Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Hong, Jichao; Wang, Zhenpo; Liu, Peng

doi:10.3390/en10070919

Cited by 62 publications

(20 citation statements)

References 36 publications

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“…Entropy theory fault diagnosis has also been studied recently. Authors in [61] implemented the Shannon entropy and the Z-score method to detect any abnormality in the battery temperature, as well as predicting the time and location of the fault, to prevent thermal runaway. Liu et al [62,63] proposed the use of a modified Shannon entropy with the Z-score method to capture abnormality in cell voltage, and predict the time and location of the voltage fault occurrence.…”

Section: Non-model-based Methodsmentioning

confidence: 99%

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

Fowler

2020

Algorithms

200

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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%

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

Fowler

2020

Algorithms

200

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“…[125], the correlation coefficient between cell voltage s can capture the abnormal voltage drop. The entropy of battery temperature [127] and voltage [128] become the features of temperature abno rmity and voltage fault, respectively.…”

Section: Overdischargementioning

confidence: 99%

“…According to the difference between the RCCs after two adjacent charges, the leakage current and MSC resistance can be obtained. Based upon a large amount of raw temperature data derived from NSMC-EV in Beijing, Hong et al [127] applied the Shannon entropy to capture the temperature abnormity of the battery pack. Besides, the abnormity coefficient, including over-temperature and excessive temperature difference, was quantitatively evaluated to predict both the time and location of the temperature faults in battery packs.…”

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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“…19 A battery thermal management system (BTMS) is needed since extreme temperature can significantly affect safety and durability of EVs. 20,21 More severely, fatal fires or explosions could be triggered by thermal runaway if the battery thermal condition can not be appropriately managed. For mitigating these potential hazards, Xu et al 22 and Lan et al 23 developed a novel BTMS based on aluminum mini-channel tubes and applied it to a single prismatic Li-ion battery under different discharge rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

et al. 2019

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Summary Faults of lithium batteries in their early stage in electric vehicles (EVs) are usually undetectable, and their characteristics are difficult to be extracted by conventional methods. This paper presents a novel synergistic diagnosis scheme for multiple battery faults using the modified multi‐scale entropy (MMSE). The proposed MMSE can effectively extract the multi‐scale features of complex battery signals in the early stages of battery faults as well as overcome the shortage of the coarse‐grained mode in the standard multi‐scale entropy. The simulation results on experimental data and the real‐world operational vehicles show that the proposed method can effectively detect and locate multiple battery faults/abnormities before they trigger the alarm thresholds. The defined sensitivity factor can implement real‐time evaluation on abnormities with high efficiency and stability, and the developed variable‐calculation‐window diagnosis scheme can synchronously detect and locate different fault types in real time. Furthermore, feasibility, stability, reliability, versatility, robustness, and practicality of the proposed method are separately verified using multiple sets of real‐world operation data. More importantly, the proposed method also provides feasibility to effectively prevent battery thermal runaway caused by multiple battery abnormities/faults. The applications of multi‐scale entropy theory is the first of its kind to battery fault diagnosis on the real‐world operational vehicles.

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Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Cited by 62 publications

References 36 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

Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

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