On-board Thermal Fault Diagnosis of Lithium-ion Batteries For Hybrid Electric Vehicle Application

Dey, Satadru; Biron, Zoleikha Abdollahi; Tatipamula, Sagar; Das, Nabarun; Mohon, Sara; Ayalew, Beshah; Pisu, Pierluigi

doi:10.1016/j.ifacol.2015.10.056

Cited by 31 publications

(16 citation statements)

References 20 publications

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“…A string of studies on thermal fault detection using the battery thermal model and the ECM was introduced by the same group of authors in [55][56][57]. In [55], the Li-ion battery was modeled via ECM and a two-state thermal model.…”

Section: Model-based Methodsmentioning

confidence: 99%

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

Fowler

2020

Algorithms

196

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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: Model-based Methodsmentioning

confidence: 99%

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

Fowler

2020

Algorithms

196

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“…A comparison of the above model-based methods is illustrated in Table 5. Various filters and observers have been applied to fault diagnosis for LIBS, such as Kalman filter (KF) [59], extended Kalman filter (EKF) [60], unscented Kalman filter [61], particle filter (PF) [62], Lunberger observer [63], and adaptive observer [64]. The state estimation method can help the state monitoring fun ction of BMS and can detect the fault with excellent real-time performance.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…For the quantitative analysis of faults, certain parameters of the battery model are regarded as fault features, such as the ISC equivalent resistance [129] and thermal model parameters [63], [130] related to convective cooling resistance fault, internal thermal resistance fault and TR fault. Liu [131] and Wu [132] analyzed the relationship between battery faults and parameter changes, and summarized the diagnostic rules for common battery faults.…”

Section: Overdischargementioning

confidence: 99%

“…But this method is accompanied by the difficulty of identifying multiple models and running EKF. Dey et al [63] added convective cooling resist ance fault, internal thermal resistance fault, and TR fault into a two-state thermal model, and the FDI is designed for these three thermal faults based on the Luenberger observer. In Ref.…”

Section: Overdischargementioning

confidence: 99%

“…Luenberger observer [63] Thermal faults Thermal model parameters corresponding to different thermal faults.…”

Section: Pf [62]mentioning

confidence: 99%

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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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Characterization of the nonlinear response of defective multi-DOF oscillators using the method of phase space topology (PST)

2016

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On-board Thermal Fault Diagnosis of Lithium-ion Batteries For Hybrid Electric Vehicle Application

Cited by 31 publications

References 20 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

Characterization of the nonlinear response of defective multi-DOF oscillators using the method of phase space topology (PST)

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