The propulsion system of vertical take-off and landing vehicles relies heavily on batteries as the main power source. The batteries’ health and remaining capacity should be carefully monitored for the safe and healthy operation of the eVTOL vehicles. State-of-the-art data-driven machine learning algorithms are increasingly used for battery status estimation. This study proposes a machine learning-based estimation technique to monitor the state of health, remaining useful life and maximum operating temperature of an eVTOL vehicle's battery. In this context, random forest and 2nd-, 3rd- and 4th-degree polynomial regression algorithms are implemented on a mission-based, publicly generated dataset. The limited data availability condition considered for each mission dataset and the hyperparameters of machine learning algorithms are also optimized to enhance the estimation accuracy. The results indicate that the least weighted average error rates in estimating battery state of health, remaining useful life and maximum operating temperature are achieved by algorithms: 2nd-degree polynomial regression, 3rd-order polynomial regression and random forest. Finally, the proposed technique achieves lower error rates in battery state estimation than prior studies, despite the limited data availability during the learning phase of the selected data-driven algorithms.
Çınar et al. (Mon,) studied this question.