The performance of rechargeable batteries is governed by processes deep within their components. A fundamental understanding of electrochemistry, structure-property-performance relationships and the effects of processing and operating conditions is essential for accelerating the development of next-generation battery technologies capable of powering electric vehicles, portable electronic devices and grid-scale energy storage.
However, laboratory exploration, design and optimization remain extremely time-consuming and expensive. In contrast, advanced modeling and simulation provide powerful tools to elucidate the complex, tightly coupled processes that govern battery performance. These approaches can accelerate rational development of advanced energy storage systems with properties tailored to specific needs.
In a recent paper, published in Chemical Reviews, researchers from Lawrence Livermore National Laboratory (LLNL) outlined how state-of-the-art computational modeling can help to unravel the fundamental relationships among battery processing, structure, properties and performance across multiple scales, ultimately paving the way for the implementation of new materials, microstructures and innovative architectural designs in next-generation electrochemical devices.
At the micron scale, battery electrodes are often composed of tiny crystalline particles that can exist in two primary forms: polycrystalline, consisting of multiple grains joined together, and single crystals, which exhibit a continuous and uninterrupted lattice structure. Polycrystalline materials resemble a snowball with many small ice crystals lumped together, while single crystals look more like a uniform ice cube with consistent properties throughout.
The team focused specifically on single-crystal battery materials. Although these materials have not yet been fully commercialized, they offer the potential for improved performance, enhanced tunability and reduced degradation over extended cycles.
"Once a fundamental understanding is obtained, single-crystal materials can be leveraged to inform design strategies for improved battery performance, such as better capacity retention, enhanced safety and longer cycling life," said author and LLNL scientist Sabrina Wan.
Many open questions remain regarding this essential underlying knowledge, but simulations provide a critical first step toward addressing them. Modeling approaches spanning length scales from the atomistic level to the full battery cell can be used to investigate the key factors governing the electrochemical behavior of single-crystal battery materials.
"It is our intention to provide a comprehensive review of state-of-the-art physics-based modeling approaches for studying properties and phenomena relevant to single-crystal applications in batteries," said Wan. "We aim to equip the community with the knowledge needed to effectively use these tools."
Coupled with experiments, computational models enable iterative refinement of material designs, yielding new insights and guiding optimization. By cycling between simulation predictions and laboratory validation, scientists can rapidly optimize battery materials without costly trial-and-error testing.
Looking ahead, the authors emphasized the importance of fully integrated, experimentally validated multiscale modeling frameworks, further enhanced by state-of-the-art machine learning and data science approaches, to enable reliable and predictive design of next-generation battery systems.
