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Record W2346174636 · doi:10.1149/ma2016-03/2/1212

Optimizing Energy Density of Li-Ion Batteries Using Thick Electrodes

2016· article· en· W2346174636 on OpenAlexaboutno aff
Zhijia Du, David L. Wood, Claus Daniel, Jianlin Li

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSeparator (oil production)AnodePower densityElectrolyteEnergy storageElectrodeCoatingCathodeSpecific energyLithium (medication)IonBattery (electricity)Composite materialSlurryChemical engineeringElectrical engineeringChemistryPower (physics)

Abstract

fetched live from OpenAlex

Low-cost, high energy density lithium-ion batteries (LIBs) have been consistently pursued in consumer electronics, electrical vehicles (EVs) and stationary (grid) energy storage. Modern Li-ion cells can have an energy density of up to 550 Wh/L, compared to only 200 Wh/L in the late 1990s [1]. The gradual energy density improvement over the last 15-20 years was mostly due to the improved engineering of the electrode coatings and, to some extent, some improvement in active material development and manufacturing. The former has significantly increased the volume ratio of active materials (which provides the capacity of the battery) from ~20% at early stages to ~45% in the state-of-art LIBs [2,3]. Thickening the cathode and anode is one effective approach to further increase the active material content, enabled from reducing current collector and separator layers in the stack, which further improves the energy density and lowers the cost of LIBs. It has been estimated that significant reduction in pack cost can be realized when compared to conventional manufacturing if electrode thickness is doubled and aqueous slurry processing is utilized [4]. However, thicker electrodes lead to poor kinetics and electrolyte salt depletion and thus underutilization of active materials. The major concern of thick coatings is electrolyte salt depletion, which results in fewer lithium ions available in the liquid phase for reaction at the active material surface. Therefore, there is an optimum thickness for maximum energy and power density. To maximize energy and power density, the effect of various coating parameters on the reaction kinetics and thus battery performance must be considered during electrode design. The operation of LIBs follows porous electrode theory and electrochemical reaction thermodynamics, and the governing equations have been summarized by Newman et al. [5]. Thus, the porous electrode model is used in the present work to investigate the effect of manufacturing parameters such as electrode thickness and porosity. In the present study, the following will be discussed: (1) The effect of thickness on active material utilization, areal capacity, voltage and energy density. (2) The effect of porosity on active material utilization, areal capacity, voltage and energy density. (3) Possible solutions to further increase the energy density of thick-electrode battery. Acknowledgement This research at Oak Ridge National Laboratory (ORNL), managed by UT Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725, was sponsored by the Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office (VTO) Applied Battery Research (ABR) subprogram (Program Managers: Peter Faguy and David Howell). References: [1] J. F. Rohan, M. Hasan, S. Patil, D. P. Casey and T. Clancy, Energy Storage: Battery Materials and Architectures at the Nanoscale, p113. [2] R. Moshtev, J. Power Sources, 91, 86 (2000). [3] M.N. Obrovac, New Metal-Ion Battery Chemistries, Workshop on Energy, Advanced Materials and Sustainability May 29, 2015, Halifax, NS Canada. [4] D.L. Wood, J. Li, and C. Daniel, J. Power Sources, 275, 234 (2015). [5] M. Doyle, T. F. Fuller, and J. Newman, J. Electrochem. Soc., 140 (6), 1526 (1993).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.236
Teacher spread0.219 · 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
Published2016
Admission routes1
Has abstractyes

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