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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
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.
Bibliographic Information
Book Title: A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries
Authors: Friedrich von Bülow
Series Title: AutoUni – Schriftenreihe
DOI: https://doi.org/10.1007/978-3-658-43188-4
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Computer Science and Engineering (German Language)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2024
Softcover ISBN: 978-3-658-43187-7Published: 02 February 2024
eBook ISBN: 978-3-658-43188-4Published: 01 February 2024
Series ISSN: 1867-3635
Series E-ISSN: 2512-1154
Edition Number: 1
Number of Pages: XXXII, 227
Number of Illustrations: 33 b/w illustrations, 26 illustrations in colour
Topics: Automotive Engineering, Mechanical Engineering, Automotive Industry, Energy Systems