Real-Time State of Power Estimation of a Lithium-Ion Battery Using Robust OCV and Internal Resistance Estimates
Bibliographic record
Abstract
This article considers the problem of estimating the state of power (SOP) of a battery in real time. The SOP refers to maximum power that a battery can absorb or release during charging and discharging, respectively. The SOP is determined using the open circuit voltage (OCV) and internal resistance of the battery, both of which change with the state of charge (SOC). Existing approaches employ nonlinear system identification techniques that require the knowledge of the battery parameters, especially the parameters of the OCV–SOC characteristics. Further, the estimates of SOP from existing approaches are sensitive to factors such as temperature, SOC, aging, and OCV–SOC characterization parameters. This article presents a novel approach to SOP estimation based on a novel observation model that is independent of temperature, SOC, aging, and OCV–SOC characterization parameters. The proposed approach does not require nonlinear approaches for both system identification and filtering; instead, a linear least squares estimation technique is utilized to estimate the two parameters needed for SOP computation. The SOP estimates computed through the proposed approach are tested using realistic battery data collected from three battery cells at the following eight different temperatures:$-\text{25}\,^\circ {\text{C}}$,$-\text{15}\,^\circ {\text{C}}$,$-\text{5}\,^\circ {\text{C}}$,$\text{5}\,^\circ {\text{C}}$,$\text{15}\,^\circ {\text{C}}$,$\text{25}\,^\circ {\text{C}}$,$\text{35}\,^\circ {\text{C}}$, and$\text{45}\,^\circ {\text{C}}$. From this analysis, it was observed that the proposed SOP estimation technique exhibited an SOP estimation error of 0.5 W across all the chosen temperatures except for very low SOC regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".