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Presented at Forum 82 — the Vertical Flight Society's Annual Forum and Technology Display
Electric Vertical Takeoff and Landing Technical Session
13 pages
Abstract:
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
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