Optimal Energy Management and Storage Sizing for Electric Vehicles With Dual Storage
Why this work is in the frame
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Bibliographic record
Abstract
Battery degradation reduces the performance and lifetime of electric vehicles (EVs). Using energy storage devices with different characteristics alongside the battery can minimize degradation while satisfying driving demands. However, this introduces the additional complexities of sizing multiple storage devices and controlling them in real time. In this brief, we first provide a computationally tractable method to manage power-sharing between dual energy storages using approximate linear programming (ALP), an approximation of infinite horizon dynamic programming (DP). We formulate a procedure to determine the optimal sizes of the two storages based on the solution to the energy management problem to account for the tradeoff between vehicle range, storage size, and weight. We validate our approaches on a numerical case study. Numerical results show that our controller shares power efficiently between the storages, and our sizing procedure provides a design with minimal cost.
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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 | 0.001 | 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 | 0.001 |
| 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