Analysis of prismatic lithium‐ion battery degradation based on an electrochemical‐thermal‐degradation model
Abstract
Understanding the performance degradation of lithium-ion batteries will be conducive to improving the endurance of electric vehicles. A one-dimensional electrochemical model (1D-EM), a three-dimensional thermal model (3D-TM) and an accurate degradation model are here coupled for a prismatic LFP/C battery for the first time. The degradation mechanisms include the formation of the solid electrolyte interface (SEI), Li plating (LP), and the loss of active material (LAM) of the negative electrode and positive electrode. This electrochemical-thermal-degradation model can accurately predict battery degradation over a wide range of temperatures ( T amb ) and current rates. The charge-discharge process is repeated up to 1000 times, and the changes in the charge-discharge curve reflect the battery degradation to some extent. Capacity loss varies from −10°C to 50°C, which can be divided into three stages. The first stage corresponds to low T amb and is dominated by the LP, the second stage corresponds to an intermediate T amb and is dominated by the LAM and SEI formation, and the third stage corresponds to high T amb and is dominated by the LAM. Through the coupled model of 1D-EM, 3D-TM, and degradation model, the degradation mechanism of prismatic LFP/C battery under different working conditions is summarized, and its causes are analyzed. The results in this study show an important reference value for the degradation analysis of prismatic LFP/C battery under different operating condit…
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Degradation diagnostics for lithium ion cells
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“Figure 1 summarizes the causes, mechanisms, modes, and effects of battery degradation.(21) It can be observed from Figure 1 that various causes trigger the mechanisms, and all these mechanisms contribute to the modes of degradation.”
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State of mission: Battery management with neural networks and electrochemical AI
Ozkan, C. S., & Ozkan, M. (2025) · iScience · doi:10.1016/j.isci.2025.113593 · Cited by 7 · see citing works
“This section details a proposed framework that synergistically combines Neural ODEs with electrochemical, thermal, and degradation sub-models, 39 informed by PINN principles and integrated with sequential deep learning architectures (LSTMs/ Transformers) for robust battery state evolution modeling.”
- Authors
- Zhang, H., Li, C., Chen, H., & Fang, H.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Fang, Haisheng
- Venue
- International Journal of Energy Research
- Publisher
- Hindawi Limited
- Published
- 2022-09-03
- Volume
- 46
- Issue
- 15
- Pages
- 23658-23681
- ISSN
- 0363-907X (ISSN-L)
- 1099-114X
- OpenAlex
- W4294379424
- Cited by
- 13
- References
- 3
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Energy Research (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Mechanical Engineering › Extraction and Separation Processes
- Keywords
- lithium-ion battery degradation, prismatic cells, solid electrolyte interphase formation, lithium plating, active material loss, ambient temperature effects, capacity fade, electric vehicle batteries, degradation mechanism
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