A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries

Buch | Softcover
XXXII, 227 Seiten
2024 | 1st ed. 2024
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-43187-7 (ISBN)

Lese- und Medienproben

A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries - Friedrich von Bülow
117,69 inkl. MwSt

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.

lt;p>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.

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.

Erscheinungsdatum
Reihe/Serie AutoUni – Schriftenreihe
Zusatzinfo XXXII, 227 p. 59 illus., 26 illus. in color. Textbook for German language market.
Verlagsort Wiesbaden
Sprache englisch
Maße 148 x 210 mm
Gewicht 344 g
Themenwelt Technik Elektrotechnik / Energietechnik
Technik Fahrzeugbau / Schiffbau
Technik Maschinenbau
Schlagworte Battery Aging • Battery Electric Vehicles • Forecasting • Lithium-Ion Battery • machine learning • State of health • transfer learning
ISBN-10 3-658-43187-3 / 3658431873
ISBN-13 978-3-658-43187-7 / 9783658431877
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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