Lithium-ion battery data for fault diagnosis in different applications are comprehensively analyzed. Fault modes and diagnosis methods across application scenarios are reviewed. Fault diagnosis methods for both laboratory and real-world applications are summarized.
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.
In general, there are three ways to transition lithium-ion battery fault diagnosis from the laboratory to the real world: unified framework of fault diagnosis method, cloud big data fusion, and application of laboratory measurement technology.
Measurement data Among the lithium-ion battery measurement data, voltage is widely used in fault diagnosis methods because of its simple acquisition, its ability to characterize the battery state, and its ease of distinguishing the lithium-ion battery fault type.
The current and temperature data also plays an important role in lithium-ion battery fault diagnosis. By mathematically processing the voltage and temperature data and calculating the cells' similarities, it is possible to realize the OC and OD diagnosis and TR early warning for EVs .
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.
The current sensor monitors the current that enters and exits the battery and sends the data to the BMS. It is important to detect a faulty current sensor as it can lead to …
Operating battery cells with defects may lead to lithium plating, degradation of the electrolyte, gas and heat generation, and in worst cases accidents, like fire. Safety is a major …
This paper provides a comprehensive review of the anomaly types and detection methods for lithium-ion batteries in electric vehicles. We classify battery anomalies into energy …
Table 5 Summarises Several studies tackle early fault detection in Lithium-ion batteries ... predict cell properties and compare them to thresholds to identify internal circuit …
This article reviews LIB fault mechanisms, features, and methods with object of providing an overview of fault diagnosis techniques, emphasizing feature extraction''s critical role in …
Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for …
4 · Specifically, in lithium battery shell defect detection, it achieves an mAP50 of 97.0%, representing a 4.6% improvement over Yolov8n. Its parameters and FLOPs are reduced by …
The current sensor monitors the current that enters and exits the battery and sends the data to the BMS. It is important to detect a faulty current sensor as it can lead to further problems.
In order to reduce the cost of lithium-ion batteries, production scrap has to be minimized. The reliable detection of electrode defects allows for a quality control and fast …
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 …
In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole …
The primary objective is to provide a comprehensive understanding of the current state of defect detection technologies, assess their effectiveness, and identify key …
defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of this methodology. A non-contact ultrasonic scanning system
This systematic review aims to explore and synthesize the existing literature on defect detection methods in lithium batteries. With the increasing demand for reliable and …
The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is …
Thirdly, it outlines the current status, main technological approaches, and challenges of ultrasonic technology in battery defect and fault diagnosis, including defect …
Therefore, efficient and accurate fault diagnosis for LIBs is very important. This paper provides a comprehensive review of the latest research progress in fault diagnosis for …
In the defect detection literature, defect segmentation approach is well-known for segmenting the defects since it works at pixel level, and thus it is more accurate than object …
Thirdly, it outlines the current status, main technological approaches, and challenges of ultrasonic technology in battery defect and fault diagnosis, including defect …
4 · Specifically, in lithium battery shell defect detection, it achieves an mAP50 of 97.0%, representing a 4.6% improvement over Yolov8n. Its parameters and FLOPs are reduced by …
The primary objective is to provide a comprehensive understanding of the current state of defect detection technologies, assess their effectiveness, and identify key challenges and future...
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 …
Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel …