Performance assessment of thermal management systems for electric and hybrid electric vehicles
Bibliographic record
Abstract
Thermal management systems (TMS) are one of the key components of electric and hybrid electric vehicles to achieve high vehicle efficiency and performance under all operating conditions. Current improvements in electric battery technology allow vehicles to have relatively long ranges, fast acceleration, and long life while keeping low-maintenance costs and considerably lower emissions. However, the vehicle performance is significantly affected by the battery operating conditions. Moreover, the cell life cycle, safety, and possibility of thermal runaway significantly depend on peak temperature rise and temperature uniformity of the battery. Therefore, various TMSs are created to keep batteries within ideal operating ranges. In this article, three different TMS systems—passive cabin cooling (via air), active moderate liquid circulation (via refrigerant), and active liquid circulation (via refrigerant and coolant)—are analyzed and compared with electric and hybrid electric vehicles. A second law analysis is used to examine the areas of low exergy efficiency in each system and minimize the entropy generation based on the system configuration. Moreover, TMS systems are compared on the basis of battery temperature increase and temperature uniformity. Various parametric studies are conducted to compare the TMS in different ambient and operating conditions. On the basis of the analysis, the active liquid circulation (via refrigerant and coolant) is determined to have the lowest battery temperature increase (3.9 °C in 30 min) and most cell temperature uniformity (2.5 °C median) as well as the lowest entropy generation rate (0.0121 W/K) among the compared systems. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".