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Accession Number ADA564788
Title Intelligent Hybrid Vehicle Power Control - Part 1: Machine Learning of Optimal Vehicle Power.
Publication Date Jun 2012
Media Count 24p
Personal Author A. Masrur J. Park M. Kuang Y. L. Murphey Z. Chen
Abstract Energy management in Hybrid Electric Vehicles (HEV) has been actively studied recently because of its potential to significantly improve fuel economy and emission control. Because of the dual-power-source nature and the complex configuration and operation modes in a HEV, energy management is more complicated and important than in a conventional vehicle. Most of the existing vehicle power optimization approaches do not incorporate knowledge about driving patterns into their vehicle energy management strategies. Our approach is to use machine learning technology combined with roadway type and traffic congestion level specific optimization to achieve quasi-optimal energy management in hybrid vehicles. In this series of two papers, we present a machine learning framework that combines Dynamic Programming with machine learning to learn about roadway type and traffic congestion level specific energy optimization, and an integrated online intelligent power controller to achieve quasi-optimal energy management in hybrid vehicles. These two papers cover the modeling of power flow in HEVs, mathematical background of optimization in energy management in HEV, machine learning algorithms and real- time optimal control of energy flow in a HEV. This first paper presents our research in machine learning for optimal energy management in HEVs. We will present a machine learning framework, ML-EMO-HEV, developed for the optimization of energy management in a HEV, machine learning algorithms for predicting driving environments and generating optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's PSAT (Powertrain Systems Analysis Toolkit). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML-EMO-HEV framework are effective in predicting roadway type and traffic congestion levels, in predicting driving trend and.
Keywords Algorithms
Congestion
Cost effectiveness
Dual power sources
Dynamic programming
Electric propulsion
Emission control
Energy management
Energy optimization
Environments
Escape systems
Experimental data
Fuel consumption
Fuel economy
Hev power management
Hev(Hybrid electric vehicles)
Hybrid systems
Learning machines
Neural nets
Real time
Roads
Vehicles

 
Source Agency Non Paid ADAS
NTIS Subject Category 85D - Transportation Safety
81B - Electric & Ion Propulsion
97K - Fuels
81G - Rocket Engines & Motors
Corporate Author Michigan Univ.-Dearborn.
Document Type Technical report
Title Note Journal.
NTIS Issue Number 1303
Contract Number N/A

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