Optimal energy management of fuel cell hybrid electric vehicle based on model predictive control and on-line mass estimation
Bibliographic record
Abstract
Energy management strategies with prediction message show great potential in optimizing control objectives for fuel cell hybrid vehicles. This paper presents a novelty model-predictive-control based energy management framework for fuel cell commercial vehicle, in which the vehicle mass is firstly introduced as a variable parameter for controller. A vehicle mass identification model is established and embed in the framework based on recursive least square algorithm, and the influence of variable algorithm parameters on estimator is evaluated. Then the effects of mass varying on vehicle performance is talked about in detail. In addition, the performance for different drive cycles on different loaded is also given. The simulation results show that the fuel consumption is positively correlated with the mass identification error, and the designed controller can reduce the additional fuel consumption to around 0.1%, which is much batter to the deterministic parameters scheme. Finally, the shortcomings of the designed controller are given. This paper provides a reliable theoretical basis to address varying-mass vehicle energy management problems.
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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 | 0.000 |
| 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".