Massively Distributed Bayesian Analysis of Electric Aircraft Battery Degradation

Electric vertical takeoff and landing (EVTOL) aircrafthave highpower and energy requirements that must be understood throughout theirbatteries' life. Using a fast electrochemical model and highthroughput computing, we independently and simultaneously sample thedegradation modes for 21392 cycles of an EVTOL aircraft battery dataset, obtaining a posterior probability distribution for each degradationmode throughout the life of the aircraft. The model shows a medianerror of 32.5 mV (or 1.9%) across all cycles. We conduct an identifiabilityanalysis on the generated mode distributions. We analyze correlationsbetween the modes and cycling characteristics, finding that depthof discharge, temperature, and charging current all have significantimpacts on degradation. We introduce a protocol for early identificationof active degradation mechanisms, identifying electrolyte oxidation,active material dissolution, and growth of a solid-electrolyteinterphase as the most likely causes of battery degradation. Finally,we discuss other applications of large-scale sampling of battery degradationmodes.

Automated summary

This study uses a fast electrochemical model and high-throughput computing to analyze the degradation modes of an EVTOL aircraft battery dataset. The research identifies the causes of battery degradation and finds that depth of discharge, temperature, and charging current have significant impacts. A protocol for early identification of active degradation mechanisms is proposed.