Advanced robotics for automated EV battery testing using electrochemical impedance spectroscopy

• Published in: Frontiers in Robotics and AI
• Main Authors: Alireza Rastegarpanah, Cesar Alan Contreras, Mohamed Ahmeid, Mohammed Eesa Asif, Enrico Villagrossi, Rustam Stolkin
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2025-01-01
• Subjects: electrochemical impedance spectroscopy; EV battery; Lithium-ion battery recycling; admittance Control; robotic disassembly
• Online Access: https://www.frontiersin.org/articles/10.3389/frobt.2024.1493869/full

IntroductionThe 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.MethodsThis 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.ResultsExperimental validation demonstrated the effectiveness of the developed robotized framework in identifying varying levels of battery degradation. Internal resistance measurements reached up to 1.5 mΩ in the most degraded cells, correlating with significant capacity reductions. The robotic setup achieved consistent and reliable EIS testing across multiple LIB modules.DiscussionThis 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.