Digital Shadows in Electric Motor Demanufacturing Systems as Preliminary Work Towards Automation and Digital Twin Implementation

The rise of electric mobility has accelerated the adoption of electric vehicles (EVs), leading to an increase in the number of motors. However, the millions of units present in the market cannot be treated through conventional waste practices. Despite reaching the end of their service, electric motors retain high residual value both functionally and materially, due to their rare earth elements, high grade steel, and critical electronic components. Thus, enabling their remanufacturing yields substantial environmental, competitive, and economic benefits, bringing sustainability and circular economy principles to the forefront. With global EV sales topping 17 million units in 2024 and continued growth expected, efficient end-of-life management of critical components is becoming increasingly urgent. Most electric motors for the automotive sector rely on rare earth materials, which account for 40 - 50% of the cost in permanent magnet motors, yet their global recycling rate remains below 3%. Addressing this challenge requires advanced demanufacturing solutions capable of reclaiming valuable materials while ensuring safe and efficient disassembly. At current, manual processes dominate, leading to inefficiencies, high costs, and difficulties in performance optimization. Automation represents an inciting solution but introduces its own hurdles, including the need to test and validate one-of-a-kind disassembly plans before execution. Furthermore, the high flexibility of demanufacturing systems, combined with heterogeneous machines using different communication protocols, complicates their integration from a control perspective. This paper presents digital shadows as a foundational step toward system control and validation in the context of automated electric motor demanufacturing. The developed IT architecture integrates disparate machines into a unified digital framework where signals are collected from each node and forwarded to a one-on-one virtual replica of the demanufacturing system where machines' kinematics have been accurately modelled. Thanks to its high level of detail and physics-based movements, the digital shadow serves as a critical enabler for future automation strategies. A practical E-Mobility application demonstrates the digital shadow as a precursor to a digital twin, enabling predictive validation and in-process feedback through bidirectional communication. This transition enhances system reliability, efficiency, and resource recovery, fostering a circular approach to EV component lifecycle management. This work lays the foundation for intelligent, adaptable demanufacturing systems, reducing environmental impact while optimizing material reuse in electric mobility.