Real-Time Ambient Temperature Estimation Using Kalman Filter and Traction Power-Aware Cabin Climate Control in Battery Electric Vehicles
Bibliographic record
Abstract
In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".