Effect of Control Strategy on the Performance of a Fuel Cell Hybrid Electric Auto Rickshaw
Bibliographic record
Abstract
The basis for this paper is a project whose objective is to examine the feasibility of converting a diesel powered auto rickshaw to fuel cell/battery hybrid electric operation. One of the most important factors that influences the performance of hybrid vehicles is the energy management and power distribution between the different energy sources. This paper examines the impact of the control strategy on performance. The optimization of the energy management system is a supervisory control problem. One of the most popular cost functions for optimization involves the sum of fuel consumption and equivalent fuel consumption from the battery state of charge (SOC), commonly referred to as the equivalent consumption minimization strategy (ECMS). In this paper, a modified ECMS is tested together with three different management control strategies on a model of a fuel cell hybrid electric rickshaw using a realistic drive cycle. The 1 st tested was a fuel cell load following strategy in which the power of the fuel cell tracked the demanded power and the role of the battery was to supplement power when demand exceeded the capacity of the fuel cell. The 2 nd tested was a battery load following strategy in which the fuel cell shuts down when the SOC is above a given threshold, and turns on when the SOC is below a given threshold. The 3rd tested was an optimized fuel cell strategy in which fuel cell operation was restricted to its most efficient region. The strategies are documented via flow charts. A performance comparison of the different strategies is presented, where the main performance measures are given by distance traveled, fuel economy and speed tracking error.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".