Self-reconfigurable battery architectures have gained a lot of interest recently in the literature, with more and more advanced functionalities. This paper describes the performance analysis of our proposed High Frequency Self-Reconfigurable Battery (HF SRB). To evaluate specific features with long-term dependencies of our system, a full functional behavioral simulator was developed. A comparison with a real 128-level HF SRB validated the simulator operation. The balancing performances obtained on vehicle test cycles showed the cell capacity discrepancy that the HF SRB is capable of handling in a single complete charge or discharge cycle. The magnitude of this gap demonstrated the extent to which the HF SRB is capable of operating with second life cells or even different chemistry mixes.

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<p>Physics-based deep learning experienced a major breakthrough a few years ago with the advent of neural operators. Beyond the traditional use of deep neural networks to predict the solution to a fixed Partial Differential Equation (PDE), these novel methods are able to learn the operator solution to a class of PDEs.</p> <p>Comparisons and analyses of popular neural operators such as Fourier Neural Operator and DeepONet have been conducted for numerical case studies. However, they are still lacking for more realistic problems in complex settings.</p> <p>In this study, we compare several neural operators to predict the propagation of seismic waves in heterogeneous media. Our database is composed of more than 12 million ground motion timeseries generated from 50,000 media. We quantify the accuracy of the neural operators, their memory requirements, and their dependence towards both the initial condition and the PDE parameters. We also propose insights on their possible extension to 3 dimensions.</p>