Predictive Health-Conscious Energy Management Strategy of a Hybrid Multi-Stack Fuel Cell Vehicle
Bibliographic record
Abstract
The use of a multi-stack fuel cell (FC) hybrid electric vehicle (HEV) has recently attracted a lot of attention as a key to enhance system modularity, efficiency, and redundancy. An essential challenge here is the high operating cost of the FC-HEV, which includes the cost of hydrogen, FC degradation, and energy storage system (ESS). For this purpose, this study proposes a predictive health-conscious (PHC) energy management strategy (EMS) to reduce the total operating cost of a multi-stack FC-HEV. The proposed EMS is a multi-layer strategy. In the upper layer, a rule-based strategy is devised to determine how many FCs should be activated considering the degradation of FCs, requested power, and battery state of charge (SOC) to decrease the hydrogen consumption and prevent extreme FC degradation. The lower layer is a real-time optimization algorithm called model predictive control (MPC) that employs its predictive ability to share the optimal power between FCs and the battery. Unlike the other studies, a new velocity predictor, stacked bidirectional long short-term memory (SBLSTM) networks, is utilized in the lower layer to provide accurate future velocity prediction for the MPC. The performance of the proposed strategy is benchmarked with dynamic programming (DP) and equivalent consumption minimization strategy (ECMS) under two different driving cycles. The obtained results indicate that the proposed PHC-EMS achieves close results to DP (with maximum of 3.728% difference) while outperforming ECMS up to 5.154%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".