The maritime sector, akin to the transportation industry at large, is increasingly tasked with achieving sustainability and climate protection objectives. In response to these demands, there is a notable rise in the construction and retrofitting of battery-electric and hybrid ocean-going vessels. Safety remains a paramount concern within this domain, particularly as the battery system becomes a critical component for maneuverability. Consequently, classification societies have mandated regular health assessments of these systems. Currently, the battery capacity is measured annually through complete charge and discharge cycles onboard the vessels.
This presentation will elucidate the findings from project DDD-Batman (Data-Driven Degradation monitoring and prediction of BATteries for Maritime ApplicatioNs; 03SX525B), which developed a method for determining the aging state based on operational data [1]. The assessment encompasses the state of health (SOH), internal resistance, and, depending on the specific procedure, the degradation mode (loss of active material LAM, loss of lithium inventory LLI) of the battery cells. The methodology involves several key steps:
1. Receiving and preparing data as well as defining an appropriate time frame for the analysis.
2. Approximating an equivalent circuit model of the battery cells´ overvoltage to determine the open circuit voltage (OCV) values.
3. Determining capacity (thereby also SOH) from the OCV-capacity relationship (see picture) or assessing electrode capacities and electrode imbalance (or LLI) based on the OCV correlation [2].

The procedure was certified in early 2024 after the project completion and now replaces the time- and cost-intensive capacity measurement (https://corvusenergy.com/corvus-energy-first-marine-ess-supplier-to-enable-data-driven-state-of-health-test-soh/).

[1] E. Vanem, Q. Liang, M. Bruch, G. Bøthun, K. Bruvik, K. Thorbjørnsen, A. Bakdi, Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships, JDMD (2024). https://doi.org/10.37965/jdmd.2024.500 .
[2] [1] C.R. Birkl, M.R. Roberts, E. McTurk, P.G. Bruce, D.A. Howey, Degradation diagnostics for lithium ion cells, Journal of Power Sources 341 (2017) 373–386. https://doi.org/10.1016/j.jpowsour.2016.12.011 .