Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery Systems
Bibliographic record
Abstract
Accurate fault diagnosis is critical to the safe and efficient operation of lithium-ion battery systems. However, various faults in battery systems are difficult to detect and isolate due to their similar features. This paper proposes a model-based multi-fault diagnosis method to detect and isolate the current, voltage, and temperature sensor faults, short circuit faults, and connection faults in the lithium-ion battery systems. An electro-thermal model with fault information is established and used to construct the structural model. Structural analysis theory is applied to design diagnostic tests sensitive to multiple faults. To improve the accuracy and robustness of residual generation, the adaptive extended Kalman filter is introduced to battery state estimation. The multi-fault detection and isolation are implemented using residual evaluation based on the cumulative sum algorithm. Furthermore, a fault indicator used to distinguish short circuit and connection faults is presented with low computational complexity. The diagnostic results of various fault tests show that the proposed diagnostic method can accurately detect and isolate multiple faults without changing the voltage measurement topology of the battery pack.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".