Projects per year
Abstract
With widespread applications for lithium-ion batteries in energy storage systems, the performance degradation of the battery attracts more and more attention. Understanding the battery’s long-term aging characteristics is essential for the extension of the service lifetime of the battery and the safe operation of the system. In this paper, lithium iron phosphate (LiFePO4) batteries were subjected to long-term (i.e., 27–43 months) calendar aging under consideration of three stress factors (i.e., time, temperature and state-of-charge (SOC) level) impact. By means of capacity measurements and resistance calculation, the battery’s long-term degradation behaviors were tracked over time. Battery aging models were established by a simple but accurate two-step nonlinear regression approach. Based on the established model, the effect of the aging temperature and SOC level on the long-term capacity fade and internal resistance increase of the battery is analyzed. Furthermore, the storage life of the battery with respect to different stress factors is predicted. The analysis results can hopefully provide suggestions for optimizing the storage condition, thereby prolonging the lifetime of batteries.
Original language | English |
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Article number | 1732 |
Journal | Energies |
Volume | 14 |
Issue number | 6 |
Number of pages | 21 |
ISSN | 1996-1073 |
DOIs | |
Publication status | Published - 20 Mar 2021 |
Keywords
- Lithium-ion battery
- Long-term calendar aging
- Capacity fade
- Internal resistance increase
- Lifetime modelling
- Nonlinear regression
Fingerprint
Dive into the research topics of 'The Degradation Behavior of LiFePO4/C Batteries during Long-Term Calendar Aging'. Together they form a unique fingerprint.Projects
- 1 Finished
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Robust State of Health Estimation for Lithium-ion Batteries using Machine Learning
Sui, X. (PI), Teodorescu, R. (Supervisor) & Stroe, D.-I. (Supervisor)
01/11/2018 → 31/10/2021
Project: PhD Project
Research output
- 46 Citations
- 1 PhD thesis
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Robust State of Health Estimation for Lithium-Ion Batteries Using Machines Learning
Sui, X., 2021, Aalborg Universitetsforlag. 119 p.Research output: PhD thesis
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