Evaluating battery cell quality in electric vehicles
Summary
This dual nature demands a testing approach that encompasses not only electrical and mechanical assessments but also a deep understanding of the electrochemical processes integral to a battery’s functionality, safety and operational lifetime. Artificial intelligence (AI) enabled automated optical inspection (AOI) offers speed, consistency, and precision, and can help streamline quality control. The results are used to characterise the AC dynamic of the cell and detect any critical defects, offering insights into electrochemical processes, ageing effects, and internal resistance variations. Hyper-automation, artificial intelligence, and machine learning play pivotal roles in long-term test strategies, enhancing accuracy and efficiency, and delivering quantifiable business results to attract investment. This approach uses statistical, machine learning, and operational research techniques to analyse massive data sets from cell to pack level, facilitating data-driven decisions, defect prevention, and performance improvement.