A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation
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Various supervised ML techniques have been utilized for battery SOH estimation, including ANN, SVM, RVM, kNN, GPR, RFR.
“Various supervised ML techniques have been utilized for battery SOH estimation, including Artificial Neural Networks (ANN) [81], Support Vector Machine (SVM) [85], relevance vector machine (RVM) [86], k-Nearest Neighbours (kNN) [87], Gaussian Process Regression (GPR) [82] or random forest regression (RFR) [83].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [85]
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- Authors
- Klass, V., Behm, M., & Lindbergh, G.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Corresponding author
- Klass, Verena
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 270
- Pages
- 262-272
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W1966039250
- Cited by
- 381
- References
- 33
- Citations by year
- 2026: 31 · 2025: 46 · 2024: 53 · 2023: 47 · 2022: 50 · 2021: 49 · 2020: 30 · 2019: 24 · 2018: 19 · 2017: 14 · 2016: 11 · 2015: 5 · 2014: 2
- FWCI
- 13.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- Swedish Hybrid Vehicle Centre (SHC)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of health estimation, lithium-ion batteries, electric vehicles, support vector machine, battery capacity estimation, battery internal resistance, temperature dependence, state of charge
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