Data-Driven Approaches to Diagnostics and State of Health Monitoring of Maritime Battery Systems

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Published Oct 26, 2023
Erik Vanem Qin Liang Carla Ferreira Christian Agrell Nikita Karandikar Shuai Wang Maximilian Bruch Clara Bertinelli Salucci Christian Grindheim Anna Kejvalova Øystein Alnes Kristian Thorbjørnsen Azzeddine Bakdi Rambabu Kandepu

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 maneuvering is growing. In order to ensure the safety of such electric ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) can be verified by independent tests. However, this paper addresses data-driven approaches to state of health monitoring of maritime battery systems based on operational sensor data. Results from various approaches to sensor-based, data-driven degradation monitoring of maritime battery systems will be presented, and advantages and challenges with the different methods will be discussed. The different approaches include cumulative degradation models and snapshot models. Some of the models need to be trained, whereas others need no prior training. Moreover, some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Different datasets have been used in order to explore the various methods, including public datasets, data from laboratory tests and operational data from ships in actual operation. Lessons learned 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.

How to Cite

Vanem, E., Liang, Q., Ferreira, C. ., Agrell, C., Karandikar, N., Wang, S. ., Bruch, M., Bertinelli Salucci, C., Grindheim, C. ., Kejvalova, A., Alnes, Øystein, Thorbjørnsen, K. ., Bakdi, A., & Kandepu, R. (2023). Data-Driven Approaches to Diagnostics and State of Health Monitoring of Maritime Battery Systems. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3437
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Keywords

Battery diagnostics, State of health, Maritime, data-driven approaches

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Section
Technical Research Papers