Clara Bertinelli Salucci

• Vanem, Erik; Liang, Qin; Ferreira, Carla; Agrell, Christian; Karandikar, Nikita & Wang, Shuai [Show all 14 contributors for this article] (2023). DATA-DRIVEN APPROACHES TO DIAGNOSTICS AND STATE OF HEALTH MONITORING OF MARITIME BATTERY SYSTEMS, PROCEEDINGS OF THE ANNUAL CONFERENCE OF THE PHM SOCIETY 2023. PHM society. ISSN 978-1-936263-29-5. doi: 10.36001/phmconf.2023.v15i1.3437. Full text in Research Archive
• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (2023). A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries. Journal of Power Sources. ISSN 0378-7753. 556. doi: 10.1016/j.jpowsour.2022.232429. Full text in Research Archive
• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (2022). A novel semi-supervised learning approach for maritime lithium-ion battery monitoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 978-88-5511-309-0. p. 85–90. Full text in Research Archive Show summary

  We propose a novel semi-supervised learning method to monitor the State of Health of lithium-ion batteries, a prominent technology for the electrification of the transport sector. Our approach enables State of Health monitoring of batteries with no labeled data, starting from a minimal set of labeled data from another similar battery. This can be achieved by exploiting the relation between a pseudo-capacity measure and the total capacity of the labeled data. Our results with operational data from maritime batteries show that the approach is valid and can lead to significant progress in failure prevention, operational optimization, and for planning batteries at the design stage.

• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (2022). Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage. ISSN 2352-152X. 52. doi: 10.1016/j.est.2022.104903. Full text in Research Archive Show summary

  Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery capacity degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying real physical processes. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite being characterised by simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are provided by the NASA Ames Prognostics Center of Excellence, and are characterised by varying loads which simulate dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent observed capacity measurement is known, the other is based only on the nominal capacity of the cell. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors ranging from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor to ageing acceleration is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.

• Vanem, Erik; Bertinelli Salucci, Clara; Bakdi, Azzeddine & Alnes, Øystein Åsheim (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. ISSN 2352-152X. 43, p. 1–27. doi: 10.1016/j.est.2021.103158. Full text in Research Archive

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