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 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 | 0.001 |
| 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".