Neural Network Predictive Control for Vanadium Redox Flow Battery
Joint Authors
Cao, Hong-fei
Zhu, Xin-Jian
Shao, Meng
Shen, Hai-Feng
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-10
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances.
The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system.
Too low or too high flowrate is unfavorable for the safety and performance of VRB.
This paper presents a neural network predictive control scheme to enhance the overall performance of the battery.
A radial basis function (RBF) network is employed to approximate the dynamics of the VRB system.
The genetic algorithm (GA) is used to obtain the optimum initial values of the RBF network parameters.
The gradient descent algorithm is used to optimize the objective function of the predictive controller.
Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
American Psychological Association (APA)
Shen, Hai-Feng& Zhu, Xin-Jian& Shao, Meng& Cao, Hong-fei. 2013. Neural Network Predictive Control for Vanadium Redox Flow Battery. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479687
Modern Language Association (MLA)
Shen, Hai-Feng…[et al.]. Neural Network Predictive Control for Vanadium Redox Flow Battery. Journal of Applied Mathematics No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-479687
American Medical Association (AMA)
Shen, Hai-Feng& Zhu, Xin-Jian& Shao, Meng& Cao, Hong-fei. Neural Network Predictive Control for Vanadium Redox Flow Battery. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479687
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479687