Abstract
As a popular type of Redox Flow Batteries (RFBs), single flow Zinc Nickel Battery (ZNB) was proposed in the last decade without requiring an expensive and complex ionic membrane in the battery. In this paper, a Radial Basis Function (RBF) neural model is proposed for modelling the behaviours of ZNBs. Both the linear and non-linear parameters in the model are tuned through a new feedback-learning phase assisted Teaching-Learning-Based Optimization (TLBO) method. Besides, the fast recursive algorithm (FRA) is applied to select the proper inputs and network structure to reduce the modelling error and computational efforts. The experimental results confirm that the proposed methods are capable of producing ZNB models with desirable performance over both training and test data.
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Acknowledgments
X Li and CK Wong would like to thank the Macao Science and Technology Development Fund (FDCT) ’s support with the project (111/2013/A3)-Flow Battery Storage System Study and Its Application in Power System. The paper is partially funded by EPSRC under grant EP/L001063/1, the NSFC under grant 61673256, NSFC under grant 61633016, and NSFC under grant 61533010 and by China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source under LAPS17018.
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Li, X., Li, K., Yang, Z., Wong, C. (2017). A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_39
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DOI: https://doi.org/10.1007/978-981-10-6364-0_39
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