Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion

IEEE Transactions on Vehicular Technology

Volumn 58, Issue 9, 2009, Pages 4741-4756

  Park, Jungme ^a   Chen, Zhihang ^a   Kiliaris, Leonidas ^a   Kuang, Ming L ^b   Masrur, M Abul ^c   Phillips, Anthony M ^b   Murphey, Yi Lu ^a  

^a Department of Electrical and Computer Engineering   (United States)

^b FORD MOTOR COMPANY   (United States)

^c U S ARMY TARDEC   (United States)

Author keywords

Fuel economy; Machine learning; Road type and traffic congestion (RT TC) level prediction; Vehicle power management

Indexed keywords

CURRENT DRIVING; DRIVE CYCLES; DRIVING STYLES; FUEL EFFICIENCY; INTELLIGENT POWER; INTELLIGENT VEHICLES; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; NEURAL LEARNING; OFFLINE; ON-MACHINES; OPTIMAL CONTROLS; OPTIMIZATION APPROACH; POWER MANAGEMENTS; POWER-TRAIN SYSTEMS; UNIVERSITY OF MICHIGAN; VEHICLE MODEL; VEHICLE STRUCTURES;

AUTOMOBILE PARTS AND EQUIPMENT; CONTROL SYSTEM ANALYSIS; CONTROLLERS; DYNAMIC PROGRAMMING; EDUCATION; ELECTRIC POWER MEASUREMENT; ENERGY MANAGEMENT; FUEL ECONOMY; FUELS; HIGHWAY ADMINISTRATION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LAND VEHICLE PROPULSION; LEARNING ALGORITHMS; NEURAL NETWORKS; POWER CONTROL; ROADS AND STREETS; ROBOT LEARNING; SIMULATORS; STEEL METALLURGY; SYSTEMS ENGINEERING; THERMAL CONDUCTIVITY; VEHICLES; WIRELESS TELECOMMUNICATION SYSTEMS;

TRAFFIC CONGESTION;

EID: 70450167530     PISSN: 00189545     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVT.2009.2027710     Document Type: Article

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