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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...
Contexts in source publication
Context 1
... ships battery system configuration can be found in Table 1. Trace plots of the operational data from one cell on Vessel C is shown in Figure 4. It is observed that there is a rather long gap in the data, and also that this system started doing fastcharging at some point, with a sudden occurrence of higher maximum currents. ...
Context 2
... each capacity measurement, data are collected from the first 25 cycles and the integrated current and the change in SOC is calculated from the complete charge and discharge cycles, as well as from partial cycles, where random segments of the charge and discharge are extracted. Figure 13 shows the extracted data for X = ∆SOC and Y = t2 t1 I(τ )dτ and estimated regression lines for an arbitrary cell and Figure 14 shows estimated SOH based on both full and partial cycles. Overall, the results are satisfactory and indicate that this approach can yield reasonable results for most of the cells. ...
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As the primary power source for ships, the reliability of electric propulsion systems directly impacts the safety, stability, and economic efficiency of maritime operations. However, the composition of ship electric propulsion systems is complex and is continuously exposed to the dynamic and variable marine environment, which complicates their reli...
Citations
This paper tests two data-driven approaches for predicting the State of Health (SoH) of Lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, nonsequential approaches are investigated and various models are tested: Ridge, Lasso, Support vector regression, and Gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature and current. Secondly, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory (LSTM), Transformer, and Temporal convolutional network (TCN). Finally, the various models and the two approaches are compared in terms of their SoH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches.