Wang, BowenBowenWangZhang, GuobinGuobinZhangWang, HuizhiHuizhiWangXuan, JinJinXuanJiao, KuiKuiJiao2021-08-022021-08-022020-08Energy and AI, 2020, 1, pp.1-132666-5468http://hdl.handle.net/10044/1/90779The 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.© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)Journal Articlehttps://www.dx.doi.org/10.1016/j.egyai.2020.100004https://www.sciencedirect.com/science/article/pii/S2666546820300045?via%3Dihub