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Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships

Journal of Dynamics, Monitoring and Diagnostics 2024年第1期3卷 11-20页

作者： Erik Vanem Qin Liang Maximilian Bruch Gjermund Bøthun Katrine Bruvik Kristian Thorbjørnsen azzeddine bakdi DNV GRD HøvikNorway Department of Mathematics University of OsloOsloNorway Fraunhofer ISE FreiburgGermany Corvus Energy BergenNorway Corvus Energy PorsgrunnNorway

Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is *** ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent *** paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor *** methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be *** different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed *** of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of *** include simple statistical models and more complicated machine learning *** from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

关键词： battery condition monitoring data-driven analytics diagnostics state of health

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