Li, S, Li, K, Xiao, E et al. (3 more authors) (2020) A novel model predictive control scheme based observer for working conditions and reconditioning monitoring of Zinc-Nickel single flow batteries. Journal of Power Sources, 445. 227282. ISSN 0378-7753
Abstract
Zinc-nickel single flow batteries (ZNBs) have been demonstrated as a promising alternative to lithium batteries for next generation grid-tied energy storage. However, due to the dendritic growth, the monitoring of working conditions in terms of the state of charge (SoC) and battery capacity is intractable. Although longer lifespan can be achieved through the periodic reconditioning maintenance, there is no mature method to identify the moment of reconditioning. Model predictive control (MPC) is a popular optimization paradigm in the process control. By incorporating the merits of model predictive control, this work presents a novel model predictive control based observer (MPCO) for the working conditions monitoring and reconditioning identification. Strong evidence from substantial experiments and simulations manifests the convergence, robustness, effectiveness and generality of the proposed method. The competitiveness is demonstrated by analytical comparisons against other three estimators. In this regard, the relationships of the proposed observer with other estimators are summarized briefly. At last, an indicator based on the capacity changes is proposed to judge the timing of reconditioning.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved. This is an author produced version of a paper published in Journal of Power Sources. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Redox flow batteries; Zinc-nickel single flow batteries; State of charge estimation; Capacity estimation; Reconditioning; Model predictive control; Observer |
Dates: |
|
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: | 15 Nov 2019 13:39 |
Last Modified: | 10 Sep 2024 13:33 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jpowsour.2019.227282 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153516 |