Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications

Electric vertical take-off and landing (eVTOL) aircrafts is one solution for the urban and peri-urban mobility. To monitor the aircraft systems and functions and to mitigate safety concerns, prognostic and health management (PHM) uses state-of-the-art machine learning approaches. Lithium-ion battery, as the main power source of eVTOL, needs to be monitored. To anticipate the battery ageing, the most important parameter to predict is its state-of-health (SoH). In this article, we present a machine learning methodology to predict and forecast the batteries SoH (regression). This study was based on a public dataset consisting of 22 cells that were tested over several hundreds of charge/discharge cycles, with power demands simulating typical eVTOL missions. After feature engineering and preprocessing using a rolling window, 5 machine learning models were adjusted to the train dataset using cross-validation and grid search (linear regression, support vector machines, k-nearest neighbors (kNN), random forest and gradient boosted trees). kNN was found to give the best validation and test scores for the lowest training time (1 μs/point). Finally, after finer hyperparameters tuning (number of observations in the rolling window and k neighbors), kNN was found accurate to forecast SoH up to 200 cycles (≈500 h) ahead (test-R2 ≈ 0.98).