Study of the electric power balance in a vehicle for the choice of the battery
Bibliographic record
Abstract
The number of electrical components in vehicles is day by day increasing, requiring a careful dimensioning of the electric system and a correct choice of the components that will have to provide electrical power to the whole system. The amount of electric energy that may be required during the usage of a car is very variable, depending on how many and which loads are active at the moment; the choice of the power generating elements must then be done in a way that guarantees a certain performance, usually relative to the most critical working conditions that may be encountered by the vehicle in its lifetime. In the present paper a study of the fundamental characteristics of the electric system of a vehicle is proposed with particular attention to the two main components, the alternator and the battery; the fundamental equations for the electric power balance in the vehicle are reported and discussed as a preamble for the implementation of a Matlab model able to simulate the behavior of the charge and discharge of a car battery just considering the overall energy balance. The graphical visualization of the trend of the battery charge over many operating cycles allows the user of the program to choose the battery with the most suitable capacity avoiding oversizing or downsizing of this fundamental component. Particular emphasis has been put on the choice of the requirements that the battery charge has to satisfy throughout its lifecycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".