Xiaokun Zhang1*, Yu Mao1, Fan Zhou1, Xinquan Wang1, Xinlong Wang1, Zhenguo Cheng1,Shijie Mei1, Chao Pang1, Yong Xiang1*

1University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China

EXTENDED ABSTRACT: Currently, the energy densities of Li-ion batteries (LIBs) cannot satisfy the requirements of emerging applications, such as electric airplanes. Cathode materials, which typically accounts for more than 40% of the total weight of a LIBs pouch cell, has been the key bottleneck for improving the energy densities of LIBs. Increasing the charging cut-off voltage and cycling capacity of cathode materials is one of the most important routes to improve the energy density of LIBs. LiCoO2 (LCO) is one of the most widely applied cathode materials in LIBs, due to its high voltage, energy density, and taped density. However, its cycling performance decayed dramatically at high voltages, because of degradation of crystal structure, loss of lattice oxygen, and harmful side-reactions with liquid electrolyte. Doping of foreign elements and interfacial modification is the major technical approaches to improve the performance of LCO at high-voltages. However, the scientific knowledge on the mechanism of doping and interfacial modification is less-developed. Thus, the technologies of high-voltage LCO remains a challenge. This work applied data-driven modeling methods to the development of high-voltage LCO. On the one hand, the relationship between the characteristic parameters of doped elements and the performance of LCO at high voltages was modeled. The accuracy of the model was improved via algorithms optimization, and thus the characteristic parameters with high influence was revealed, helping the understanding of doping mechanisms and the design of high-voltage LCO. On the other hand, the relationship between the process parameters and the performance of products was modeled, potentially helping to detection of process failures, reduce the production cost, and improve the yield.

Keywords: Materials Genomic Engineering; Data-driven; LiCoO2; High Voltage

REFERENCES

[l] Xiaokun Zhang*, et al, J. Power. Sources, 505, (2021) 230067.

[2] Xiaokun Zhang*, et al, Nanoscale Res. Lett., 15, (2020) 110.

[3] Jin Xie and Xiaokun Zhang, et al, Acs Nano, 11, (2017) 7019.