Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Adaptive Self-Configuring Simulation Model for Planning and Optimizing Disassembly Scenarios for Electric Vehicle Batteries

Version 1 : Received: 20 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:28:33 CEST)

A peer-reviewed article of this Preprint also exists.

Baazouzi, S.; Grimm, J.; Birke, K.P. Multi-Method Model for the Investigation of Disassembly Scenarios for Electric Vehicle Batteries. Batteries 2023, 9, 587. Baazouzi, S.; Grimm, J.; Birke, K.P. Multi-Method Model for the Investigation of Disassembly Scenarios for Electric Vehicle Batteries. Batteries 2023, 9, 587.

Abstract

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Keywords

electric vehicle batteries; disassembly; disassembly scenarios; automated disassembly, self-configuring model, multi-method simulation, optimization

Subject

Engineering, Mechanical Engineering

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