原文信息：
Towards a safer lithium-ion batteries: A critical review on cause,characteristics, warning and disposal strategy for thermal runaway
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792423000252
Abstract
Lithium-ion batteries have become the best choice for battery energy storage systems and electric vehicles due to their excellent electrical performances and important contributions to achieving the carbon-neutral goal. With the large-scale application, safety accidents are increasingly caused by lithium-ion batteries. As the core component for battery energy storage systems and electric vehicles, lithium-ion batteries account for about 60% of vehicular failures and have the characteristics of the rapid spread of failure, short escape time, and easy initiation of fires, so the safety improvement of lithium-ion batteries is urgent. This study analyses the causes and mechanisms of lithium-ion batteries failures from design, production, and application, investigates its failure features and warning algorithms for thermal runaway, and the concept of long-medium-short graded warning is proposed based on the battery failure mechanism and its evolution to provide a basis for failure warning. As lithium-ion batteries fires are difficult to completely avoid, the characteristics of lithium-ion batteries fires are explored to improve battery structure and develop fire extinguishing agents and methods for fire prevention and suppression.Improving the safety of batteries is a systematic project, and at a time when there has been no breakthrough in the chemical system, improvements, such as build a practical graded warning system, are needed in all aspects of design, production, use and disposal to improve battery safety and minimize the risk of failure.
Keywords
Lithium-ion battery
Fault diagnosis
Graded warning
Fire suppression
Graphics
Fig. 1. The flowchart of framework.
Fig. 2. Model-based RUL prediction framework.
Fig. 3. Data-driven RUL prediction framework.
Fig. 4. Battery temperature-based mid-term warning method.
Fig. 5. Short-term warning characteristics and methods.
作者简介
团队介绍：
本研究由哈尔滨工业大学汽车工程学院、北京理工大学机械工程学院先进储能与应用联合实验室和斯威本科技大学科学计算与工程技术学院的研究人员共同完成。
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