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SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization
Open accessAbstract
State of charge (SOC) is very important parameter for monitoring the battery charge and discharge operation and estimating the drive distance of electric vehicle. Especially, with the cycle number increasing, the precision estimation of SOC for battery management system is still not well resolved. Therefore, in this study, aim at accurate sampling of voltage, current and temperature signals based on LTC6803-3 chip, the paper proposed a support vector machine (SVM) optimized by particle swarm optimization (PSO) to improve SOC estimation accuracy. The results demonstrate that the proposed PSO-SVM model has good forecasting performance.
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SVM is considered inflexible compared to other data mining techniques like neural networks and may lack efficacy in intricate or fluctuating environments.
“analytically, SVM is considered an inflexible model compared to some other approaches of data mining techniques like neural networks; it maybe lacks efficacy specifically in intricate or fluctuating environments [54].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [54]
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Cited by
Sorted by citations
Estimation of state of charge for lithium-ion batteries - A Review
A.M.S.M.H.S.Attanayaka, Department of Electrical Engineering, U. O. M., J.P.Karunadasa, & K.T.M.U.Hemapala (2019) · AIMS Energy · doi:10.3934/energy.2019.2.186 · Cited by 72
Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12
Metadata
- Authors
- Guo, G. F., Shui, L., Wu, X. L., & Cao, B. G.
- Affiliations
- Xi'an Jiaotong University (CN)
- Xi'an University of Architecture and Technology (CN)
- Corresponding author
- Guo, Gui Fang
- Venue
- Advanced Materials Research
- Publisher
- Trans Tech Publications, Ltd.
- Published
- 2014-10-27
- Volume
- 1051
- Pages
- 1004-1008
- ISSN
- 1022-6680 (ISSN-L)
- 1662-8985
- OpenAlex
- W2061593837
- Cited by
- 20
- References
- 0
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 3 · 2019: 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced materials research (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Else Kröner-Fresenius-Stiftung
- Division of Materials Research
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- 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, lithium-ion batteries, support vector machine, particle swarm optimization, hyperparameter optimization, battery management systems, electric vehicles
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