Energy-Efficient Adaptive Cruise Control for Electric Connected and Autonomous Vehicles

USDOT T3e Webinar

Hosted by Dr. Jing DongPresented by Liang Hu and Chaoru Lu

Focal point for transportation research

49 faculty, 257 students from 11 departments across ISU

Collaborative culture and structure

Innovation in streaming data, analytics, and decision support tools 2

Outline

Introduction

Fuel and Energy consumption model

Adaptive cruise control

Simulation

Conclusion

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Introduction

In the United States, the fuel economy of personal vehicles is estimated as 24.7 miles per gallon (mpg) in 2016 and is projected to be 54.5 mpg in 2025.

Battery electric vehicle• High energy efficiency• Zero tailpipe emissions

Driver behavior could also affect the fuel economy of vehicles by 10~40%

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Introduction

For ICEV Smoother deceleration and acceleration rate leads

to better fuel efficiency (Wu et al. 2015). ACC-equipped vehicle decreased the emissions

(Ioannou and Stefanovic 2005)

For BEV Ability to recover energy while braking using a

regenerative braking system (Fiori et al. 2016)

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Introduction

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Car-following modelsLead vehicle

Velocity & Acceleration

Fuel/Energy consumption models

On-road fuel/energy

economy data

Energy efficiency

#1 #2 #3 #16

Energy Consumption Models

ICEV fuel consumption model• a linear regression model, taking speed &

acceleration as predictors• there is an optimal speed range for fuel consumption

BEV energy consumption model• braking regenerates electricity• energy consumption increases with speed

Different ACCs for ICEVs and BEVs are needed

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ICEV Fuel Consumption Model

Calibrated the VT-Micro model (Ahn et al., 2002), which uses speed (𝑣𝑣) & acceleration (𝑎𝑎) to estimate vehicle fuel consumption

Used on-board diagnostics II (OBD-II) data, e.g. speed, acceleration, and fuel consumption rate, collected from a gasoline car for a year

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ln𝐹𝐹𝐹𝐹 = �𝑖𝑖=0

3�

𝑗𝑗=0

3𝐿𝐿𝑖𝑖,𝑗𝑗𝑣𝑣𝑖𝑖𝑎𝑎𝑗𝑗 , 𝑖𝑖𝑖𝑖 𝑎𝑎 ≥ 0

ln𝐹𝐹𝐹𝐹 = �𝑖𝑖=0

3�

𝑗𝑗=0

3𝑀𝑀𝑖𝑖,𝑗𝑗𝑣𝑣𝑖𝑖𝑎𝑎𝑗𝑗 , 𝑖𝑖𝑖𝑖 𝑎𝑎 < 0

ICEV Fuel Consumption Model Validated the model on the trip basis Compared the actual trip fuel consumption with the

estimated trip fuel consumption

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BEV Energy Consumption Model The regenerative braking feature of electric motors:

kinetic energy converts to electricity during braking Vehicle specific power (VSP) < 0, when regenerative

braking takes effect

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(Fiori et al., 2016)

If maintain the deceleration at high energy efficiency range for a long time period, BEVs are likely more energy efficient.

BEV Energy Consumption Model EV energy consumption is more sensitive to ambient

temperature (Dong and Hu, 2017; Greene et al., 2017) Ambient temperature influences auxiliaries, e.g. air

conditioning; Auxiliaries consume considerable electricity There is an optimal temperature for energy consumption,

e.g., 20 °C (or 68 F)

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BEV Energy Consumption Model Use VSP and auxiliary power to estimate energy

consumption rate (ECR)𝐸𝐸𝐹𝐹𝐸𝐸 = ℎ0 + ℎ1𝑉𝑉𝑉𝑉𝑉𝑉 + ℎ2𝑉𝑉𝑎𝑎𝑎𝑎𝑎𝑎

Result in better estimation than other models, e.g. Yang et al., 2014 and Yao et al., 2014

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Energy Consumption Models MAPE RMSE

Proposed model 13.3% 0.296 kWh

Yao’s model 19.5% 0.495 kWh

Yang et al.’s model 16.7% 0.511 kWh

Car-Following Models

Human-driver models• Newell Model• Gipps Model• Optimal Velocity Model• Intelligent Driver Model

Adaptive cruise control• Adaptive cruise control based on IDM (IDM-

ACC)• Nissan Model

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Adaptive Cruise Control

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Source: https://res.cloudinary.com/engineering-com/image/upload/w_640,h_640,c_limit/Driverless_Car_Tech_2_zabzmt.jpg

Proposed Adaptive Cruise Control

Assumptions:

Only CAVs are capable of communicating with other CAVs through V2V communication

Ignore computational, sensor, and communication delays for CAVs

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Platoon with Mixed CAV and Human-Driven Vehicles

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#1 #2 #M #2#1 #1

Vehicle Set 3Vehicle Set 1 Vehicle Set 2

Adaptive Cruise Control

Gasoline-CAVEcological Smart Driver Model (Eco-SDM)

e-CAVEnergy-Efficient Electric Driving Model (E3DM)

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String Stability (Acceleration profiles)

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a) String stable b) Unstable string

Reference: Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143-163.

Comparison of ACCs

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Nissan IDM

Eco-SDM E3DM

Simulation

traffic stream with 1000 vehicles

a single lane

the platoon size ranges from 14 to 81 vehicles

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Lead vehicle follows a driving cycle

Urban Dynamometer Driving Schedule (UDDS)• city test• distance: 12 km• length: 1369 sec• average speed: 31.5 km/h

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0

20

40

60

80

100

0 200 400 600 800 1000 1200

Spe

ed (k

m/h

)

Time (s)

Scenario 1: All Gasoline-CAVs

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0.92

0.96

1

1.04

1.08

1.12

1.16

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Fuel

con

sum

ptio

n (L

)

Position of the following vehicles

Manual IDM-ACC Nissan-ACC Eco-SDM

Scenario 2: One CAV at different position

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-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Flee

t fue

l con

sum

ptio

n ch

ange

Location of the CAV

IDM-ACC Nissan-ACC Eco-SDM

Scenario 3: Different % of CAVs

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-16%-14%-12%-10%-8%-6%-4%-2%0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Flee

t fue

l con

sum

ptio

n ch

ange

Market penetration of CAVs

Eco-SDM Nissan-ACC IDM-ACC

Scenario 4: All e-CAVs

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1.7

1.75

1.8

1.85

1.9

1.95

2

2.05

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Ener

gy c

onsu

mpt

ion

(kW

h)

Position of the following vehicles

Manual IDM-ACC Nissan-ACC E³DM

Scenario 5: One e-CAV at different position

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-2.5%-2.0%-1.5%-1.0%-0.5%0.0%0.5%1.0%1.5%2.0%

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Flee

t ene

rgy

cons

umpt

ion

chan

ge

Position of the CAV

IDM-ACC Nissan-ACC E³DM

Scenario 6: Different % of e-CAVs

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-8%-7%-6%-5%-4%-3%-2%-1%0%1%

0% 20% 40% 60% 80% 100%

Ener

gy c

onsu

mpt

ion

chan

ge

Market penetration of e-CAVs

IDM-ACC Nissan-ACC E³DM

Conclusion

Gasoline vehicles• a CAV fleet consumes less fuel than a manual

vehicle fleet;• 1 CAV at the front of a mixed fleet has larger

impacts on the fleet fuel efficiency;• higher % of CAV leads to more fuel savings, but

the marginal benefit diminishes after about 30%.

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Conclusion

Electric vehicles• a E3DM-equipped CAV fleet consumes less

energy than a manual vehicle fleet;• 1 e-CAV at the front of a mixed fleet has larger

impacts on the energy efficiency;• The higher % of e-CAVs may not result in better

energy efficiency of the entire fleet.• With E3DM, the highest fleet-level energy

efficiency is achieved when the market penetration of e-CAVs is 20%.

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Thank you

Corresponding author:Dr. Jing Dong

jingdong@iastate.edu

ReferenceYao, E., Wang, M., Song, Y. and Zhang, Y., 2014. Estimating energy consumption on the basis of microscopic driving parameters for electric vehicles. Transportation Research Record: Journal of the Transportation Research Board, (2454), pp.84-91.Yang, S.C., Li, M., Lin, Y. and Tang, T.Q., 2014. Electric vehicle’s electricity consumption on a road with different slope. Physica A: Statistical Mechanics and its Applications, 402, pp.41-48.Ahn, K., Rakha, H., Trani, A. and Van Aerde, M., 2002. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of transportation engineering, 128(2), pp.182-190.Dong, J. and Hu, L., 2017. Investigation of the Link Between Macroscopic Traffic Flow Characteristics and Individual Vehicle Fuel Consumption.Greene, D.L., Liu, J., Khattak, A.J., Wali, B., Hopson, J.L. and Goeltz, R., 2017. How does on-road fuel economy vary with vehicle cumulative mileage and daily use?. Transportation Research Part D: Transport and Environment, 55, pp.142-161.Fiori, C., Ahn, K. and Rakha, H.A., 2016. Power-based electric vehicle energy consumption model: Model development and validation. Applied Energy, 168, pp.257-268.S. Akhegaonkar, L. Nouvelière, S. Glaser, and F. Holzmann, “Smart and Green ACC : Energy and Safety Optimization Strategies for EVs,” IEEE Trans. Syst. Man, Cybern. Syst., vol. PP, no. 99, pp. 1–12, 2016.Z. Wang, X. M. Chen, Y. Ouyang, and M. Li, “Emission Mitigation via Longitudinal Control of Intelligent Vehicles in a Congested Platoon,” Comput. Civ. Infrastruct. Eng., vol. 30, no. 6, pp. 490–506, 2015

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ReferenceTalebpour and H. S. Mahmassani, “Influence of connected and autonomous vehicles on traffic flow stability and throughput,” Transp. Res. Part C Emerg. Technol., vol. 71, pp. 143–163, 2016P. A. Ioannou and M. Stefanovic, “Evaluation of ACC vehicles in mixed traffic: Lane change effects and sensitivity analysis,” IEEE Trans. Intell. Transp. Syst., vol. 6, no. 1, pp. 79–89, 2005.Wu, X., X. He, G. Yu, A. Harmandayan, and Y. Wang. Energy-Optimal Speed Control for Electric Vehicles on Signalized Arterials. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 5, pp. 2786–2796, 2015S. Park, H. A. Rakha, K. Ahn, K. Moran, B. Saerens, and E. Van Den Bulck, “Predictive Ecocruise Control System Model Logic and Preliminary Testing,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2270, pp. 113–123, 2012.K. Ahn, H. A. Rakha, and S. Park, “Ecodrive Application Algorithmic Development and Preliminary Testing,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2341, pp. 1–11, 2013.C. Lu, L. Hu, and J. Dong, “Ecological Adaptive Cruise Control in a Traffic Stream with Mixed Autonomous and Manually Driven Vehicles.” Presented at Transportation Research Board 97th Annual Meeting, January 7-11, 2018-Washington, D.C.U.S Environmental Protection Agency. Dynamometer Drive Schedules. https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules. Accessed Jul. 28, 2017.

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