Advanced robotics for automated EV battery testing using electrochemical impedance spectroscopy

Area of Science:

• Robotics and Automation
• Materials Science
• Electrochemistry

Background:

• Electric vehicle (EV) adoption necessitates reliable methods for assessing lithium-ion battery (LIB) health.
• Electrochemical Impedance Spectroscopy (EIS) is a key non-invasive technique for battery degradation analysis.
• Automating EIS testing in industrial settings presents significant challenges.

Purpose of the Study:

• To develop and validate a robotic framework for automated EIS testing of LIBs.
• To enhance the efficiency and accuracy of battery state-of-health (SoH) diagnostics.
• To facilitate industrial-scale battery testing for reuse and recycling.

Main Methods:

• A KUKA KR20 robot arm on a rail track equipped with a force/torque sensor and custom End-of-Arm Potentiostat (EOAT).
• Shared-control system allowing autonomous, semi-autonomous, and human-controlled operation.
• Admittance controller for stable connections and optimized force application; FEA for EOAT validation.

Main Results:

• The robotic framework successfully identified varying levels of LIB degradation.
• Measured internal resistance reached up to 1.5 $$ in degraded cells, correlating with capacity loss.
• Consistent and reliable EIS testing was achieved across multiple LIB modules.

Conclusions:

• The automated robotic system improves battery diagnostic accuracy and safety while reducing human intervention.
• This approach is scalable for industrial EIS testing, supporting EV battery lifecycle management.
• The framework contributes to efficient battery reuse and recycling processes.