Home/Case Studies/EV Battery Cell Quality and Full Genealogy Traceability
ManufacturingSolution

EV Battery Cell Quality and Full Genealogy Traceability

Capture cell-level process and test data with full genealogy from cell to module to pack, so a suspect condition is contained to the exact units affected rather than an entire build period.

Solution
Machine Learning
Industry
Manufacturing
Company Size

Solution Overview

industry
Manufacturing
solution
Machine Learning
technologies
Azure IoT Hub, Microsoft Fabric, Azure Machine Learning, Azure Data Explorer, Power BI, Microsoft Purview

The Challenge

Battery manufacturing produces quality escapes that only become apparent in the field, and without cell-level genealogy the only safe containment is every pack built in the suspect window, which can be a hundred times the number actually affected.

  • 1Containment scoped to entire build windows because cell-level genealogy does not exist
  • 2Process data captured per batch, so field failures cannot be correlated to causal parameters
  • 3Cell to module to pack relationships not retained, breaking the traceability chain

Our Approach

CloudGate captures process and test data at cell level through every station, maintaining parent-child genealogy from raw material lot through cell, module and pack.

  • Cell-level process and test capture at every station rather than batch-level sampling
  • Full genealogy from material lot through cell, module and pack, retained end to end
  • Process parameter correlation against end-of-line test and field performance

Typical Outcomes

Cell-level
GENEALOGY THROUGH MODULE AND PACK
Minutes
TO SCOPE AN AFFECTED POPULATION
Immutable
RETENTION FOR REGULATORY LIFETIME

Typical outcomes for this solution pattern, not the results of a named client engagement.

Technologies Used

Azure IoT HubMicrosoft FabricAzure Machine LearningAzure Data ExplorerPower BIMicrosoft Purview
Full architecture and data flow in the Data & AI Catalog

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