Motor vehicles – Power – Electric
Reexamination Certificate
2011-06-07
2011-06-07
Shriver, II, J. Allen (Department: 3618)
Motor vehicles
Power
Electric
C180S065275, C903S930000, C701S022000
Reexamination Certificate
active
07954579
ABSTRACT:
This invention relates a control strategy for a hybrid electric vehicle having an electric motor, a battery and an internal combustion engine. The control strategy improves fuel economy and reduces emissions while providing sufficient acceleration over a varying set of driving conditions through an adaptive control unit with an artificial neural network. The artificial neural network is trained on a pre-processed training set based on the highest fuel economies of multiple control strategies and multiple driving profiles. Training the artificial neural network includes a training algorithm and a learning algorithm. The invention also includes a method of operating a hybrid electric vehicle with an adaptive control strategy using an artificial neural network.
REFERENCES:
patent: 6054710 (2000-04-01), Bruggeman
patent: 6424961 (2002-07-01), Ayala
patent: 6442535 (2002-08-01), Yifan
patent: 6534954 (2003-03-01), Plett
patent: 7069256 (2006-06-01), Campos
patent: 7193385 (2007-03-01), Emadi et al.
patent: 7467117 (2008-12-01), Kermani
patent: 2004/0034460 (2004-02-01), Folkerts et al.
patent: 2005/0052080 (2005-03-01), Maslov et al.
patent: 2008/0319933 (2008-12-01), Moussa et al.
A. Rajagopalan et al., Development of Fuzzy Logic and Neural Network Control and Advanced Emissions modeling for parallel Hybrid Vehicles, NREL Subcontractor Report, NREL/SR-540-32919, Dec. 2003.
S. Lukic et al., Optimization of the Control Strategy of a Parallel Hybrid Electric Vehicle using Neural Networks, IIT course ECE 764-501: Vehicular Power Systems, Feb. 2007 (29 pages).
B. Baumann et al., Intelligent Control of Hybrid Vehicles Using Neural Networks and Fuzzy Logic, SAE Technical Document 981061 (Abstract).
F. Harmon et al., The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network, Neural Networks, 2005, vol. 18, Issue 5-6, pp. 772-780 (Abstract).
www.greencarcongress.com, Neural-Network Engine Controller for Higher Efficiency and Lower Emissions, Emissions, Engines, Fuel Efficiency, Vehicle Systems, Jan. 2006 pp. 1-2.
Neural Networks, Matlab neural network toolbox from software help file, pp. 23-24.
Neuron Model and Network Architectures, Matlab neural network toolbox from software help file, pp. 39-62.
Co-Pending U.S. Appl. No. 11/977,118, filed Oct. 23, 2007.
V.H. Johnson et al., HEV Control Strategy for Real-Time Optimization of Fuel Economy and Emissions, Society of Automotive Engineers, 2000, vol. 1, 15 pages.
F.G. Harmon et al., Application of CMAC Neural Network to the Control of a Parallel Hybrid-Electric Propulsion System for a Small Unmanned Aerial Vehicle, IEEE Conference Jul. 2005, pp. 355-360.
B. Baumann et al., Intelligent Control of Hybrid Vehicles Using Neural Networks and Fuzzy Logic, SAE Technical Paper 981061, 1998, Feb. 23-26, 9 pages.
Co-pending U.S. Appl. No. 12/214,032, filed Jun. 16, 2008, Digital Control of Motor Drives.
Emadi Ali
Lukic Srdjan M.
Rodriguez Fernando
Illinois Institute of Technology
Pauley Petersen & Erickson
Shriver II J. Allen
Walters John D
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