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Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles
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“While conventional Backpropagation (BP) neural networks handle nonlinear inputs effectively, they struggle with temporal relationships in timeseries data due to gradient issues [29][30][31].”
From SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm (2025) · cites this paper as [29]
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Metadata
- Authors
- He, H., Xiong, R., & Guo, H.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- He, Hongwen
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 89
- Issue
- 1
- Pages
- 413-420
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2057708672
- Cited by
- 388
- References
- 19
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 23 · 2023: 31 · 2022: 42 · 2021: 41 · 2020: 30 · 2019: 23 · 2018: 35 · 2017: 27 · 2016: 15 · 2015: 29 · 2014: 25 · 2013: 19 · 2012: 12
- FWCI
- 36.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- 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
- lithium iron phosphate batteries, state of charge estimation, online parameter estimation, adaptive extended Kalman filter, Thevenin equivalent circuit model, battery management systems, electric vehicles, open-circuit voltage, terminal voltage
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