Realistic lifetime prediction approach for Li-ion batteries
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Most existing studies model battery cyclic and calendar aging independently and combine the two to make predictions under a dynamic load profile.
“Most of the existing studies model the battery cyclic and calendar aging independently, and combine the two to make predictions under a dynamic load profile [6,94,95].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [94]
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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“Most of the existing studies model the battery cyclic and calendar aging independently, and combine the two to make predictions under a dynamic load profile [6,94,95].”
BackgroundSelf-citationde Hoog, J., Timmermans, J.-M., Ioan-Stroe, D., Swierczynski, M., Jaguemont, J., Goutam, S., Omar, N., Van Mierlo, J., & Van Den Bossche, P. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.018 · Cited by 254 · see citing works
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Online Health Diagnosis of Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network
Khaleghi, S., Karimi, D., Beheshti, S. H., Hosen, M. S., Behi, H., Berecibar, M., & Van Mierlo, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116159 · Cited by 161 · see citing works
Self-citation
- Authors
- Sarasketa-Zabala, E., Martinez-Laserna, E., Berecibar, M., Gandiaga, I., Rodriguez-Martinez, L. M., & Villarreal, I.
- Affiliations
- Ikerlan (ES)
- Corresponding author
- Sarasketa-Zabala, E.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 162
- Pages
- 839-852
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2191802622
- Cited by
- 191
- References
- 31
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 22 · 2023: 25 · 2022: 12 · 2021: 24 · 2020: 30 · 2019: 28 · 2018: 16 · 2017: 7 · 2016: 7
- FWCI
- 10.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- ETORTEK
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- lithium-ion batteries, battery lifetime prediction, aging models, calendar aging, dynamic validation, model error analysis, lithium iron phosphate cells, vehicle-to-grid
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