Two-Stage Energy Management Control of Fuel Cell Plug-In Hybrid Electric Vehicles Considering Fuel Cell Longevity
Bibliographic record
Abstract
As the dependence on fossil fuel increases in the transportation sector, more attention has been paid to the energy management control of proton exchange membrane fuel cell (PEMFC) plug-in hybrid electric vehicles (PHEVs). In this paper, the energy management control problem for a series plug-in PEMFC/Li-ion battery hybrid midsize sedan is formulated and investigated using a two-stage controller (TSC). The control objective is to minimize hydrogen consumption and simultaneously protect PEMFC health. The proposed TSC consists of two controllers designed in two stages with different control functions. During the first design stage, a predictive controller is developed using the telemetry equivalent consumption minimization strategy (T-ECMS) approach to predict the global battery state-of-charge (SOC) optimality trend and local control reference, without regard for the PEMFC health constraints. During the second stage of design, a tracking controller is designed to track the local control reference with respect to the PEMFC health constraints and other physical limitations at the current control step, which ensures that the system follows the optimal battery SOC reference over a long time horizon. Finally, the effectiveness of the proposed TSC is compared with the T-ECMS and an electric vehicle controller (EVC) under the Matlab/Simulink software environment. The results demonstrate that the TSC achieves a reasonable tradeoff between hydrogen fuel consumption and PEMFC health protection.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".