Robust Sensor Fault Diagnosis for Lithium-Ion Battery Systems Using an Electro-Thermal Model
Bibliographic record
Abstract
Sensor faults can have a significant negative impact on battery system performance, and if not properly detected, may result in failure or safety issues. Hence, it is important to diagnose these faults in real time. Fault detection and isolation methods in battery systems mainly depend on current and voltage sensor measurements. This paper presents a model-based fault diagnosis approach for a Li-ion battery cell using an electro-thermal model. The proposed method uses the Extended Kalman Filter to estimate the state of charge and terminal voltage in real time. In addition, by incorporating a thermal model, the system not only enhances its fault detection capability by considering temperature effects but also effectively isolates various sensor faults, including current, voltage, and temperature sensor faults. Temperature-adaptive cumulative sum control charts are proposed to detect any small deviation between measured and estimated data, which indicates faults through adaptive process-related parameters. Moreover, the method demonstrates greater robustness against false alarms compared to the threshold method and maintains high fault detection performance even under noisy sensor measurements. The proposed fault diagnosis scheme is then validated by investigating different fault scenarios for each sensor through Li-ion battery cell experimental US06 drive cycle data, which demonstrates the effectiveness of the proposed method in modeling and fault diagnosis for various operating conditions in an electric vehicle battery system. The results demonstrate that the proposed method can be systematically tuned for different fault scenarios and operating temperatures, enabling accurate detection of various sensor faults across a wide temperature range with zero false alarms and minimal detection delay.
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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) | 0.000 | 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 | 0.000 |
| 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".