Dual-health protocol modelling of Li-ion battery degradation for UAVs with capacity–resistance coupled metrics in aviation duty cycles

Batteries must be able to hold enough usable energy, and have enough power to operate, for high power UAVs and electric VTOLs. But state-of-health based on capacity can deem resistance degraded batteries in good condition for use when there is significant voltage sag risk. This work proposes a dual-health protocol-aware model using both the reference-discharge capacity retention and resistance-derived power retention to assess and schedule the battery degradation. Chronological time, current, cumulative discharge capacity and pulse-resistance measurements were used to reconstruct cycling and reference-performance data for 22 Sony–Murata VTC6 cells based on different aviation duty protocols. The analysis resulted in 439 valid capacity checkpoints, 388 impedance checkpoints and 415 paired health states. A generalized estimating-equation model was used to quantify the nonlinear cycling and protocol effects and to account for the repeated within-cell observations. The leakage-safe leave-one-cell-out validation was used to assess a random-forest predictor. Although there was a strong association between the two degradation measures (Spearman coefficient of 0.882), capacity loss was strongly correlated with resistance growth. The trajectory of the capacity-health was down 2.468% points per 100 mission-equivalent cycles and was highly curved. At the last checkpoint observed, 20 out of 21 cells were resistance-limited for power retention and 1 cell was capacity-limited. The median dual-health scores at 500 cycles varied between 73.52% for extended cruise operation to 90.05% for lower voltage charging. The cell-wise validation showed a mean absolute error of 0.800% point, a root-mean-square error of 1.101% point and coefficient of determination of 0.975. The dual-health metric has the benefit of avoiding over-stating the operational fitness and capacity of a UAV, while providing a conservative state metric for battery eligibility screening, swapping, and risk-aware UAV mission allocation.