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A comprehensive single-particle-degradation model for battery state-of-health prediction
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“These models can accurately simulate the internal behavior of batteries, but due to the complexity of battery aging processes and individual variability, they face many limitations in practical applications.24,25,26,27”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [24]
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- Lin, 2013, A comprehensive capacity fade model and analysis for Li-ion batteries, J. Electrochem. Soc., 160, A1701, 10.1149/2.040310jes · DOI
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Elmahallawy, M., Elfouly, T., Alouani, A. T., & Massoud, A. M. (2022) · IEEE Access · doi:10.1109/access.2022.3221137 · Cited by 260
Machine learning for predicting battery capacity for electric vehicles
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Metadata
- Authors
- Li, J., Landers, R. G., & Park, J.
- Affiliations
- Missouri University of Science and Technology (US)
- Corresponding author
- Park, J.
- ORCID
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 456
- Pages
- 227950
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3012214204
- Cited by
- 116
- References
- 33
- Citations by year
- 2026: 8 · 2025: 23 · 2024: 22 · 2023: 21 · 2022: 25 · 2021: 14 · 2020: 3
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Power Sources (acceptedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Materials Science › Metals and Alloys › Extraction and Separation Processes
- Keywords
- lithium-ion battery degradation, battery state of health estimation, single particle model, battery management systems, capacity fade, metal dissolution, lithium loss, anode degradation, cathode degradation, effective transport properties
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