EV Battery Manufacturing: Digital Twins, AI, & Advanced Battery Chemistries

With the push for US adoption of zero-emissions vehicles, despite the current 42% gap between electric vehicles (EVs) and gas cars, it’s increasingly crucial for automakers to find ways to make EVs more affordable for the average US citizen.

This is easier said than done for US manufacturers facing steep competition from Asia, which has dominated the EV industry, particularly the supply chain, for years. To increase their competitive advantage and close the gap, US manufacturers are increasingly looking to emerging technologies to help them scale. Battery manufacturers rely on artificial intelligence (AI), digital twins, and developments in advanced battery chemistries to increase production efficiency, improve quality (such as charging times, range, lifecycle length, and overall safety), and reduce waste and cost. Reducing reliance on foreign supply chains should be a priority for the US as it develops a robust domestic EV industry.

As the industry looks to the future, these technologies will help revolutionize EV battery production – reducing costs and accelerating mainstream adoption.

Energy density and safety with advanced chemistries

Battery chemistries influence safety, charging speed, energy density, and more - and as a result, advancements in EV performance heavily rely on advancements in battery chemistry. Many manufacturers are currently shifting from traditional Nickel-Manganese-Cobalt (NMC) lithium-ion batteries, which have dominated the EV market, to newer chemistries.

One emerging chemistry is the solid-state battery. To improve battery safety, the solid-state battery replaces the lithium-ion battery’s highly flammable liquid electrolyte with a solid one, allowing for greater energy density, and enhancing vehicle range. Solid-state batteries also reduce fire risks, an issue for 25 out of every 100,000 EVs.

Lithium-iron phosphate batteries are another emerging battery chemistry that manufacturers are exploring. While lithium-iron phosphate batteries have a lower energy capacity per unit volume than NMC lithium-ion batteries, they are more stable, affordable, and lengthen battery lifecycles.

To boost the sustainability of battery production, some manufacturers are also testing out dry battery electrode production techniques that reduce the need for toxic solvents and, most importantly, drastically reduce energy consumption (and bill), as the dry room is one of the biggest energy consumer processes.

Quality control with AI

 Manufacturers are using AI to speed up quality control in EV battery manufacturing. Traditional human-performed quality checks make the process vulnerable to various interpretations from person to person and human error. Using AI for battery production helps reduce cost over time, avoid material waste, optimize performance, improve efficiency, and identify problems earlier in the battery’s manufacturing and lifecycle. Leveraging AI for visual quality checks is not only quicker, more accurate, and less subjective, but it also allows for far greater detail, catching flaws the human eye would miss and improving overall quality control robustness.

Battery Intelligence often relies on AI for predictive maintenance. If manufacturers can collect and analyze data from EV battery behavior and charging cycles, these analytics can feed AI to help:

AI insights also support the design stage of the product, facilitate ongoing maintenance, and even provide information on aging to assess battery recycling. Involving AI in the battery design and testing process can aid manufacturers in introducing new chemistries that are less critical in raw material supply and potentially less harmful, reducing costs and improving performance and safety.  AI is fundamentally a driver to accelerate innovation.

Craddle computational fluid dynamics (CFD) simulation and visualization software. Courtesy of Hexagon.

Innovation with digital twins

 Where AI can leverage quality control, digital twins are useful for spurring innovation. By playing with simulations—like virtual geometry electrode designs to evaluate overpotential distributions by optimizing thickness and porosity—battery engineers can assess the best performance or compromise, reducing physical testing. Another field of using digital twins is in the production environment: Simulating the process can enhance productivity and time to market by optimizing operations, and significantly reducing scrap rate, which is crucial to reduce the currently high cost of battery manufacturing. Utilizing digital reality to visualize possible shop floor layouts can also greatly improve implementation choices, particularly when scaling up production is a key topic.

To meet the growing demand for EVs, Manufacturers can use digital twins to ensure that workflows, systems, and machines are in lockstep to improve operational efficiency. There is already a high level of automation in the EV battery manufacturing industry. Leaders like CATL and ProLogium claim more than 85% automation in their lines, showing the industry’s commitment to increasing productivity with emerging technologies. Using digital twins to refine and align these automated processes, manufacturers can accelerate growth in the EV industry, reducing downtime, improving quality, and scaling production.

Lifecycle management and recycling

Using AI, digital twins, and advanced chemistries in EV battery production not only creates an opportunity for minimized scrap rates and cost savings but also helps improve lifecycle management and battery recyclability. Digital twins can keep close track of an EV battery’s lifecycle from production to wear and tear on the road to disposal. By analyzing data on battery usage, AI can predict smarter charging algorithms, potential aging, and state of health. These insights help EV automakers understand how and when batteries will reach the end of their life. This shows EV automakers the ideal conditions for use and recycling, helping them decide to repair, re-use, re-purpose, or recycle the battery. This, in turn, will help them build more sustainable batteries over time.

A data-driven approach to battery circularity is key for the industry, as growing EV adoption spurs the need for battery disposal, recycling, and data traceability for carbon footprint monitoring. To achieve a closed-loop economy that reduces critical raw material content wherever possible, manufacturers should enlist the help of digital twins and AI to recover and recycle precious materials like cobalt and lithium. Reusing these materials in new batteries addresses issues of scarce resources and helps manufacturers meet environmental goals.

Limitations of EV battery manufacturing with emerging technology

 Refining how EV batteries are designed, manufactured, and maintained, these innovations can optimize the production process, battery performance, sustainability, and safety; however, there are limitations on how far these technologies can go. Given the involvement of advanced chemistries, developing EV batteries is extremely complex. So, while scaling up production is a key aspect of EV battery manufacturing, it should be handled with care on pilot lines before producing at scale.

Battery thermo-fluid simulation. Courtesy of Hexagon.

Think of it like this: If you’re baking loaves of bread for 10 people, you cannot scale the same recipe with simple multiplication for 400 people. It’s the same with battery production; manufacturers cannot mass produce without recalculating and adjusting the chemistries and processes, or they risk compromising performance and quality, potentially leading to defective products.

Because of the complexity and lack of available data to run simulations, EV battery production still requires physical testing. However, AI and digital twins can be used to accelerate innovation and time to market. As these technologies become more advanced, more manufacturers develop ways to integrate them into their processes. Increasing the use of automation will help manufacturers reach their goals of a high production rate, producing less expensive batteries while reducing scrap rate and energy consumption, ultimately reducing EV prices to facilitate access to and adoption of EVs.

Next Steps in EV manufacturing

As EVs continue to increase in popularity, implementing smart manufacturing innovations with research, testing, and emerging technologies will be essential to success.  Investing in R&D for solid-state, lithium-iron phosphate, and other advanced chemistries will further improve EVs' long-term viability, further strengthening the US standing in the EV race. Implementing AI, digital twins, and future emerging technologies is also crucial for giving the US the upper hand in the EV race and meeting the country’s sustainability and EV adoption goals.