Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis

Ma, Mina; Wang, Yu; Duan, Qiangling; Wu, Tangqin; Sun, Jinhua; Wang, Qingsong

doi:10.1016/j.energy.2018.09.047

Cited by 85 publications

(24 citation statements)

References 46 publications

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“…However, this method can be easily affected by the set value of the interval parameters. Ma et al [80] proposed the use of a modified Z-score test to analyze the abnormal voltage coefficients to detect cell connection fault. The temperature rise rate was utilized as a secondary parameter to determine the severity of the fault.…”

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

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%

“…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

200

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“…The contact resistance can be monitored by fault diagnosis approaches based on voltage characteristics. 58 For the control of other cases of thermal abuse, we may return to the situation of TR control shown in Figure 3. Overall, TR mitigation encompasses many issues, and here, a closed loop is formed.…”

Section: E-iii External Short Circuitmentioning

confidence: 99%

Mitigating Thermal Runaway of Lithium-Ion Batteries

Feng

Ren

et al. 2020

Joule

952

377

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This paper summarizes the mitigation strategies for the thermal runaway of lithium-ion batteries. The mitigation strategies function at the material level, cell level, and system level. A time-sequence map with states and flows that describe the evolution of the physical and/or chemical processes has been proposed to interpret the mechanisms, both at the cell level and at the system level. At the cell level, the time-sequence map helps clarify the relationship between thermal runaway and fire. At the system level, the time-sequence map depicts the relationship between the expected thermal runaway propagation and the undesired fire pathway. Mitigation strategies are fulfilled by cutting off a specific transformation flow between the states in the time sequence map. The abuse conditions that may trigger thermal runaway are also summarized for the complete protection of lithium-ion batteries. This perspective provides directions for guaranteeing the safety of lithium-ion batteries for electrical energy storage applications in the future.

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“…39 Due to the impact of accelerated stress on fittings, losing connection between cells is easy to be caused after long-term operation of EVs. 39 Due to the impact of accelerated stress on fittings, losing connection between cells is easy to be caused after long-term operation of EVs.…”

Section: Analysis Of Battery Cells' Connection Issuesmentioning

confidence: 99%

“…When hundreds of cells are connected in a certain manner to form a battery pack, they need to be connected through metal pieces and nuts. 39 Due to the impact of accelerated stress on fittings, losing connection between cells is easy to be caused after long-term operation of EVs. This will produce lots of calories, which leads to sparkle, combustion, explosion, or other serious consequences.…”

Section: Analysis Of Battery Cells' Connection Issuesmentioning

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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Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis

Cited by 85 publications

References 46 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

Mitigating Thermal Runaway of Lithium-Ion Batteries

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

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