Comprehensive Modeling of Vehicle Air Conditioning Loads Using Heat Balance Method
Bibliographic record
Abstract
The Heat Balance Method (HBM) is used for estimating the heating and cooling loads encountered in a vehicle cabin. A load estimation model is proposed as a comprehensive standalone model which uses the cabin geometry and material properties as the inputs. The model is implemented in a computer code applicable to arbitrary driving conditions. Using a lumped-body approach for the cabin, the present model is capable of estimating the thermal loads for the simulation period in real-time. Typical materials and a simplified geometry of a specific hybrid electric vehicle are considered for parametric studies. Two different driving and ambient conditions are simulated to find the contribution and importance of each of the thermal load categories. The Supplemental Federal Test Procedure (SFTP) standard driving cycle is implemented in the simulations for two North American cities and the results are compared. It is concluded that a predictive algorithm can be devised according to the driving conditions, vehicle speed, orientation, and geographical location. By using this model, the pattern of upcoming changes in the comfort level can be predicted in real-time in order to intelligently reduce the overall AC power consumption while maintaining driver thermal comfort.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".