State‐of‐Charge estimation of Li‐ion battery at different temperatures using particle filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
State‐of‐Charge (SOC) estimation is one of the fundamental functions undertaken by Battery Management System (BMS) in an Electric Vehicle (EV) to assess the residual service time of the battery during operation. Thus, an accurate model of the battery that efficiently describes its dynamic characteristics is necessary for precise SOC estimation. The variation in temperature effects battery parameters, and consequently, the estimation of SOC is subject to change in temperature. In this paper, the identification of parameters of battery model is considered as an optimisation problem and solved using meta‐heuristic Ageist Spider Monkey Algorithm (ASMO) under the influence of varying temperature. The developed model is used for SOC estimation using three Recursive Bayesian filtering based adaptive filter algorithms. Further, the efficiency of the implemented adaptive filter algorithms is compared in terms of solution quality and computation time required for evaluation of SOC.
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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 it