Experimental study of internal and external short circuits of commercial automotive pouch lithium-ion cells

Abaza, Ahmed; Ferrari, Stefania; Wong, Hin Kwan; Lyness, Chris; Moore, Andrew D.; Weaving, Julia S.; Blanco-Martin, Maria; Dashwood, R. J.; Bhagat, Rohit

doi:10.1016/j.est.2018.01.015

Cited by 93 publications

(43 citation statements)

References 11 publications

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“…An internal short circuit occurs when the insulating separator layer between the electrodes fails. This failure of the separator can be attributed to melting due to high temperature, cell deformation, the formation of dendrite, or compressive shock [7,19,20]. All of these can cause penetration through the separator layer, or cause the lithium ions and electrons to be released at the anode and travel across the electrolyte toward the cathode, which triggers contact between the anode and the cathode, leading to internal short-circuiting.…”

Section: Internal Short Circuitmentioning

confidence: 99%

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

Fowler

2020

Algorithms

177

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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 Short Circuitmentioning

confidence: 99%

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

Fowler

2020

Algorithms

177

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“…Recently, many studies have been aimed at detecting external short circuit (ESCr) and ISCr in lithium-ion batteries. When the ESCr (R ISCr < 5.4 m ) exists in the batteries, significant changes in measurements such as decrease in terminal voltage and increase in both current and temperature are monitored in short time [12]. ESCr detection methods are developed via using the aforementioned evident changes [13]- [18].…”

Section: Abbreviationsmentioning

confidence: 99%

“…After estimating the R ISCr s with the various SOC(p)s and the fixed SOC ini online, a representative value among the estimates of R ISCr should be determined as a final fault index R fi to detect the soft ISCr. The mean value R ISCr of diverse R end s is calculated in (11) where R end denotes the R ISCr estimated at the last iteration with a SOC(p) and h is the number of R end s. In (12), d denotes deviation of the R end and is then calculated. The R end which has the largest d is considered as an outlier.…”

Section: Final Fault Index Calculationmentioning

confidence: 99%

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

et al. 2020

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Soft internal short circuit (ISCr) in lithium-ion batteries is a latent risk, and it is a primary reason for thermal runaway with blaze and explosion. Early detection of ISCr is necessary to ensure safe utilization of the batteries. Based on the applications of batteries, load currents are considerably diverse and occasionally do not satisfy the persistent excitation condition, resulting in inaccurate detection of soft ISCr with existing model-based methods. Using constant current for standard charging of the batteries, this study proposes a novel and accurate model-based algorithm to detect soft ISCr online irrespective of the specific type of load currents. An equivalent circuit model of the battery with ISCr is used to extract open circuit voltage of the battery. Enhanced relationship between open circuit voltage and state of charge is obtained to estimate ISCr resistance as a fault index. To improve estimation accuracy of the fault index, factors affecting the ISCr resistance are analyzed and considered. Experiments incorporating various charging ranges and soft ISCr conditions below 100 are configured, and the proposed method is verified with experimental data. The results of the study indicate that the relative error of the estimated fault index does not exceed 6.4%; thereby, the battery management system is enabled to accurately detect an ISCr early.

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“…Based on multiphysics model of the LIBs, efficient thermal management system and cooling strategies for LIBs are proposed in References 36‐38. Exploration of short‐circuit modes . In addition to finite element models of LIBs, some exploration of short‐circuit modes 39 and mechanisms has been identified through experiments, 40 numerical simulations, and analyses 41,42 …”

Section: Introductionmentioning

confidence: 99%

A fast‐validated computational model for cylindrical lithium‐ion batteries under multidirectional mechanical loading

Xia

Tang

2020

Int J Energy Res

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Summary In the current electric vehicle (EV) market, cylindrical lithium‐ion batteries (LIBs) have played an indispensable role due to their high capacity and stability. However, LIBs are generally recognized as fragile and may cause fires or explosions when broken. Upon this issue, most studies focus on revealing the detailed reaction and interior change in the LIB during using or abuse conditions. However, in this study, we would like to provide a highly accessible model for automotive cylindrical LIBs that is practical in engineering application and can reduce computational effort in the entire EV crash simulation. In order to achieve this goal, the LIB model is built and meshed as a whole and a specific anisotropic material model is set so as the model can apply to multidirectional mechanical loading. Considering various operating condition of LIBs, state of charge (SOC) dependence and dynamic effect is termed as factors worth noticing when establishing this model. Furthermore, to predict the electrochemical responses and failures of LIBs, two optional short‐circuit criteria are defined. The proposed modeling method provides a powerful tool for analyzing the safety of batteries and battery packs with less computational and analytic effort than previous approaches.

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Experimental study of internal and external short circuits of commercial automotive pouch lithium-ion cells

Cited by 93 publications

References 11 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

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

A fast‐validated computational model for cylindrical lithium‐ion batteries under multidirectional mechanical loading

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