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  • 2010/5/19

    1

    Ecological Driver Assistance System

    (EDAS)Presented by

    M. A. S. Kamal

    Researcher, Fukuoka Industry, Science & Technology Foundation

    Email: kamal@lab-ist.jp;

    URL: http://terra.ees.kyushu-u.ac.jp/~kamal/

    MAS Kamal, Fukuoka IST

    ISIT 2010514

    Presentation Outline

    Background & Motivation Ecological Driver Assistance System Case Studies and Simulation

    Driving on a flat urban road with crowded traffic Driving on a freeway with up-down slope Driving on crowded road with up-down slope

    Conclusions

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    2

    Background

    &

    Motivation

    MAS Kamal, Fukuoka IST

    Emission From Cars

    MAS Kamal, Fukuoka IST

    Emission From CarsEmission From Cars

    Emission of CO2 from Transportation is one of the major sources of Environment Pollution and Global Warming.

    It is a demand of time to make Transportation

    Systems more Environmentally friendly

    Transportation

    PSIndustry

  • 2010/5/19

    3

    Fuel Efficient Vehicles

    MAS Kamal, Fukuoka IST

    Progress for efficient Vehicles have been continuing

    Realization of Eco-Driving

    Major Factors influencing consumptions

    Vehicle maintenance, Route Selections, Driving Style.

    Proper Driving or Vehicle Control Style may

    save fuel consumption significantly.

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    4

    Ecological Driving

    According to Recent Studies:

    Eco-Driving may save fuel

    By 10-25%.

    Various Approaches are introduced to Motivate a

    driver for Eco-Driving:

    Example:

    Driving Tips; ECO indicator; Fuel Ranking;

    MAS Kamal, Fukuoka IST

    Eco-Driving Assistance

    MAS Kamal, Fukuoka IST

    Driving TipsOn-board Assistance

    for Driver

    ECOECO indicator

    ECOECO

  • 2010/5/19

    5

    On-board Performance Indicator

    MAS Kamal, Fukuoka IST

    Driving Efficiency

    Support on Sloppy Road

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    6

    On-Board Assistance

    MAS Kamal, Fukuoka IST

    CARWINGS and ECO Pedal

    Limitation of Existing Assistance

    They only focus on fuel consumption characteristics of the Engine

    They do not analyze current road traffic situation They do not anticipate future traffic conditions They do not provide exact support

    MAS Kamal, Fukuoka IST

    Therefore, A more Comprehensive EcoTherefore, A more Comprehensive Eco--Driving System is necessary for Optimum Driving System is necessary for Optimum AchievementAchievement

  • 2010/5/19

    7

    EDAS

    Ecological Driver Assistance System

    MAS Kamal, Fukuoka IST

    Proposed EDAS

    An EDAS should Assist a Driver based

    on

    Fuel Consumption Characteristics of the Engine

    Road gradients, alignment and Lanes

    Situation of current traffic Anticipation of Future Situation Traffic Signal ahead Safety

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    8

    ITS Technologies

    MAS Kamal, Fukuoka IST

    Necessary InformationPosition, Speed, acceleration of surrounding vehicles.Status of Signal (or Timing)Road gradient and alignment

    Possible TechnologiesGPS, Camera, Laser, etcCommunication Among VehiclesCommunication with Infrastructure

    Algorithm ?

    Vehicle control problemVehicle control problem NonNon--linear linear Requires AnticipationAnticipation of traffic Discontinuous Events Constraints

    Model Predictive Control Model Predictive Control is the most suitable Options.

    Selection of Algorithm

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    9

    At t, using current state and initial inputs, states in the prediction horizon t to t+T is predicted.

    Inputs are optimized using performance index.

    Only the first input is used to control the vehilce.

    The process is repeated in next steps.

    Implmentation

    t0t tt1 t2 inputState

    Prediction Horizon TPrediction Horizon T

    t

    Model Predictive Control

    MAS Kamal, Fukuoka IST11

    Fuel Consumption Estimation

    C

    P

    +++

    ++++

    = )(1

    1

    012

    2

    012

    23

    3

    )(cvcvca

    bvbvbvb

    eg

    uV

    MAS Kamal, Fukuoka IST

    29.1%28.5%

    27%

    25.5%24%

    Best efficiency points

    Efficiency

    Engine power

    Efficiency () depends on torque and speed.

    For a CVT Vehicle, it is assumed, the gear ratio is maintained at maximum efficiency point for any output power.

    Therefore, the consumption [ml/s] can be obtained as: [ml/s]

    Where C: calorific value of gasoline.

    Using data of the engine Map, Fuel consumption is approximated as:

    vWhere, is velocity, and

    The sigmoid function indicates, no fuel consumption at input

    sin gva += &

    0u

  • 2010/5/19

    10

    Modeling

    )( maxmax uuu

    MAS Kamal, Fukuoka IST

    pAssumption time dependent variable, at t, remains constant for a while.

    Constraint :

    Modeling

    +==

    p

    x

    uxgxgxACm

    x

    puxfx vaD

    3

    1122

    2

    ))(sin()(cos2

    1),,(

    &

    MAS Kamal, Fukuoka IST

    g

    DC

    A

    um

    Air density

    Drag Coefficient

    Frontal Area

    Slope angel

    Rolling Coefficient

    Gravitational forceControl inputVehicle Mass

  • 2010/5/19

    11

    Performance Index

    ( )+

    =Tt

    tdtpuxLJ ,,min

    MAS Kamal, Fukuoka IST

    Fuel Economy

    Cost for acceleration/brakingand road gradient

    Desired Speed*

    Dynamic weights w1, w2, w3, w4 focus their relative contextual merits.

    ( )( ) ( )( ) .

    2

    1

    201324

    213

    2222021

    222

    323

    2

    1

    VdR

    vaD

    lxxxhwvxw

    gxACm

    uwbxbxbxbx

    wL

    ++

    ++++=

    Safe Clearance

    Optimization Problem

    ),(),(),(:),,,( uxCuxfuxLuxH TT ++=

    MAS Kamal, Fukuoka IST

    .0

    ),(

    ),,,(

    :

    ),(

    ),,,(

    :),,(

    11

    111

    00

    0010

    =

    =

    NN

    NNNNTu

    Tu

    uxC

    uxH

    uxC

    uxH

    txUF

    Condition for Optimal solution with given initial values

    Continuation/Generalized Minimum Residual (C/GMRES)Continuation/Generalized Minimum Residual (C/GMRES) [5] is used to finds the solutions of the above.

    [9] T. Ohtsuka, A Continuation/GMRES method for fast computation of nonlinear [9] T. Ohtsuka, A Continuation/GMRES method for fast computation of nonlinear receding horizon control, Automatica 40 (2004) 563receding horizon control, Automatica 40 (2004) 563--574.574.

    Hamiltonian:

  • 2010/5/19

    12

    Flow of Vehicle Control Process

    MAS Kamal, Fukuoka IST

    Measure states of the vehicle,

    At time t=kh

    Using the model of vehicle dynamics, Information of road slope, Performance index and Constraint,

    For a prediction horizon T, from t=kh to, t=kh+T

    Optimize the current and future vehicle control inputs using C/GMRES

    Implement best current input to control the vehicle

    k=k+1

    Test Environment

    MAS Kamal, Fukuoka IST

    Functions can be Extended through API Routine to control a car in a special way

    AIMSUNAIMSUN Microscopic Traffic SimulatorMicroscopic Traffic Simulator

    Vehicles run as per Gipps model

  • 2010/5/19

    13

    AIMSUN NG

    Host Vehicle

    Model Predictive

    Control

    Other Traffic

    Interactions

    API

    Control input

    Measurement

    EDASTraffic Signal

    Interactions

    Interactions

    Simulation Interface

    MAS Kamal, Fukuoka IST

    Case I

    Eco-Driving on Flat Urban Road with Traffic

    Signal at Junctions

    MAS Kamal, Fukuoka IST

  • 2010/5/19

    14

    Test Route & Network Setting

    MAS Kamal, Fukuoka IST

    S1 S2 S3 S12 S13

    J1 J2J13

    S14

    J12J11

    Test Route 4.00 km

    Test Route 4.0 km14 sections13 junctions2-3 Lanes90 sec Signal cycle50 sec Green Timing

    Vehicle flow3000+ vehicle/hourVehicle TypesTruck, Car, and Taxi

    Simulations

    MAS Kamal, Fukuoka IST

    =1200[kg]

    =1.184[kg/m3]

    =0.012

    =34.5e+6[J/l]

    =0.7[PS] =514.85[W]

    =9.8[m/s2]

    =2.5[m2]

    =0.32

    Modeling Parameters

    M

    DC

    A

    g

    C

    cP

    8

    =12[s]

    =50[km/h]

    =110

    =7.7

    =0.39

    = 0.1[s]

    = 2.75[m/s2]

    Algorithms Setting

    Tdv

    0w

    maxu

    h

    1w

    2w

    Constraint is converted into inequality constraint as:

    ( ) 02

    1),( 2max

    22

    2 =+= uuuuxC

  • 2010/5/19

    15

    04.

    Signal status

    0 60 120 180 240 300 360 420 480 5400

    4

    8

    12

    16

    .

    Road sections

    0 60 120 180 240 300 360 420 480 540

    20

    40

    60

    NV

    No. of vehicles on the road section

    0 60 120 180 240 300 360 420 480 5400

    20

    40

    60

    x4 [

    km

    /h]

    Velocity of Preceding Vehicle

    0 60 120 180 240 300 360 420 480 5400

    20

    40

    60

    80

    x3-x

    1 [m

    ]

    Range clearance

    0

    0.8.

    Change of Preceding Vehicles

    0 60 120 180 240 300 360 420 480 5400

    20

    40

    60

    x2 [

    km

    /h]

    Velocity of Host Car

    0 60 120 180 240 300 360 420 480 540-4