Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network

2025-10-02

AI Summary

Low remanufacturing and recycling efficiency of end-of-life electric vehicle (EV) batteries stems from manufacturers’ closed data ecosystems. Method: This paper proposes a disassembly optimization framework integrating product digital twins with a bidirectional Product–Process–Resource Asset Network (Bi-PAN). Unlike conventional unidirectional PANs, Bi-PAN explicitly models dynamic couplings among products, processes, and resources across manufacturing, remanufacturing, and recycling stages. Leveraging digital twin–driven dynamic modeling and real-time optimization of multi-battery-type disassembly processes, the method enhances resource recovery rates and disassembly flexibility while reducing ecological footprint. Contribution/Results: The framework provides a scalable, intelligent decision-support system for EV battery closed-loop supply chains and represents an innovative application of digital twin technology to sustainable remanufacturing.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the context of the circular economy, products in their end-of-life phase should be either remanufactured or recycled. Both of these processes are crucial for sustainability and environmental conservation. However, manufacturers often do not support these processes enough by not sharing relevant data. This paper proposes use of a digital twin technology, which is capable to help optimizing the disassembly processes to reduce ecological impact and enhance sustainability. The proposed approach is demonstrated through a disassembly use-case of the product digital twin of an electric vehicle battery. By utilizing product digital twins, challenges associated with the disassembly of electric vehicle batteries can be solved flexibly and efficiently for various battery types. As a backbone for the product digital twin representation, the paper uses the paradigm of product-process-resource asset networks (PAN). Such networks enable to model relevant relationships across products, production resources, manufacturing processes, and specific production operations that have to be done in the manufacturing phase of a product. This paper introduces a Bi-Flow Product-Process-Resource Asset Network (Bi-PAN) representation, which extends the PAN paradigm to cover not only the manufacturing, but also the remanufacturing/recycling phase.

Problem

• Optimizing disassembly processes for electric vehicle batteries
• Addressing data sharing gaps in end-of-life battery management
• Extending digital twin coverage to remanufacturing and recycling phases

Innovation

• Digital twin technology optimizes electric vehicle battery disassembly
• Product-process-resource asset network models manufacturing and recycling relationships
• Bi-Flow PAN representation extends digital twin to remanufacturing phase