Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries Article Swipe
Joonchul Kim
,
Eunsong Kim
,
Junghwan Park
,
Kyoung‐Tak Kim
,
Joung‐Hu Park
,
Taesic Kim
,
Kyoungmin Min
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/9305309
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/9305309
Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability, the charge–discharge information of cathode materials with different chemical elements was adopted from previous research, including lithium iron phosphate, lithium nickel cobalt aluminum oxide, and lithium nickel cobalt manganese oxide cells. Among the partitioning procedures, the method of adding predicted data from the surrogate model to the training set exhibited the best accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which only used certain cycles before the test set as the training set, exhibited the worst MAE value of 60 cycles. In conclusion, the proposed data partitioning method could be implemented to predict the RUL of batteries to develop next-generation cathode materials with improved performance and stability, shorten the quality assessment time, and achieve stable predictive maintenance.
Related Topics
Concepts
Lithium iron phosphate
Reliability engineering
Stability (learning theory)
Battery (electricity)
Cobalt oxide
Computer science
Lithium (medication)
Reliability (semiconductor)
Cobalt
Materials science
Engineering
Metallurgy
Machine learning
Quantum mechanics
Endocrinology
Physics
Power (physics)
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2023/9305309
- https://downloads.hindawi.com/journals/ijer/2023/9305309.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320482443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320482443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2023/9305309Digital Object Identifier
- Title
-
Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion BatteriesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-13Full publication date if available
- Authors
-
Joonchul Kim, Eunsong Kim, Junghwan Park, Kyoung‐Tak Kim, Joung‐Hu Park, Taesic Kim, Kyoungmin MinList of authors in order
- Landing page
-
https://doi.org/10.1155/2023/9305309Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/ijer/2023/9305309.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/ijer/2023/9305309.pdfDirect OA link when available
- Concepts
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Lithium iron phosphate, Reliability engineering, Stability (learning theory), Battery (electricity), Cobalt oxide, Computer science, Lithium (medication), Reliability (semiconductor), Cobalt, Materials science, Engineering, Metallurgy, Machine learning, Quantum mechanics, Endocrinology, Physics, Power (physics), MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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