Battery modeling approaches and management techniques for Plug-in Hybrid Electric Vehicles
Bibliographic record
Abstract
Batteries play a critical role in Hybrid Electric Vehicles (HEV) and Plug-In HEV (PHEV) as one of the main energy sources because of their high energy density. Therefore, for designing purposes suitable battery models are necessary. Batteries have very nonlinear behavior and are dependent on many factors such as chemistry, temperature, load profile, charge/discharge algorithm, age, etc. Modeling the behaviors of the batteries can be achieved using different approaches and techniques. The more accurate battery model is, the more reliable results are obtained using simulation softwares. However, increasing the accuracy of the model increases the complexity of the whole system model and also the time of the simulations. In the case of vehicular applications batteries are used as packs of hundreds of cells which adds to the complexity of the model, since some behaviors are exaggerated. Besides, some effects such as the difference in state-of-charge of different cells which leads to the age reductions of the whole battery pack, or for example the temperature difference of the cells in different places of the pack cannot be ignored. For optimal designing purposes there should be a kind of tradeoff between accuracy and complexity. There are various battery models based on various approaches and techniques in the literature which may cause confusion. This paper tries to summarize and categorize different battery models with main focus on vehicular applications.
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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 | 0.000 |
| 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 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".