LEADER 03489nam 22005895 450 001 9910831500403321 005 20240201125923.0 010 $a3-658-43188-1 024 7 $a10.1007/978-3-658-43188-4 035 $a(MiAaPQ)EBC31106845 035 $a(Au-PeEL)EBL31106845 035 $a(DE-He213)978-3-658-43188-4 035 $a(EXLCZ)9930316549800041 100 $a20240201d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries$b[electronic resource] /$fby Friedrich von Bülow 205 $a1st ed. 2024. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2024. 215 $a1 online resource (0 pages) 225 1 $aAutoUni ? Schriftenreihe,$x2512-1154 ;$v170 311 08$aPrint 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 327 $aTowards 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. 330 $aGiven the limitations of state-of-the-art methods, this book presents a state of health (SOH) forecasting method that is suitable for lithium-ion battery (LIB) systems in real-world battery electric vehicle operation. Its histogram-based features can capture the higher operational variability compared to constant and controlled laboratory operation. Also, the transferability of a trained machine learning model to new LIB cell types and new operational domains is investigated. The presented SOH forecasting method can be provided as a cloud service via a web or smartphone app to fleet managers. Forecasting the SOH enables fleet managers of battery electric vehicle fleets to forecast and plan vehicle replacements. About the author Friedrich von Bülow studied mechanical engineering and automation engineering at RWTH Aachen University. He completed his doctoral thesis at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal (BUW) while working in the automotive industry as a data scientist with a special interest in the analysis of time series data and applications of machine learning. 410 0$aAutoUni ? Schriftenreihe,$x2512-1154 ;$v170 606 $aAutomotive engineering 606 $aVehicles 606 $aAutomobile industry and trade 606 $aElectric power production 606 $aAutomotive Engineering 606 $aVehicle Engineering 606 $aAutomotive Industry 606 $aElectrical Power Engineering 615 0$aAutomotive engineering. 615 0$aVehicles. 615 0$aAutomobile industry and trade. 615 0$aElectric power production. 615 14$aAutomotive Engineering. 615 24$aVehicle Engineering. 615 24$aAutomotive Industry. 615 24$aElectrical Power Engineering. 676 $a629.2 700 $avon Bülow$b Friedrich$01669355 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831500403321 997 $aUNINA