Zhang, L, Li, K, Yang, Z et al. (4 more authors) (2018) Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization. In: Proceedings of CEC 2018. 2018 IEEE Congress on Evolutionary Computation (CEC), 08-13 Jul 2018, Rio de Janeiro, Brazil. IEEE , pp. 851-856. ISBN 978-1-5090-6017-7
Abstract
As a novel family member of the redox flow batteries (RFBs), the single flow zinc-nickel battery (ZNB) without ion exchange membranes has attracted a lot of interests in recent years due to the high charging and discharging efficiencies. To understand the electrical behaviour is a key for proper battery management system. Unlike the electrochemical mechanism models and equivalent circuit models, the neural network based black-box model does not need knowledge about the electrochemical reactions and is a promising and adaptive approach for the ZNB battery modelling. In this paper, a compact radial basis function neural network is developed using a two-stage layer selection strategy to determine the network structure. While Jaya optimization is utilized to determine the non-linear parameters in the selected hidden nodes of the resultant RBF neural network (RBF-NN) model. The proposed method is implemented to model the ZNB to capture the non-linear electric behaviours through the readily measurable input signals. Experimental results manifest the accurate prediction capability of the resultant neural model and confirm the effectiveness of the proposed approach.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in 2018 IEEE Congress on Evolutionary Computation (CEC). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Jan 2019 14:36 |
Last Modified: | 16 Jan 2019 04:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CEC.2018.8477707 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141010 |