Offline Parameter Identification and SOC Estimation for New and Aged Electric Vehicles Batteries
Bibliographic record
Abstract
The hybrid (HEVs) and battery electric vehicles (BEVs) represent a sustainable alternative in compare to conventional, fossil fuel-based vehicles. Battery pack is a major and the most expensive part of electric vehicles. It requires accuracy, real-time monitoring, and control. Parameters such as state of charge (SOC) and state of health (SOH) have to monitor accurately to guarantee battery safety and reliability and avoid overcharge or under-discharge conditions. These conditions can lead to irreversible capacity degradation and power fade. An accurate battery model with robust estimation method is needed for battery condition monitoring. In this paper, a way to estimate battery parameters is proposed for the first order equivalent circuit battery model (OCV-R-RC) using genetic algorithm (GA) optimization at various ages of the battery to track the changes. The smooth variable structure filter (SVSF) strategy has been considered to estimate the state of charge based on the optimized model. An aging test has been conducted over a period of 12 months using real-world driving scenarios. Experimental results are provided to show the efficacy of the proposed approach.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".