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 scen. 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.••Causes and mechanisms of battery faults and failures are concisely reviewed.••The gap between lab tests and real-world battery safety is succinctly summarized.••A cloud-based hierarchical framework for enhancing battery safety is outlined.••Challenges, including data-centric and machine learning issues, are discussed.••Future perspectives are provided, covering materials, cells, and system levels.BatterySafetyFaultFailureThermal runawayDiagnosisThe urgent need to reduce emissions and lessen our dependence on fossil fuels in the transportation sector has brought electrification to the forefront as a crucial strategy. Electric vehicles (EVs) and green energy storage have become pivotal in this electrification drive, representing a significant step towards a more sustainable and environmentally friendly future for transportation [,, ]. Lithium-ion batteries have played a central role in this transformation, practically synonymous with the power systems of modern EVs. Over the past decade, there has been a surge of innovation, resulting in remarkable enhancements in battery performance. These improvements have been made possible by the introduction of cutting-edge electrode materials and innovative cell designs. However, despite these advancements, safety concerns continue to cast a shadow over lithium-ion batteries.Battery faults represent a broad spectrum of issues that can occur in a battery system, significantly impacting its performance, safety, and longevity. These anomalies, often complex and multifaceted, range from minor deviations in battery operation to severe incidents, leading to catastrophic damage and failure. On the lighter end of the spectrum, faults such as calibration errors, temperature fluctuations, or minor overcharging may occur, typically resulting i. 2.1. Analysis from laboratory testsThanks to advancements in manufacturing processes and meticulous assembly techniques, battery failure is rare. Estimates suggest that the hazardous failure rate for NCA-type lithium-ion cells under normal operations is about 1 in 10 million cells. However, collecting relevant experimental data can be challenging, as it requires cycling millions of commercial lithium-ion cells. One method to investigate battery fault and failure behaviors and mechanisms is to intentionally induce various abuse conditions, such as mechanical, electrical, and thermal abuse. These laboratory tests are designed to simulate abuse patterns that batteries might encounter in real-world applications and to investigate their responses in specific cases (Fig. 1).2.2. Analysis from real-world scenariosIn recent years, there has been growing concern about battery safety, which has attracted significant attention from academia, industry, and social media [101,102]. In the automotive industry, battery safety issues have led to the recall of hundreds of thousands of EVs made by various companies, resulting in multi-billion dollar costs [103,104]. Despite ongoing progress, the underlying failure.
