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Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries
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“One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [11]
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Metadata
- Authors
- Schwunk, S., Armbruster, N., Straub, S., Kehl, J., & Vetter, M.
- Affiliations
- Fraunhofer Institute for Solar Energy Systems (DE)
- Corresponding author
- Schwunk, Simon
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 239
- Pages
- 705-710
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2000885517
- Cited by
- 250
- References
- 16
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 27 · 2023: 28 · 2022: 20 · 2021: 21 · 2020: 23 · 2019: 29 · 2018: 20 · 2017: 19 · 2016: 12 · 2015: 15 · 2014: 8 · 2013: 4
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- particle filter, state of charge estimation, state of health estimation, lithium iron phosphate batteries, hysteresis, battery degradation, Monte Carlo sampling, electric vehicles, off-grid power supply
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