State of Charge Estimation in Li-ion Batteries Using a Robust Nonlinear Observer with Low-Cost Sensors
Bibliographic record
Abstract
This paper presents a robust Luenberger observer for accurate State of Charge (SOC) estimation in lithium-ion batteries. A Lyapunov-based strategy is used to make the system work appropriately with model uncertainties as well as measurement problems. The proposed observer jointly estimates SOC, terminal voltage, and sensor-induced measurement errors using a second-order battery model. This method is particularly useful for stationary energy storage systems using low-cost sensors. Low-cost sensors typically have a large bias with inaccurate measurements. We conducted the experiment using a 25 A lowcost current sensor. In the experiment, we added bias as an error to the current measurement. The experimental results show that, compared to the conventional Sliding Mode Observer (SMO), the proposed Luenberger observer significantly decreases the measurement error, effectively improving the accuracy of the SOC estimation. In addition, the observer decreases chattering effects and offers robust performance. The proposed observer can be used to reduce the battery management systems using low-cost sensors, without compromising the SOC estimation accuracy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".