Advanced Real-Time Battery State Estimation for a Hybrid Aircraft
Bibliographic record
Abstract
Canada's aviation industry aims to reduce its carbon footprint while maintaining safety and operational standards. Hybrid and electric aircraft offer a promising solution to reducing the environmental impact of conventional aviation. However, current limitations in battery technology and the robustness of battery management systems in monitoring and estimating battery states cause significant challenges for their adoption in aviation applications. This research focuses on developing an advanced state of charge (SoC) estimation method for Li-ion batteries used in hybrid aircraft. A new approach is proposed that integrates a merged set of equivalent circuit models capable of simulating battery dynamics at both the cell and module levels. In addition, an innovative dual filter involved in interactive multiple model (IMM) frameworks is introduced, equipped with advanced filtering approaches such as smooth variable structure filter (SVSF). This framework ensures accurate SoC estimation even under varying and harsh operating conditions by testing all developed algorithms in real-time. The developed model is much more accurate than the existing SoC estimation algorithms in the aircraft battery management system. These findings increase the understanding of battery performance in specific aviation conditions and help develop safer and more advanced condition monitoring and estimation methods for hybrid and electric aircrafts.
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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 | 0.000 |
| Open science | 0.000 | 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".