Ageing Estimation of Lithium-Ion Batteries Applied to a Three-Wheel PHEV Roadster
Bibliographic record
Abstract
Predicting the ageing behaviour of a lithium-ion battery is a challenging difficulty in plug-in hybrid electric vehicles applications. Invoking the expensive price of the energy density linked with considerable performances degradation over cycling, it is worth considering a battery lifetime optimization in the design process. The degradation will materialize by a capacity loss and an increase of the internal resistance. Capacity loss will affect the autonomy of the vehicle while an increase of the internal resistance will lead to an increase of the battery power losses and overheating. In the paper, the lifetime of a PHEV roadster battery pack is estimated by applying a real-life current discharge pattern, instead of the common ageing experiments based on constant current discharge patterns. A current cycle corresponding to a roadster power load has been used as input. This work involves an accelerated ageing experiment of individual LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> battery cells through cycling. The cycle applied is derived from a speed cycle comprised of three parts: urban roads, rural roads and highways. For more than 1400 cycles, the capacity, internal resistance, voltage, current and temperature have been monitored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".