Failure modes and mechanisms for rechargeable Lithium-based batteries: a state-of-the-art review

Lyu, Dandan; Ren, Bo; Li, Shaofan

doi:10.1007/s00707-018-2327-8

Cited by 63 publications

(27 citation statements)

References 143 publications

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“…Internal battery faults are difficult to detect since the operation within a Li-ion cell is still not fully understood [11]. Some examples of internal battery faults are overcharge, overdischarge, internal and external short circuit, overheating, accelerated degradation, and thermal runaway.…”

Section: Internal Battery Faultsmentioning

confidence: 99%

“…Thermal runaway is mainly caused by heat build-up from the Algorithms 2020, 13, 62 4 of 18 short circuit. In general, high capacity cells are at a higher risk of thermal runaway from internal short circuits than normal capacity cells [11].…”

Section: Internal Short Circuitmentioning

confidence: 99%

“…An external short circuit generally occurs when the tabs are connected by a low resistance path [11]. Another cause is electrolyte leakage from cell swelling due to gas generation from side reactions during overcharge [22].…”

Section: External Short Circuitmentioning

confidence: 99%

“…A study [22] concluded that due to external short circuit, the Li-ion diffusion in the negative electrode leads to limited current, and the heat generated by the electrolyte decomposition in the positive electrode is responsible for thermal runaway occurrence. An external short circuit also leads to excessive discharge of the energy that is being stored in a cell [11].…”

Section: External Short Circuitmentioning

confidence: 99%

“…">Types of Fault in the Li-Ion Battery SystemThis paper categorizes Li-ion battery faults into internal and external faults. Lyu et al [11] presented a detailed review of failure modes and mechanisms for rechargeable Li-based batteries. This section only briefly presents some causes and mechanisms of the faults.…”

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confidence: 99%

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A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

Fowler

2020

Algorithms

197

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The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.Algorithms 2020, 13, 62 2 of 18 combustion and explosion [7]. The consequences of Li-ion battery faults can be minimized or eliminated by the BMS, as it prevents the battery from functioning outside its safe operational range, and also detects faults using various fault diagnostic methods [8]. It is an indispensable component in the Li-ion battery system since it can handle failures safely by going into failure mode, providing a safer environment for the users of Li-ion battery applications.Since fault diagnosis is a crucial part of Li-ion battery advancements, various methods for fault diagnosis have been studied and developed. Many fault diagnostic algorithms have been proposed, which can be categorized into model-based and non-model-based methods. Model-based methods include parameter estimation, state estimation, parity space, and structural analysis. Non-model-based methods include signal processing and knowledge-based methods [1,9]. Fault diagnosis research in other fields has shown that the most effective approach is often a combination of more than one method [9].Lu et al.[8] briefly presented fault diagnosis as one of the key issues for Li-ion battery management in electric vehicles. In [10], a review of sensor fault diagnosis for Li-ion battery systems was provided, but other types of faults were not discussed. Wu et al.[1] conducted a review on fault mechanism and diagnosis for Li-ion battery, but there have been many new developments in the field since then. Researchers have made significant progress in understanding the mechanism and operation of Li-ion batteries, and with that, many innovations in battery fault diagnosis have emerged. Therefore, there is a need to identify the current progress and future direction of Li-ion battery fault diagnosis research. The contribution of this paper is the comprehensive review of recent developments in Li-ion battery fault diagnosis, as well as the discussion of some future challenges that need to be addressed.The rest of this paper is organized as follows. Section 2 introduces different types of faults in Li-ion battery, and their causes, ...

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Section: Internal Battery Faultsmentioning

confidence: 99%

Section: Internal Short Circuitmentioning

confidence: 99%

Section: External Short Circuitmentioning

confidence: 99%

Section: External Short Circuitmentioning

confidence: 99%

mentioning

confidence: 99%

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A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

Fowler

2020

Algorithms

197

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A universal anisotropic model for a lithium‐ion cylindrical cell validated under axial, lateral, and bending loads

Song

Gilaki

Keshavarzi

et al. 2022

Energy Science & Engineering

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Li‐ion batteries are widely used in electric vehicles (EVs) propulsion. Therefore, ensuring their safety under mechanical abuse and accidental loads is a major challenge for the industry. To get a better understanding of the battery behavior in such cases, material calibration and computational modeling of the battery cells are essential. This paper aims to develop a universal homogenized model for an 18,650 cell that can predict cell behavior under both axial and lateral loading cases as well as three‐point bending. Previous homogenized models presented in the literature have covered one or two of these cases, but none have been validated in all these three major loading scenarios. To achieve this, precise shell casing and jellyroll material calibrations were performed. The features included in this universal model are (I) uncoupled calibration of axial and lateral properties for the cylindrical jellyroll from experiments performed in these two loading directions and employment of an anisotropic crushable foam model to simulate these features, (II) using Hill's anisotropic yield criteria and modified Mohr–Coulomb fracture criteria for the shell casing. The universal model developed here was able to predict the response of the cell in all lateral, axial, and bending loading scenarios. A comparison of this model with the previously developed isotropic models shows the special advantage of the new model in cases of axial loading and bending. However, for lateral compression cases, even the isotropic model provides a very close prediction. The experiments used for this study were all performed on fresh discharged cells under quasi‐static loading.

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The Concept of Li-Ion Battery Control Strategies to Improve Reliability in Electric Vehicle (EV) Applications

Gandoman

Nasiriyan

Mohammadi‐Ivatloo

et al. 2022

Electric Vehicle Integration via Smart Charging

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Failure modes and mechanisms for rechargeable Lithium-based batteries: a state-of-the-art review

Cited by 63 publications

References 143 publications

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

A universal anisotropic model for a lithium‐ion cylindrical cell validated under axial, lateral, and bending loads

The Concept of Li-Ion Battery Control Strategies to Improve Reliability in Electric Vehicle (EV) Applications

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