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Lookup NU author(s): Dr Mohamed Ahmeid
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2025 Rastegarpanah, Contreras, Ahmeid, Asif, Villagrossi and Stolkin. Introduction: The transition to electric vehicles (EVs) has highlighted the need for efficient diagnostic methods to assess the state of health (SoH) of lithium-ion batteries (LIBs) at the end of their life cycle. Electrochemical Impedance Spectroscopy (EIS) offers a non-invasive technique for determining battery degradation. However, automating this process in industrial settings remains a challenge. Methods: This study proposes a robotic framework for automating EIS testing using a KUKA KR20 robot arm mounted on a 5 m rail track, equipped with a force/torque sensor and a custom-designed End-of-Arm Potentiostat (EOAT). The system operates in a shared-control mode, enabling the robot to function both autonomously and semi-autonomously, with the option for human intervention to assume control as needed. An admittance controller ensures stable connections, with forces optimized for accuracy and safety. The EOAT’s mechanical strength was validated through finite element analysis. Results: Experimental validation demonstrated the effectiveness of the developed robotized framework in identifying varying levels of battery degradation. Internal resistance measurements reached up to 1.5 (Formula presented.) in the most degraded cells, correlating with significant capacity reductions. The robotic setup achieved consistent and reliable EIS testing across multiple LIB modules. Discussion: This automated robotic framework enhances battery diagnostics by improving testing accuracy, reducing human intervention, and minimizing safety risks. The proposed approach shows promise for scaling EIS testing in industrial environments, contributing to efficient EV battery reuse and recycling processes.
Author(s): Rastegarpanah A, Contreras CA, Ahmeid M, Asif ME, Villagrossi E, Stolkin R
Publication type: Article
Publication status: Published
Journal: Frontiers in Robotics and AI
Year: 2024
Volume: 11
Online publication date: 10/01/2025
Acceptance date: 08/11/2024
Date deposited: 03/02/2025
ISSN (electronic): 2296-9144
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/frobt.2024.1493869
DOI: 10.3389/frobt.2024.1493869
Data Access Statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
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