Panasonic AI Accelerates Solid State Battery Development -Lithium - Ion Battery Equipment
The method developed this time is a way to visualize the action state of the internal materials of lithium ion battery in the battery working process at high speed and high resolution. The visualization of this state will greatly affect the improvement of capacity density, charging and discharging speed, life and other performances of lithium ion battery.(Lithium - Ion Battery Equipment)
For example, it is possible to display the charging and discharging parts and irrelevant parts of the electrode in a highly decomposed state through space and time dimensions.
Using this method, researchers can immediately identify the effect of applying new materials, and more accurate data can be fed back to the database when materials are developed by AI (Artificial Intelligence).
Panasonic expects that the competitiveness of "material intelligence" will be greatly improved through such AI development of materials.
The performance of the battery can be improved by visualizing the work state of electrode materials (LiCoO2, graphite, etc.) that affect the charge discharge performance and capacity density.
Analysis by electron microscope
The method developed uses an electron microscope. By releasing electrons, scanning and irradiating them on the detected object material, and through quantitative analysis of EELS (Electronic Energy Loss Spectroscopy), the reduced electron energy distribution caused by collisions with atoms is imaged in 2D.
In traditional practice, in order to obtain the Li ion distribution image, it is generally necessary to use a large radiation device (such as "SPRING-8") to irradiate X-ray. Moreover, it is very difficult to improve the resolution to atomic level through X-ray imaging.
Therefore, in order to confirm the impact of new materials on the development of lithium ion batteries, indirect observation methods such as making samples and measuring changes in battery capacity and thickness are usually used.
Fast imaging with AI
Panasonic can shoot in a short time by combining EELS and AI machine learning. At present, Panasonic has not released the details of its implementation method, but it can be learned that this method can obtain the observation data that requires tens of minutes of long observation in a short time of tens of seconds through machine learning.
And other measurement conditions can also be included in the learning object. It seems that a series of original algorithms are used to remove noise and extract useful signals from incomplete data in a short time.
The spatial resolution of Li ion concentration in electrodes and electrolytes is nm, which is about 100 times higher than that of conventional methods using X-ray. The imaging time is 20 seconds each.
Pre applied in solid state battery research and development
At present, Panasonic has applied this method to the research and development of all solid state batteries in advance, and confirmed it on specific topics.
All solid state battery is the most important next generation technology in the cooperative research and development between Panasonic and Toyota. The change near the electrode surface in contact with the electrolyte is a major topic in mass production applications. Panasonic analyzed the change of positive electrode through lithium ion concentration distribution.
At this time, Panasonic also used another analysis method to focus on the material formation process and ionic conductivity near the cathode and electrolyte interface, and confirmed that it would promote the solution of the above issues.
Moreover, it is possible to clarify the Li ion conduction characteristics of the solid-state lithium ion battery different from the lithium ion battery using the liquid electrolyte.
At present, LiCo2O3 is the common cathode material of lithium ion batteries, and LASGTP, an oxide ceramic material commonly used in small capacity batteries, is used as the electrolyte. Co3O4 is formed at the interface due to the side reaction.