An Exergoeconomic Analysis of Hybrid Electric Vehicle Thermal Management Systems
Bibliographic record
Abstract
In this paper, exergy analysis of a hybrid electric vehicle thermal management system (TMS) is initially investigated in order to find the areas of inefficiencies and exergy destruction within each system component. In the analysis, advanced exergy modeling is utilized to study both endogenous/exogenous and avoidable/unavoidable exergy destructions for each component of the system and further understand the interactions among the TMS components and determine the underlying reasons behind the exergy destructions. Moreover, this approach is also used to enhance exergoeconomic analyses by calculating the endogenous/exogenous and avoidable/unavoidable portion of the investment and exergy destruction costs (so-called advanced exergoeconomic analysis) in order to improve the cost effectiveness of the system and provide information on how much of the cost can be avoided for each component. Based on the analysis, it is determined that exogenous exergy destruction is small but significant portion of the total exergy destruction in each component (up to 40%, in the chiller and thermal expansion valves) and that large portion of the exergy destruction within the components (up to 70%, in the compressor) could be potentially avoided. Moreover, it is determined that electric battery, compressor, and chiller are dominated by investment cost, whereas the condenser and evaporator are dominated by the cost of exergy destruction in the system.
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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 | 0.001 |
| 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) | 0.003 | 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".