Economic Evaluation of Using Ultracapacitors in Electric Vehicles
Bibliographic record
Abstract
The main challenge of hybridizing ultracapacitors (UCs) with batteries in electric vehicles is their uncertain economic viability, besides their complexity and weight, which should be fully addressed. Therefore, this article determines the general condition for achieving a justified economic system, which is held when the average annual cost (AAC) of a battery‐UC system over a vehicle's useful life is lower than the annual cost of a sole‐battery for a specific system design, energy management strategy, vehicle type, and driving style. As such, the energy storage system is designed in a case study vehicle, and the optimal current distribution is found by dynamic programming (DP) under UDDS, HWFET, and US06 driving cycles. Then, by economic analysis, it is indicated that although adding an UC incurs additional costs, it saves the AAC by improving the battery health and prolonging its lifespan up to a maximum of 15‐year calendar life, which proves its economic justification. Investing in UCs is more economically viable for vehicles with severe driving cycles and high current stress. Finally, the DP optimal trajectory is implemented into an experimental setup under the US06 driving cycle to verify the evaluated strategy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".