Fault Detection and Isolation for Lithium-Ion Battery System Using Structural Analysis and Sequential Residual Generation

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

doi:10.1115/dscc2014-6101

Cited by 29 publications

(27 citation statements)

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“…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%

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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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Section: Sensor Fault Featuresmentioning

confidence: 99%

“…Potential act uator faults in LIBS, including the terminal connector fault, cooling system fault, CAN bus fault, high voltage contactor fault, and fuse fault, are summarized in Ref. [20]. If the cooling system fails, the battery cannot be maintained within the proper operating temperature range, and it may even trigger TR.…”

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confidence: 99%

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Hu¹,

Zhang²,

Liu³

et al. 2020

Preprint

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“…Any fault in these sensors, which are often neglected, can result in fatal consequences [5]. Some works were proposed for component-level analysis of faults [14]- [18]. However, limited attentions were given to sensor fault diagnosis for lithium-ion battery system at the system level.…”

Section: Nomenclaturementioning

confidence: 99%

“…1) ME-based KS: Define the properties of Median expectation operator from (3) to (14). Deriving the state and covariance matrices for filter from (25) to (26), following by a smoother from (27) to (28).…”

Section: E Summary Of Meda Approachmentioning

confidence: 99%

Health Monitoring of Li-Ion Battery Systems: A Median Expectation Diagnosis Approach (MEDA)

Khalid¹,

Ahmed

Peng³

2015

IEEE Trans. Transp. Electrific.

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The operations of Li-ion battery management system (BMS) are highly dependent on installed sensors. Malfunctions in sensors could lead to a deterioration in battery performance. This paper proposed an effective health monitoring scheme using a median expectation-based diagnosis approach (MEDA). MEDA calculates the median of a possible set of values, rather than taking their weighted average as in the case of a standard expected mean operator. Furthermore, a smoother was developed to capture important patterns in the estimation. The resulting filter was first derived using an one-dimensional (1-D) system example, where the iterative convergence of median-based proposed filter was proved. Performance evaluations were subsequently conducted by analyzing real-time measurements collected from Li-ion battery cells used in hybrid electric vehicles (HEV) and plug-in HEVs (PHEV) duty cycles. Results showed that the proposed filter was more effective and less sensitive to small sample size and curves with outliers.

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“…Model-based methods utilize a battery model (e.g., electrochemical models [32,33], electrical circuit models [34], multiple models [35], and thermal models [36]) and estimate parameters and/or evaluate residuals which can be good indicators for battery faults. Model-based condition monitoring algorithm have been applied for the model-based fault diagnosis (e.g., a sliding mode observer (SMO) [37], an adaptive unscented KF [38], and a structural analysis and sequential residual generators [39]). Model-free methods extract fault symptoms from signals by using signal processing methods (e.g., wavelet transform [40] and Shannon entropy [41]) and using artificial intelligence [31] (e.g., fuzzy logic and artificial neural network).…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems

et al. 2018

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Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.

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Fault Detection and Isolation for Lithium-Ion Battery System Using Structural Analysis and Sequential Residual Generation

Cited by 29 publications

References 0 publications

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Health Monitoring of Li-Ion Battery Systems: A Median Expectation Diagnosis Approach (MEDA)

Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems

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