Battery state of charge estimation based on variable-order battery model and ZOA-EKF Article Swipe
Xinghua Huang
,
Shanchao Wang
,
Jirong Qin
,
Weiguang Zheng
,
Jianghua Wei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/tse/tdaf024
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/tse/tdaf024
This study is based on the Hannan-Quinn information criterion (HQIC) and introduces the contrast factor. It balances structural accuracy and complexity of the RC order of the lithium-ion battery model across different regions. Furthermore, the Zebra optimization algorithm (ZOA) is integrated to optimize the measurement noise of the extended Kalman filter (EKF), resulting in a hybrid estimation algorithm (ZOA-EKF). The performance of this algorithm is assessed for state of charge (SOC) estimation under dynamic stress test (DST) and federal urban driving schedule (FUDS) operating conditions, revealing that the variable-order equivalent circuit model combined with the ZOA-EKF algorithm achieves superior accuracy, with the minimum mean absolute error (MAE) and root mean square error (RMSE) of only 0.217% and 0.4168%, respectively.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/tse/tdaf024
- https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdaf024/63029776/tdaf024.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409981085
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409981085Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/tse/tdaf024Digital Object Identifier
- Title
-
Battery state of charge estimation based on variable-order battery model and ZOA-EKFWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-29Full publication date if available
- Authors
-
Xinghua Huang, Shanchao Wang, Jirong Qin, Weiguang Zheng, Jianghua WeiList of authors in order
- Landing page
-
https://doi.org/10.1093/tse/tdaf024Publisher landing page
- PDF URL
-
https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdaf024/63029776/tdaf024.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdaf024/63029776/tdaf024.pdfDirect OA link when available
- Concepts
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Battery (electricity), Extended Kalman filter, State of charge, Variable (mathematics), State variable, Computer science, Power (physics), Physics, Mathematics, Kalman filter, Artificial intelligence, Mathematical analysis, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 4 |
| abstract_inverted_index.error | 106, 112 |
| abstract_inverted_index.model | 30, 92 |
| abstract_inverted_index.noise | 46 |
| abstract_inverted_index.order | 25 |
| abstract_inverted_index.state | 68 |
| abstract_inverted_index.study | 2 |
| abstract_inverted_index.under | 73 |
| abstract_inverted_index.urban | 80 |
| abstract_inverted_index.(EKF), | 52 |
| abstract_inverted_index.(FUDS) | 83 |
| abstract_inverted_index.(HQIC) | 10 |
| abstract_inverted_index.(RMSE) | 113 |
| abstract_inverted_index.0.217% | 116 |
| abstract_inverted_index.Kalman | 50 |
| abstract_inverted_index.across | 31 |
| abstract_inverted_index.charge | 70 |
| abstract_inverted_index.filter | 51 |
| abstract_inverted_index.hybrid | 56 |
| abstract_inverted_index.square | 111 |
| abstract_inverted_index.stress | 75 |
| abstract_inverted_index.ZOA-EKF | 96 |
| abstract_inverted_index.battery | 29 |
| abstract_inverted_index.circuit | 91 |
| abstract_inverted_index.driving | 81 |
| abstract_inverted_index.dynamic | 74 |
| abstract_inverted_index.factor. | 15 |
| abstract_inverted_index.federal | 79 |
| abstract_inverted_index.minimum | 103 |
| abstract_inverted_index.0.4168%, | 118 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.absolute | 105 |
| abstract_inverted_index.accuracy | 19 |
| abstract_inverted_index.achieves | 98 |
| abstract_inverted_index.assessed | 66 |
| abstract_inverted_index.balances | 17 |
| abstract_inverted_index.combined | 93 |
| abstract_inverted_index.contrast | 14 |
| abstract_inverted_index.extended | 49 |
| abstract_inverted_index.optimize | 43 |
| abstract_inverted_index.regions. | 33 |
| abstract_inverted_index.schedule | 82 |
| abstract_inverted_index.superior | 99 |
| abstract_inverted_index.accuracy, | 100 |
| abstract_inverted_index.algorithm | 38, 58, 64, 97 |
| abstract_inverted_index.criterion | 9 |
| abstract_inverted_index.different | 32 |
| abstract_inverted_index.operating | 84 |
| abstract_inverted_index.resulting | 53 |
| abstract_inverted_index.revealing | 86 |
| abstract_inverted_index.(ZOA-EKF). | 59 |
| abstract_inverted_index.complexity | 21 |
| abstract_inverted_index.equivalent | 90 |
| abstract_inverted_index.estimation | 57, 72 |
| abstract_inverted_index.integrated | 41 |
| abstract_inverted_index.introduces | 12 |
| abstract_inverted_index.structural | 18 |
| abstract_inverted_index.conditions, | 85 |
| abstract_inverted_index.information | 8 |
| abstract_inverted_index.lithium-ion | 28 |
| abstract_inverted_index.measurement | 45 |
| abstract_inverted_index.performance | 61 |
| abstract_inverted_index.Furthermore, | 34 |
| abstract_inverted_index.Hannan-Quinn | 7 |
| abstract_inverted_index.optimization | 37 |
| abstract_inverted_index.respectively. | 119 |
| abstract_inverted_index.variable-order | 89 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.16256874 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |