How Digital Modeling Can Move the Battery Industry Forward

Modern manufacturing is in the midst of a revolution as more and more companies embrace digital transformation. However, it is still early days, especially when it comes to the materials that manufacturers use for their products.  

Many manufacturers still rely on materials developed last century for their products or use half-century-old technology for the development of new materials. In recent years, though, digital modeling has been responsible for a number of breakthroughs in the design of new materials critical to industries like aerospace, energy and medical devices. There is good reason to believe that more breakthroughs are waiting in the wings.

The batteries industry is among those most ripe for major materials advancements through digital modeling – the kind that will usher in a new era of batteries with higher efficiency and greater energy density. 

The reason is that much of the science for these advancements has been established. The manufacturers who bolster their research and development with digital technologies are most likely to turn this science into something truly transformative.  

Evolution of materials design 

The introduction of Integrated Computational Materials Engineering (ICME) in the early 2000s was a watershed moment in physics-based digital modeling, allowing for the quickest and best possible outcomes in the development of new materials. Now the materials engineering field is in the middle of another revolution that is being driven by two advances in digital modeling.  

Related:Easing Battery Design Through Digital Twin and Simulation Tools

The first is the application of artificial intelligence (AI) and machine learning (ML) to the ICME process. Used as an assisting technology in the identification of potential compounds for a comprehensive materials solution, ML and AI have been critical in the development of next-generation turbines, high-strength shape-memory ceramics and rock nozzles.  

The second development comes from the availability of digital tools that allow manufacturers to bring materials development, a highly specialized undertaking, in house. This advancement is driven by products like Siemens NX, which uses digital twin technology to speed up the design process, and Ansys Granta MI, which helps with materials data management, and QuesTek’s ICMD, which allows users to bring full ICME capability into their own research and development departments. 

By using these technologies, upstream manufacturers and OEMs can drive their own materials creation. 

Using lithium-ion data to drive sodium-ion development 

Related:Material Modeling Promotes Advances in Energy Storage

Sodium-ion batteries are a major trend in battery manufacturing and are moving swiftly into commercialization as a replacement for lithium in certain applications. While lithium is dominant in the battery market, powering mobile phones and electric vehicles, it remains relatively expensive, difficult to acquire and dangerous. Sodium, on the other hand, is much more available and cheaper, in addition to being more stable.  

CATL is one of the many companies developing sodium-ion batteries. CATL

The problem with sodium, of course, is that the energy density is much lower than lithium. Many engineers are working to move sodium ion from its current limited use, primarily for storage, to higher technology readiness levels. It is a tall order. Scientists started with lithium because that is the smallest, most reactive element. But still, it has taken decades and trial-and-error experimentation to arrive at the best anode, cathode and electrolyte system to achieve the energy, density and efficiency currently exhibited by lithium-ion. 

Using the same approach for sodium will require as much time, if not more. Yet, that process can be greatly reduced through digital modeling. 

The key is to take the lessons learned from the development of the lithium-ion system, collecting key data gathered from experimentation on that compound’s electrochemical reactions and processes. By feeding that data into computational models for sodium-ion systems and using it to run simulations, researchers can fast track its development. 

This is not an easy process by any means. There are similarities between lithium and sodium and the underlying mechanics of all electrochemical battery systems, but there will still need to be significant modifications to develop a new material system. Key in bridging these gaps between ion and sodium is the predictive function of AI. Battery manufacturers will also benefit from applying their own proprietary data to the process. Taken together, these technologies can result in a shortcut that could lower development costs and advance development by decades.  

Broadening the horizon for solid-state batteries 

The development of sodium-ion battery systems with higher technology readiness levels is an incremental development; the roadmap exists and materials engineers using digital modeling can use that map to push the technology further, quicker. A completely new innovation like the solid state battery (SSB) presents a different challenge, but one that can also be aided by digital transformation.

Robotic arm with solid-state battery cells manufacturing PhonlamaiPhoto/iStock / Getty Images Plus

As with any truly new technology, the development of a solid-state battery that is safer and more efficient than a lithium-ion battery requires a blank-slate approach. Engineers and scientists need to rethink every material and redesign every component. Yet, researchers need to start somewhere. 

There are efforts to develop a solid-state battery taking place around the world, from Japan to China, Europe to the U.S., and they are each betting on one route to solid state that they think are most promising based on the previous experience and data that they have been building to this point. One group is experimenting with a polymer-based approach, another is sulfide based, yet another is oxide based. This is a traditional mode of experimentation. A team selects the approach they think is the most promising and they stick with it throughout their experimentation. Faced with limitless possibilities and limited material resources, they narrow their approach.  

Digital modeling and simulation can allow for another approach that broadens the horizon of possibility.  

Papers recently published by Google Deepmind and Microsoft’s Azure Quantum show that researchers can use AI with large-scale computation to quickly search vast spaces of possibilities at a fraction of the cost of traditional methods. Applied to materials development, this approach may allow engineers to more efficiently explore the full horizon of what is possible.  

This is an ideal application for modern digital modeling. Available material models and other data anchors the exploration of what is possible in reality, while AI presents probabilities that help leapfrog traditional trial-and-error experimentation.

Turning soda cans into batteries 

Solid-state battery technology is the holy grail of battery technologies, but it is important to note that digital transformation can help solve materials problems that are much more immediate for battery manufacturers.  

Take the use of copper for conductivity. Many batteries currently use high-grade copper for conductivity because it is effective. Yet, copper is expensive.   

Aluminum soda can - stock image Dobrincu Manuel / BuzzPixel via Getty Images

A cheaper alternative is in hand. Aluminum, and in particular recycled aluminum from beer and soda cans, presents a low-cost alternative. Yet, recycling and batteries has long been a vexing problem for battery manufacturers. While aluminum is plentiful and easily recyclable, the impurities in recycled material make it more challenging for use in batteries. 

Using digital modeling, manufacturers can fine tune the microstructure and processing of recycled aluminum so the end result can be a very high-conductivity, reliable aluminum alloy

As is the case with sodium ion and even solid state, the answers to the materials challenge presented by recycled aluminum are out there. Manufacturers who adopt and embrace digital transformation in the research and development of new materials are more likely to find them faster and at reduced cost.