Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9393965/ |
| _version_ | 1818429801092874240 |
|---|---|
| author | Joanna Sobon Bruce Stephen |
| author_facet | Joanna Sobon Bruce Stephen |
| author_sort | Joanna Sobon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T15:23:17Z |
| format | Article |
| id | doaj.art-c08b520768ff46d0abc042117bd38992 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:23:17Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c08b520768ff46d0abc042117bd389922022-12-21T22:56:05ZengIEEEIEEE Access2169-35362021-01-019545795459010.1109/ACCESS.2021.30705749393965Model-Free Non-Invasive Health Assessment for Battery Energy Storage AssetsJoanna Sobon0https://orcid.org/0000-0002-3243-4992Bruce Stephen1https://orcid.org/0000-0001-7502-8129Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.KDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.KWith 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.https://ieeexplore.ieee.org/document/9393965/Battery energy storagebattery limitationsstorage longevitysecondary batteriesinput-output hidden Markov model |
| spellingShingle | Joanna Sobon Bruce Stephen Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets IEEE Access Battery energy storage battery limitations storage longevity secondary batteries input-output hidden Markov model |
| title | Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets |
| title_full | Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets |
| title_fullStr | Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets |
| title_full_unstemmed | Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets |
| title_short | Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets |
| title_sort | model free non invasive health assessment for battery energy storage assets |
| topic | Battery energy storage battery limitations storage longevity secondary batteries input-output hidden Markov model |
| url | https://ieeexplore.ieee.org/document/9393965/ |
| work_keys_str_mv | AT joannasobon modelfreenoninvasivehealthassessmentforbatteryenergystorageassets AT brucestephen modelfreenoninvasivehealthassessmentforbatteryenergystorageassets |