A Data-Driven Snapshot Method for State of Health Modelling and Diagnostics of Maritime Battery Systems

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Published Oct 28, 2022
Erik Vanem Maximilian Bruch Qin Liang David Vicente Reyes Gonzalez Øystein Åsheim Alnes

Abstract

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 and manoeuvring 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. There are different strategies for such data-driven diagnostics. Some approaches, referred to as cumulative damage models, require full operational history of the batteries in order to predict state of health, and this may be impractical due to several reason. Thus, snapshot methods that are able to give reliable estimation of state of health based on only snapshots of the data streams are attractive candidates for data-driven diagnostics of battery systems on board ships. In this paper, data-driven snapshot methods are explored and applied to a novel set of degradation data from battery cells cycled in laboratory tests. The paper presents the laboratory tests, the resulting battery data, shows how relevant features can be extracted from snapshots of the data and presents data-driven models for state of health prediction. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for 40% of the tested cells. This is greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. Nevertheless, improved accuracy is required for such snapshot methods to be recommended for ships in actual operation.

How to Cite

Vanem, E., Bruch, M., Liang, Q., Gonzalez, D. V. R. ., & Åsheim Alnes, Øystein. (2022). A Data-Driven Snapshot Method for State of Health Modelling and Diagnostics of Maritime Battery Systems. Annual Conference of the PHM Society, 14(1). https://doi.org/10.36001/phmconf.2022.v14i1.3154
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Keywords

Battery diagnostics, state of health, data-driven methods, maritime

Section
Technical Papers