The Standards Map — Performance Parameters Across Applications
Safety rows in a DVP usually have a threshold to look up. Performance rows usually don't. Across eVTOL, drones, humanoid robotics, and UUV, published standards govern how cycle life, C-rate, energy density, temperature envelope, SOH endpoints, and reserve policy get tested, then hand the numeric acceptance value back to the program. This cross-application map covers all six parameter classes, cites what exists at the clause level, and names every gap where the engineering team is setting the threshold rather than inheriting one.
The Standards Map — Performance Parameters Across Applications
Safety rows in a DVP usually have a threshold to look up. Performance rows usually don't. Across eVTOL, drones, humanoid robotics, and UUV, published standards govern how cycle life, C-rate, energy density, temperature envelope, SOH endpoints, and reserve policy get tested, then hand the numeric acceptance value back to the program. This cross-application map covers all six parameter classes, cites what exists at the clause level, and names every gap where the engineering team is setting the threshold rather than inheriting one.
Characterization Sources
A Battery Dataset for Electric Vertical Takeoff and Landing Aircraft
Cell-level degradation from 22 cells, one chemistry, OFAT design, with voltage/temperature end criteria rather than SOH-based endpoints.
74+ citing papers have gradually treated this notional mission profile as a de facto C-rate requirement. Trace provenance before citing.
Characterization Sources
Leading the Pack: Next-Generation Batteries for Humanoid Robotics
Peer-reviewed rationale documenting why specific humanoid DVP rows remain program-defined, usable as a citable basis for gap documentation.
Cycle-life thresholds, C-rate requirements, shock/fall profiles, and hot-swap interlock specs. The void is identified but unfilled.
Characterization Sources
High-Power Lithium-Ion Battery Characterization Dataset for Stochastic Battery Modeling
Confirms that DVP teams citing Bills' 5C aging data are working from one of very few public high-rate degradation sources.
The scarcity of independent high-C-rate datasets means eVTOL mission-profile aging results are difficult to corroborate externally.
Characterization Sources
Massively Distributed Bayesian Analysis of Electric Aircraft Battery Degradation
Independently verified Bills cell chemistry and applied degradation-mode decomposition, giving DVP teams a second interpretive layer on the same data.
Re-analysis carries forward the same 22 cells, single chemistry, and cell-level-only scope as the parent dataset.
