Details

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

AutoUni - Schriftenreihe, Band 170

von: Friedrich von Bülow
109,99 €
Verlag:	Springer Vieweg
Format:	PDF
Veröffentl.:	01.02.2024
ISBN/EAN:	9783658431884
Sprache:	englisch

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Beschreibungen

Titelbeschreibung

<p>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.</p>

Inhaltsverzeichnis

<p>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.</p>

Autorenportrait

<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.</p>

Back cover copy

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.<div><br></div><div><b>About the author <br></b></div><div><b>Friedrich von Bülow</b> 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.<br></div>