Covalently Joined Electrode Architectures for Extreme Fast Charging Li-Ion Batteries
Bibliographic record
Abstract
Li-ion batteries are the backbone of all electric vehicles (EVs) in production today [1]. However, they compete poorly against the few-minute refueling time of fossil fuel powered vehicles [2, 3]. To successfully replace fossil fuel powered vehicles, the EV batteries must support charging rates under the 15-minute mark as determined by the eXtreme Fast Charging (XFC) standard defined by the U.S. Department of Energy [4]. When subjected to XFC charging rates, well-known problems of lithium plating and delamination plague graphite anodes due to local potential gradients and binder degradation respectively [5]. Our work replaces the traditional polymer binder with conductive titanium carbide interconnects that covalently join graphite particles as well as bond them to the current collector [6]. This new architecture demonstrates ~400% increase in electrical conductivity compared to traditional architectures with polymer binders while maintaining interparticle integrity and improving adhesion. The adhesion and mechanical properties are provided by conductive chemical bonds mitigating delamination as these novel bonds suffer less mechanical and chemical degradation under high current rates than adhesion provided by polymer binders. The improved conductivity was also utilized to form a solid electrolyte interphase (SEI) at rates up to 4C reducing the ionic impedance of the SEI interface significantly [7]. The improved graphite electrodes can sustain 15-minute charge maintaining 80% of the specific capacity of the graphite particles for 800 cycles at commercially relevant loadings. Our studies introduce an effective strategy to engineer Li-ion battery electrode architectures that simultaneously improve electrical and ionic conductivity mitigating lithium plating and delamination under XFC charging rate needed for the mass adoption of EVs. Habib, A.A., S. Motakabber, and M.I. Ibrahimy. A comparative study of electrochemical battery for electric vehicles applications. in 2019 IEEE International Conference on Power, Electrical, and Electronics and Industrial Applications (PEEIACON). 2019. IEEE. Balali, Y. and S. Stegen, Review of energy storage systems for vehicles based on technology, environmental impacts, and costs. Renewable and Sustainable Energy Reviews, 2021. 135: p. 110185. Andwari, A.M., et al., A review of Battery Electric Vehicle technology and readiness levels. Renewable and Sustainable Energy Reviews, 2017. 78: p. 414-430. Dufek, E.J., et al., Developing extreme fast charge battery protocols–A review spanning materials to systems. Journal of Power Sources, 2022: p. 231129. Ahmed, S., et al., Enabling fast charging–A battery technology gap assessment. Journal of Power Sources, 2017. 367: p. 250-262. Rangom, Y., Covalently Joined Carbonaceous and Mettalloid Powders by Carbide-Based Interconnects and Method of Fabrication for High-Performance Electrodes. 2021: United States preliminary patent #63/248,293. Rangom, Y., T.T. Duignan, and X. Zhao, Lithium-ion transport behavior in thin-film graphite electrodes with SEI layers formed at different current densities. ACS Applied Materials & Interfaces, 2021. 13(36): p. 42662-42669. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".