Model-free non-invasive health assessment for battery energy storage assets
Sobon, Joanna and Stephen, Bruce (2021) Model-free non-invasive health assessment for battery energy storage assets. IEEE Access, 9. pp. 54579-54590. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2021.3070574)
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Abstract
With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.
ORCID iDs
Sobon, Joanna and Stephen, Bruce ORCID: https://orcid.org/0000-0001-7502-8129;-
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Item type: Article ID code: 75997 Dates: DateEvent2 April 2021Published27 March 2021AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 01 Apr 2021 13:22 Last modified: 22 Sep 2024 01:12 URI: https://strathprints.strath.ac.uk/id/eprint/75997