Hitachi High-Tech has successfully demonstrated a physical AI platform designed to optimize cathode coating processes for next-generation all-solid-state batteries.
Developed in collaboration with Powrex Corporation, the system applies process informatics to eliminate costly experimental trial-and-error cycles. The technology targets the delicate oxide coating layer required to stabilize high-energy solid electrolytes.
Overcoming Degradation in Solid-State Cathode Coating
All-solid-state cells require an ultra-uniform lithium-conductive oxide film coated across individual cathode active particles. Inconsistent film thickness often triggers interfacial resistance and causes rapid battery capacity degradation during fast-charging cycles.
Conventional optimization methods historically required months of empirical testing conducted by specialized chemical engineers and material scientists.
Hitachi PROACCELA and Powrex Process Informatics
The joint framework integrates Hitachi advanced analytical instruments with Powrex fluidized bed coating manufacturing equipment. Data streams from scanning electron microscopes and X-ray fluorescence analyzers feed directly into predictive AI models.
The proprietary PROACCELA informatics platform quantitatively calculates interfacial parameters to pinpoint optimal manufacturing variables within hours.
Accelerating Commercial Mass Production from Lab to Fab
By automating the discovery of ideal coating conditions, battery manufacturers can drastically compress new cell development schedules. The AI architecture establishes direct mathematical correlations between raw material characteristics, factory equipment settings, and final energy retention.
As global automakers race to commercialize solid-state electric vehicles by 2027–2030, manufacturing yields remain the primary commercial bottleneck. Hitachi data-driven physical AI tooling provides the scalable quality assurance needed to transition solid-state chemistry from research laboratories into gigawatt-scale factories.