A Cloud-based State of Health Estimation Method for Lithium-ion Batteries Using Sparse Charging Data Article Swipe
Wenchao Guo
,
Lin Yang
,
Zhongwei Deng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2456/1/012026
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2456/1/012026
Under the current background of the national standard requirements for electric vehicle remote management and the companies’ needs for their digital platform services, automobile factories habitually transmit battery data to a cloud in a sparse way for state of health (SOH) estimation. Compared with the dynamic discharge condition of the vehicle, the charging process has been noticed in recent years due to its relatively stable advantages. In this paper, a cloud-based SOH estimation method for lithium-ion batteries using sparse charging data is proposed. A HI feature that can be extracted from the sparse data is derived and only the constant voltage charging process is considered. By using the experimental data deployed on the cloud computing system, the correlation of battery health with the HI is verified and SOH can be efficiently estimated by taking as this feature an input to three data-driven methods. The results show that all methods can achieve a low test error of 2% under 10s sparsity, which is promising to be applied in cloud sparse charging conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2456/1/012026
- https://iopscience.iop.org/article/10.1088/1742-6596/2456/1/012026/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361279609
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4361279609Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2456/1/012026Digital Object Identifier
- Title
-
A Cloud-based State of Health Estimation Method for Lithium-ion Batteries Using Sparse Charging DataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-01Full publication date if available
- Authors
-
Wenchao Guo, Lin Yang, Zhongwei DengList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2456/1/012026Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2456/1/012026/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2456/1/012026/pdfDirect OA link when available
- Concepts
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Cloud computing, State of health, Computer science, Battery (electricity), Process (computing), Feature (linguistics), State of charge, Real-time computing, Automotive engineering, Engineering, Power (physics), Physics, Linguistics, Philosophy, Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.needs | 18 |
| abstract_inverted_index.state | 38 |
| abstract_inverted_index.their | 20 |
| abstract_inverted_index.three | 141 |
| abstract_inverted_index.under | 158 |
| abstract_inverted_index.using | 78, 107 |
| abstract_inverted_index.which | 161 |
| abstract_inverted_index.years | 60 |
| abstract_inverted_index.health | 40, 121 |
| abstract_inverted_index.method | 74 |
| abstract_inverted_index.paper, | 69 |
| abstract_inverted_index.recent | 59 |
| abstract_inverted_index.remote | 13 |
| abstract_inverted_index.sparse | 35, 79, 93, 169 |
| abstract_inverted_index.stable | 65 |
| abstract_inverted_index.taking | 134 |
| abstract_inverted_index.achieve | 151 |
| abstract_inverted_index.applied | 166 |
| abstract_inverted_index.battery | 28, 120 |
| abstract_inverted_index.current | 3 |
| abstract_inverted_index.derived | 96 |
| abstract_inverted_index.digital | 21 |
| abstract_inverted_index.dynamic | 46 |
| abstract_inverted_index.feature | 86, 137 |
| abstract_inverted_index.methods | 149 |
| abstract_inverted_index.noticed | 57 |
| abstract_inverted_index.process | 54, 103 |
| abstract_inverted_index.results | 145 |
| abstract_inverted_index.system, | 116 |
| abstract_inverted_index.vehicle | 12 |
| abstract_inverted_index.voltage | 101 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 43 |
| abstract_inverted_index.charging | 53, 80, 102, 170 |
| abstract_inverted_index.constant | 100 |
| abstract_inverted_index.deployed | 111 |
| abstract_inverted_index.electric | 11 |
| abstract_inverted_index.methods. | 143 |
| abstract_inverted_index.national | 7 |
| abstract_inverted_index.platform | 22 |
| abstract_inverted_index.standard | 8 |
| abstract_inverted_index.transmit | 27 |
| abstract_inverted_index.vehicle, | 51 |
| abstract_inverted_index.verified | 126 |
| abstract_inverted_index.batteries | 77 |
| abstract_inverted_index.computing | 115 |
| abstract_inverted_index.condition | 48 |
| abstract_inverted_index.discharge | 47 |
| abstract_inverted_index.estimated | 132 |
| abstract_inverted_index.extracted | 90 |
| abstract_inverted_index.factories | 25 |
| abstract_inverted_index.promising | 163 |
| abstract_inverted_index.proposed. | 83 |
| abstract_inverted_index.services, | 23 |
| abstract_inverted_index.sparsity, | 160 |
| abstract_inverted_index.automobile | 24 |
| abstract_inverted_index.background | 4 |
| abstract_inverted_index.estimation | 73 |
| abstract_inverted_index.habitually | 26 |
| abstract_inverted_index.management | 14 |
| abstract_inverted_index.relatively | 64 |
| abstract_inverted_index.advantages. | 66 |
| abstract_inverted_index.cloud-based | 71 |
| abstract_inverted_index.conditions. | 171 |
| abstract_inverted_index.considered. | 105 |
| abstract_inverted_index.correlation | 118 |
| abstract_inverted_index.data-driven | 142 |
| abstract_inverted_index.efficiently | 131 |
| abstract_inverted_index.estimation. | 42 |
| abstract_inverted_index.lithium-ion | 76 |
| abstract_inverted_index.companies’ | 17 |
| abstract_inverted_index.experimental | 109 |
| abstract_inverted_index.requirements | 9 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5058866803 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I183067930 |
| citation_normalized_percentile.value | 0.50887248 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |