Efficient Artificial Neural Network for Predicting Power and Range in Fuel Cell Electric Vehicles
Bibliographic record
Abstract
Fuel cell electric vehicles (FCEVs) generate electricity through a chemical reaction between hydrogen and oxygen, powering an electric motor.These vehicles emit only water vapor, making them an eco-friendly transportation option.But the problem is the prediction of true power demand and the remaining range of vehicles hence, to address these issues a novel Competent Recurrent Neural Network is proposed.The transient response of fuel cells induces sudden changes in power demand, so the accurate prediction of power demand is bargained due to the irregular nature of the transient response.Thus, a novel Adaptive Erratic Model Predictive Control effectively addresses the challenges posed by the transient response of fuel cells in Fuel Cell Electric Vehicles (FCEVs) by predicting and adjusting to rapid power demand fluctuations with high accuracy.Furthermore, the fluctuating power demand in FCEVs, coupled with the non-linear relationship between current density and voltage, undermines the accuracy of range prediction, contributing to unpredictable efficiency levels.So, a novel, Provision Vector XGBoost Lapse captures linear and non-linear relationships between driving dynamics and fuel cell performance thereby predicting their impact on fuel cell efficiency over time.Moreover, the time delay inherent in the response of fuel cell systems to changes in range worsens the challenge.So, the novel Autoregressive Integrated Seasonal Decomposition of Time Series is proposed to reduce time delays and enhance prediction accuracy by adapting the dynamic nature of driving conditions and fuel cell responses in real time.As a result, when compared to other existing models the proposed model achieves an accuracy of 99.5%, SOC of 92%, and a low RMSE score of 1.12, MRE of 0.015, R^2 of 1.05 with a minimal computation time of 3s, which proves its robustness for fuel cell EV applications..
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".