Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives

Lai, Xin; Jin, Changyong; Wei, Yi; Han, Xiao; Feng, Xuning; Zheng, Yuejiu; Ouyang, Minggao

doi:10.1016/j.ensm.2020.11.026

Cited by 252 publications

(84 citation statements)

References 184 publications

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“…LIBs are involved in structure deformation and failure of battery materials in mechanical abuse conditions. Generally, mechanical abuse behaviors include collision, pinch and piercing, 47 which can lead to ISC and cause TR as accidents No. 2, No.…”

Section: Inducements Of Iscmentioning

confidence: 99%

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Huang

Liu

et al. 2021

Int J Energy Res

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Summary Internal short circuit (ISC) of lithium‐ion battery is one of the most common reasons for thermal runaway, commonly caused by mechanical abuse, electrical abuse and thermal abuse. This study comprehensively summarizes the inducement, detection and prevention of the ISC. Firstly, the fault tree is utilized to analyze the ISC inducement, including the abuse conditions, improper manufacture and design defects. Secondly, the ISC substitute triggering methods (utilizing mechanical deformation from the outside or utilizing controllable components introduced inside) are summarized considering the randomness of ISC, which have high repeatability. Moreover, these methods can be used to investigate the evolution and the hazard boundary of ISC. The ISC severity is determined by the ISC type, ISC location, ISC area, battery state of charge, capacity, material and structure. There are four types of ISC mode, the danger extends ranking is aluminum‐anode > aluminum‐copper > cathode‐copper > cathode‐anode. The ISC evolution is presented based on the upper summary. Then, the ISC detection methods are reviewed: (1) comparing the measured data with the predicted value from the model; (2) detecting whether the battery has self‐discharge; (3) comparing based on the battery inconsistency and (4) other signals. Finally, the prevention strategies are summarized, which can be used to reduce the ISC risk by blocking electron or lithium‐ion channels in the battery cell.

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Section: Inducements Of Iscmentioning

confidence: 99%

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Huang

Liu

et al. 2021

Int J Energy Res

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“…When the temperature rises up to the detectable temperature of self-heating (T SH = 116.74°C), the metastable components (such as polymers, ROLi, (CH 2 OCO 2 Li) 2 , and ROCO 2 Li) decompose, which results in the fracture of SEI layer ( Leroy et al., 2005 ; McArthur et al., 2012 ; Wang et al., 2006 ). The major chemical reactions are summarized as follows ( Aurbach et al., 1997 ; Fu et al., 2015 ; Lai et al., 2021 ):

…”

Section: Resultsmentioning

confidence: 99%

Investigating the critical characteristics of thermal runaway process for LiFePO4/graphite batteries by a ceased segmented method

et al. 2021

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Summary Lithium-ion batteries (LIBs) are widely used as the energy carrier in our daily life. However, the higher energy density of LIBs results in poor safety performance. Thermal runaway (TR) is the critical problem which hinders the further application of LIBs. Clarifying the mechanism of TR evolution is beneficial to safer cell design and safety management. In this paper, liquid nitrogen spray is proved to be an effective way to stop the violent reaction of LIBs during the TR process. Based on extended-volume accelerating rate calorimetry, the liquid nitrogen ceasing combined with non-atmospheric exposure analysis is used to investigate the TR evolution about LiFePO 4 /graphite batteries at critical temperature. Specifically, the geometrical shape, voltage, and impedance change are monitored during the TR process on the cell level. The morphologies/constitution of electrodes and separators are presented on the component level. Utilizing the gas analysis, the failure mechanism of the prismatic LiFePO 4 /graphite battery is studied comprehensively.

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“…The datadriven methods usually use machine learning and a multimodel fusion strategy for fault detection [116]. Data-driven algorithms include random forest (RF) and principal component analysis (PCA) which are used for detecting battery internal short-circuit faults [117]. The multiclass relevance vector machine is also used for internal short-circuit faults in batteries [118].…”

Section: H Battery Management Systemmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives

Cited by 252 publications

References 184 publications

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Investigating the critical characteristics of thermal runaway process for LiFePO4/graphite batteries by a ceased segmented method

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

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