A Critical Review and Future Prospects of Control-Oriented HVAC Modeling Strategies in Electric Vehicles
Bibliographic record
Abstract
Electric Vehicles (EVs) have emerged as a promising solution in the transportation industry, but their adoption is hindered by range anxiety due to uncertainty in driving range. Specifically, severe weather conditions can result in a high requirement for the use of Heating, Ventilation and Air Conditioning (HVAC) to regulate cabin’s thermal comfort, leading to significant demand for battery power. To address this, understanding real-time HVAC power usage can help precise range prediction and control. Furthermore, achieving real-time capability involves exploring simplified control-oriented models for EV HVAC systems. Therefore, this research aims to address the gap between current and previous HVAC modelling research for EVs by providing a detailed discussion of three modelling techniques: physics-based, data-driven, and hybrid models. Later, various evaluation metrics such as modelling level capability, accuracy, complexity, generalization, adaptability, cost, and required effort are defined and used to compare these models. The potential of using control-oriented models for design optimization, synthetic data generation, fault detection, diagnosis, prognosis, and Failure Mode Effect Analysis (FMEA) is also discussed, and the need for further research in this area is noted. Overall, this paper provides a comprehensive overview of control-oriented HVAC modelling for EVs and offers insights for researchers in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".