Analysis of degradation mechanism of lithium iron phosphate battery

Kaneko, Genki; Inoue, Soichiro; Taniguchi, Koichiro; Hirota, Toshio; Kamiya, Yushi; Daisho, Yasuhiro; Inami, Shoichi

doi:10.1109/evs.2013.6914847

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…This impedance can, then, be represented by electrical elements, with varying degrees of accuracy and performance. Good examples of EIS being used to parameterize an EEC model can be found in [18,[42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

Open data model parameterization of a second-life Li-ion battery

Seger

Coron²,

Thivel³

et al. 2022

Journal of Energy Storage

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With the fast expansion of Electric and Hybrid Vehicles, the demand of Li-ion batteries is increasing exponentially. This creates an opportunity for a second life of these cells once they are no longer suitable for their original application. An accurate modelling of the second life can be a powerful tool for these new applications. In this work, we propose a complete second life model parameterization for a Li-Ion cell, with its parameters based on experimental data of NMC/LMO cells cycled through their first and second lives. The resulting model parameters span a State of Health from 80 % to 50 % of the cell original capacity, a State of Charge from 0 to 100 %, two current values and two first life temperature points. The model parameters are presented in an open data fashion in the form of look-up tables, ready to be implemented in simulation softwares.

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Section: Literature Reviewmentioning

confidence: 99%

Open data model parameterization of a second-life Li-ion battery

Seger

Coron²,

Thivel³

et al. 2022

Journal of Energy Storage

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“…Until now, we analysed influence of temperature and SOC. As a result, it was clear to inhibit drastically the degradation by change the operation of the SOC range at actual running for BEV [1]. Degradation factors of lithium-ion batteries have been reported by previous researches [2] [3] [4].…”

Section: Introductionmentioning

confidence: 97%

Degradation Predictions of Lithium Iron Phosphate Battery

Hato

Chen

Hirota

et al. 2015

WEVJ

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Degradation mechanisms of lithium iron phosphate battery have been analyzed with calendar tests and cycle tests. To quantify capacity loss with the life prediction equation, it is seen from the aspect of separating the total capacity loss into calendar capacity and real cycle capacity loss. The real cycle capacity loss of total capacity loss was derived by subtracting the calendar capacity loss parts during cycle tests. It is considered that calendar capacity loss is dominated by SEI formation. On the other hand, real cycle capacity loss includes structure disorder of electrodes and promotion of SEI growth such as delamination and regrowth. Generally, the test results indicated that capacity loss increases under high temperature and SOC condition, and SOC range (ΔSOC) is not related to the loss. However, we founded that the test results under 5℃ condition do not exactly show the same tendency of degradation. As a result, the life prediction equation is based on the chemical kinetics and it can only be adopted only beyond the 15℃ temperature limitation. At this time in life prediction equation, to take ΔSOC into consideration and describe the real cycle capacity loss specifically with amounts of lithium-ion intercalation/deintercalation, the processing amount of current is adopted as the standard of capacity degradation instead of the cycle number. Finally, it is considered to be possible that certain reactions such as further structure disorder or lithium plating caused under low temperature. However, we also founded that DC internal resistance tests results indicated that only calendar capacity loss can apply to chemical kinetics. It is necessary to consider the other construction method of the life prediction equation in the future.

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“…Arrhenius‐based correlations are also proven to represent capacity loss during calendar aging for other cell chemistries . For instance, Kaneko et al performed a set of accelerated calendar tests on 6.2‐Ah lithium iron phosphate (LFP) cells, and they fitted the capacity loss using an Arrhenius‐based correlation, where a capacity loss coefficient is correlated with the time term with an exponent equals to 0.5. Similarly, Redondo‐Iglesias et al developed an empirical approach for estimating capacity loss during calendar aging based on a linear approximation of the Eyring equation, which has the same mathematical form as the Arrhenius equation.…”

Section: Introductionmentioning

confidence: 99%

Challenges in data‐based degradation models for lithium‐ion batteries

et al. 2020

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Summary This work summarizes the findings resulting from applying an aging modeling approach to four different capacity loss experimental datasets of lithium‐ion batteries (LIBs). This approach assumes that the degradation trajectory of the capacity is a function of three variables: time, kinetic constant, and time‐dependent factor. The analysis shows that the time‐dependent factor α is cell‐chemistry dependent and cannot be averaged for calendar and cycling modes and combined modes. This factor was also found to be a function of the stress factors. A quadratic model was used to obtain the kinetic constants per test, and statistical metrics were provided to evaluate the quality of the fitting, which was significantly affected when using averaged values of α and refitted kinetic constants. A set of test matrices is proposed for calendar, cycling, and mixed aging modes to overcome the challenges of data‐based models developed from accelerated test approaches for modeling aging in LIBs. This work also proposes a methodology to develop these data‐based aging models.

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Analysis of degradation mechanism of lithium iron phosphate battery

Cited by 8 publications

References 3 publications

Open data model parameterization of a second-life Li-ion battery

Open data model parameterization of a second-life Li-ion battery

Degradation Predictions of Lithium Iron Phosphate Battery

Challenges in data‐based degradation models for lithium‐ion batteries

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