Estimation of Battery Pack Layout and Dimensions for the Conceptual Design of Hybrid-Electric Aircraft
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1362.vid The aerospace community is invested in research into hybrid-electric aircraft to meet its challenging emission reduction targets. These hybrid-electric aircraft provide several design challenges, such as lower battery energy density than typical aviation fuel, both from a mass and volume point of view. In addition, aircraft fuel can easily fill out complex shapes of the wing and fuselage tanks. To allocate sufficient space for batteries, the conceptual designer must consider the battery cell types, arrangements, thermal management system and other physical constraints. This paper proposes a method to estimate the battery pack size and dimensions suitable for conceptual design. The battery layout is defined based on individual cells grouped to form many modules that form the overall pack. The battery pack sizing method accounts for the volumetric and gravimetric contributions of energy-producing components (cells) and non-energy-producing components (such as cooling to meet aircraft certification requirements). The method is validated for lithium-ion battery packs; pack size and mass predictions are compared with the manufacturer data for electric aircraft and electric ground vehicles. The achieved accuracy is satisfactory; the approach achieves conceptual design needs, enabling battery volume and layout considerations in addition to weight. This new capability is demonstrated in a hybrid-electric retrofit case study on the Dornier DO-228 aircraft, in which the lithium-ion batteries replace sections of the wing fuel tanks. Overall, the proposed method is the first step to closing a gap in conceptual design tools for electric and hybrid-electric aircraft.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".