The improved interleaved voltage measurement method for series connected battery packs

Xia, Bing; Nguyen, Truong X.; Yang, Jufeng; Mi, Chris

doi:10.1016/j.jpowsour.2016.09.167

Cited by 48 publications

(8 citation statements)

References 17 publications

(27 reference statements)

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“…A matrix interpretation of the sensor topology was developed to isolate sensor and cell faults by locating abnormal signals. Since this method is sensitive to measurement noises, the authors further developed an improved measurement topology in [82], where the noise limit and trend of the interleaved voltage measurement method were derived to improve the noise sensitivity. In [83], a multi-fault diagnostic strategy based on an interleaved voltage measurement topology and an improved correlation coefficient method was presented, which can diagnose several types of faults, such as internal and external short circuits, sensor faults and connection faults.…”

Section: Non-model-based Methodsmentioning

confidence: 99%

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

Fowler

2020

Algorithms

157

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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%

“…Wavelet transform [58] Correlation coefficient [59,60,83] Shannon entropy [39,[61][62][63][78][79][80] Sensor topology [81][82][83] Knowledge-based These algorithms use the knowledge obtained from observations or data coming from the system to establish rules or train data to detect a fault.…”

Section: Structural Analysismentioning

confidence: 99%

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

Fowler

2020

Algorithms

157

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“…The sensor topology-based method mainly relies on the sensor configuration and redundancy of sensor functionalities, which is easy to implement. Xia et al [157], [158] proposed a fault-tolerant voltage measurement method for series-connected battery packs by measuring the total voltage of multiple cells instead of measuring the voltage of individual cells. Then, a matrix interpretation of the sensor topology was developed.…”

Section: Sensor Fault Featuresmentioning

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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“…An advanced BMS system will monitor individual batteries inside a series configuration and identify the independent voltages and current contributions as well as the SOC levels of each battery [23]. An improved and fault tolerant voltage measurement for battery management systems has been introduced in [24]. A module-integrated distributed BMS in the battery cells without the need for an additional battery equalizer or a converter interface has been introduced in [25], and a high-efficiency BMS that applies active charge equalization to balance the charge of all cells in the pack has been proposed in [26].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes

Haq

Kurniadi

Leksono

et al. 2019

J. Eng. Technol. Sci.

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In this work, a historical data based battery management system (BMS) was successfully developed and implemented using an embedded system for condition monitoring of a battery energy storage system in a smart microgrid. The performance was assessed for 28 days of operating time with a one-minute sampling time. The historical data showed that the maximum temperature increment and the maximum temperature difference between the batteries were 4.5 °C and 2.8 °C. One of the batteries had a high voltage rate of change, i.e. above 3.0 V/min, and its temperature rate of change was very sensitive, even at low voltage rate of changes. This phenomenon tends to indicate problems that may deplete the battery energy storage system's total capacity. The primary findings of this study are that the voltage and temperature rates of change of individual batteries in real operating conditions can be used to diagnose and foresee imminent failure, and in the event of a failure occurring the root cause of the problem can be found by using the historical data based BMS. To ensure further safety and reliability of acceptable practical operating conditions, rate of change limits are proposed based on battery characteristics for temperatures below 0.5 °C/min and voltages below 3.0 V/min.

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The improved interleaved voltage measurement method for series connected battery packs

Cited by 48 publications

References 17 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

Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes

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