Battery manufacturing lies at the heart of industrial transformation. Whether for electric mobility or stationary energy storage, the quality and scalability of cell production determine innovation and competitiveness. The reality, however, often looks different: fragmented data landscapes, limited transparency, and time-consuming coordination between laboratories, pilot lines, and gigafactories.

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In our whitepaper “AI-Driven Digital Thread in Battery Manufacturing”, we demonstrate how the explore platform addresses these challenges together with i AI agent Lora. Using standardized interfaces (OPC-UA, REST, MQTT) and a semantic knowledge graph, data from engineering, manufacturing, and quality assurance are seamlessly connected. This creates a continuous digital thread that not only integrates data but also models processes and automatically documents them.

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A central element is complete traceability: every process step is logged, and material batches as well as machine parameters can be traced back to the individual cell ID. Manufacturers thus not only establish the foundation for the digital product passport required by EU battery regulation but also gain valuable insights into their CO₂ footprint.

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The real innovation: Lora acts as a proactive AI agent that detects issues, suggests corrective actions, and integrates seamlessly into existing system landscapes – from Jira to ERP. Humans remain in the loop: critical decisions are prepared but never made autonomously. This interplay of automation, governance, and transparency unlocks new levels of efficiency, scalability, and sustainability in battery production.

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