Time-dependent deep learning predictions of 3D electrode particle-resolved microstructure effect on voltage discharge curves

Abstract

Battery electrode particle-resolved microstructure featured with a 3D active material (AM) particle network bonded by carbon and binder (CBD) phase plays a significant role in determining battery performances. Tremendous physical computational models were developed to investigate these microstructure effects on voltage discharge curves, which however are computationally expensive. Machine learning (ML) could be the solution to address the high computational cost, but they still suffer from two challenges. (1) 3D particle-resolved microstructure that cannot be modelled in traditional ML that only correlate the scalar inputs and outs, (2) the outputs (voltage discharge curves), are time dependent. In this work, a deep learning (DL) approach was developed to tackle these two challenges. Using NMC 111 as one example, the results show a great match between the predictions and ground truth with a 99.98% validation accuracy. The superb extrapolation capabilities were demonstrated through the accurate prediction of microstructures fabricated with distinct process conditions and graded microstructures that exhibit completely different probability density function (PDF) of particle size. The trained DL model can be employed to boost the optimization of fabrication process parameters and microstructure design of electrodes. Moreover, the proposed framework can be extended to predict other dynamic behaviors of particle-resolved electrode microstructures.