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Record W2273601043 · doi:10.1149/ma2015-01/2/473

Memory-Effect in Li-Ion Battery Electrodes Unraveled

2015· article· en· W2273601043 on OpenAlexaff
M. Farkhondeh, Mark Pritzker, Michael Fowler, Mohammadhosein Safari, Charles Delacourt

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrodeBattery (electricity)Materials scienceMesoscopic physicsIonPhase (matter)CrystalliteParticle (ecology)Charge (physics)ChemistryPhysicsCondensed matter physicsThermodynamicsPhysical chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

The memory effect in Li-ion batteries first observed by Sasaki and co-workers1 in a LiFePO4electrode is defined as an abnormal potential overshoot in the charge–discharge cycle immediately following a partial charge–discharge cycle. This phenomenon is observed when the electrode undergoes 3 sequential cycles: i) memory-writing cycle where the electrode is first charged to a certain state-of-charge (SOC), then allowed to rest before being discharged to a given SOC, ii) memory-release cycle consisting of a full charge–discharge cycle which follows a rest step after memory writing and iii) a regular cycle which is simply a repetition of the memory-release cycle as a control test. A potential bump appears during the memory-release cycle which coincides with and tracks the depth of the memory-writing cycle (Fig. 1). A mathematical model has been developed2 to describe the dynamics of a phase-separating electrode (e.g., LiFePO4) and relies on two main features: i) non-equilibrium single-phase lithiation/delithiation of elementary units making up the active material and ii) non-uniformity of at least one physical property among the elementary units or across the electrode. An elementary unit may be interpreted as a single Li channel lying along the b-axis, a crystallite within a polycrystalline particle or even a nanoparticle in a nanoparticulate porous electrode. This mesoscopic unit is the smallest entity that can exchange species with its counterparts across the electrode. Model simulations reveal that the necessary condition for the memory effect to occur is the existence of a non-zero residual capacity at the onset of the memory-release charge which may originate either from a non-zero initial SOC or from an imbalanced writing cycle (unequal initial and final SOC). More importantly, it is shown that the collective electrochemical lithiation/delithiation of units in an electrode leaves it in a condition which is at the heart of the memory effect and other unusual observations in phase-separating electrodes (e.g., LiFePO4). Such unusual behavior is of critical importance in designing battery management algorithms which inevitably rely on cell voltage in addition to coulomb counting for SOC estimation. In this presentation, we shed light on the lithiation/delithation dynamics of LiFePO4 electrodes through a simple mathematical mesoscopic model. This model can simultaneously explain several other unique observed features associated with LiFePO4electrochemical performance: - quasi-static potential hysteresis - high rate-capability - cycle-path dependence - polarization overshoot in GITT compared with continuous cycling at the same current - bell-shaped current response in PITT and - existence of several apparent equilibria - memory effect To the best of our knowledge, this is the first successful description of the electrode response to multiple operating modes including continuous/intermittent partial/full galvanostatic and potentiostatic cycles that predicts the above features (Fig. 1). Figure 1: (a) Memory effect simulated for a 30% deep memory-writing cycle followed by a 10 min rest and memory-release cycles carried out at a rate of C/2. (b) Simulated open-circuit potential of the electrode undergoing different charge-discharge paths. References: 1. T. Sasaki, Y. Ukyo, and P. Novak. Nature Mater., 12:569–575, 2013. 2. M. Farkhondeh, M. Pritzker, M. Fowler, M. Safari, and C. Delacourt. Phys. Chem. Chem. Phys., 16:22555–22565, 2014. Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.267
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes1
Has abstractyes

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