Boosting battery state of health estimation based on self-supervised learning Article Swipe
Yunhong Che
,
Yusheng Zheng
,
Xin Sui
,
Remus Teodorescu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jechem.2023.05.034
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jechem.2023.05.034
State of health (SoH) estimation plays a key role in smart battery health prognostic and management. However, poor generalization, lack of labeled data, and unused measurements during aging are still major challenges to accurate SoH estimation. Toward this end, this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation. Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells, the proposed method achieves accurate and robust estimations using limited labeled data. A filter-based data preprocessing technique, which enables the extraction of partial capacity-voltage curves under dynamic charging profiles, is applied at first. Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder. The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data, which boosts the performance of the estimation framework. The proposed method has been validated under different battery chemistries, formats, operating conditions, and ambient. The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles, with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%, and robustness increases with aging. Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method. This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jechem.2023.05.034
- https://ars.els-cdn.com/content/image/1-s2.0-S2095495623003212-ga1_lrg.jpg
- OA Status
- hybrid
- Cited By
- 49
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380050937
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380050937Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jechem.2023.05.034Digital Object Identifier
- Title
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Boosting battery state of health estimation based on self-supervised learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-07Full publication date if available
- Authors
-
Yunhong Che, Yusheng Zheng, Xin Sui, Remus TeodorescuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jechem.2023.05.034Publisher landing page
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https://ars.els-cdn.com/content/image/1-s2.0-S2095495623003212-ga1_lrg.jpgDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ars.els-cdn.com/content/image/1-s2.0-S2095495623003212-ga1_lrg.jpgDirect OA link when available
- Concepts
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Boosting (machine learning), Artificial intelligence, Machine learning, Estimation, Computer science, Supervised learning, Engineering, Artificial neural network, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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49Total citation count in OpenAlex
- Citations by year (recent)
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2025: 19, 2024: 24, 2023: 6Per-year citation counts (last 5 years)
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28Number 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.very | 137 |
| abstract_inverted_index.with | 136, 183, 203, 206 |
| abstract_inverted_index.(SoH) | 3 |
| abstract_inverted_index.1.14% | 188 |
| abstract_inverted_index.State | 0 |
| abstract_inverted_index.aging | 27, 112 |
| abstract_inverted_index.boost | 47 |
| abstract_inverted_index.data, | 22, 141 |
| abstract_inverted_index.data. | 82 |
| abstract_inverted_index.error | 190 |
| abstract_inverted_index.learn | 110 |
| abstract_inverted_index.major | 30 |
| abstract_inverted_index.other | 207 |
| abstract_inverted_index.paper | 40 |
| abstract_inverted_index.plays | 5 |
| abstract_inverted_index.smart | 10 |
| abstract_inverted_index.still | 29 |
| abstract_inverted_index.three | 174 |
| abstract_inverted_index.under | 96, 156, 231 |
| abstract_inverted_index.using | 79, 172 |
| abstract_inverted_index.which | 59, 88, 142 |
| abstract_inverted_index.Toward | 36 |
| abstract_inverted_index.aging. | 204 |
| abstract_inverted_index.boosts | 143 |
| abstract_inverted_index.cells, | 70 |
| abstract_inverted_index.curves | 95 |
| abstract_inverted_index.during | 26 |
| abstract_inverted_index.errors | 185 |
| abstract_inverted_index.first. | 103 |
| abstract_inverted_index.health | 2, 12 |
| abstract_inverted_index.method | 73, 152 |
| abstract_inverted_index.robust | 77 |
| abstract_inverted_index.simple | 221 |
| abstract_inverted_index.unused | 24 |
| abstract_inverted_index.applied | 101 |
| abstract_inverted_index.battery | 11, 51, 69, 158 |
| abstract_inverted_index.cycles, | 182 |
| abstract_inverted_index.dataset | 65 |
| abstract_inverted_index.dynamic | 97 |
| abstract_inverted_index.enables | 89 |
| abstract_inverted_index.initial | 179 |
| abstract_inverted_index.labeled | 21, 81, 140, 175 |
| abstract_inverted_index.learned | 122 |
| abstract_inverted_index.limited | 80 |
| abstract_inverted_index.machine | 210 |
| abstract_inverted_index.method. | 219 |
| abstract_inverted_index.methods | 58, 212 |
| abstract_inverted_index.network | 123 |
| abstract_inverted_index.overall | 184 |
| abstract_inverted_index.partial | 93 |
| abstract_inverted_index.testing | 194 |
| abstract_inverted_index.through | 118 |
| abstract_inverted_index.variety | 233 |
| abstract_inverted_index.However, | 16 |
| abstract_inverted_index.accuracy | 167 |
| abstract_inverted_index.accurate | 33, 75 |
| abstract_inverted_index.achieves | 74 |
| abstract_inverted_index.ambient. | 164 |
| abstract_inverted_index.charging | 98 |
| abstract_inverted_index.formats, | 160 |
| abstract_inverted_index.learning | 44, 105, 211 |
| abstract_inverted_index.numerous | 68 |
| abstract_inverted_index.obtained | 66 |
| abstract_inverted_index.proposed | 72, 151, 218 |
| abstract_inverted_index.proposes | 41 |
| abstract_inverted_index.sparsely | 139 |
| abstract_inverted_index.training | 64 |
| abstract_inverted_index.Different | 54 |
| abstract_inverted_index.different | 157 |
| abstract_inverted_index.framework | 45, 225 |
| abstract_inverted_index.increases | 202 |
| abstract_inverted_index.operating | 161 |
| abstract_inverted_index.profiles, | 99 |
| abstract_inverted_index.promising | 227 |
| abstract_inverted_index.scenarios | 195 |
| abstract_inverted_index.unlabeled | 116 |
| abstract_inverted_index.validated | 155 |
| abstract_inverted_index.challenges | 31 |
| abstract_inverted_index.downstream | 129 |
| abstract_inverted_index.estimation | 4, 131, 148, 166, 224 |
| abstract_inverted_index.extraction | 91 |
| abstract_inverted_index.fine-tuned | 135 |
| abstract_inverted_index.framework. | 149 |
| abstract_inverted_index.guaranteed | 170 |
| abstract_inverted_index.parameters | 124 |
| abstract_inverted_index.prognostic | 13 |
| abstract_inverted_index.real-world | 229 |
| abstract_inverted_index.robustness | 201 |
| abstract_inverted_index.scenarios. | 235 |
| abstract_inverted_index.supervised | 209 |
| abstract_inverted_index.technique, | 87 |
| abstract_inverted_index.Comparisons | 205 |
| abstract_inverted_index.conditions, | 162 |
| abstract_inverted_index.data-driven | 57 |
| abstract_inverted_index.demonstrate | 213 |
| abstract_inverted_index.estimation. | 35, 53 |
| abstract_inverted_index.estimations | 78 |
| abstract_inverted_index.maintaining | 196 |
| abstract_inverted_index.management. | 15 |
| abstract_inverted_index.performance | 49, 145 |
| abstract_inverted_index.superiority | 215 |
| abstract_inverted_index.traditional | 56 |
| abstract_inverted_index.transferred | 126 |
| abstract_inverted_index.Unsupervised | 104 |
| abstract_inverted_index.applications | 230 |
| abstract_inverted_index.chemistries, | 159 |
| abstract_inverted_index.considerable | 63 |
| abstract_inverted_index.distribution | 191 |
| abstract_inverted_index.filter-based | 84 |
| abstract_inverted_index.measurements | 25 |
| abstract_inverted_index.preprocessing | 86 |
| abstract_inverted_index.data-efficient | 223 |
| abstract_inverted_index.characteristics | 113 |
| abstract_inverted_index.generalization, | 18 |
| abstract_inverted_index.self-supervised | 43 |
| abstract_inverted_index.capacity-voltage | 94 |
| abstract_inverted_index.auto-encoder-decoder. | 120 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5041097181 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I891191580 |
| citation_normalized_percentile.value | 0.97428431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |