Experimental Performance Analysis of LG E-66 Cells from a Fast-Charging Porsche Taycan Battery Module
Bibliographic record
Abstract
The Porsche Taycan, which is capable of up to 270 kW DC fast charging, can be charged from 5 to 80% state of charge (SOC) within 23 minutes. To better evaluate the battery storage system performance of the Taycan and explore ways to improve fast charging capability for electric vehicles (EVs), this work comprehensively tested a battery module as well as a battery cell taken from the Porsche Taycan EV. To monitor the thermal performance of the module during charging, three thermocouples are placed at the bottom of the module, and nine more on the cell surface within the actual module. Battery cell characterization tests were performed under -10, 0, 10, 25 and 40°C, and the module was evaluated based on the charging efficiency and the power loss at different charging rates. The results show that charging efficiency at 0.5C, 1C, and 1.5C is 97.9%, 97.1%, and 95.9%, respectively. Furthermore, a rescaled fast charge profile from the Porsche Taycan EV was applied to charge the module. The results show that, compared to a constant 1.5C charge, the fast-charge profile can charge the module to 80% SOC 1.4 times faster, with a cell surface temperature distribution of only ±1.9°C.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".