery SOC

SAE 2013-01-1458 108 Test Data Analysis for Control Model - Engine operating target analysis . Engine operating target from all tests.

Engine power demand From all tests

Engine speed optimal

Series mode Eng Temp < 76C Split mode Eng Temp > 76C

Engine torque desired

SAE 2013-01-1458 109 Test Data Analysis for Control Model - Transmission operation modes . GB Mode on electric mode (EV) / Extended range (ER) driving

EV driving ER driving SR = 1.43

Series mode Split mode

After we convert ER driving results into new map by using vehicle speed and engine speed indexes, the mode selection rule can be defined based on the speed ratio

(Note that GB is the gearbox)

SAE 2013-01-1458 110 Test Data Analysis for Control Model - Engine warm up strategy (-6.7 ) . Constant speed and constant power for 150 seconds used to warm-up battery and heat on under cold conditions (@ high SOC)℃ 30 Vehicle Speed, m/s UDDS 25 Fuel Rate, g/s x10 Warm up strategy Engine Speed, ra/ds x0.1

20 • 1450 rpm

15 • 1.1 g/s fuel • 150 seconds to 10 reach 50C of en 5 gine temp 0 50 100 150 200 250 300 Warm up phase Time, sec

25 90 HWY 80 20 70 60 15 50

10 30 20

5 Vehicle Speed, m/s C Temp, Coolant Engine 10 Fuel Rate, g/s x10 UDDS 0 Engine Speed, rad/s x0.1 HWY 0 -10 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Time, sec Time, sec SAE 2013-01-1458 111 Model Validation in Autonomie - urban driving schedule (EV) : 35 . Battery SOC & power

UDDS CD (95 degF) - Battery SOC ℃

Battery SOC (Simulation) 0.9 Battery SOC (ANL Test) 0.85

0.8

SOC 0.75

0.7

0.65

0.6 0 200 400 600 800 1000 1200 1400 Time, sec

UDDS CD (95 degF) - Battery Power 60 Battery Power (Simulation) Battery Power (ANL Test) 40

0 Power, kW

-20

-40 0 200 400 600 800 1000 1200 1400 Time, sec

SAE 2013-01-1458 112 Model Validation in Autonomie - urban driving schedule (ER) : 35 . Engine speed & torque UDDS CS (95 degF) - Vehicle Speed 100 ℃Vehicle Speed (Simulation) 80 Vehicle Speed (ANL Test) 60

40 Speed, kphSpeed, 20

0 0 200 400 600 800 1000 1200 1400 Time, sec Warm up phase UDDS CS (95 degF) - Engine Speed 400 to reach the engine temperature Engine Speed (Simulation) 300 Engine Speed (ANL Test)

200

Speed, rad/s Speed, 100

0 0 200 400 600 800 1000 1200 1400 Time, sec UDDS CS (95 degF) - Engine Torque 150 Engine Torque (Simulation) Engine Torque (ANL Test) 100

50 Torque, Nm Torque,

0 0 200 400 600 800 1000 1200 1400 SAE 2013-01-1458 Time, sec 113 Model Validation in Autonomie - urban driving schedule (EV) : -6.7 . Engine speed & torque UDDS CD (20 degF) - Vehicle Speed 100 Vehicle℃ Speed (Simulation) 80 Vehicle Speed (ANL Test)

40 Speed, kphSpeed, 20

0 0 200 400 600 800 1000 1200 1400 Time, sec UDDS CD (20 degF) - Engine Speed 400 Warm up phase Engine Speed (Simulation) 300 Engine Speed (ANL Test) To reach the engine temperature 200

Speed, rad/s Speed, 100

0 0 200 400 600 800 1000 1200 1400 Time, sec UDDS CD (20 degF) - Engine Torque 150 Engine Torque (Simulation) Engine Torque (ANL Test) 100

50 Torque, Nm Torque,

0 0 200 400 600 800 1000 1200 1400 SAE 2013-01-1458 Time, sec 114 Model Validation in Autonomie - urban driving schedule (ER) : -6.7 . Battery SOC, engine fuel

UDDS CS (20 degF) - Battery SOC ℃

Battery SOC (Simulation) Battery SOC (ANL Test) 0.23

0.22 SOC

0.21

0.2 0 200 400 600 800 1000 1200 1400 Time, sec

UDDS CS (20 degF) - Engine Integrated Fuel 0.8 Integrated Fuel (Simulation) Integrated Fuel (ANL Test) 0.6

0.4

Integrated Fuel, kg Fuel, Integrated 0.2

0 0 200 400 600 800 1000 1200 1400 Time, sec

SAE 2013-01-1458 115 Model Validation in Autonomie - summary (Urban driving schedule)

Battery Electric consumption (Wh/mi) Conditions Test Simulation EV(CD) : 22.2 214.3 201.9 (- 5.7 %)

EV(CD) : 35 ℃(A/C on) 302.3 312.3 (+ 3.3 %)

EV(CD) : 35 ℃ (A/C off) 203.3 195.4 (- 3.9 %)

EV(CD) : -6.7℃ (Heater on) 416.2 414.1 (- 0.5 %)

℃ Fuel Consumption (kg) Conditions Test Simulation

ER(CS) : 22.2 0.454 0.435 (- 4.2 %)

ER(CS) : 35 ℃(A/C on) 0.758 0.772 (+ 1.8 %)

ER(CS) : -6.7℃ (Heater on) 0.555 0.529 (- 4.8 %)

℃ (Note that EV is the electric vehicle driving mode and ER is the extended range mode. A/C is an air conditioner )

SAE 2013-01-1458 116 Transmission Thermal Management System

. Thermal system in GB_Plant : Heat transfer between TM & Oil

 Input : , Heat flow between𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒐𝒐𝒐𝒐𝒐𝒐 Motors & Oil𝒒𝒒 Coolant heat flow :

= , _ _

𝒒𝒒𝒄𝒄𝒐𝒐𝒐𝒐𝒐𝒐 𝒎𝒎̇ 𝒄𝒄𝒑𝒑 𝒐𝒐𝒐𝒐𝒐𝒐 𝑻𝑻𝒐𝒐𝒐𝒐𝒐𝒐 𝒐𝒐𝒐𝒐𝒐𝒐 − 𝑻𝑻𝒐𝒐𝒐𝒐𝒐𝒐 𝒊𝒊𝒊𝒊

𝒎𝒎̇ 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄_ Convective heat flow _ 𝑻𝑻𝒐𝒐𝒐𝒐𝒐𝒐 𝒊𝒊𝒊𝒊 between TM & Ambient Air 𝑻𝑻𝒐𝒐𝒐𝒐𝒐𝒐 𝒐𝒐𝒐𝒐𝒐𝒐

 Input : 𝑻𝑻𝑻𝑻 Transmission oil as a TM losses from GB 𝑻𝑻 thermal capacitor 𝑻𝑻𝑻𝑻 Convective heat flow plant block𝑸𝑸 Lumped TM mass as a thermal capacitor between TM & Oil

SAE 2013-01-1458 117 Test Data Analysis for Control Model - Charge sustaining operation on Hot UDDS Prius 04 (UDDS) Distinctive Volt Operation: • Longer EV only operation • Engine operates at higher loads

60 Volt CS mode driving (UDDS) Veh Spd, m/s 40 Eng Pwr, kW Battery Pwr, kW

0 Power, kW -20

-40

0 200 400 600 800 1000 1200 1400 Time, sec

SAE 2013-01-1458 118 Test Data Analysis for Control Model - motor operating on regeneration mode . Motor operating as a function of coolant temp.

Current limit Battery maximum on low/high temperature regenerative power ~ 70kW

SAE 2013-01-1458 119 Model Validation in Autonomie - urban driving schedule (ER) : 35 . Battery SOC, engine fuel

UDDS CD (95 degF) - Battery SOC ℃

Battery SOC (Simulation) Battery SOC (ANL Test) 0.23

0.22 SOC

0.21

0.2 0 200 400 600 800 1000 1200 1400 Time, sec

UDDS CS (95 degF) - Engine Integrated Fuel

0.8

0.6

0.4

Integrated Fuel, kg Fuel, Integrated 0.2 Integrated Fuel (Simulation) Integrated Fuel (ANL Test) 0 0 200 400 600 800 1000 1200 1400 Time, sec

SAE 2013-01-1458 120 Model Validation in Autonomie - urban driving schedule (EV) : -6.7 . Battery SOC & power

UDDS CD (20 degF) - Battery SOC ℃ 0.9 Battery SOC (Simulation) Battery SOC (ANL Test) 0.8

0.7 SOC

0.6

0.5 0 200 400 600 800 1000 1200 1400 Time, sec

UDDS CD (20 degF) - Battery Power 60 Battery Power (Simulation) Battery Power (ANL Test) 40

0 Power, kW

-20

-40 0 200 400 600 800 1000 1200 1400 Time, sec

SAE 2013-01-1458 121 Outline

122 Ford Focus BEV

123 Import Test Data into Autonomie Assumption to Calculate Additional Signals Motor Efficiency Map (Torque) 250 40 90 50 40

Tuned Driving Motor 50 90 Vehicle Specifications 200 80

60 Parameters Value For the Ford BEV 80 150 60

70 Tire radius 0.321 m 90 90 100 Final drive ratio 7.82 50 40 50 Final drive efficiency 97.5% 90 90 80 90 90

60 70 80 80 60 80 70 60 0 60 50 70 50 7060 7080 70 80 50 60 40 90 Torque coupler ratio 1:1 90 80 80

Torque (N.m) -50

Torque coupler efficiency 98% 90 90 -100 90 90 Vehicle mass 1700 kg -150 Motor Efficiency Map

2 90 50 Frontal Area 2.42 m Propeling Max Torque Curve(N.m)40 90 70

-200 60 50 Regen Max Torque Curve(N.m)40 80 70

60 Drag coefficient 0.26 80 -250 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 Driving motor power/torque 107 kW/250 N-m Speed (rad/s) Battery pack capacity 23 kWh Nissan Leaf Ford BEV (Public) (Assumption) Li-ion Li-ion Motor efficiency map Total cell # 192 430 cell # per module 4 86  Newly generated Ford BEV motor map (Assumption) cell V (max) 3.8 (4.2) 3.8 (4.2)  Based on the init file of Nissan Leaf motor and test data cell Ah 33.1 13 module # 48 5 module series # 24 1 Battery pack efficiency map module parallel # 2 5  Newly generated Ford BEV battery map (Assumption) module V (max) 15.2 326 (361) module Ah 33.1 13  Based on the init file of Nissan Leaf battery data and test data pack V 364.8 326 (361) pack Ah 66.2 65 Transmission efficiency pack kWh 24.1 23.4 Max Current [A] 264.8 305.5  1 speed gear ratio (FD ratio) Max Power [W] 96599.04 110346.6

124 Import Test Data into Autonomie Battery Performance Estimation

Comparison of Open Circuit Voltage (OCV) 420 Leaf BEV Battery Pack 400 Focus BEV Battery Pack (Assumed data from Test) Battery Model : 380 Internal Resistance Model 360 340

Voltage [V] 320

300

280

260 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 SOC (Normalized) Comparison of Internal Resistance of the Battery Pack 0.14 Leaf BEV Battery Pack - Charging Leaf BEV Battery Pack - Discharging 0.12 Ford BEV Battery Pack (Assumed) - Charging Ford BEV Battery Pack (Assumed) - Discharging To calculate Vout , the battery model needs Vocv and Rint as the initialization data. 0.1 Therefore, we have newly developed the characteristic data based on the test results of 0.08 the Ford BEV, and by modifying the original Resistance [ohm] Leaf battery data. 0.06

0.04 File name : 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ess_plant_li_65_430_Ford_Focus_EV.m SOC (Nomalized) ess_plant_li_65_430_Ford_Focus_EV.init

125 Import Test Data into Autonomie Results of Estimated OCV from MCT Data

UDDS 1 - 61301070 360 30 Vout - Test V - Estimated value 355 oc 25 Vehicle Speed

350 20

345 15 Voltage [V] 340 10 Vehicle Speed [m/s]

335 5

330 0 0 200 400 600 800 1000 1200 1400

HWY 1 - 61301071 355 30

Vout - Test

Voc - Estimated value 350 Vehicle Speed 20

345 10 Voltage [V] Vehicle Speed [m/s] 340 0

335 -10 0 100 200 300 400 500 600 700 800

126 Verification of Estimated Signals

Motor Efficiency Map (Torque) 250 40 90 50 40

50 90 Comparison between Test Data and Calculated Signals 200 80

60 80

150 60

70 90 90 100

50 40 50 Verification of Electric Current Input to the Driving Motor 90 90 80 90 90 60 70 80 80 60 80 70 60 0 70 60708050 50807060

50 70 60 40 90 90 80 80

Torque (N.m) -50

90 90  -100 90 90 61301071_HWY -150

Motor Efficiency90 Map 50 40 90 Propeling Max Torque Curve(N.m)70 -200 60 50 40 80

Regen Max Torque Curve(N.m)60 80 -250 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 Validation of Input Current for Motor - 61301071 Speed (rad/s) 150

100

0 Current[A]

-50 ESS curr out - PC curr in (measured) Mot curr in (calculated) Veh Spd [km/h] -100

0 100 200 300 400 500 600 Time(sec)

Validation of Input Current for Motor - 61301071 150 ESS curr out - PC curr in (measured) 100 Mot curr in (calculated) Veh Spd [km/h]

0 Current[A]

-50

-100

100 120 140 160 180 200 220 240 260 280 300 Time(sec)

127 Verification of Estimated Signals

Motor Efficiency Map (Torque) 250 40 90 50 40

50 90 Comparison between Test Data and Calculated Signals 200 80

60 80

150 60

70 90 90 100

50 40 50 Verification of Electric Current Input to the Driving Motor 90 90 80 90 90 60 70 80 80 60 80 70 60 0 70 60708050 50807060

50 70 60 40 90 90 80 80

Torque (N.m) -50

90 90  -100 90 90 61301072_UDDS -150

Motor Efficiency90 Map 50 40 90

-200 60 50

Propeling Max Torque Curve(N.m)40 80

60 Regen Max Torque Curve(N.m)80 Validation of Input Current for Motor - 61301072 -250 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 150 Speed (rad/s) ESS curr out - PC curr in (measured) 100 Mot curr in (calculated) Veh Spd [km/h]

0 Current[A]

-50

-100

0 100 200 300 400 500 600 Time(sec) Validation of Input Current for Motor - 61301072 150 ESS curr out - PC curr in (measured) 100 Mot curr in (calculated) Veh Spd [km/h]

0 Current[A]

-50

-100

100 120 140 160 180 200 220 240 260 280 300 Time(sec) Verification Conclusion: Compared signals between measured data and calculated value show good coincident result. Therefore, we can think that the Ford motor map data developed is reasonable and can be applied to a vehicle model.

128 Verification of Estimated Signals Example of Energy Balance of Vehicle System Energy valance results of each component calculated by using test data

UDDS - 61301073 : 1 bag, cycle 4 of J1634 full charge test Estimated initial SOC: 75.28% Unit : kWh Parameters Value

Electrical consumption [DC Wh/mile] 281.72 Electrical consumption [DC Wh/km] 175.09 Initial SOC [%] 75.28 Final SOC [%] 64.70 SOC Swing (max-min) [%p] 11.01 Percent of Regen. Braking at Battery [%] 82.66 Percent of Regen. Braking at Wheel [%] 94.15 Regen Braking Energy Recovered at Battery [Wh] -641.79

Regen Braking Energy Available at Wheel [Wh] -727.54 Total Braking Energy at Wheel [Wh] -772.75

‘DC Wh/mile’ represents the total output energy of the battery divided by distance, where the battery internal resistance is not considered in the electric consumption value.

129 Vehicle Model Development and Validation Introduction of Vehicle Model in Autonomie

Parameters Value Tire radius 0.321 m Final drive ratio 7.82 Final drive efficiency 97.5% Torque coupler ratio 1:1 Vehicle Torque coupler Powertrain 98% efficiency Controller

Electric accessory loss 400W

Vehicle mass 1700kg Battery pack capacity 23kWh ess mot tc fd whl chas Init file: ess_plant_li_66_430_Ford_Focus_EV.m modified from the Leaf battery data

pc accelec

Init file: mot_plant_pm_53_107_Ford_Focus_EV.m modified from the Leaf motor data

Electrical accessory loss: 400W

130 Vehicle Model Development and Validation Validation Results : HWFET #1 - 61301071

90 87.5 ess_plant_soc_simu [%] x 1 88 87 ess_plant_soc_test [%] x 1 86 86.5 SOC 84 86 [%] 82 85.5 80 NCCP = 0.9882 85

78 84.5 0 100 200 300 400 500 600 700 800 200 220 240 260 280 300 320 340 360 380 400 time_simu time_simu 355 352 ess_plant_volt_out_simu [V] x 1 350 350 ess_plant_volt_out_test [V] x 1 348 Battery 346 Voltage 345 344 [V] 340 NCCP = 0.9903 342

335 340 0 100 200 300 400 500 600 700 800 200 220 240 260 280 300 320 340 360 380 400 time_simu time_simu 100 100 ess_plant_curr_out_simu [A] x 1 50 ess_plant_curr_out_test [A] x 1 Battery 50 0 Current 0 [A] -50 -50 -100 NCCP = 0.9653 -150 -100 0 100 200 300 400 500 600 700 800 200 220 240 260 280 300 320 340 360 380 400 time_simu time_simu 40 40

20 20 Battery 0 0 Power -20

[kW] -20 -40 NCCP = 0.9687 ess_plant_pwr_out_simu [kW] x 1 ess_plant_pwr_out_test [kW] x 1 -60 -40 0 100 200 300 400 500 600 700 800 200 220 240 260 280 300 320 340 360 380 400 time_simu time_simu

131 Vehicle Model Development and Validation Validation Results : UDDS #2 - 61301072

82 78.5 ess_plant_soc_simu [%] x 1 78 80 ess_plant_soc_test [%] x 1

77.5 SOC 78 [%] 77 76 76.5 NCCP = 0.9936 74 76 0 200 400 600 800 1000 1200 1400 400 450 500 550 600 650 700 750 800 time_simu time_simu 350 350 ess_plant_volt_out_simu [V] x 1 ess_plant_volt_out_test [V] x 1 345 345 Battery Voltage 340 340 [V] NCCP = 0.9945 335 335 0 200 400 600 800 1000 1200 1400 400 450 500 550 600 650 700 750 800 time_simu time_simu

150 150 ess_plant_curr_out_simu [A] x 1 100 100 ess_plant_curr_out_test [A] x 1 Battery 50 50

Current 0 0

[A] -50 -50

-100 NCCP = 0.9516 -100 -150 -150 0 200 400 600 800 1000 1200 1400 400 450 500 550 600 650 700 750 800 time_simu time_simu

60 40 ess_plant_pwr_out_simu [kW] x 1 40 Battery ess_plant_pwr_out_test [kW] x 1 20 20 Power 0 [kW] 0 -20 -20 NCCP = 0.9539 -40 -40 0 200 400 600 800 1000 1200 1400 400 450 500 550 600 650 700 750 800 time_simu time_simu 132 Vehicle Model Development and Validation Verification Results – NCCP

NCCP Value for Each Signal

Driving Cycle Test Number ESS SOC ESS Voltage Out ESS Current Out ESS Power Out

WOTx4 61301057 0.9696 0.9976 0.9652 0.9630

UDDS #1 61301070 0.9948 0.9941 0.9362 0.9386

UDDS #2 61301072 0.9936 0.9945 0.9516 0.9539

UDDS #3 61301077 0.9895 0.9955 0.9560 0.9567

UDDS #4 61301079 0.9866 0.9926 0.9573 0.9575

Hwy #1 61301071 0.9882 0.9903 0.9653 0.9687

Hwy #2 61301078 0.9861 0.9909 0.9804 0.9828

US06 #1 61301073 0.9872 0.9888 0.9462 0.9533

US06 #2 61301076 0.9867 0.9914 0.9531 0.9589

SSS 55MPH #1 61301075 0.9892 0.9949 0.9919 0.9879

SSS 55MPH #2 61301080 0.9822 0.9864 0.9890 0.9925

SAE 2014-01-1809 133 Conclusion

Analysis of preliminary test data for the missing signals of each component – Newly generated Ford BEV motor map – Motor efficiency map (Assumption) – Newly generated Ford BEV battery pack data – Open Circuit Voltage and Internal Resistance (Assumption) – Auxiliary loss power – average 400~450W / frequent peak power loss 600W ~ 1200W – Rolling resistance force of tires - Front 130~150N / Rear 80~120 N – The mechanical brake operation in a rear wheel– Less than around 10km/h (6.2mi/h) Effort-Flow of each component (Energy Balance) and verification of estimated missing signals – Compared signals between measured data and calculated value show good coincident result. Therefore, we can think that the motor efficiency map data developed is reasonable and can be applied to a vehicle model. Development of a vehicle model and validation with test data – Based on the default BEV model in Autonomie – Using of ‘[DC Wh/mile]’ at the output terminal of the battery system (not internally consumed energy) due to the test method – For 11 preliminary test data, good estimated results within around 3% except for one US06

134 Outline

135 Thermal Models and Controls Developed Over Multiple Years

Ford Fusion Conv. Compressor Tamb Toyota Prius HEV Toyota Prius PHEV coolant loop heatercore loop GM Volt Ford Focus BEV

Engine Cabin Engine radiator radiator condenser ventilation Evaporator Heatercore coolant loop heatercore loop Teng Tcab Tamb Fan

T Heatercore eng_room Valve Engine Radiator A/C coolant Teng Tamb Teng_room Valve Battery coolant Active Tamb Climate control system Compressor Battery Pump Pump Battery Fan Active

Tbat Cabin ventilation Condenser Evaporator Battery Tcab Fan Radiator C coolant A / C Tbat Battery coolant Battery Tamb Expander Valve

136 Multi-Entity Collaboration Leveraged

Engine : Compressor Tamb APRF & OEM Cabin: OEM & Labs. coolant loop heatercore loop

Engine Cabin radiator radiator condenser ventilation Evaporator Heatercore Teng Tcab Tamb T eng_room Valve Electric Machine: A/C coolant OEM and ANL Battery coolant Active Air Conditioner : OEM & Labs

Pump Battery Transmission :

Tbat OEM Battery : Battery Funded under this project Supplier Funded under another project Collaboration with Other Institutions

137 Multiple Powertrain Configurations Considered

• Conventional Vehicle – Ford Fusion HEV & PHEV • Extended Range Electric Vehicles (E-REV) – GM Volt • Hybrid Electric Vehicles (HEV) – Toyota Prius Hybrid • Battery Electric Vehicles (BEV) – Ford Focus BEV • Plug-In HEVs (PHEV) – Toyota Prius Plug-in Hybrid Conventional Toyota Prius

Ford Fusion E-REV

Electric Vehicle

Ford Focus BEV GM VOLT 138 Standard Model Validation Process Developed

UDDS 0.4

0.3 Test data from APRF (ANL) 0.2 Test data 0.1 Test Simu UDDS 0 0 200 400 600 800 1000 1200

30 (kg) consumption fuel Test 20 70 Simu Test 10 65 Simu

0 60

-10 SOC (%) 55

vehicle speed (m/s) vehicle 0 200 400 600 800 1000 1200 50 0 200 400 600 800 1000 1200 ºC 400 Test 300 Simu

200 80 Engine(Test) 100 Engine(Simu) -7 60 Battery(Test) 0 Battery(Simu)

0 200 400 600 800 1000 120040 engine speed (rad/s) engine

temperature (C) temperature Test0 200 400 600 800 1000 1200 150 21 Simu time (s) 100

35 -50

engine torque (Nm) torque engine 0 200 400 600 800 1000 1200 Modeltime Validation (s) Simulation data Control and Performance Analysis Model Development (Autonomie) component 120 coolant loop heatercore loop engine operation target Fan Heatercore 100 Heat capacity estimation

Engine 80 Radiator Teng Tamb Teng_room 60 Valve

Engine torque (Nm) 40

0 50 100 150 200 250 300 350 400 450 Engine speed (rad/s)

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off)

Engine performance analysis e.g. Prius PHEV

Engine efficiency decreases as the coolant temperature decreases.

Battery performance analysis 110 Round-trip efficiencies @ V base Round-trip efficiencies @ V out 105

100

Roundtrip efficiency (%) Vp + - io 85 Cp + + Ri + Rp V Vo ut 80 b ase -5 0 5 10 15 20 25 30 35 40 45 Vs Battery pack temperature (C)

Round-trip efficiency decreases as battery temperature decreases 140 Vehicle Level Controls Analysis Performed

Mode Control (Engine On/Off) The engine is forced to be turned on if the coolant temperature is low. (a) (b)

Vehicle speed (m/s) [x 10] Not turned off Engine speed (rad/s) 250 Engine torque (N.m) 70 250 70 Coolant temperature (C)

200 65 200 65 Turned on

150 60 150 60

100 55 100 55 Speed, Torque Temperature (C) Temperature

50 50 50 50

0 45 0 45

950 1000 1050 1100 300 350 400 450 Time (s) Time (s) The engine is not turned off if the coolant temperature is low. Desired battery power is proportional to SOC. SOC Balancing

Only when engine is on

e.g. Prius HEV Battery power is constrained by the battery temperature. 141 Comparative Analysis for Ctrl. & Perfo.

Target Control (Engine Operating)

Prius PHEV Prius HEV

Performance of battery

Prius PHEV Prius HEV

142 Component & Vehicle Model Developed

Component model development Component level validation

e.g. Battery Control analysis Instant power Rating power Pump Fan Active Internal resistance

Battery Regenerative power Radiator C coolant A / C Tbat Battery coolant Battery Tamb Valve Validations with test data in component level

Example Thermal System System Integration and vehicle level validation

Compressor Tamb

coolant loop heatercore loop Vehicle level validation with test data

Engine Cabin radiator radiator condenser ventilation Evaporator Heatercore Teng Tcab Tamb T eng_room Valve Control model development A/C coolant

Driver power demand Engine on/off demand Engine on/off demand SOC Mode decision (Engine on/off) Battery coolant Active Energy Engine management power demand (SOC balancing) Engine torque demand Engine target Engine torque demand Engine Motor 2 Thermal conditions generating speed demand torque demand Motor 2: Motor 2 torque demand • Control development Engine speed tracking Pump Battery • Component model integration Motor: Battery power demand torque target Motor torque demand • Vehicle model validation Driver power demand generation Tbat 143 Components Integrated into Vehicle

Qgb_loss Pump Fan Qcoolant Qmot1 Tmot1

Ttra Battery Qmot2 Tmot2 Radiator Transmission oil Tbat Qair Tamb Prius PHEV

Prolling_resistance

Pconvection

Pconduction Carcass Tread

Pconvection Pfriction

fuel

Qfuel Qcoolant Fan Heatercore

Pmech T Qair eng Radiator Tenv Teng_room QTheatingeng Qexhausted Tamb Valve Compressor exhausted gas

Cabin ventilation Condenser Evaporator Tcab Fan

Expander

144 Models Validated within Test to Test Uncertainty

Conv. 0.8 HEV 1 Test Simulation Test Simulation 0.7

0.8 0.6 0.5 0.6 0.4

0.4 0.3 0.2 0.2 0.1 Fuel consumption (kg) Fuel consumption (kg) 0 0 -77ºC°C 21ºC 22°C 35ºC35°C -77ºC°C 21ºC 22°C 35ºC35°C

0.8 PHEV (CS) 0.8 EREV (CS) Test Simulation Test Simulation 0.7 0.7

0.6 0.6

0.5 0.5

0.4 0.4

0.3 0.3

0.2 0.2

0.1 Fuel consumption (kg) 0.1 Fuel consumption (kg) 0 0 -77ºC°C 21ºC 22°C 35ºC35°C -77ºC°C 21ºC 22°C 35ºC35°C

145 Models Validated within Test to Test Uncertainty

Conv. 0.8 HEV 1 Test Simulation Test Simulation 0.7

0.8 0.6 0.5 0.6 0.4

0.4 0.3 0.2 0.2 0.1 Fuel consumption (kg) Fuel consumption (kg) 0 0 -77ºC°C 21ºC 22°C 35ºC35°C -77ºC°C 21ºC 22°C 35ºC35°C

0.8 PHEV (CS) 5 EV Test Simulation Test Simulation 0.7 4 0.6

0.5 3 0.4 2 0.3 (kWh)

0.2 1 0.1 Fuel consumption (kg) Electrical consumption 0 0 -77ºC°C 21ºC 22°C 35ºC35°C -77ºC°C 21ºC 22°C 35ºC35°C

146 Thermal Impact on Energy Consumption

VTMS Development Driving Conditions Simulation techniques • Ambient temperature Vehicle tests at APRF • Driving cycles • Large scale simulation process • Database analysis • Starting conditions Control and performance analysis • Technologies • Fleet Distribution Thermal Impact on Energy Thermal model developments From various • Energy consumptions studies • Impact of driving conditions Model integration and validation Tech. impact

Conv. PHEV E-REV

Cold Start 1.2 Hot Start

0.8

0.6 Fuel consumption (kg) Advanced 0.4

Powertrains 0.2

0 with VTMS HEV EV -20 -10 0 10 20 30 40 Temp. impact Ambient temperature (C)

147 Thermal Impact On Energy Consumption

Conv. HEV

Cold Start Cold Start Hot Start 1.2 Hot Start

1.2

1 1

0.8 0.8

0.6 0.6 Cold Start initial condition Fuel consumption (kg)  all initial temperatures of components are the same Fuel consumption (kg) 0.4 0.4 as the ambient temperature

Hot Start initial condition  Engine and coolant temp. (90C) Initial condition is the same as the Conv. 0.2 0.2  Cabin temp. (22C) Hot Start initial condition  Transmission and oil temp. (60C)  Battery temp. (30C)

0 0 -20 -10 0 10 20 30 40 -20 -10 0 10 20 30 40 Ambient temperature (C) Ambient temperature (C)

Cabin is sized, so that all vehicles have the similar hvac power consumption 148 Thermal Impact On Energy Consumption

PHEV EV

7 1.2 Cold Start Cold Start Hot Start Hot Start 1 Cold Start initial condition The same initial condition as HEV 6 0.8  the same as the ambient temp. Engine should be turned on for heating the cabin.  Not cold when ambient temp > 30C 0.6

0.4 5 Hot Start initial condition  Battery temperature (30C)

Fuel consumption (kg) 0.2  Cabin temperature (22C)

0 -20 -10 0 10 20 30 40 4 Ambient temperature (C)

2 3

1.5 There is a drastic change of the pattern by Electrical consumption (kWh) 2 the engine on threshold for Cold Start 1

1 0.5 Cold Start Hot Start

Electrical consumption (kWh) 0 0 -20 -10 0 10 20 30 40 -20 -10 0 10 20 30 40 Ambient temperature (C) Ambient temperature (C)

149 Real-World Scenario with VTMS

Cycle synthesizing Distribution of Vehicle Mass(Conventional Vehicle (Automatic)) 1500

Drive Cycles: HWFET Date: 18-Apr-2014

schcycle

50 1000 Real World 40

Driving Cycles 10

0 Number of appearance 500 0 200 400 600 800 1000 1200 1400 1600 Drive Cycles: UDDS User: nakim Time Copyright Program 1.0 Date: 18-Apr-2014

40 0

30 1420 1440 1460 1480 1500 1520 1540 1560 1580 Vehicle Mass(kg)

0 0 200 400 600 800 1000 1200 1400 Drive Cycles: US06 Time User: nakim Copyright Program 1.0Date: 18-Apr-2014Drive Cycles: SC03 Date: 18-Apr-2014

90 sch ycle 60 c sch ycle 80 c

50 Temp. 70

60 ASSUMPTIONS 40 50

30 40

30 Conditions 20 20

10 10

0 0 100 200 300 400 500 600 0 0 100 200 300 400 500 600 User: nakim Time Copyright Program 1.0 from multi-resources User: nakim Time Copyright Program 1.0

Conv. PHEV E-REV

AUTONOMIE on high performance computing HEV EV

Comparative studies

ANALYSIS Energy distribution by database tool 150 Validation Methodology Summary

. Accurate validation requires significant constant investment (Inc sensors, testing, modeling, control, calibration…) over a very long period of time . Matching component operating conditions over a wide rage of driving cycles and temperatures is critical . NCCP used by Argonne to quantify the validation quality (all core parameters should be >0.9) . Using validated individual vehicle models does not mean one can properly estimate future technology benefits – Generic algorithms sometimes cannot be used for multiple technologies (i.e. 6 speed vs 8 speed) – Calibration has to be carefully defined to take into account individual advanced technologies (I.e. light weighting, advanced engines…) as well as combined (i.e. adv ICE + BISG) – Drive quality (i.e. # ICE ON/OF, # shifting events, pedal tip-in, reserve torque…) – …

151 List of References

. N. Kim, J. Jeong, A. Rousseau, and H. Lohse-Busch, “Control Analysis and Thermal Model Development of PHEV”, SAE 2015-01- 1157, SAE World Congress, Detroit, April15 . N. Kim, A. Rousseau, and H. Lohse-Busch, “Advanced Automatic Transmission Model Validation Using Dynamometer Test Data”, SAE 2014-01-1778, SAE World Congress, Detroit, Apr14 . N. Kim, E. Rask and A. Rousseau, “Control Analysis under Different Driving Conditions for Peugeot 3008 Hybrid 4”, SAE 2014-01- 1818, SAE World Congress, Detroit, Apr14 . D. Lee, A. Rousseau, E. Rask, “Development and Validation of the Ford Focus BEV Vehicle Model”, 2014-01-1809, SAE World Congress, Detroit, Apr14 . N. Kim, A. Rousseau, D. Lee, and H. Lohse-Busch, “Thermal Model Development & Validation for the 2010 Toyota Prius”, 2014-01- 1784, SAE World Congress, Detroit, Apr14 . N. Kim, N. Kim, A. Rousseau, M. Duoba, “Validating Volt PHEV Model with Dynamometer Test Data using Autonomie”, SAE 2013- 01-1458, SAE World Congress, Detroit, Apr13 . N. Kim, A. Rousseau, E. Rask, “Autonomie Model Validation with Test Data for 2010 Toyota Prius”, SAE 2012-01-1040, SAE World Congress, Detroit, Apr12 . N. Kim, R. Carlson, F. Jehlik, A. Rousseau, “Tahoe HEV Model Development in PSAT”, SAE paper 2009-01-1307, SAE World Congress, Detroit (April 2009). . Cao, Q., Pagerit, S., Carlson, R., Rousseau, A., "PHEV hymotion Prius model validation and control improvements," 23rd International Electric Vehicle Symposium (EVS23), Anaheim, CA, (Dec. 2007). . Rousseau, A., Sharer, P., Pagerit, S., Duoba, M., "Integrating Data, Performing Quality Assurance, and Validating the Vehicle Model for the 2004 Prius Using PSAT," SAE paper 2006-01-0667, SAE World Congress, Detroit (April 2006). . Pasquier, M., Rousseau, A., Duoba, M, "Validating Simulation Tools for Vehicle System Studies Using Advanced Control and Testing Procedures," 18th International Electric Vehicle Symposium (EVS18), Berlin, Germany, 12 pgs. (October 2001). . Rousseau, A., Deville, B., Zini, G., Kern, J., Anderson, J., and Duoba, M., "Honda Insight Validation Using PSAT," Future Transportation Technology Conference, Costa-Mesa, 01–FTT49 (August 2001). . Rousseau, A., and Pasquier, M., "Validation of a Hybrid Modeling Software (PSAT) Using Its Extension for Prototyping (PSAT- PRO)," Global Powertrain Congress, Detroit (June 2001).

152