Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
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A common way to estimate SOH is through models simulating cell behaviours, followed by optimization algorithms and observers such as Kalman filter family and particle filter to identify parameters and SOH states.
“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 [10]
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- Authors
- Plett, G. L.
- Affiliations
- University of Colorado Colorado Springs (US)
- Corresponding author
- Plett, Gregory L.
- ORCID
- Plett, Gregory L.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2004-08
- Volume
- 134
- Issue
- 2
- Pages
- 277-292
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2147428031
- Cited by
- 1,768
- References
- 28
- Citations by year
- 2026: 119 · 2025: 103 · 2024: 125 · 2023: 127 · 2022: 114 · 2021: 128 · 2020: 104 · 2019: 119 · 2018: 123 · 2017: 104 · 2016: 99 · 2015: 119 · 2014: 100 · 2013: 98 · 2012: 63
- FWCI
- 22.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- battery management systems, hybrid electric vehicles, lithium polymer batteries, extended Kalman filter, state of charge estimation, power fade, capacity fade, state of health estimation, battery balancing, battery degradation, cell modeling, system identification, error bounds
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