Energy management of battery electric vehicles hybridized with supercapacitor using stochastic dynamic programming
Bibliographic record
Abstract
In this study, a stochastic Energy Management System (EMS) for Battery Electric Vehicles (BEVs) hybridized with the supercapacitor is proposed. At each moment, the EMS should determine an optimal power distribution between the supercapacitor and the battery. Because of the uncertain nature of the power demand, an effective EMS should be able to handle uncertainties. As a result, a Stochastic Dynamic Programming (SDP) approach has been proposed and demonstrated to be successful. In this investigation, the power demand has been predicted based on a Markov chain assumption using some real drive cycles data points. The used drive cycles are categorized in two groups, which are training drive cycles and test ones. The Transition Probability Matrix (TPM) is built by the training cycles; meanwhile simulation results are based on the test drive cycles. In comparison to the results of other methods, the SDP results show more improvements. In addition, in terms of computational costs, it has a significant advantage over the other rival approaches.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".