Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles
Bibliographic record
Abstract
Each electric vehicle (EV) requires a low-voltage (e.g., 12V) auxiliary battery to provide electric power to onboard electronic control units, lighting systems, and various sensors during power off. Therefore, when the low-voltage battery is in low state of health (SOH) or low state of charge (SOC), it may cause no-start events. The existing OnStar Proactive Alert service can effectively predict low SOC or low SOH events for low-voltage batteries in Internal Combustion Engine vehicles using cranking signals. However, it does not work for EVs since there is no cranking event. In this work, a diagnostic and prognostic solution for the low-voltage battery of EVs is proposed. Four novel health indicators (HIs) along with the decision-making system are developed based on equivalent circuit models. Furthermore, the selection process of appropriate HIs tailored to various operational states of the vehicle is described. The validation results based on GM test EV data have demonstrated the effectiveness and robustness of the proposed solution.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".