NDLI: DC to DC converter with neural network control for on-board electrical energy management

Content Provider	IEEE Xplore Digital Library
Author	Marie-Francoise, J.-N. Gualous, H. Berthon, A.
Copyright Year	2004
Description	Author affiliation: Franche-Comte Univ., Belfort, France (Marie-Francoise, J.-N.; Gualous, H.; Berthon, A.)
Abstract	Neural network (ANN) methodology is proposed to control DC/DC static converters. These converters are used to adapt the voltage levels and currents between sources in parallel (battery, ultracapacitors and fuel cells) and loads (DC bus bar) in an electric or hybrid vehicle. The update of the parameters is carried out using the method of Levenberg-Marquardt; the training and the validation of the network by the model assumption NARX (Nonlinear Autoregressive with exogenous input). The system is split in two parts: the First is composed of a battery (12 V), a DC/DC boost converter and a load (DC bus bar 42 V). The second consists on a pack of 8 ultracapacitors in series, a two quadrants DC/DC buck-boost converter and the load (DC bus bar 42 V). The system is designed for power up to 10 KW on the level DC bus bar. Mathematical models for the battery, the ultracapacitors, the two converters, the classical command and the neural command, are developed using MATLAB/SIMULINK/spl reg/ software. Comparisons with experimental results are presented in order to validate these models. Simulations give very satisfactory results in term of stability.
Sponsorship	China Electrotechnical Soc. (CES) National Natural Sci. Found. of China (NSFC)
Starting Page	521
Ending Page	525
File Size	352708
Page Count	5
File Format	PDF
ISBN	7560518699
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Xi'an Jiaotong Univ. Press
Subject Keyword	DC-DC power converters Neural networks Energy management Supercapacitors Mathematical model Artificial neural networks Voltage Fuel cells Battery powered vehicles Fuel cell vehicles
Content Type	Text
Resource Type	Article

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