An overview of safety for laboratory testing of lithium-ion batteries

Fantham, Thomas L.; Gladwin, Daniel T.

doi:10.1016/j.egyr.2021.03.004

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…This is the cornerstone of the physics-of-failure (PoF) approach for evaluating the reliability of systems, subsystems, and components. In comparison to the conventional Failure Mode and Effects Analysis (FMEA) [97]- [100], which was developed to identify and categorize failures with a focus on mission success and safety, FMMEA sets itself apart by considering failure mechanisms and their importance in evaluating the potential risks associated with the system. The four key components describing the FMMEA principle are illustrated in Fig.…”

Section: ) Failure Modes Mechanisms and Effects Analysismentioning

confidence: 99%

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

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Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research.INDEX TERMS Capacity fade, causes of failure, data-driven, degradation trajectory, electric vehicle, electrochemical impedance spectroscopy, failure modes mechanisms and effects analysis, incremental capacity and differential voltage analysis, Li-ion batteries, machine learning, model-based, power fade, qualitative analysis, quantitative analysis, remaining useful life, reliability, state of health, state of charge, transfer learning, X-ray computed tomography.

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Section: ) Failure Modes Mechanisms and Effects Analysismentioning

confidence: 99%

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

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“…구개발 단계에서도 많은 화재사고가 발생하고 있는 실정이다. 연구개발 단계에서는 코인셀 또는 파우치셀이라 불리는 프로 토 타입의 셀을 제조하여 충방전 성능 테스트를 수행하고 있 는데 완제품 전지와 달리 안전장치의 부재로 화재 사고 발생 , 가능성이 높다 (10) FMEA handbook 을 시험용 셀에 적용하였다 (14) .…”

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A Study on dFMEA-based Fire Risk Assessment of Lithium-ion Secondary Battery Testing Cells

Kim,

Kim

2023

Fire Sci. Eng.

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In this study, Design Failure Mode and Effects Analysis (dFMEA) was performed to evaluate the fire risk of lithium-ion secondary battery testing cells used during the research and development stage. dFMEA was used for the failure risk analysis, risk assessment, and risk priority analysis of the anode, cathode, separator, electrolyte, and cell design of the testing cell. In addition, the evaluation criteria, including severity, occurrence, and detection, was reorganized into three categories and optimized for the testing cell application. Based on the failure risk analysis, a total of 11 causes of failure were identified. In particular, explosion and thermal runaway were predicted when a nickel-cobalt-manganese (NCM)-based anode material with a high nickel content was used, the separator had a high thermal contraction rate and low tensile strength characteristics, and there were errors in the electrolyte composition optimization. This cause of failure was classified as a moderate risk with a high fire risk by the risk priority analysis. Therefore, at the research and development stage, it is critical to evaluate the mutual reactivity and preliminary physical properties of battery materials and to construct safety equipment to reduce fire damage.

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An overview of safety for laboratory testing of lithium-ion batteries

Cited by 10 publications

References 9 publications

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

A Study on dFMEA-based Fire Risk Assessment of Lithium-ion Secondary Battery Testing Cells

Electric vehicle charging station using fuel cell technology: Two different scenarios and thermodynamic analysis

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