HYBRID ENERGY STORAGE SYSTEMS FOR ENERGY EFFICIENT ELECTRIC DRIVE VEHICLES: CONVENTIONAL APPROACHES AND MUTLI-SOURCE TOPOLOGIES
Bibliographic record
Abstract
With the increasing global awareness and call for reduced dependence on fossil fuels and their polluting byproducts, energy efficient electric vehicles are gaining more attention for replacing internal combustion engine vehicles. Current battery technologies such as the Li-ion batteries, as the main source of energy in the electrified vehicles offer good energy density features, which fulfills the energy requirements for covering long mileages. However, other performance metrics for batteries such as power density, thermal operating range, and cyclic life span is not sufficiently high. In this regard, electric vehicle manufacturers have been overdesigning the battery packs in their products to offer adequate degree of confidence to the market for more appeal towards the electrified vehicles. This has led to an increase in the weight, volume, and the price of the battery packs. As an attempt to address the aforementioned challenges, hybridization of the battery packs with super-capacitors has been suggested. Super-capacitors offer complementary characteristics to the battery packs such as high-power density, wide thermal operating range, and high number of cyclic life span. Nevertheless, hybridization of the battery packs as the energy source and the super capacitors banks as the power source to take the advantages of both sources introduces new challenges. The need for electrical converters for compliance of these sources, and the requirement for the control system to share the power demand between these two sources based on their inherent features are among the concerns to be addressed. This thesis introduces new electronic hardware structure for incorporative utilization of battery packs and super-capacitor banks, moderated by supervisory control systems for power sharing purposes between the energy and the power sources in electric vehicle applications.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".