An Optimal Energy Management System for Battery Electric Vehicles
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".