A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries
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SVM is particularly appealing for its capability of handling small training datasets.
“SVM is particularly appealing for its capability of handling small training datasets [135].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [135]
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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Estimation of state of charge for lithium-ion batteries - A Review
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- Authors
- Zhao, Q., Qin, X., Zhao, H., & Feng, W.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Zhao, Hongbo
- Venue
- Microelectronics Reliability
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 85
- Pages
- 99-108
- ISSN
- 0026-2714 (ISSN-L)
- OpenAlex
- W2799353157
- Cited by
- 199
- References
- 26
- Citations by year
- 2026: 11 · 2025: 33 · 2024: 33 · 2023: 35 · 2022: 28 · 2021: 28 · 2020: 15 · 2019: 15 · 2018: 1
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Microelectronics Reliability (publishedVersion)
- Funders
- Aerospace Innovation Fund (20160357)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- lithium-ion batteries, remaining useful life prediction, state of health estimation, battery prognostics, support vector regression, feature vector selection, capacity fade, health indicators
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