Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy …
Applying the laboratory simulation to a real-world scenario is one of the primary challenges in lithium-ion battery fault diagnosis, and there are few solutions available. Gan et al. realized the accurate diagnosis of OD fault by training the unified framework of voltage prediction based on the predicted voltage residual.
With the development of data-driven-based fault diagnosis methods, a large amount of lithium-ion battery normal data or fault data is needed for training and testing the model to improve the accuracy and generalization performance. However, the current lithium-ion battery fault data is mainly obtained by artificial triggering in a laboratory.
For multi-fault diagnosis and localization of lithium-ion batteries, the voltage sensor measurement topology of the series-connected battery pack is designed. Then the connection fault (CF), ESC, ISC, and voltage sensor fault (VSF) diagnosis only require the voltage data [47, 48].
Zhang et al. obtained five types of lithium-ion battery fault data—namely CSF, VSF, temperature sensor faults (TSF), ESC, and CF—through the joint simulation of AutoLion-ST and Simulink software and implemented multi-fault diagnosis and isolation based on the data.
4.3. Current Progress and Future Challenges of Li-Ion Battery Fault Diagnosis In summary, the fault diagnostic algorithms that were discussed have made certain progress on improving Li-ion battery safety, but they still have some limitations in real-life applications. A summary of all the reviewed algorithms is shown in Table 1.
Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. Table 1. Summary of Lithium-ion (Li-ion) fault diagnostic algorithms.
Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy …
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of …
An Anyscan-30 is ideal for use in Flaw Detection and Quality control applications. Careful thought was applied to keystroke use and menu options. Regularly used functions are allocated specific keys. Multiple on-board reporting tools and a …
cylindrical coated lithium batteries, resulting in poor detection reliability. The other uses deep learning techniques to detect defects. X. Y. Feng[6] conducted a comparative investigation on the detection of surface flaws in battery steel shells using three models: Fast R -CNN, Cascade R CNN, and YOLOv3. J.
A lithium iron phosphate battery with a rated capacity of 1.1 Ah is used as the simulation object, and battery fault data are collected under different driving cycles. To enhance the realism of …
A lithium iron phosphate battery with a rated capacity of 1.1 Ah is used as the simulation object, and battery fault data are collected under different driving cycles. To enhance the realism of the simulation, the experimental design is based on previous studies ( Feng et al., 2018, Xiong et al., 2019, Zhang et al., 2019 ), incorporating fault fusion based on the fault characteristics.
Aiming at the phenomenon of individual battery abnormalities during the actual operation of electric vehicles, this paper proposes a lithium-ion battery anomaly detection method based on the STL and improved Manhattan …
Lithium-ion batteries (LIBs) have been extensively used in electronic devices, electric vehicles, and energy storage systems due to their high energy density, environmental friendliness, and longevity. However, LIBs are sensitive to environmental conditions and prone to thermal runaway (TR), fire, and even explosion under conditions of mechanical, electrical, …
Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light microscopy images of the sectioned cells. The results demonstrate that deep learning models are able to learn accurate representations of the microstructure images well enough to ...
Scientific Reports - Lithium-ion Battery Thermal Safety by Early Internal Detection, Prediction and Prevention Skip to main content Thank you for visiting nature .
To investigate the consequences, mechanisms, and features of the causes, lithium-ion battery fault experiments under mechanical abuse, electrical abuse, and thermal …
To investigate the consequences, mechanisms, and features of the causes, lithium-ion battery fault experiments under mechanical abuse, electrical abuse, and thermal abuse conditions are conducted in the laboratory. Mechanical abuse mainly includes bending, indentation, collision, penetration, and compression [1].
In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and …
This paper presents an automatic flaw inspection scheme for online real-time detection of the defects on the surface of lithium-ion battery electrode (LIBE) in actual industrial production. Firstly, based on the conventional methods of region extraction, ROI (region of LIBE) could be extracted from the captured LIBE original image. Secondly, in ...
Abstract: This paper presents an automatic flaw inspection scheme for online real-time detection of the defects on the surface of lithium-ion battery electrode (LIBE) in actual industrial production. Firstly, based on the conventional methods of region extraction, ROI (region of LIBE) could be extracted from the captured LIBE original image.
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task ...
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. …
3 · Achieving comprehensive and accurate detection of battery anomalies is crucial for battery management systems. However, the complexity of electrical structures and limited computational resources often pose significant challenges for direct on-board diagnostics. A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, …
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of …
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.
In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and the identification of system parameters; (2) an elaborate exposition of design principles underlying various model-based state observers and their ...
Xue, Q. et al. Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution. J. Power Sources 482, 228964 (2021). Article CAS Google Scholar Zheng, Y ...
Stay updated with the latest news and trends in solar energy and storage. Explore our insightful articles to learn more about how solar technology is transforming the world.