Modelling and optimal energy-saving control of automotive air-conditioning and refrigeration systems
Bibliographic record
Abstract
Air-conditioning and refrigeration systems are extensively adopted in homes, industry and vehicles. An important step in achieving a better performance and a higher energy efficiency for air-conditioning and refrigeration systems is a control-based model and a suitable control strategy. As a result, a dynamic model based on the moving-boundary and lumped-parameter method is developed in this paper. Unlike existing models, the proposed model lumps the effects of the fins into two equivalent parameters without adding any complexity and considers the effect produced by the superheated section of the condenser, resulting in a model that is not only simpler but also more accurate than the existing models. In addition, a model predictive controller is designed on the basis of the proposed model to enhance the energy efficiency of the air-conditioning and refrigeration systems. Simulations and experimental results are presented to demonstrate the accuracy of the model. The experiments show that an energy saving of about 8% can be achieved by using the proposed model predictive controller compared with the conventional on–off controller under the examined scenario. The better performance of the proposed controller requires electrification of the automotive air-conditioning and refrigeration systems so as to eliminate the idling caused by running the air-conditioning and refrigeration systems when a vehicle stops.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".