Parameter optimisation for robotic disassembly of snap-fit plug connectors in end-of-life electric vehicle batteries

Abstract

With the growth of the new energy sectors, recycling and remanufacturing end-of-life (EoL) electric vehicle (EV) batteries have become increasingly important. Disassembly is the initial step in this process. Snap-fit plug connectors are common components in EV batteries, which are currently disassembled manually. The accuracy of the machine vision system’s positioning affects the precision of robotic disassembly. This paper introduces a disassembly parameter optimisation method based on the Bees algorithm (BA) for robotic disassembly of snap-fit plug connectors. By combining six-axis force/torque sensor data with a limited set of experimental samples, the search direction could be iteratively optimised from the initial contact point to an effective disassembly pose of the robotic tool. Experimental results show that the method converges faster than that using proximal policy optimisation (PPO) with the same number of iterations. The proposed approach could improve disassembly robustness and adaptability, enabling the automation of snap-fit plug connector disassembly.