An Optimal Energy Management System for Battery 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
Environmental pollution and high fuel costs have increased demands for an alternative energy source for transportation. Battery Electric Vehicles (BEVs) are attracting the attention of researchers of automotive engineering field to address these concerns because of their reputation for being fully green as well as more efficient than Internal Combustion Engine Vehicles (ICEVs). However, two major problems with BEVs are their short driving range and the limited service life of their costly batteries. Enhancing BEVs’ driving range and their batteries’ lifetime are possible through developing more effective energy management systems (EMSs) for them. This study proposes an optimal EMS for a BEV, the Toyota RAV4 EV, by considering the power flow between the energy consumers inside the vehicle. Dynamic programming (DP) is used to find an optimal power distribution between the vehicle drivetrain and the heating system for a standard driving cycle. A high-fidelity model of the vehicle in Autonomie is also employed to demonstrate the effectiveness of the devised EMS. The results show that the proposed strategy can improve the battery health of the considered BEV.
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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 | 0.000 |
| 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 it