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Driving Assist System for Ecological Driving Using Model Predictive Control Presented by ー M.A.S. Kamal - Fukuoka IST Co-Authors ー Maskazu Mukai - Kyushu University Junichi Murata - Kyushu University Taketoshi Kawabe - Kyushu University SICE 9 th Annual Conference on Control System Hiroshima, March 05, 2009
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2 Outline Ecological Driving Needs of an Assist System Modeling of the System Controlling a Vehicle Simulation Results Conclusions and Future Work
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3 Ecological Driving Scarce of Oil, Global warming, Environment pollutions force us to an idea of Eco-Driving. Eco Driving Aims in: Increase in Mileage. Reduce Emissions of CO 2. Reduce noise pollution. Reduce accidents. Reduce impact on environment. Environment Friendly System
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4 Realization of Eco-Driving By Proper Vehicle maintenance, Route Selections, Traffic Signaling Systems, Driving Style Vehicle Control. A good Driving or Vehicle Control Style may save fuel consumption significantly.
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5 Eco-Vehicle Control Strategy Desired behavior includes: Minimize acceleration and braking. Smooth and higher acceleration at starting. Cruise at the best economy speed. Stop by coasting or little braking. Inevitable Situations: At urban traffic or in traffic congestion. A red signal. Braking preceding vehicle.
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6 Remarkable feature Eco Driving Tips based on Vehicle Engine fuel consumption characteristics. Qualitative Assistance without rigorous reasoning: “do not accelerate very hard” Limitations: No indication what should be the exact acceleration; no a quantitative value (e.g. 2.3 m/s 2 ). No analysis of the traffic trends, which greatly influenced on acceleration/braking on urban traffic. Conventional Eco-Strategy Solutions ?
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7 Concept of the Proposed Eco-Strategy Control the Vehicle by Anticipating future situations. A Driving Assist System can help a driver to attain or refine his Eco-Driving Skills. “Slow-and-go is always better than stop-and-go” Driving Assist System: Generation of optimal action using model predictive control. Assistance to the driver through human interface.
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8 Problem Formulation ホス ト Scope and Assumptions Single Lane. Only the immediate Preceding Car. Flat road, no slope. Longitudinal Motion Control. Preceding Vehicle will move at its current acceleration/deceleration. A Dummy vehicle stopped at red signal.
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9 Problem Formulation H P1P1 HV position HV Speed PV position PV Speed Input : Assumption : time dependent variable, at t, remains constant for a while.
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10 Model Predictive Control 0 1 N u0u0 u N-1 u1u1 ∆u∆u The problem is discretized in N step Prediction control Optimize [u 0,u 1,…u N-1 ] T to minimize the cost: HV PV Model Predictive Vehicle Control x(t) u(t) Model of Vehicle Control System Performance Index Optimization of Control Inputs Sensors
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11 Fuel Consumption Model A continuous function for approximation of fuel consumption is derived as:
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12 Performance Index Fuel Economy Safe Clearance Reference Speed* Dynamic weights w 1, w 2, w 3 focus their relative contextual merits.
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13 Optimization of control inputs Continuation method combined with Gneralized Minimum Residual Algorithm is used to derive the solution with a given initial value vector. Required condition in finding the optimum control inputs: The Hamiltonian Function is given by :
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14 Simulations Prediction Horizon: T= 10 sec, N=10, and h=1.0. Simulation step 0.1 sec. Control input constraint: -2.75 u 2.75 Time headway in car following h d =1.3sec. Parameters of a Ford Feista Car. Observation 1: Starting from Standstill with no Preceding Vehicle. Highest initial acceleration. Reaches full speed at about 10 sec. Continues cruising at the best economy speed.
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15 Simulations Observation 2: A typical starting situation HV PV 13 m Both Vehicle start from standstill. HV Started with higher acceleration. Control input is adjusted without any braking at closing range.
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16 Test Environment Test Environment Functions can be Extended through API Routine to control a car in a special way AIMSUNMicroscopic Traffic Simulator AIMSUN Microscopic Traffic Simulator
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17 Simulations Application Program Interface Routine Info of the Host and surrounding vehicles Control Interface Fix a car as Host Vehicle Model Predictive Control Interface Observation 3: Comparison with Gipps based method, a default control system in AIMSUN. Fuel consumption ml and Mileages km/l are monitored.
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18 Simulations 728 m 510 m 285 m Test Route 90 Vh/h 585 Vh/h 465 Vh/h 90 Vh/h A two lane road section. Lane changes are not controlled. A car is forced to stop at the beginning. Then it controlled by MPC and Gipps methods in separate run.
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19 Simulations Average fuel consumption: Gipps : 66.35 ml; MPC: 59.96 ml. Savings :6.39 ml or 10.65% Average Millage/ economy : Gipps : 9.84 km/l; MPC: 10.96 km/l. Improvement : 11.39% 728 m 510 m 285 m Test Route 90 Vh/h 585 Vh/h 465 Vh/h 90 Vh/h
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20 Conclusions A novel concept of Eco-Driving Assist System using Model predictive control has been presented. Vehicle control assistance is based on both Fuel consumption and traffic trends. The algorithm has been tested in AIMSUN, a traffic simulator with pseudo-realistic environment. In a single road section of 700m, about 11.39% improvement in Mileage.
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21 Future Work Refinement of the system to cope additional situations such as: Roads with up-down slopes. With known signal timing a priori. Experimenting on real road systems. Thank you
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