Degradation of lithium-ion batteries and how to fight it: A review

Kanevskii, L. S.; Dubasova, V. S.

doi:10.1007/s11175-005-0042-y

Cited by 60 publications

(21 citation statements)

References 128 publications

(116 reference statements)

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“…It can also cause surface layer formation and contact deterioration, which results in electrode disintegration, material disintegration, and loss of lithium. These phenomena can lead to transport barriers, which result in penetration of the separator and cause an internal short circuit and, ultimately, a thermal runaway [30].…”

Section: Accelerated Degradationmentioning

confidence: 99%

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: Accelerated Degradationmentioning

confidence: 99%

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

Fowler

2020

Algorithms

197

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“…Particularly evident changes at the consideration of the same problem take place at the higher values of the potential с U , namely at с U = 0.7 (see Figure 4e). Specific changes occur also with the 2 d width increasing of the barrier с U . At the same time the comparing of the results with the results of the problem ʺparticle in an infinitely wellʺ is less obvious (see Figure 4f).…”

Section: Virtual Statesmentioning

confidence: 90%

“…Changing the dimensions 2 d and values of the potential c U it is possible to simulate the amount x of introduced guest. In the wells with finite thickness and height of barriers an electron is not fully localized in the wells, and there is a finite probability of its penetration outside of the wells.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…The increasing of the barrier thickness 2 d simultaneously is accompanied by the narrowing of wells on both sides с U , reducing overlap of the wave functions between them. Therefore, these wells can be regarded as independent.…”

Section: Virtual Statesmentioning

confidence: 99%

“…Today transition to nanoscale objects as energy or charge storages allows to solve this problem. Really, application of the nanodispersed FeS2 in an energy storage device with a lithium anode increases the specific capacity almost by 20% in comparison with a coarsegrained homologue [1], and the nanosized -Fe2O3 possesses the high recirculated capacity 200 mA hour/g and good cycling in a range 1,5-4,0 V regarding Li + /Li in comparison with the macrostructured α -Fe2O3, γ -Fe2O3 and α -Fe3O4 [2]. The concept of ʺelectrochemical graftingʺ [3,4] explains the effect by the features density of nanoobjects.…”

Section: Introductionmentioning

confidence: 99%

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Peculiarities of kinetic characteristics in a supramolecular system, in particular, in a double-matrix structure observed at change of the guest content in a matrix-host are investigated. Results obtained within the framework of a time-independent one-dimensional Schrödinger equation with three barrier potential qualitatively explain experimental data. They indicate the importance of size quantization of a system, correlation between energy and geometric characteristics of both guest and host in this case.

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Degradation of lithium-ion batteries and how to fight it: A review

Cited by 60 publications

References 128 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

Current Generation in Double-Matrix Structure: A Theoretical Simulation

Energy Storage and Materials

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