Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution

Xue, Qiao; Li, Guang; Zhang, Hongjie; Shen, Shiquan; Chen, Zheng; Liu, Yonggang

doi:10.1016/j.jpowsour.2020.228964

Cited by 75 publications

(32 citation statements)

References 35 publications

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“…The Gaussian distribution and multi-level screening strategy are applied to detect abnormal voltage fluctuations [121]. For battery-package fault diagnosis, the K-means clustering algorithm, and 3 screening approach are exploited to detect and locate the abnormal cells [122].…”

Section: H Battery Management Systemmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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Section: H Battery Management Systemmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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“…The main object in this study was a battery containing two important elements: current and voltage, which were then examined for their characteristics from several experiments that have been designed [24]. An important part before detection was to create experimental data directly from sensors and model or simulation data [25].…”

Section: Fig 2 Diagram Block Of Fault Detection and Isolation Of Batterymentioning

confidence: 99%

Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime

Amifia¹,

Bismoko²,

Daely

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The battery is the main component both as an energy provider and as an interface for several systems in an electric vehicle. It has three important parameters: current, voltage, and temperature that must be maintained as the battery can have a harmful reaction that can lead to overcurrent. The battery must also not overcharging or discharging for too long because it can cause damage and affect its lifetime. Another error that can arise is sensor failure due to the interference or noise that can cause an error in data reading. To prevent this problem, it needs protection by means of isolation in operating the battery. In this research, planning in optimizing battery work was conducted by designing the process of detection and isolation of faults that occurred in batteries, particularly lithium polymer battery to reach their more optimal and good performance. Battery modeling was needed as the parameter identification, and the Kalman Filter algorithm was applied to help to reduce the detection rate and fault isolation. The results of detection and isolation of overcurrent and sensor failure using Kalman Filter were found quite accurate. In overcurrent isolation, a discharge current of 6A was obtained from the maximum current limit of 10 A, and for sensor failure isolation, the Kalman Filter algorithm succeeded in improving the results of the previous reading.

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“…The current research on battery for electric vehicles has been mentioned in many types of literature, such as battery fault diagnosis, estimation of remaining useful life for batteries, state of the health estimation, etc. − And the research approaches in the literature about fault diagnosis can be broadly classified into three categories: knowledge-based, model-based, and data-driven fault diagnosis approaches. − Among them, the knowledge-based fault diagnosis method uses some historical and empirical knowledge of the battery to design some diagnostic rules for fault diagnosis . The model-based approach is to establish a physical model of the battery, which is generally capable of accurately calculating the values of the parameters of the battery.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

Zhong

et al. 2022

ACS Omega

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Battery failure has traditionally been a major concern for electric vehicle (EV) safety, and early fault diagnosis will reduce many EV safety accidents. However, the short-circuit signal is generally very weak, so it is still a challenge to achieve a timely warning of battery failure. In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning. Third, a reference cell selection method based on K-means clustering is proposed, which can effectively reduce the false alarms caused by the inconsistency of each cell. Fourth, the Fréchet algorithm is introduced into the field of battery pack fault diagnosis and combined with thresholds for battery pack fault diagnosis and localization to accomplish the diagnosis and early warning of minor faults. Finally, the fault diagnosis method is validated by three actual running electric vehicles to verify the effectiveness, reliability, and robustness of the method.

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Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution

Cited by 75 publications

References 35 publications

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Battery Insulation Performance Analysis in Electric Vehicles for the Improvement of Battery Lifetime

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

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