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
Battery diagnostics, State of health, Maritime, data-driven approaches
Statistics and Computing, 31(27). Bertinelli Salucci, C., Bakdi, A., Glad, I. K., Vanem, E., &De Bin, R. (2022). Multivariable fractional polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage, 52, 104903.
Bertinelli Salucci, C., Bakdi, A., Glad, I. K., Vanem, E., & De Bin, R. (2023). A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries. Journal of Power Sources, 556, 232429.
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F.,Pradhan, N., Karaletsos, T., . . . Goodman, N. D. (2018).
Pyro: Deep Universal Probabilistic Programming. Journal of Machine Learning Research.
Bole, B., Kulkarni, C. S., & Daigle, M. (2014). Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use. In Proceedings of the annual conference of the prognostics and health management society 2014. PHM Society.
Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement error in nonlinear models. A modern perspective (Second ed.). Chapman & Hall.
dos Reis, G., Strange, C., Yadav, M., & Li, S. (2021). Lithium-ion battery data and where to find it. Energy and AI, 5, 100081.
Feng, X., Li, J., Ouyang, M., Lu, L., Li, J., & He, X. (2013). Using probability density function to evaluate the state of health of lithium-ion batteries. Journal of Power Sources,
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, B., David, Vehtari, A., & Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). CRC Press.
Goldstein, M., & Wooff, D. (2007). Bayes linear statistics: Theory and methods (1st ed.). Wiley.
Grindheim, C. A. (2022). Methods for battery state of health estimation (Master’s thesis). Department of Mathemathics, University of Oslo.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.
Hill, D., Agarwal, A., & Gully, B. (2015). A review of engineering and safety considerations for hybrid-power (lithium-ion) systems in offshore applications. Oil and Gas Facilities, 4(3), 68-77.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Ibraheem, R., Strange, C., & dos Reis, G. (2023). Capacity and internal resistance of lithium-ion batteries: Full degradation curve prediction from voltage response at constant current at discharge. Journal of Power Sources, 556, 232477.
Jiang, B., Dai, H., & Wei, X. (2020). Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition. Applied Energy,
Kejvalova, A. (2022a). Maritime battery capacity estimation (Technical report No. 2022-0945). DNV.
Kejvalova, A. (2022b). Total least squares estimation of maritime battery capacity (Master’s thesis). Department of Mathemathics, University of Oslo.
Liang, Q., Vanem, E., Alnes, Ø., Xue, Y., Zhang, H., Lam, J.,& Bruvik, K. (2022, October). Data-driven state of health monitoring for maritime battery systems – a case study on sensor data from a ship in operation. In Proc. of the international conference on ships and offshore structures 2022 (icsos 2022) (p. 128-145).
Liang, Q., Vanem, E., Alnes, Ø., Xue, Y., Zhang, H., Lam, J., & Bruvik, K. (2023). Data-driven state of health monitoring for maritime battery systems – a case study on sensor data from a ship in operation. Ships and Offshore Structures, Latest articles, 1-13.
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kavukcuoglu, K. (2016).
Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.
Plett, G. L. (2011). Recursive approximate weighted total least squares estimation of battery cell total capacity. Journal of Power Sources, 196, 2319-2331.
Pop, V., Bergveld, H. J., Danilov, D., Regiten, P. P. L., & Notten, P. H. L. (2008). Battery aging process. In Battery management systems. Accurate state-of-charge indication for battery powered applications (p. 111-143). Dordrecht: Springer.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. The MIT Press.
Rozas, H., Troncoso-Kurtovic, D., Ley, C. P., & Orchard, M. E. (2021). Lithium-ion battery State-of-Latent-Energy
(SoLE): A fresh new look to the problem of energy autonomy prognostics in storage systems. Journal of Energy Storage, 40, 102735.
Vanem, E., Alnes, Ø. Å., & Lam, J. (2021, November- December). Data-driven diagnostics and prognostics for modelling the state of health of maritime battery systems– a review. In Proc. annual conference of the prognostics and health management society 2021 (phm 2021).
Vanem, E., Bertinelli Salucci, C., Bakdi, A., & Alnes, Ø. Å. (2021). Data-driven state of health modelling – a review of state of the art and reflections on applications for maritime battery systems. Journal of Energy Storage, 43, 103158.
Vanem, E., Bruch, M., Liang, Q., Reyes Gonzalez, D. V., & Alnes, Ø. Å. (2022, October-November). A data-driven snapshot method for state of health modelling and diagnostics of maritime battery systems. In Proc. annual conference of the prognostics and health management society
2022 (phm 2022).
Vanem, E., Bruch, M., Liang, Q., Thorbjørnsen, K., Valøen, L..O., & Alnes, Ø. Å. (2023). Data-driven snapshot methods leveraging data fusion to estimate state of health for
maritime battery systems. Energy Storage, Early View.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).
Weng, C., Cui, Y., Sun, J., & Peng, H. (2013). On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression.
Journal of Power Sources, 36-44.
Xue, Y., Zhou, H., Luo, Y., & Lam, J. (2022, March). Battery degradation modelling and prediction with combination of machine learning and semi-empirical methods. In Proc.
12th International conference on Power, Energy and Electrical Engineering (CPEEE 2022) (p. 78-85).
Yamamoto, T., Hatano, H., Maruchi, K., & Mitsumoto, K. (2022). Soundness monitoring for battery energy storage system by voltage deviation method (in Japanese). IEEJ
Transactions on Power and Energy, 142(1), 51-57.
Zheng, L., Zhu, J., Lu, D. D.-C., Wang, G., & He, T. (2018). Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries. Energy, 150, 759-769.
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