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).

电动垂直起降 (eVTOL) 飞机是城市和城郊机动性的一种解决方案。为了监控飞机系统和功能并减轻安全问题，预测和健康管理 (PHM) 使用最先进的机器学习方法。锂离子电池作为 eVTOL 的主要动力源，需要进行监控。要预测电池老化，最重要的预测参数是其健康状态 (SoH)。在本文中，我们提出了一种机器学习方法来预测和预测电池 SoH（回归）。这项研究基于一个公共数据集，该数据集由 22 个电池组成，这些电池在数百个充电/放电循环中进行了测试，电源需求模拟了典型的 eVTOL 任务。在使用滚动窗口进行特征工程和预处理之后，使用交叉验证和网格搜索（线性回归、支持向量机、k-最近邻 (kNN)、随机森林和梯度提升树）将 5 个机器学习模型调整为训练数据集。发现 kNN 在最短的训练时间（1 μs/点）下给出了最好的验证和测试分数。最后，经过更精细的超参数调整（滚动窗口中的观察次数和 k 个邻居），发现 kNN 可以准确预测多达 200 个周期（≈500 小时）的 SoH（测试-R ²  ≈ 0.98)。