Unified control architecture for resilient hydrogen mobility with heterogeneous storage under realistic market logistics delays
Bibliographic record
Abstract
The growing adoption of hydrogen-powered transport demands scalable and robust control strategies for hydrogen refueling stations (HRSs) operating under uncertainty in supply, demand, and market conditions. This study presents a delay-aware predictive control framework for renewable-integrated HRSs equipped with heterogeneous hydrogen storage systems and dual interaction with electricity and hydrogen markets. The station architecture enables simultaneous refueling across multiple fuel cell electric vehicle (FCEV) classes, each served by a dedicated high-pressure storage unit, while auxiliary tanks function as buffers and market reserves. Inter-storage coordination is managed using receding horizon control across layered decision stages, allowing flexible hydrogen routing under dynamic operating conditions. A central challenge is hydrogen delivery delays, which introduce a temporal gap between procurement actions and actual availability. The proposed formulation incorporates these delays within the control horizon, classified as deterministic (fixed lead times), stochastic (modeled via discrete uncertainty sets), and logistics-based (dependent on route planning, fleet capacity, and congestion, captured through time-dependent concave functions). A mode-switching mechanism allows the operator to activate one of four control strategies: deterministic MPC (DMPC), scenario-based stochastic MPC (SMPC), convex relaxed MPC (RMPC), and scaled risk-averse SMPC (SRA-SMPC) with conditional value-at-risk and chance constraints. Convex relaxation techniques are applied to address combinatorial complexity from binary variables, nonlinear tank dynamics, and inter-market constraints, ensuring real-time tractability while preserving constraint feasibility and economic performance. Numerical simulations confirm the framework’s effectiveness in coordinating storage operations, meeting demand, and reducing costs under uncertainty, with substantial computational benefits compared to conventional approaches. • A unified control framework manages delivery delays in hydrogen refueling stations. • Delay types are categorized to enable adaptive controller selection. • Predictive strategies balance dispatch accuracy and computational efficiency. • Market interaction and renewable coordination improve cost and sustainability. • The framework supports future expansion to multi-station hydrogen networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".