Experimental Results of Battery Power Capability Measurement on Cells with Different State of Health Levels
Bibliographic record
Abstract
Nowadays, lithium-ion batteries are widely used in electric vehicles (EVs). For safer and more efficient battery operation, a battery management system (BMS) that estimates the state of charge (SOC), state of health (SOH) and power capability (state of power, SOP) is required. Accurately estimating SOP is particularly challenging due to its susceptibility to various factors, including temperature, SOC, and aging. To gain insight into how various factors affect power capability, this study conducts an SOP measurement test throughout a battery aging test at two different temperatures. The SOP measurement technique utilized is developed from our previous patented work. Analysis of the impact of aging, SOC, SOH, and temperature on SOP dynamics are conducted. Furthermore, our study scales the cell-level results to a plug-in hybrid electric vehicle, offering practical insights into SOP estimation algorithms for usable energy and driving range scenarios. Given the absence of comparable data online, these experimental results serve as a valuable resource for developing an accurate power capability estimation algorithm.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".