Integration of Water Management Into the Energy Management Strategy for a Fuel Cell Vehicle
Bibliographic record
Abstract
Unlike other studies that focus solely on determining the optimal power distribution for a fuel cell hybrid electric vehicle (FC-HEV), this article aims to integrate water management into the energy management strategy (EMS) by designing an adaptive efficient purge strategy. This article investigates the impact of integrated water management into the EMS (IWM-EMS) on the performance and operating cost of FC-HEV. The IWM-EMS consists of two levels: the first level is the EMS, which determines the optimal power, and the second level is the proposed purge strategy. The proposed IWM-EMS in this article is the adaptive IWM-EMS (AIWM-EMS), which utilizes an adaptive efficient purge strategy that can adjust based on the fuel cell (FC) reference current. Another IWM-EMS is the nonadaptive IWM-EMS (NAIWM-EMS), which utilizes a constant purge strategy suggested by the FC manufacturer for all current levels. The results show that the FC-HEV under AIWM-EMS consumes 4.09% less hydrogen than the NAIWM-EMS and reduces total operating cost by up to 5.21%. In addition, 78.36% of the FCs’ operating time is spent in the high-efficiency zone.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".