Fault Detection for Lithium-Ion Battery Using Smooth Variable Structure Filters
Bibliographic record
Abstract
Batteries are prone to faults that may arise because of vibrations, deformations, collisions, or improper usage. These faults can be sorted into two main categories: internal and external faults. In this study, external battery faults, particularly sensor faults that affect the measurement of current and voltage, are investigated. This paper proposes a fault-detection strategy that is built on different variants of the Smooth Variable Structure Filter (SVSF) for the detection of such faults in a battery cell. SVSF is applied to estimate the State of Charge (SoC) and terminal voltage of the battery. A modified decision signal is calculated using the residual signals of the filters to detect faults using the Cumulative Sum (CUSUM) strategy. The performance of the SVSF is compared with that of the Extended Kalman Filter (EKF). The effectiveness of the proposed method is demonstrated for detecting and isolating different external faults in a wide range of fault scenarios. The proposed SVSF-based methods not only improve fault-detection accuracy but also significantly decrease the fault-detection time in some scenarios compared to EKF, which is a critical factor for safety.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".