Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model
By Bowen Wang, Guobin Zhang, Huizhi Wang, J. Xuan, K. Jiao. 2020
169 citations recorded by the source corpus.
Abstract The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells (PEMFCs) is significant for the advancement of this technology. Here, to solve this scientific issue, a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is proposed. The surrogate modelling prediction results demonstrate that the test-set relative root mean square errors (rRMSEs) of the multi-physics fields range from 3.88% to 24.80% and can mirror the multi-physics field distribution characteristics well. In summary, for multi-physics field prediction, the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model; however, it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved digital-twin. Two model-based designs based on the as-developed digital twin framework, i.e. the PEMFC healthy operation envelope and the PEMFC state map, are demonstrated. This study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems, such as PEMFCs.
Subfields: Electrical and Electronic Engineering, Materials Chemistry, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/118924e978ef0f1d90382bacf514f57b3c636f6e
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In Electrical and Electronic Engineering
Citations in the corpus
- Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, background): “For instance, Wang et al. [62] showed how a high fidelity multi-physics model of a fuel cell could be used to train a ANN and SVM to mimic the model performance but with significantly reduced computational cost, opening the possibility for more accurate optimisation.”