Analysis of the battery performance in hybrid electric vehicle for different traction motors
Bibliographic record
Abstract
Hybrid electric vehicles (HEVs) have gained immense popularity due to the rapidly increasing stringent emission norms and global environmental concerns. Unlike conventional vehicles, the presence of high charge sustaining rechargeable energy storage system (battery) in HEV distributes the vehicular power demand, thus considerably reducing the size of the internal combustion engine and in turn the fuel consumption. The HEV is equipped with a high power traction motor that is powered by the battery or the generator and is directly connected to the transmission. This motor also has regenerative braking capability, thus transferring energy back into the battery which otherwise would have been wasted in the form of heat. The present automotive industry is using different types of motors for HEV application, depending upon the extent of their use and power requirement. The battery performance is a function of the motor operation. In this paper, the characteristics of different types of traction motors will be discussed. DC and induction motors will be then tested against a designed electric circuit model of battery in order to determine the respective charge-discharge characteristics. The designing of the battery management system is dependent on the analysis of this charge-discharge variation for determining the precision battery parameters such as voltage and current limits, and permissible state-of-charge.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".