Advancements in Temperature-Sensing Technologies for Lithium-Ion Batteries in Electric Vehicle Thermal Management Systems: A Comprehensive Review
Bibliographic record
Abstract
Recently, the growing popularity of electric vehicles (EVs) has drawn attention to the significance of adequate battery thermal management systems (BTMS) for lithium-ion batteries (LIBs), which play a critical role in providing safety, performance, and longevity. However, due to the recent incidents of EV explosions caused by the thermal runaway of LIBs, a solid BTMS is designed to face these expected challenges to ensure battery safety. Temperature sensors are one of the most critical components of BTMS to ensure the efficient and safe operation of the battery system. An adequate temperature sensing system ensures the optimal thermal condition of the batteries, providing a faster time response and greater accuracy to detect abnormalities and non-homogeneous temperature variations. This prevents overheating and ensures an ideal working temperature for safe operation. This study provides a comprehensive review of temperaturesensing technologies, including direct and indirect techniques. The study introduces the working principle of each sensor and its application in BTMS. Additionally, the current work discusses the temperature range, accuracy, data filtration, and data transmission of each sensor. Moreover, the study reviews the temperature sensing location either on the battery surface or inside the battery cell. Lastly, the review describes the challenges and prospects of research ideas for utilizing these temperature sensors to contribute to the development of safer and more efficient BTMS.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".