Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

@article{He2022EnhancedPD,
  title={Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery},
  author={Qizhi He and Yucheng Fu and Panos Stinis and Alexandre M. Tartakovsky},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.01985}
}
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization and control of flow batteries. In this study, we propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach [1] to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional… 
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