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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00378752" target="_blank" >RIV/68407700:21220/24:00378752 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/24:00378752

Result on the web

<a href="https://doi.org/10.1109/INDIN58382.2024.10774436" target="_blank" >https://doi.org/10.1109/INDIN58382.2024.10774436</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INDIN58382.2024.10774436" target="_blank" >10.1109/INDIN58382.2024.10774436</a>

Result language

angličtina

Original language name

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

Original language description

In a 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 frequently do not support these processes enough in terms of not sharing relevant data about the products nor their (re-)manufacturing processes. This paper proposes to accompany each product with a digital twin technology, specifically the Product Digital Twin (PDT), which can carry information for facilitating and optimizing production and remanufacturing processes. This paper introduces a knowledge representation called Bi-Flow Product-Process-Resource Asset Network (Bi-PAN). Bi-PAN extends a well-proven Product-Process-Resource Asset Network (PAN) paradigm by integrating both assembly and disassembly workflows into a single information model. Such networks enable capturing relevant relationships across products, production resources, manufacturing processes, and specific production operations that have to be done in the manufacturing phase of a product. The proposed approach is demonstrated in a use-case of disassembling electric vehicle (EV) batteries. By utilizing PDTs with Bi-PAN knowledge models, challenges associated with disassembling of EV batteries can be solved flexibly and efficiently for various battery types, enhancing the sustainability of the EV battery life-cycle management.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)

ISBN

979-8-3315-2747-1

ISSN

1935-4576

e-ISSN

2378-363X

Number of pages

6

Pages from-to

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Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Peking

Event date

Aug 17, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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