Battery Thermal Management of Electric Vehicles: An Optimal Control Approach
Bibliographic record
Abstract
The main goal of the current investigation is to evaluate the potential of the optimal control techniques to improve the quality of battery thermal management (BTM) in electric vehicles (EVs). The BTM is of profound importance in EVs, where battery is the sole power source and any unexpected change in the battery temperature could dramatically affect the vehicle performance and its driving range. As a result, the battery temperature should be maintained within a specific range to lead to the best vehicle performance. Despite the obvious importance of this issue, there exist rare reports in the literature addressing the design of optimal controllers for BTM of EVs. Here, the authors intend to apply and compare two controllers to solve this problem for an EV: an optimal controller designed upon dynamic programming (DP) and a proportional integral-derivative (PID) controller. The results indicate that by using DP, a less amount of the battery power will be demanded, and therefore, the vehicle’s driving range can be increased.
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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".