Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs

Kong, Xiangdong; Zheng, Yuejiu; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Zhang, Zhendong

doi:10.1016/j.jpowsour.2018.05.097

Cited by 137 publications

(34 citation statements)

References 30 publications

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“…Several recent works implement signal processing for Li-ion battery fault diagnosis. Kong et al [58] obtained the remaining charging capacity, which can increase due to extra charge depletion caused by micro-short circuiting, from charging cell voltage curve transformation. The micro-short circuit fault was then identified from the remaining charging capacities between adjacent charges, which was used to obtain the leakage current of the shorted cell.…”

Section: Non-model-based Methodsmentioning

confidence: 99%

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

Fowler

2020

Algorithms

185

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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: Non-model-based Methodsmentioning

confidence: 99%

“…Wavelet transform [58] Correlation coefficient [59,60,83] Shannon entropy [39,[61][62][63][78][79][80] Sensor topology [81][82][83] Knowledge-based These algorithms use the knowledge obtained from observations or data coming from the system to establish rules or train data to detect a fault.…”

Section: Structural Analysismentioning

confidence: 99%

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

Fowler

2020

Algorithms

185

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“…Using the real-time voltage data extracted from the National Service and Management Center of Electric Vehicles (NSMC-EV) in Beijing, Li et al [142] verified the voltage fault detection of the battery pack based on the interclass correlation coefficient method. Considering that the remaining charging capacity (RCC) of the MSC cell will increase when the battery pack is fully charged each time due to the extra charge depletion, Kong et al [143] estimated the RCC of each cell based on the uniform charging cell voltage curve (CCVC) hypothesis. According to the difference between the RCCs after two adjacent charges, the leakage current and MSC resistance can be obtained.…”

Section: Parameter Identificationmentioning

confidence: 99%

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Hu¹,

Zhang²,

Liu³

et al. 2020

Preprint

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Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed. IntroductionAs one of the most promising energy storage systems, lithium-ion batteries have been widely used in various applications, such as electric vehicles (EVs) and smart grids. Currently, lithium-ion batteries have become the mainstream energy storage solution, owing to their inherent benefits such as high energy density, high power density, and long lifespan. However, the potential r isks, due to the abusive operating conditions and harsh environment, pose a huge challenge to the safety of Li-ion battery systems (LIBSs). A real-time effective battery management system (BMS) is critical to ensure the safety of LIBSs. BMS has several functionalities, such as state of charge monitoring, thermal management, charging management, and equalization management. It also tracks the health status and monitors the potential faults of LIBS. Without suitable diagnostics and fault handling, a minor fault could eventually lead to severe damages of LIBS [1]. The importance of the fault diagnostics and fault handling has been demonstrated repeatedly in several severe incidents recently [2]- [4].There are different fault modes in the LIBS, and fault mechanisms are usually very complex. From a control perspective, these fault modes can be divided into battery fault, sensor fault, and actuator fault. The battery faults, which include overcharge , overdischarge, overheat, external short circuit (ESC), internal short circuit (ISC), electrolyte le akage, battery swelling, battery accelerated degradation, and thermal runaway (TR), are the most critical faults in the LIBS. These faults are also intertwine d. Overcharge and overdischarge could lead to various undesirable battery side reactions, resultin g in accelerated degradation. These side reactions and gases generated by the chain reactions during TR may eventually cause the battery swelling. Such a swellin g along with mechanical damage may, in turn, lead to electrolyte leakage. The ISC is typically caused by separator failure due to manufacturing defects, overheat, mechanical collisions, or penetration by metal dendrites or mechanical punctures. Fortunatel y, the Joule heat generated by ISC develops into TR only when the equivalent ISC resistance reac hes a very low level [5]. Abnormal heat generation occurs under various conditions, such as side reactions during overcharge/over -di...

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“…Evaluation and accurate estimation of the battery condition have been of great interest for battery manufacturers and for road safety purpose because an early detection of short circuit or cycle life of battery based on state of health (SOH) value unforeseen occurrence of dire events . This is important for safety design of EVs and prevents unforeseen accidents such as those based on thermal failure, short circuit, and external impact from happening . Therefore, developing the novel strategy for an effective evaluation of residual energy in the battery pack shall pave the way for an efficient time‐based recycling for use of spend batteries for either reuse, remanufacturing, or recovery of materials.…”

Section: Introductionmentioning

confidence: 99%

“…7 This is important for safety design of EVs and prevents unforeseen accidents such as those based on thermal failure, short circuit, and external impact from happening. [8][9][10] Therefore, developing the novel strategy for an effective evaluation of residual energy in the battery pack shall pave the way for an efficient time-based recycling for use of spend batteries for either reuse, remanufacturing, or recovery of materials.…”

Section: Introductionmentioning

confidence: 99%

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Garg

Liu

et al. 2019

Int J Energy Res

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Summary The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.

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Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs

Cited by 137 publications

References 30 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

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

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