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


  • Erik Vanem DNV GRD, Høvik, Norway & University of Oslo, Department of Mathematics, Oslo, Norway
  • Qin Liang DNV GRD, Høvik, Norway
  • Maximilian Bruch Fraunhofer ISE, Freiburg, Germany
  • Gjermund Bøthun Corvus Energy, Bergen, Norway
  • Katrine Bruvik Corvus Energy, Bergen, Norway
  • Kristian Thorbjørnsen Corvus Energy, Porsgrunn, Norway
  • Azzeddine Bakdi Corvus Energy, Porsgrunn, Norway



State of Health, Battery, Condition Monitoring, Diagnostics, Data-driven analytics


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 growing. To 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 tests. This paper addresses statistical modelling of SOH for maritime lithium-ion batteries based on operational sensor data. Various methods for sensor-based, data-driven degradation monitoring will be presented, and advantages and challenges with the different approaches will be discussed. The 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 models. 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. Insight 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.


Conflict of Interest Statement

The authors declare no conflicts of interest.


Metrics Loading ...




How to Cite

Vanem, E., Liang, Q., Bruch, M., Bøthun, G., Bruvik, K., Thorbjørnsen, K., & Bakdi, A. (2024). Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships. Journal of Dynamics, Monitoring and Diagnostics, 3(1), 11–20.



Regular Articles