The digital twin technique has been broadly utilized to efficiently and effectively predict the performance and problems associated with real objects via a virtual replica. However, the digitalization of twin electrochemical systems has not been achieved thus far, owing to the large amount of required calculations of numerous and complex differential equations in multiple dimensions. Nevertheless, with the help of continuous progress in hardware and software technologies, the fabrication of a digital twin-driven electrochemical system and its effective utilization have become a possibility. Herein, a digital twin-driven all-solid-state battery with a solid sulfide electrolyte is built based on a voxel-based microstructure. Its validity is verified using experimental data, such as effective electronic/ionic conductivities and electrochemical performance, for LiNi0.70Co0.15Mn0.15O2 composite electrodes employing Li6PS5Cl. The fundamental performance of the all-solid-state battery is scrutinized by analyzing simulated physical and electrochemical behaviors in terms of mass transport and interfacial electrochemical reaction kinetics. The digital twin model herein reveals valuable but experimentally inaccessible time- and space-resolved information including dead particles, specific contact area, and charge distribution in the 3D domain. Thus, this new computational model is bound to rapidly improve the all-solid-state battery technology by saving the research resources and providing valuable insights.
Bibliographical noteFunding Information:
J.P. and K.T.K. contributed equally to this work. This research was supported by the Technology Development Program to Solve Climate Changes of the National Research Foundation (NRF) funded by the Ministry of Science & ICT (NRF-2017M1A2A2044493 and 2017M1A2A2044501) and Creative Materials Discovery Program of the National Research Foundation (NRF) funded by the Ministry of Science & ICT (2020M3D1A1068764). The authors are also very thankful for the support from the DGIST Supercomputing and Bigdata Center.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Materials Science(all)