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To gain an in-depth understanding of the thermal decomposition mechanism of lithium-ion battery cathode materials, this study utilized Thermogravimetric analysis(TG) data of LiNi0.5Co0.2Mn0.3O2(NCM523) obtained at multiple heating rates and integrated these data with a Chemical Reaction Neural Network(CRNN) for kinetic modeling of the pyrolysis process. Through iterative training and validation, a 4-4 model comprising four species and four principal reactions was successfully developed. The model accurately captures the thermal mass-loss behavior of NCM523 and achieves a relative error of less than 1% across all heating rates. Additionally, the modeling results suggest that the pyrolysis follows an irreversible phase-transition pathway, progressing from a layered structure to a lithiated spinel, and ultimately to a rock-salt phase, reflecting the intrinsic structural evolution of the material. The thermal decomposition process can be divided into four distinct stages. In the first stage, the layered structure partially transforms into a lithiated spinel, with a relatively slow reaction rate due to the initial rearrangement of the lattice. In the second stage, both the lithiated spinel and the residual layered phases continue converting into a delithiated spinel, accompanied by an accelerated reaction rate as the system gains thermal activation. The third stage involves the decomposition of intermediate phases into a rock-salt structure, with the concurrent release of oxygen. During this stage, the reaction rate increases sharply in the high-temperature region before becoming diffusion-limited. In the final stage, all intermediate phases fully convert into the rock-salt structure, and the reaction rate gradually stabilizes as structural reorganization is completed. Key kinetic parameters, including activation energies and pre-exponential factors for each reaction step, were autonomously extracted by the CRNN model. These results demonstrate that the CRNN approach not only identifies complex thermal decomposition pathways but also provides physically interpretable kinetic constants without requiring prior assumptions about the reaction mechanisms. Overall, this work highlights the potential of the CRNN as an effective, data-driven tool for analyzing the thermal decomposition behavior of lithium-ion battery cathode materials.
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Basic Information:
DOI：10.13637/j.issn.1009-6094.2025.1429
China Classification Code:TQ131.11;TM912
Citation Information:
[1]Wang Yan,Zhang Jingyue,Yu Tianmin ,et al.Modelling of thermal decomposition kinetics and study of reaction mechanisms of NCM523[J].Journal of Safety and Environment,2026,26(08):2969-2977.DOI:10.13637/j.issn.1009-6094.2025.1429.
Fund Information:
国家重点研发计划青年科学家项目(2022YFC3080700); 河南省高校科技创新团队支持计划项目(25IRTSTHN005); 河南省重点研发专项(241111322400)
2026-05-22
2026-05-22
2026-05-22