Li, Y, Li, K, Li, S et al. (1 more author) (2018) FRA and EKF Based State of Charge Estimation of Zinc-nickel Single Flow Batteries. In: ICSEE 2018, IMIOT 2018: Advances in Green Energy Systems and Smart Grid. International Conference on Intelligent Computing for Sustainable Energy and Environment International Conference on Intelligent Manufacturing and Internet of Things, 21-23 Sep 2018, Chongqing, China. Springer, Singapore , pp. 183-191.
Abstract
The reliable state of charge (SOC) estimation is indispensable for flow batteries to maintain the safe and reliable operation. The widely adopted Extended Kalman filter (EKF) algorithm is a model-based method, however, the uncertainties in battery model will cause large errors in SOC estimation. An accurate battery model is the essence to capture the behaviors of batteries. In this paper, a novel framework for the SOC estimation of Zinc-nickel flow batteries is proposed based on the fast recursive algorithm (FRA) and extended Kalman filter (EKF). The FRA is firstly used to determine the model structure and identify the model parameters. Due to merits of FRA, a compact and accurate model of flow battery is built. Then, the SOC is estimated using the EKF based on the proposed linear-in-the-parameter model. Experimental studies and resultant simulations manifest the modelling accuracy of the proposed SOC estimation framework.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2018. This is a post-peer-review, pre-copyedit version of an article published in CCIS Vol 925. The final authenticated version is available online at: https://doi.org/10.1007/978-981-13-2381-2_17. |
Keywords: | flow battery; state of charge(SOC); fast recursive algorithm (FRA); extended Kalman fi lter(EKF) |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Aug 2018 14:30 |
Last Modified: | 05 Sep 2019 00:42 |
Status: | Published |
Publisher: | Springer, Singapore |
Identification Number: | 10.1007/978-981-13-2381-2_17 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134483 |