A multi-particle cellular automaton modeling method for grain dynamics evolution of nickel-rich cathode material

Nickel-rich cathode materials preparation requires a timewise lengthy and complicated chemical reaction, which results in challenges in determining a suitable sintering schedule to obtain a high-quality product. In this article, a multiparticle cellular automaton modeling method is proposed to capture and track the complex grain dynamics evolution process. According to the chemical reaction mechanism, the sintering process is divided into three main reactions, with each reaction's starting and ending temperature points determined based on thermal hysteresis. Then multiparticle state transition rules based on the Arrhenius formula and experiments data are constructed to obtain a multiparticle cellular automaton with kinetic properties. To realize dynamic simulation under multiple heating rates, a kinetic parameter transfer method is designed, in which particle swarm optimization and least squares estimation algorithms are adopted. Based on the above model, the final Li/Ni cation mixing parameter of the preparation is predicted by combining X-ray diffraction experiments analysis with the Li/Ni cation mixing law in reactions. The simulation results show that the proposed modeling method can accurately simulate the grain dynamics evolution of the cathode materials, which can provide a valuable reference for optimizing the sintering schedule in the sintering process of the cathode materials.