hardware-in-the-loop (hil) testbed for evaluating ... · fpirc 2015 1 hardware-in-the-loop (hil)...

Department of Mechanical EngineeringUniversity of Minnesota

Date : 10/16/15 (Friday)FPIRC 2015

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Hardware-In-the-Loop (HIL) Testbedfor Evaluating Connected Vehicle Applications

Project Members :Mohd Azrin Mohd Zulkefli

Pratik MukherjeeYunli Shao

Prof. Zongxuan Sun

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Presentation Outline

• Introduction

• Powertrain Research Platform

• Hardware-In-the-Loop (HIL) Testbed

Introduction

Test Results with Simple Traffic Network

Test Results with Complex Traffic Network

• Conclusions

• Future Directions

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• Introduction



Introduction



• Conclusions


4

Background

Travel direction

Intelligent Vehicles

Detectors

Traffic Center

Road Site Unit

• IVC & VII are introduced to improve safety and mobility.

• Information exchange between vehicles are supported by :

• DSRC communication standards [1] :

• IEEE 802.11p – Wireless Access in Vehicular Environments (WAVE).• IEEE 1609 – Security, Network Service & Multi Channel Operation.• SAE J2735 – Message Set Dictionary for Basic Safety Message (BSM).

• FCC Allocate 5.85–5.925 GHz band for DSRC communication.

Glossary IVC : Inter Vehicle CommunicationVII : Vehicle-Infrastructure-IntegrationDSRC : Dedicated Short Range Communication

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Motivation

• Evaluation of connected-vehicle application in real traffic is difficult and time consuming with safety and legal concerns.

• Inaccurate fuel and emission maps requires the use of real engine.• Microscopic traffic simulation can mimic actual traffic if calibrated and driven by

real traffic inputs.

Previous Methods and Challenges

• Inaccurate fuel and emission maps in simulations [2].

• Difficulties and space requirements to instrument on-road vehicles with big measurement devices [3-4].

• Safety and legal concerns to test connected vehicle in real traffic [5].

Proposed Research Previous Methods Deficiencies HIL Testbed

Development of HiLS for EMS Evaluation

Inaccurate fuel-use and emission maps. HiLS measures real engine fuel-use and emissions.

Difficulties & space requirements to instrument on-road vehicles.

Testing done in lab & engine is easily instrumented and replaced.

Safety and legal concerns.Realistic simulated traffic does not pose safety or

legal concerns.

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• Introduction



Introduction



• Conclusions


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Powertrain Research Platform

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Working PrinciplesHardware Components

Main Dynamics

Valve opening 𝑤𝐻𝑆 is controlled to track 𝜔𝑒

and engine throttle angle is used to control 𝑇𝑒

𝜔𝑒 =𝑇𝑒𝐽𝑒−𝑇𝑝𝑢𝑚𝑝

𝐽𝑒−𝑇𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛

𝐽𝑒

=𝑇𝑒𝐽𝑒−

𝐷𝑀2𝜋𝐽𝑒

𝑃𝑜𝑢𝑡 +𝐷𝑀2𝜋𝐽𝑒

𝑃𝑖𝑛 −𝑇𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛

𝐽𝑒

𝑃𝑜𝑢𝑡 =𝛽𝑒𝑉𝑡2

𝑞𝑖𝑛 −𝛽𝑒𝑉𝑡2

𝑞𝑜𝑢𝑡 −𝛽𝑒𝑉𝑡2

𝑞𝑙𝑒𝑎𝑘

=𝛽𝑒𝐷𝑀2𝜋𝑉𝑡2

𝜔𝑒 −𝛽𝑒𝐶𝑑𝐴𝐻𝑆

𝑉𝑡2

2

𝜌𝑃𝑜𝑢𝑡 𝑤𝐻𝑆

−𝛽𝑒𝑉𝑡2

𝑞𝑙𝑒𝑎𝑘Wang, Y., Sun, Z., and Stelson, K.A., “Modeling, Control, and Experimental Validation of a TransientHydrostatic Dynamometer,” Control Systems Technology, IEEE Transactions on , v19, n6, pp. 1578-1586,Nov 2011.

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Control Architecture

Three-level control architecture :

1) High Level : EMS to optimize reference (𝑇𝑒 , 𝜔𝑒).

2) Middle Level : Virtual Powertrain Model – calculate desired engine load.

3) Low Level : Dynamometer control - track desired engine load.

Wang, Y., Sun, Z., and Stelson, K.A., “Nonlinear Tracking Control of a TransientHydrostatic Dynamometer for Hybrid Powertrain Research,” Proceedings of the ASME2010 Dynamic Systems and Control Conference, pp. 61-68, September 12-15 2010.

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• Introduction



Introduction



• Conclusions


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Overview of Hardware in the Loop System (HiLS)

HiLS Component Purpose Ownership

Powertrain Research Platform Controls load to real engine for fuel & emission measurements. U of MN

Microscopic Traffic Sim (VISSIM) Simulate traffic & provide speed trajectory to Powertrain Res. Platform. BOTH

Connected Vehicle Controller Controls vehicles in VISSIM for connected vehicle applications. U of MN

SMART-SIGNAL Provide real traffic input to VISSIM simulation. U of MI

Signal Controller Cabinet Controls a virtual intersection in VISSIM. U of MI

Signal Controller

Cabinet

Controller Middleware

SMART-SIGNAL

Field Data Processer

Connected Veh Controller

Connected VehMiddleware

Powertrain Research Platform

Powertrain Middleware

Zoom-In intersection

Microscopic Traffic Simulator (VISSIM)

Middlewares to connect the different HiLS components

Physical Components

Software/Hardware Components

Glossary

• Desired-veh-speed is extracted from VISSIM, while actual-veh-speed is calculated from the powertrain dynamics using actual engine speed and torque.

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• Solid arrows indicate local communication.• Dashed arrows indicate remote communication via C# Socket Programming.

VISSIM COM

VISSIM Traffic

Simulator

Dyno(Hardware)

Desired/Optimized 𝑇𝑒, 𝜔𝑒

Control/Optimization

Traffic

Control of Powertrain model &

Optimization

Actual 𝑇𝑒 , 𝜔𝑒

MATLAB-Simulink

Powertrain Research Platform Remote computer running VISSIM

Pow

ertr

ain

CO

M

VISSIM Input

Powertrain Dynamics

(Simulation) Actual Veh Speed

Desired Veh

Speed

Desired Veh

Speed

Des Veh

Speed

VISSIM Input

HIL Testbed Powertrain Middleware

• Currently, one-way communication is implemented for testing before implementing two-way communication.

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• Solid arrows indicate local communication.• Dashed arrows indicate remote communication via C# Socket Programming.

VISSIM COM

VISSIM Traffic

Simulator

Dyno(Hardware)



Traffic


Optimization


MATLAB-Simulink


Pow

ertr

ain

CO

MPowertrain Dynamics

(Simulation)

Desired Veh

Speed

Desired Veh

Speed

Desired Veh

Speed

HIL Testbed Powertrain Middleware

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• Introduction



Introduction



• Conclusions


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• 1700 meters long with 7 traffic-lights (fixed-timing) at every 200m between 300m and 1500m.

• Vehicle speed data was transferred from a computer running VISSIM to powertrain research testbed remotely at every 0.2 seconds.

• Vehicle with no-stop, 1-stop, 2-stops and 3-stops were identified before tests were conducted for each vehicle.

• HIL Video

300m

1500m

Test Setup : Traffic Network

https://www.youtube.com/watch?v=Bkr7gWk7dwk&feature=youtu.be

Test 1 : No Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

Test 2 : 1-Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

Test 3 : 2-Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

Test 4 : 3-Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

20

0.0

4

0.0

5

0.0

6

0.0

90.2

3

0.2

7

0.3

4

0.4

3

1.0

1 1.2

8

1.6

0

2.0

9

1.1

7 1.3

6 1.6

4 1.9

5

2.0

3

2.3

7

2.8

5

3.4

3

18

3.4

2

18

4.7

9

24

2.8

1

32

9.7

5

0

50

100

150

200

250

300

350

400

0

0.5

1

1.5

2

2.5

3

3.5

4

no-stop 1-stop 2-stops 3-stops

Gra

ms

of

CO

2

Gra

ms

of

HC

HO

, NO

2, C

O, N

O a

nd

NO

X

HCHO NO2 CO

NO NOx CO2

Test Results : Fuel Consumption & Emissions

Fuel Consumption Emissions5

5.2

6

57

.97

76

.59

10

2.3

0

20

40

60

80

100

120

Gra

ms

of

die

sel f

uel

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• Introduction



Introduction



• Conclusions


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Test Setup : Traffic Network

• 3.5km stretch on Medical Drive between BabcockRoad & Fredericksburg Road in San Antonio, TX.

• Traffic Simulation Complexities :• Multiple vehicle types : cars, busses & trucks.• Multiple lanes with lane-changing.• Varying speed limits for roads & lanes.• 7 signalized & 6 non-signalized intersections.• Reduced vehicle speeds, right-of-ways &

pedestrian crossings at intersections.• Stop signs at non-signalized intersections.• Public transportation stops.

• Two vehicles with 2-stops & 3-stops traveling thesame route are selected for test cases.

Test 1 : 2-Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

Test 2 : 3-Stop

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Veh

icle

Dyn

amic

sFu

el U

se &

Em

issi

on

s

Test Results : Fuel Consumption & Emissions

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0.1

1

0.1

20.5

6

0.6

9

2.6

9 3.2

4

2.5

7

3.4

5

4.5

1

6.0

0

35

3.5

8

43

7.6

2

0

50

100

150

200

250

300

350

400

450

500

0

1

2

3

4

5

6

7

2-stops 3-stops

Gra

ms

of

CO

2

Gra

ms

of

HC

HO

, NO

2, C

O, N

O a

nd

NO

X

HCHO NO2 CO

NO NOx CO2

Fuel Consumption Emissions

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2.2

13

8.4

0

20

40

60

80

100

120

140

160

2-stops 3-stops

Gra

ms

of

die

sel f

uel

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• Introduction



Introduction



• Conclusions


27

Conclusions

• Vehicle data from remote traffic simulation extracted and transferred in real-time tothe powertrain research platform over the internet through COM interfaces andsocket programming.

• Different vehicle speed profiles accurately tracked by powertrain research platformto represent the target vehicle in VISSIM simulation.

• Simple powertrain optimization employed in powertrain research platform tooptimize engine operating points in real-time, which can be extended to complexoptimization methods utilizing traffic data in the future.

• Real fuel and emissions measurements are recorded, which can be used to evaluateoptimization methods for connected vehicle applications in the future.

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• Introduction



Introduction



• Conclusions


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Future Directions

• Upgrade one-directional communication to two-directional to reflect actual vehiclespeed from powertrain research platform in VISSIM simulation.

• Build connected vehicle controller and middleware to process traffic data fromVISSIM simulation.

• Calibrate VISSIM traffic simulation with real-traffic from data collected oninstrumented vehicle and highway (cooperation with MnDOT).

• Support the benefits evaluations of connected vehicle technologies from accuratefuel consumption and emissions measurements on the testbed.

• Support benefit assessments of several USDOT’s connected vehicle applications :Eco-Approach, CACC, Eco-Driving and Speed Harmonization.

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1. Kenney, J.B., “Dedicated Short-Range Communications (DSRC) Standards in the United States,” Proceedings of the IEEE, v99, n7, pp. 1162-1182, July 2011.

2. Filipi, Z., Fathy, H., Hagena, J., Knafl, A. et al., “Engine-in-the-Loop Testing for Evaluating Hybrid Propulsion Concepts and Transient Emissions - HMMWV Case Study,” SAE Technical Paper 2006-01-0443, 2006.

3. Duoba, M., Ng, H., and Larsen, R., “Characterization and Comparison of Two Hybrid Electric Vehicles (HEVs) - Honda Insight and Toyota Prius,” SAE Technical Paper 2001-01-1335, 2001.

4. Hu, H., Zou, Z., and Yang, H., “On-board Measurements of City Buses with Hybrid Electric Powertrain, Conventional Diesel and LPG Engines,” SAE Technical Paper 2009-01-2719, 2009.

5. Hall, R.W. and Tsao, H.S.J., “Automated Highway System Deployment: A Preliminary Assessment of Uncertainties,” Automated Highway Systems, pp. 325-334, 1997.

References

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Backup Slide 1 : CACC Controller

𝑥𝑑 = 𝑘𝑝(𝑥𝑝−𝑥𝑑_𝑎 − 𝑑0) + 𝑘𝑑( 𝑥𝑝 − 𝑥𝑑_𝑎) + 𝑥𝑝

By choosing appropriate 𝑘𝑝 and 𝑘𝑑 gains, the error dynamics will stabilize to zero.

Therefore, the distance between preceding and following vehicle can be kept constant.

𝑥𝑝 = Preceding-vehicle speed (from VISSIM)

𝑥𝑝 = Preceding-vehicle acceleration (from VISSIM)

𝑥𝑝 = Preceding-vehicle distance travelled (from VISSIM)

𝑥𝑑_𝑎 = Actual follower-vehicle speed (from HIL)𝑥𝑑_𝑎 = Actual follower-vehicle distance travelled (from HIL) 𝑥𝑑 = Desired follower-vehicle speed (CACC controller output)𝑑0 = Desired spacing (constant)

𝑥𝑑 = 𝑥𝑑 𝑑𝑡

• Vehicle speed extracted from VISSIM is assumed to be lead vehicle speed• CACC controller use this information to calculate follower vehicle speed using fixed-

spacing car-following policy.• Dyno acts as the follower car.

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Backup Slide 2 : HIL Testbed with Embedded CACC Controller

VISSIM COM

VISSIM Traffic

Simulator

Dyno(Hardware)




Optimization


MATLAB-Simulink


Pow

ertr

ain

CO

M

Powertrain Dynamics

(Simulation)

LeadVeh

Speed

LeadVeh

Speed

FollowerVeh

Speed

CACC Controller

LeadVeh

Speed

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Test Results• Follower vehicle enters traffic network 20s after lead vehicle enters• Follower vehicle catches-up with lead and maintain 3-meters (10 feet) spacing

Zoom In between 80s - 200s

Results with CACC Controller (Different Rule-Based EMS)

34

Veh

icle

Dyn

amic

sEm

issi

on

s

Fuel or Emission Gas

Total

Fuel

Consumed (g)

58.47

NOx (g)2.7719

NO (g)1.5775

NO2 (g)0.3505

HCHO (g)0.0669

CO (g)1.7031

CO2 (g)182.6519

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Backup 3 : Rule Based Method

𝑃𝑤ℎ𝑒𝑒𝑙 = 𝑇𝑣𝜔𝑣 𝑃𝑆𝑂𝐶 = 𝑆𝑂𝐶𝑡𝑎𝑟𝑔𝑒𝑡 − 𝑆𝑂𝐶 𝐾𝑓𝑖𝑡

𝑃𝑟𝑒𝑞 = 𝑃𝑤ℎ𝑒𝑒𝑙 + 𝑃𝑆𝑂𝐶

𝑇𝑒 =𝑃𝑒𝜔𝑒

=𝑃𝑟𝑒𝑞

𝜔𝑒

• Iterate 𝜔𝑒 and select minimum 𝑚𝑓𝑢𝑒𝑙 𝑇𝑒 , 𝜔𝑒 .

• A Rule-Based map can be iterated offline at different values of 𝑃𝑟𝑒𝑞 to create a mapped correlation

between minimum 𝑚𝑓𝑢𝑒𝑙 𝑇𝑒 , 𝜔𝑒 and 𝑃𝑟𝑒𝑞 (see Figure above)

Rule-Based Map for John Deere Engine

hardware-in-the-loop (hil) testbed for evaluating ... · fpirc 2015 1 hardware-in-the-loop (hil)...

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