Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

Liu, Zhentong; Ahmed, Qadeer; Zhang, Jiyu; Rizzoni, Giorgio; He, Haibo

doi:10.1016/j.conengprac.2016.03.015

Cited by 71 publications

(38 citation statements)

References 25 publications

(44 reference statements)

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“…The parity space method c an be used to verify this relationship by analyzing the input and output measurements of the battery system [50 ], [51]. Structural analysis theory finds and utilizes the structural over-determined part of system dynamic equations [52], and then achieves the structura l detectability and isolability analysis of faults [53]- [58]. A comparison of the above model-based methods is illustrated in Table 5.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…After analyzing and evaluating the residuals, the magnitude, type, and location of faults can be dete rmined. Typical battery models for sensor fault diagnosis include the EM [160]- [162], ECM [163]- [167], lumped-parameter thermal models [168], [169], and two-state lumped parameter thermal models [52]. Lombardi et al [13] tested the electrical relationship between the current sensor and voltage sensor measurements based on Kirchhoff's law to generate residuals, and achieve the FDI of voltage and current sensors according to the battery pack structure and the residual set associated with each sensor.…”

Section: Sensor Fault Featuresmentioning

confidence: 99%

“…Finally, the residuals are generated by checking the analytical redundancy relationship in each test. Structural analysis theory [20 ], [52], [169] can effectively reduce the workload in selecting residual generators. However, this type of analysis is easily affected by noise and model uncertainty.…”

Section: Sensor Fault Featuresmentioning

confidence: 99%

“…Strategies combining multiple model-based methods can compensate for the inherent flaws in a single method. For example, Liu et al [52] constructed two diagnostic tests based on the structural analysis theory. Then, the residuals were generated based on EKF in each diagnostic test.…”

Section: Sensor Fault Featuresmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Model-based Methodsmentioning

confidence: 99%

Section: Sensor Fault Featuresmentioning

confidence: 99%

Section: Sensor Fault Featuresmentioning

confidence: 99%

Section: Sensor Fault Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Hu¹,

Zhang²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This is also illustrated in Figure 7: enabling the balancing circuit (Section 3) leads to a voltage change at the adjacent cells, whose magnitude is much larger than normal and can easily be detected by the AFE. Other types of faults, for example defective sensors, can be detected by appropriate diagnostic algorithms [75]. Knowledge of the electrical behaviour of batteries can also be used to check sensor signals for validity.…”

Section: Safety and Reliabilitymentioning

confidence: 99%

Battery Management System Hardware Concepts: An Overview

et al. 2018

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This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of general requirements, several possible topologies for battery packs and their consequences for the BMS' complexity are examined. Four battery packs that were taken from commercially available electric vehicles are shown as examples. Later, implementation aspects regarding measurement of needed physical variables (voltage, current, temperature, etc.) are discussed, as well as balancing issues and strategies. Finally, safety considerations and reliability aspects are investigated.

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Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft

Hashemi

Baghbadorani

Esmaeeli

et al. 2020

Intl J of Energy Research

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Summary Reliable operation and control of battery packs can lead to increasing applications of batteries as energy sources for mobile power systems such as electric/hybrid electric aircraft. If the operation of a battery pack is controlled and monitored thoroughly, the safety in the battery system of an electrified aircraft can be guaranteed. The battery model has many applications in battery management systems such as battery performance analysis and fault detection. To achieve an accurate fault diagnosis for electric aircraft, an intelligent fault detection scheme within an accurate battery cell model is required. In this study, an adaptive lithium‐ion battery model is proposed in which models' parameters are estimated by a supervised machine learning paradigm. This adaptive battery model is developed based on a second order equivalent circuit model, which has a good representation of lithium‐ion batteries dynamics. Comparative verification experiments show good accuracy and robustness of the machine learning‐based parameter estimator lead to an accurate battery model with an average error less than 0.4%. Moreover, to see the effectiveness of this machine learning‐based model in fault detection applications, a model‐based fault diagnosis scheme is developed. Finally, the analysis of fault diagnosis tests under different test conditions proves that the proposed adaptive battery model can significantly improve the fault diagnosis accuracy of batteries.

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Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

Cited by 71 publications

References 25 publications

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Battery Management System Hardware Concepts: An Overview

Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft

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