Presentation on theme: "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."— Presentation transcript:
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
2 Outline Ecological Driving Needs of an Assist System Modeling of the System Controlling a Vehicle Simulation Results Conclusions and Future Work
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
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.
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.
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 ?
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.
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.
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.
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
11 Fuel Consumption Model A continuous function for approximation of fuel consumption is derived as:
12 Performance Index Fuel Economy Safe Clearance Reference Speed* Dynamic weights w 1, w 2, w 3 focus their relative contextual merits.
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 :
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.
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.
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
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.
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.
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
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.
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