Dovetail, as part of its development of a net zero powertrain for the retrofit of small regional aircraft (such as the Cessna Caravan) to full electric, is developing an inhouse solution for its propulsion battery.

Within that battery design, the correct prediction of the battery state of health and time to overhaul are key elements to allow customers to reliably operate their aircraft and limit the battery replacement and therefore improve their useful life. As a secondary consequence of an accurate state of health prediction, end-of-life batteries can be better used for second life applications due to a better understanding of their remaining life and characteristics.

Within this research, Dovetail is looking at developing algorithms using experimental battery and cell datasets and Machine Learning/Artificial Intelligence to provide a fast and accurate prediction of the battery life in the field based on a limited number of parameters.

The research project may look at different data analysis methodologies for the individual cell but also for the integrated pack and evaluate different algorithmic solutions to extract valuable information on the battery state of health and time to overhaul.