Laser ablation on lithium-ion battery electrode solid electrolyte interface removal

Abstract

The formation of a solid electrolyte interface (SEI) on a Li-ion battery electrode during usage cycling is a critical reason for battery capacity loss. In this paper, laser ablation technology is applied to remove SEI from a graphite electrode surface. Characterization methods including scanning electron microscopy, Raman microscopy, and Fourier transform infrared spectroscopy are used to study the structure and morphology changes of the SEI on the electrode surface. The results show that laser ablation can successfully remove the SEI, indicating a feasible method to clean the electrode surface.

Review on Action Mechanism and Characterization of Natural/Artificial Solid Electrolyte Interphase of Lithium Battery: Latest Progress and Future Directions

“…Li put forward new thoughts on high energy density rechargeable batteries, expounded the influence of unstable SEI on the service life of lithium metal batteries, and made his own insights on fluorinated SEI, and carried out characterization and electrochemical experiments. Zhang et al − are studying the use of a neodymium-doped yttrium aluminum garnet (Nd: YAG) pulsed laser to remove the SEI film on the electrode surface of a lithium-ion battery. Quantify changes in the structure and morphology of graphite electrode materials through different characterization methods.…”

Multi-Parameter Optimization Method for Remaining Useful Life Prediction of Lithium-Ion Batteries

“…Therefore, the thickness of SEI film and the loss of active substances will change with the increase of the service time of lithium-ion batteries, i.e. the number of charge-discharge cycles N. In many references [17][18][19], the number of chargedischarge cycles of lithium-ion batteries is used as the first prediction parameter for estimating RUL of batteries.…”

“…Batteries operating at room temperature (24℃) and at low temperature (4℃) were selected to fit the curve according to formula (19). The results are shown in Fig.…”

“…9. However, most references do not change the predictive parameters according to different working conditions and the changes of parameter data such as references [13,[17][18][19]. And this will have a certain impact on the accuracy of battery prediction.…”

“…The data-driven RUL predicted mentioned above are too subjective in the parameter selection of the prediction model and had little scientific basis. Only the number of cycles was selected as the model parameter in [3,10,[18][19]. In this case, the degradation mechanism and environmental factors of the battery are not considered.…”