03489nam 22005895 450 991083150040332120240201125923.03-658-43188-110.1007/978-3-658-43188-4(MiAaPQ)EBC31106845(Au-PeEL)EBL31106845(DE-He213)978-3-658-43188-4(EXLCZ)993031654980004120240201d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierA Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries[electronic resource] /by Friedrich von Bülow1st ed. 2024.Wiesbaden :Springer Fachmedien Wiesbaden :Imprint: Springer Vieweg,2024.1 online resource (0 pages)AutoUni – Schriftenreihe,2512-1154 ;170Print version: von Bülow, Friedrich A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries Wiesbaden : Springer Vieweg. in Springer Fachmedien Wiesbaden GmbH,c2024 9783658431877 Towards State of Health Forecasting of Lithium-Ion Batteries -- Structure Literature Survey of Related Work -- Battery Cell State of Health Forecasting -- Transfer of Battery Cell State of Health Forecasting -- Battery System State of Health Forecasting -- Concept for a Technical Implementation.Given the limitations of state-of-the-art methods, this book presents a state of health (SOH) forecasting method that is suitable for lithium-ion battery (LIB) systems in real-world battery electric vehicle operation. Its histogram-based features can capture the higher operational variability compared to constant and controlled laboratory operation. Also, the transferability of a trained machine learning model to new LIB cell types and new operational domains is investigated. The presented SOH forecasting method can be provided as a cloud service via a web or smartphone app to fleet managers. Forecasting the SOH enables fleet managers of battery electric vehicle fleets to forecast and plan vehicle replacements. About the author Friedrich von Bülow studied mechanical engineering and automation engineering at RWTH Aachen University. He completed his doctoral thesis at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal (BUW) while working in the automotive industry as a data scientist with a special interest in the analysis of time series data and applications of machine learning.AutoUni – Schriftenreihe,2512-1154 ;170Automotive engineeringVehiclesAutomobile industry and tradeElectric power productionAutomotive EngineeringVehicle EngineeringAutomotive IndustryElectrical Power EngineeringAutomotive engineering.Vehicles.Automobile industry and trade.Electric power production.Automotive Engineering.Vehicle Engineering.Automotive Industry.Electrical Power Engineering.629.2von Bülow Friedrich1669355MiAaPQMiAaPQMiAaPQBOOK9910831500403321UNINA