Wang, Z. orcid.org/0000-0002-0666-3724, Gladwin, D.T. orcid.org/0000-0001-7195-5435, Smith, M.J. et al. (1 more author) (2021) Practical state estimation using Kalman filter methods for large-scale battery systems. Applied Energy, 294. 117022. ISSN 0306-2619
Abstract
The system states of battery energy storage systems (BESSs) such as state of charge (SOC) and state of health (SOH) are essential for the functions of the system, such as frequency support services and energy trading. However, the complexity of a large-scale battery system makes the estimations more difficult than at the cell-level. This is further compounded by real-world limitations on system monitoring data granularity, accuracy and quality. In this paper it is shown how cell-level state estimation techniques can be utilised on large-scale BESSs using experimental data from a 2MW, 1MWh BESS. The results show how a Dual Sigma point Kalman Filter (DSPKF) SOC estimation provides more accurate results compared to the commercial BESS battery management system SOC. It is shown how the DSPKF parameters can be tuned by a genetic algorithm to simplify selection and generalise the approach for different BESSs. Furthermore, it shows how this method of SOC estimation can be combined with a total least-squares (TLS) method for capacity estimation to less than 1% error. Online system state estimation is demonstrated using both designed tests and real-world operational profiles where the BESS has provided contracted frequency response services to the national electricity grid in the UK.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Applied Energy. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Battery system; State of charge; State of health; Kalman filter; Total least squares |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/R512175/1; 2110436 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Dec 2021 07:10 |
Last Modified: | 30 Apr 2022 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.apenergy.2021.117022 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181117 |
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