Modeling and Simulation of Fuel Cell Elevator Backup Power Systems Using Advanced Vehicle Simulator
Bibliographic record
Abstract
With limited available space in the city and increasing land cost, multi-storey and high-rise buildings now dominate most urban areas of the world. The irresistible trend to build taller and taller buildings to leverage increasing land cost turns elevator from a tool of convenience to a necessity of life. This dependence of elevator further requires its continuous function in spite of power failure caused by a variety of reasons. Reliable and effective elevator power backup system becomes an urgent need today. In this work, advanced electric power backup technologies, including battery, ultracapacitor and hydrogen fuel cells, are examined. To design a functional elevator backup power system, and to assess the feasibility of a battery–ultracapacitor–fuel cell hybrid elevator backup power system with superior performance, the modeling and simulation of an elevator and its backup power system are carried out. Based on its resemblance to an electric vehicle traveling vertically, the elevator, its power need and performance are modeled using the MatLab/Simulink based hybrid vehicle design and analysis tool, ADvanced VehIcle SimulatOR (ADVISOR). The modeling and simulation provide guidelines for selecting and sizing energy storage and conversion devices. More importantly, the quantitative analysis allows complex battery–ultracapacitor–fuel cell hybrid backup power system to be optimized to reach the best potential of each components for a given elevator usage cycle. To explore the feasibility of wide commercial applications of this technology, the initial cost, maintain costs and reliability of the battery–ultracapacitor–fuel cell hybrid elevator backup power system are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".