The Role of Long Lifetime Li-Ion Cells in a Sustainable Future
Bibliographic record
Abstract
State-of-the-art Li-ion cells can have decades of lifetime (>40 years) and tremendous cycle life greater than 10000 cycles1. Such incredible cells greatly exceed the goal of 80% capacity retention after 800 cycles, promoted by some as sufficient for electric vehicles (EVs). However, by 2030 it is projected that more than 90% of all Li-ion batteries will be used to power vehicles2, with very few remaining for energy storage from intermittent renewables which must displace fossil fuels for power generation. The batteries in electric vehicles will represent a vast amount of energy storage capacity, which can be harnessed using vehicle to grid (V2G) technology. Batteries with that can maintain 80% of their capacity after a mere 800 cycles are unsustainable since most of the charge-discharge cycles in V2G will occur when the EV is stationary. For this reason, ultra-long-lived cells are a critical component of a sustainable future. In this talk, we will discuss the important role of graphite material and upper cut-off voltage (UCV) on the lifetime of NMC811 cells, and how avoiding volume contraction in Ni-rich materials yields cells that should be eminently suitable V2G applications3,4. Additionally, we will discuss the results of a two-year study on the impact of C-rate, depth of discharge (DoD), UCV, and temperature on the lifetime of NMC811 cells. Finally, we will demonstrate that NMC cells with a low UCV, with an emphasis on Ni-rich materials, yield greater energy throughput compared to their higher energy counterparts due to their prolonged lifetime. The $/kWh metric is surely important when selecting a battery technology, but the levelized cost of energy over the battery lifetime is more important when batteries last for many decades. References Harlow, J. E. et al. A Wide Range of Testing Results on an Excellent Lithium-Ion Cell Chemistry to be used as Benchmarks for New Battery Technologies. Journal of The Electrochemical Society 166, A3031–A3044 (2019). Pillot, C. The Rechargeable Battery Market and Main Trends 2018-2030. https://rechargebatteries.org/wp-content/uploads/2019/02/Keynote_2_AVICENNE_Christophe-Pillot.pdf (2019). Eldesoky, A. et al. Impact of Graphite Materials on the Lifetime of NMC811/Graphite Pouch Cells: Part I. Material Properties, ARC Safety Tests, Gas Generation, and Room Temperature Cycling. Journal of The Electrochemical Society 168, 110543 (2021). Eldesoky, A. et al. Impact of Graphite Materials on the Lifetime of NMC811/Graphite Pouch Cells: Part II. Long-Term Cycling, Stack Pressure Growth, Isothermal Microcalorimetry, and Lifetime Projection. Journal of The Electrochemical Society 169, 010501 (2022).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".