Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

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Published Nov 24, 2021
Erik Vanem Øystein Åsheim Alnes James Lam

Abstract

Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented. 

How to Cite

Vanem, E., Alnes, Øystein Åsheim, & Lam, J. (2021). Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.2972
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Keywords

Battery systems, degradation modelling, diagnostics, capacity fade, data-driven models

Section
Technical Papers