Battery safety: Fault diagnosis from laboratory to real world

Battery failures, although rare, can significantly impact applications such as electric vehicles. Minor faults at cell level might lead to catastrophic failures and thermal runaway over time, underscoring the importance of early detection and real-time diagnosis. This article offers a concise yet comprehensive review and analysis of the mechanisms that cause battery faults and failures. It emphasizes the distinctions between controlled laboratory tests and practical scenarios, where safety hazards can occur during manufacturing and operational failures. Addressing the urgent need to transition technology from academic laboratories to practical applications is a key objective of this review. The cloud-based, AI-enhanced hierarchical framework leverages emerging technologies to predict battery behavior, enabling qualitative and quantitative diagnostics throughout the entire cycle. The goal is to address safety concerns in large-scale real-world applications by applying observational, empirical, physical, and mathematical understanding of the battery system. This framework provides holistic tools for the early detection of defective cells at the multiphysics level (mechanical, electrical, thermal behaviors) during manufacturing, offers digital diagnostic solutions at multiple scales (cell, pack, and system), and facilitates safety assessments for second-life cells. Finally, we discuss emerging trends, significant challenges, and opportunities for improving battery safety diagnostics using big data and machine learning.