Edith Cowan University (ECU) researchers are advancing the future of clean energy storage by using artificial intelligence to address one of the most significant barriers to solid-state batteries reaching large-scale production
Their latest work shows how AI and machine learning can improve the reliability and efficiency of next-generation batteries, bringing them closer to widespread use in electric vehicles, renewable energy systems, and consumer electronics.
Battery innovation
Solid-state batteries are emerging as the next major leap in energy storage. Unlike today’s lithium-ion batteries, which use liquid electrolytes, solid-state versions rely on solid materials to transport charged particles. This design promises longer battery life, improved safety, and faster charging. The technology also reduces the risk of leaks and overheating, two common concerns associated with conventional batteries.
Despite these advantages, solid-state batteries have struggled to move beyond the laboratory. A key challenge lies at the interface, where the cathode, anode, and solid electrolyte meet.
This interface determines how smoothly ions move through the battery and how well the structure tolerates stress during charging and discharging. Poorly engineered interfaces can cause resistance to increase, reduce performance, or lead to the growth of lithium dendrites.
AI’s role in battery interface engineering
ECU researchers are applying AI and machine learning tools that can evaluate battery materials and designs far more quickly than traditional experimental methods.
By modelling how components respond to different temperatures, pressures, and mechanical stresses, these systems can identify configurations that improve safety, stability, and energy flow.
The approach allows researchers to detect failure points early and predict how various electrolyte materials will behave under specific conditions. This insight reduces the need for extensive trial-and-error testing and speeds up the discovery of more durable interface designs.
AI also helps researchers understand how micro-scale material interactions influence overall battery performance. By analysing large datasets generated from simulations and laboratory measurements, machine learning models can reveal patterns that would be difficult to detect manually. These patterns guide engineers in selecting materials that are more compatible, less prone to degradation, and better suited for commercial-scale production.
From the lab to large-scale manufacturing
While scientists around the world have made progress in producing high-performance solid-state batteries in controlled laboratory environments, manufacturing them at an industrial scale is still a slower process.
Creating consistent, defect-free interfaces across thousands or millions of units is particularly challenging. ECU’s work is in place to close this gap by ensuring that each part of the battery is optimised for repeatable production.
Australia’s growing interest in domestic battery manufacturing underscores the urgency of this research. As global leaders in China and Europe push ahead with large-scale development of solid-state batteries, Australian researchers and manufacturers are seeking ways to stay competitive.
Improving interface engineering with AI could accelerate local production while ensuring the resulting batteries are robust enough for real-world use.
Longer-lasting energy storage
The combination of advanced interface engineering and AI-powered modelling is shaping a more straightforward path toward commercially viable solid-state batteries. By enhancing safety, durability, and efficiency, ECU’s research contributes to a future where electric vehicles travel farther, renewable energy storage becomes more reliable, and consumers benefit from longer-lasting power solutions.
