To boost the circular economy of the electric vehicle battery industry, an accurate assessment of the state of health of retired batteries is essential to assign them an appropriate value in the post automotive market and material degradation before recycling. In practice, the advanced battery testing techniques are usually limited to laboratory benches at the battery cell level and hardly used in the industrial environment at the battery module or pack level. This necessitates developing battery recycling facilities that can handle the assessment and testing undertakings for many batteries with different form factors. Towards this goal, for the first time, this article proposes proof of concept to automate the process of collecting the impedance data from a retired 24kWh Nissan LEAF battery module. The procedure entails the development of robot end-of-arm tooling that was connected to a Potentiostat. In this study, the robot was guided towards a fixed battery module using visual servoing technique, and then impedance control system was applied to create compliance between the end-of-arm tooling and the battery terminals. Moreover, an alarm system was designed and mounted on the robot’s wrist to check the connectivity between a Potentiostat and the battery terminals. Subsequently, the electrochemical impedance spectroscopy test was run over a wide range of frequencies at a 5% state of charge. The electrochemical impedance spectroscopy data obtained from the automated test is validated by means of the three criteria (linearity, causality and stability) and compared with manually collected measurements under the same conditions. Results suggested the proposed automated configuration can accurately accomplish the electrochemical impedance spectroscopy test at the battery module level with no human intervention, which ensures safety and allows this advanced testing technique to be adopted in grading retired battery modules.

为了促进电动汽车电池行业的循环经济，必须对退休电池的健康状况进行准确评估，以便为它们在后汽车市场中分配适当的价值，并在回收之前对材料进行降解。实际上，先进的电池测试技术通常仅限于电池单元级别的实验室工作台，并且几乎不用于电池模块或电池组级别的工业环境。这就需要开发电池回收设施，以处理许多具有不同外形尺寸的电池的评估和测试工作。为了实现这一目标，本文首次提出了概念验证，以自动化从已淘汰的24kWh Nissan LEAF电池模块收集阻抗数据的过程。该程序需要开发与恒电位仪连接的机器人手臂末端工具。在这项研究中，使用视觉伺服技术将机器人引导至固定的电池模块，然后应用阻抗控制系统在臂端工具和电池端子之间建立顺应性。此外，还设计了一个警报系统并将其安装在机器人的手腕上，以检查恒电位仪与电池端子之间的连接。随后，以5％的荷电状态在较宽的频率范围内进行电化学阻抗谱测试。从自动化测试中获得的电化学阻抗谱数据通过三个标准（线性，因果关系和稳定性）进行验证，并与在相同条件下手动收集的测量结果进行比较。