Support vector based battery state of charge estimator
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The major weakness of SVM is that performance depends greatly on the kernel function that defines the shape of the hyperplane line.
“The major weakness of Support Vector Machines is that their performance depends greatly on the kernel function that defines the shape of the hyperplane line [53].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [53]
ML methods have been applied to battery energy management and to detect faults and lateral misalignment in wireless charging of EVs.
“ML methods have also been applied to battery energy management (for example charge estimation [330] or optimization in hybrid EVs [15]), and to detect faults and lateral misalignment in wireless charging of EVs [780].”
From Tackling climate change with machine learning (2023) · cites this paper as [330]
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“Hansen and Wang [30] applied a support vector machine (SVM) using both classification and regression to estimate the SOC without establishing a battery circuit model.”
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- Authors
- Hansen, T., & Wang, C.-J.
- Affiliations
- University of Colorado Colorado Springs (US)
- Corresponding author
- Wang, Chia-Jiu
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2005-03
- Volume
- 141
- Issue
- 2
- Pages
- 351-358
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2170977282
- Cited by
- 347
- References
- 10
- Citations by year
- 2026: 8 · 2025: 24 · 2024: 18 · 2023: 18 · 2022: 20 · 2021: 19 · 2020: 18 · 2019: 26 · 2018: 18 · 2017: 26 · 2016: 20 · 2015: 33 · 2014: 17 · 2013: 24 · 2012: 13
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- U.S. Department of Energy (DE-AC36-99GO-10337)
- National Renewable Energy Laboratory
- 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 charge estimation, support vector machine, lithium polymer batteries, battery pack, Coulomb counting, US06 driving cycle, real-time embedded systems, low computational load
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