Adaptive online state-of-charge determination based on neuro-controller and neural network

This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model’s predictive control input unit, radial basis function neural network, which can adjust its structure to predic...

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Bibliographic Details
Published in:	Energy conversion and management Vol. 51; no. 5; pp. 1093 - 1098
Main Author:	Shen, Yanqing
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.05.2010 Elsevier
Subjects:	Applied sciences Direct energy conversion and energy accumulation Electrical engineering. Electrical power engineering Electrical machines Electrical power engineering Electrochemical conversion: primary and secondary batteries, fuel cells Exact sciences and technology Lead-acid batteries Neuro-controller Radial Basis Function Neural Network (RBFNN) Recursive least square algorithm Regulation and control State of Charge (SOC) Radial Basis Function Neural Network (RBFNN) State of Charge (SOC) Lead-acid batteries Recursive least square algorithm Neuro-controller Lead acid batteries Control system On-line systems Secondary cell Neural network Recursive algorithm Adaptive method Radial Basis Function Neural Network Control unit Backpropagation algorithm RBFNN Least squares method Radial basis function networks Differential integral proportional control Comparative study Predictive control
ISSN:	0196-8904, 1879-2227
Online Access:	Get full text
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