A Systematic Review of Lithium Battery Defect Detection Techniques and Technologies

Abstract

This systematic review aims to explore and synthesize the existing literature on defect detection methods in lithium batteries. With the increasing demand for reliable and efficient lithium batteries in various applications, ensuring their safety and performance through effective defect detection is critical. This review categorizes and evaluates different detection techniques, including electrochemical, non-destructive testing (NDT), electrical, acoustic emission, optical methods, and machine learning. The primary objective is to provide a comprehensive understanding of the current state of defect detection technologies, assess their effectiveness, and identify key challenges and future research directions. The review covers various defect types, including manufacturing, operational, and environmental defects, and discusses the methodologies used for defect detection, including their sensitivity, accuracy, speed, cost, and practicality. Additionally, the review highlights real-world applications, case studies, and the integration challenges of these technologies with Battery Management Systems (BMS). By examining these aspects, the review aims to offer valuable insights for researchers, manufacturers, and practitioners in the field of lithium battery technology. Key findings suggest that while significant advancements have been made, there remain substantial challenges, particularly in the areas of data acquisition, standardization, and integration with existing battery management systems. Future research should focus on improving the robustness, scalability, and cost-effectiveness of defect detection methods, as well as developing comprehensive regulatory frameworks to ensure the safe deployment of lithium batteries.