Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning  

作　　者：Mihaela Mitici Birgitte Hennink Marilena Pavel Jianning Dong 

机构地区：[1]Faculty of Science,Utrecht University,Heidelberglaan 8,3584 CS,Utrecht,The Netherlands [2]Faculty of Aerospace Engineering,Delft University of Technology,Kluyverweg 1,HS 2926,Delft,The Netherlands [3]Electrical Engineering,Mathematics and Computer Science Faculty,TU Delft,2628 CD,Delft,The Netherlands

出　　处：《Energy and AI》2023年第2期145-162,共18页能源与人工智能（英文）

摘　　要：The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and remaining-useful-lifetime.Using several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

关 键 词：Electric Vertical Take-off and Landing vehicles Lithium-ion battery STATE-OF-HEALTH Machine learning Remaining-useful-life 

分 类 号：TM9[电气工程—电力电子与电力传动]