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Transcript of Energy-Efficient Adaptive Cruise Control for Electric Connected · PDF file Energy-Efficient...

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

    USDOT T3e Webinar

    Hosted by Dr. Jing Dong Presented 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

    3

  • 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%

    4

  • 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)

    5

  • Introduction

    6

    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

    7

  • 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

    8

    ln𝐹𝐹𝐹𝐹 = � 𝑖𝑖=0

    3 �

    𝑗𝑗=0

    3 𝐿𝐿𝑖𝑖,𝑗𝑗𝑣𝑣𝑖𝑖𝑎𝑎𝑗𝑗 , 𝑖𝑖𝑖𝑖 𝑎𝑎 ≥ 0

    ln𝐹𝐹𝐹𝐹 = � 𝑖𝑖=0

    3 �

    𝑗𝑗=0

    3 𝑀𝑀𝑖𝑖,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

    9

  • 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

    10

    (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)

    11

  • 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

    12

    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

    13

  • Adaptive Cruise Control

    14

    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

    15

  • Platoon with Mixed CAV and Human- Driven Vehicles

    16

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

    Vehicle Set 3Vehicle Set 1 Vehicle Set 2

  • Adaptive Cruise Control

     Gasoline-CAV Ecological Smart Driver Model (Eco-SDM)

     e-CAV Energy-Efficient Electric Driving Model (E3DM)

    17

  • String Stability (Acceleration profiles)

    18

    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

    19

    Nissan IDM

    Eco-SDM E3DM

  • Simulation

     traffic stream with 1000 vehicles

     a single lane

     the platoon size ranges from 14 to 81 vehicles

    20

  • 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

    21

    0

    20

    40

    60

    80

    100

    0 200 400 600 800 1000 1200

    S pe

    ed (k

    m /h

    )

    Time (s)

  • Scenario 1: All Gasoline-CAVs

    22

    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

    Fu el

    c on

    su m

    pt io

    n (L

    )

    Position of the following vehicles

    Manual IDM-ACC Nissan-ACC Eco-SDM

  • Scenario 2: One CAV at different position

    23

    -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 16Fl ee

    t f ue

    l c on

    su m

    pt io

    n ch

    an ge

    Location of the CAV

    IDM-ACC Nissan-ACC Eco-SDM

  • Scenario 3: Different % of CAVs

    24

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

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

    t f ue

    l c on

    su m

    pt io

    n ch

    an ge

    Market penetration of CAVs

    Eco-SDM Nissan-ACC IDM-ACC

  • Scenario 4: All e-CAVs

    25

    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

    En er

    gy c

    on su

    m pt

    io n

    (k W

    h)

    Position of the following vehicles

    Manual IDM-ACC Nissan-ACC E³DM

  • Scenario 5: One e-CAV at different position

    26

    -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

    Fl ee

    t e ne

    rg y

    co ns

    um pt

    io n

    ch an

    ge

    Position of the CAV

    IDM-ACC Nissan-ACC E³DM

  • Scenario 6: Different % of e-CAVs

    27

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

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

    En er

    gy c

    on su

    m pt

    io n

    ch an

    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%.

    28

  • 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%.

    29

  • Thank you

    Corresponding author: Dr. Jing Dong

    [email protected]

  • Reference Yao, 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