Figure 6 - available via license: Creative Commons Attribution 3.0 Unported
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SOH as predicted by the Gaussian process model (left) and the Bayesian neural network (right) compared to two identical experiments (excluded from training)
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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...
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... assumption might not be entirely true and adds uncertainties to the model predictions. Examples of model predictions against pairs of experiments that were held out during training are shown in Figure 6 (LOO denotes leave one out). The degree of uncertainty shown in Figure 6 illustrates one main challenge with this approach. ...
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... of model predictions against pairs of experiments that were held out during training are shown in Figure 6 (LOO denotes leave one out). The degree of uncertainty shown in Figure 6 illustrates one main challenge with this approach. For most loading scenarios (excluding those the model has been trained on), the SOH degradation can vary a lot. ...
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... can be seen, despite an overall decreasing trend, there is much variability and the uncertainty increases as the time period for the data decreases. Figure 16 shows estimated capacity based on yearly data for eight different battery packs (from two arrays). For these estimates, all data from a whole year is combined to yield and average annual capacity estimate. ...
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... uncertainties can be due to mea- Figure 15. Daily, weekly and monthly estimates of total capacity based on simple linear model (from Kejvalova (2022a)) Figure 16. Yearly capacity estimates surement error, and it is known that ignoring this in a linear regression model will tend to a bias towards zero of the regression coefficient -an attenuation bias (see Carroll et al. (2006)). ...
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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.
