This model will help analyze the journey that a lithium ion makes inside a battery allowing researchers to develop them so that they can withstand extremely fast charging conditions while avoiding accelerated degradation.Researchers at the National Renewable Energy Laboratory (NREL) in the United States have spent years trying to make precise and exact measurements of what happens inside a lithium-ion battery.A crucial task to understand and optimize them since in data analysis you can only control what can be measured.Although microscopy methods had been used for this purpose until now, new research has managed to artificially generate the architecture of a lithium-ion electrode particle, which will allow researchers to manipulate the model and improve the battery design to achieve extremely fast charges without accelerated degradation.Lithium-ion batteries are currently used in many devices.From its smallest size, in the mobile devices that we all carry, to the large ones such as those implemented in electric vehicles or in stationary energy storage systems.Precisely, to meet changing energy needs, researchers are focusing on improving their performance, their safety and the energy density they are able to accumulate.A recent study conducted by the NREL (US National Renewable Energy Laboratory) and the University of Ulm and published by npj Computational Materials, advances a new way to measure and analyze battery materials.The team of researchers artificially generated the representative architecture of a lithium-ion electrode particle down to subparticle detail.This artificial electrode, the first of its kind, will allow researchers to manipulate the model to evaluate opportunities for improved battery design.According to NREL researcher Donal Finegan, who is leading the project, the morphology and orientation of the grains within the cell can greatly affect the performance and service life of the electrode."With this model, we can evaluate the physical conditions that lead to improved batteries."One of the biggest challenges when researching lithium-ion batteries is on the microscopic scale of the work.Small details often have a big impact on battery performance.No existing technique allows researchers to measure subparticle information in a comprehensive way to understand the behavior of lithium-ion batteries.For years, NREL researchers have pushed the limits of specialized imaging capabilities by improving their understanding of electrode-level structures, but these tools have so far failed to provide a complete picture of subparticle detail.This is how Finegan himself explains it: “Tools that measure particle morphology miss vital information about chemical properties or crystal structure due to systematic limitations.There is no way to get all the information we need in one place. "NREL researcher Donal Finegan uses X-ray computed tomography to diagnose lithium-ion batteries.Photo by Dennis Schroeder, NREL.This is not the first time that NREL has combined microscopy techniques to closely observe the behavior of batteries.He has often used state-of-the-art X-ray diagnostic capabilities to examine the composition of materials.However, this previous research focused on imaging results, while the new project used complicated characterization to merge the data flow offered by each of the tools used."To generate the model, the team characterized the information from each data source and had to translate it into a completely new format."Initial analysis performed with this artificial model has already provided a greater understanding of the physical conditions that affect the way lithium travels between electrodes through the electrolyte.The details of the subparticles in the model will help analyze the journey of an ion and allow the development of batteries capable of withstanding extremely fast charging conditions without causing accelerated degradation.This project is just the beginning of the artificial model that NREL aims to generate.Future research will apply machine learning techniques to acquire and translate data more quickly, leading to higher quality models that will provide the insights needed to improve batteries in the future.® Hybrids and Electric |Edited by Tecnofisis Global, SL