Energy News Monitoring

For years engineers have tried to discover new methods of improving the life cycle of batteries used in mobile phones, electric vehicles and power grids so that they can store renewable energy for future use in a more efficient way. So far, their efforts have not proved successful as more reliable cathode materials have yet to be invented. To date, the typical strategy for enhancing cathode materials has been to alter their chemical composition.

Now (2020), chemists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have made a new finding about battery performance which points to a different strategy for optimizing cathode materials. They focused their research on controlling the number of structural defects in the cathode material.

This allowed them to eventually alter the structure of the cathode. The cathode material has two properties: ionic conductivity and electronic conductivity. If there is a defect present in the material, the lithium ions and electrons cannot only move freely in three dimensions across the layers. To come to this conclusion, the scientists carried out high-precision experiments that measured the concentration of defects in a cathode material with far greater accuracy than has ever been done before. To achieve this precision, the scientists conducted powder diffraction analyses using data from two DOE Office of Science User Facilities, the Advanced Photon Source (APS) at DOE’s Argonne National Laboratory and the Spallation Neutron Source (SNS) at DOE’s Oak Ridge National Laboratory.

This work has developed a new way of visualizing structural defects and their relationship to diffraction and scattering strength. The scientists believe that in the future this technique could be used in the battery community to understand defects and structural characterizations of cathode materials.

Scientists have long been researching for ways to make battery performance more sustainable. In 2016, researchers developed a semi-empirical lithium-ion battery degradation model that assessed battery cell life loss from operating profiles. They formulated the model by combining fundamental theories of battery degradation and observations in battery aging test results. The model was adaptable to different types of lithium-ion batteries, and methods for tuning the model coefficients based on manufacturer's data were designed. A cycle-counting method was incorporated to identify stress cycles from irregular operations, allowing the degradation model to be applied to any battery energy storage (BES) applications.

In 2019, scientists found that combining comprehensive experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities started to wane. After the researchers had trained their machine-learning model with a few hundred million data points of batteries charging and discharging, the algorithm predicted how many more cycles each battery would last, based on voltage declines and a few other factors among the early cycles.
The advantages of the new design are clear: with such accurate measurements of defect concentrations, the scientists could study the relationship between defects and cathode material chemistry. The ability to determine the concentration of weakly scattering elements with the sensitivity of a tenth of a percent could also be useful for many other areas of research, such as measuring oxygen vacancies in superconducting materials or catalysts.
It appears that with these new insights scientists have developed a “recipe” for achieving any defect concentration, which in the future could enable scientists to create cathodes from more affordable and environmentally-friendly materials and then tune their defect concentrations for optimal battery performance.