Impact of cell spreading on second‐life of lithium‐ion batteries
Bibliographic record
Abstract
Abstract The evolution of the degradation paths of the cells in battery packs is shaped by both intrinsic cell‐to‐cell variations, also known as cell spreading, and spatial–temporal cell‐to‐cell differences in temperature and other stress factors. To account for these variations and differences in degradation, we propose a statistical approach for modelling the degradation of lithium‐ion batteries (LIBs) that utilizes a three‐parameter non‐homogeneous gamma process, allowing for the prediction of the capacity fade or time‐to‐failure for any LIB architecture. This degradation modelling approach has been integrated into a cost model to investigate the sensitivity of the battery lifetime's economic outcome, aiming to maximize the added value of stationary battery storage composed of degraded electric vehicle batteries. Thus, the impact of cell spreading was quantified for three simulation scenarios on second‐life applications: (i) performing a simplified cost analysis to evaluate the business case for second‐life, (ii) performing an economic analysis to visualize the impact of spreading, and (iii) evaluating an existing power flow control strategy between LIB modules. The information derived from the spreading‐cost integration approach is valuable to support the technical and economic analyses in the decision‐making process of designing, installing, and running efficiently second‐life applications.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".