Energy Efficiency and Performance of Cabin Thermal Management in Electric Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The energy used for cabin cooling and heating can drastically reduce the operating range of electric vehicles. The energy efficiency and performance of the cabin heating, ventilation and air conditioning (HVAC) system depend on the system configuration and ambient conditions. The presented research investigates the energy efficiency and performance of cabin thermal management in electric vehicles. A simulation model of cabin heating and cooling systems was developed in the AMESim software. Simulations were carried out in the standard test cycles and one real-world driving cycle to take into account different driving behaviors and environments. The cabin thermal management performance was analyzed in relation to ambient temperature, system efficiency and cabin thermal balance. The simulation results showed that the driving range can shorten more than 50% in extreme cold conditions. The energy efficiency of cabin thermal management can be improved by using a heat pump and recovering waste heat from powertrain components. According to the simulations results, a heat pump system with an electric heater can significantly reduce the HVAC system energy consumption. In mild ambient temperatures, between -5 °C and 10 °C, the driving range was increased by 6-22% depending on the driving cycle. Waste heat recovery from powertrain components further improved the energy efficiency of the heat pump system resulting in a decrease of 2-4% in the vehicle energy consumption. Simulation results also show that the battery heating in cold conditions can increase the energy consumption more than 20%.</div></div>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it