Investigation of Li-Ion Batteries for Fast Charging at Low Temperature
Notice bibliographique
Résumé
Fast-charging of Li-ion batteries has become a major interest, since it is seen as the strategy of choice to enable a wider adoption of electric vehicles (EVs) by bringing a realistic solution to long-range transit. However, very little is known about how repeated fast-charging affects the battery performances in the long term, and how winter temperatures, more extreme in northern countries such as Canada, may impact the fast charging ability of the battery. Optimization of both the chemistry and the manufacturing parameters are needed to develop EV batteries capable of sustaining repeated fast-charging. The work was carried out during a four years project at the National Research Council of Canada, with the overall objective to get a better understanding of the parameters affecting the ability of a Li-ion battery to charge at high rate and sustain repeated fast-charging, especially at low temperatures. A first set of experiments investigated the comparative fast charging performance of a number of 18650 format commercial cells at various temperatures. Figure 1 shows a table summarizing the ability of 11 of these cells to charge under different regimes at room temperature or -10 °C. Interestingly, the variability in the results seemed to be related to multiple parameters. Also, the capability rating for fast-charging was not the same at ambient, sub-ambient or sub-zero temperatures. Best contenders were repeatedly fast-charged for 300 cycles at various temperatures. Here again the results showed differences in how the cells are able to sustain repeated fast-charging. The cells were then disassembled for an in-depth post-mortem study of the failure modes due to fast-charging. A number of degradation mechanisms were revealed, including macroscopic deterioration of the electrodes at the core of the jelly-roll, aluminum current collector corrosion, exfoliation of graphite and formation of thick SEI films at the anode surface. Figure 2 shows example of results from the post-mortem analysis. In a second set of experiments, the fast-charging performance of selected components was comparatively investigated in laboratory cells, including various active materials, separators and electrolyte formulations. In an example of results to be presented, Figure 3 displays the fast-charging capability of several cathode materials in full cells at 23 and 0 °C. LFP and NCA seem to be the best choices for fast-charging Li-ion cells at ambient temperature whereas LCO shows the lowest performances for rates over C/3. However, LFP showed some limitations at 0 °C, whereas LCO happened to be the best performing cathode. These results clearly show that even if the graphite anode bears the most significant challenges relative to fast-charging, cathode materials should not be overlooked in the optimization of fast-charging capabilities. The third set of experiments, currently being carried out, studies the effects of electrode manufacturing parameters (loading, porosity, N/P ratio) using a pilot-scale pouch cell prototyping line, and supported by model simulations. The presentation will highlight the results of all three sets of experiments, including results from simulation, electrochemical testing and post-mortem analysis, in an attempt to give the audience a comprehensive guidance towards the chemistries and manufacturing parameters of Li-ion cells better suited for fast-charging. Figure 1
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».