Failure analysis of lithium battery electrode ear defects based on Knowledge Graph
Lithium battery electrode connection quality remains a critical challenge in battery manufacturing, directly impacting battery performance, safety, and operational lifespan. This research introduces an innovative Knowledge Graph-based approach to systematically analyze and predict electrode ear defects, leveraging advanced data integration and knowledge reasoning techniques.
The study develops a comprehensive Knowledge Graph framework that synthesizes multidimensional data sources, including production process records, failure analysis reports, expert interviews, literature materials, and equipment monitoring data. By constructing an intricate network of entities and relationships, the research enables a more sophisticated understanding of electrode ear failure mechanisms.
The Knowledge Graph schema focuses on key entities such as manufacturing processes, equipment, and testing systems, creating an interrelationship digraph that reveals complex interactions contributing to electrode ear defects. The research identifies multiple failure modes, including electrode ear position deviation, deformation, fracture, welding issues, material detachment, and surface oxidation.
Three primary analytical techniques are applied to the Knowledge Graph:
- Failure Mode Correlation Analysis: Utilizing graph algorithms to extract causal chains and identify potential failure pathways by examining relationships between manufacturing parameters and specific defect types.
- Critical Path Recognition: Developing a systematic approach to trace root causes, quantify factor contributions, and predict failure probabilities through computational analysis.
- Knowledge Reasoning Mechanism: Implementing advanced inference techniques to generate insights and potential solutions based on interconnected knowledge elements.
The research highlights two primary sources of electrode ear defects: winding tension fluctuations and electrode alignment problems. To address these challenges, the study proposes innovative solutions:
Variable Tension Winding Technology: A dynamic tension control system that adjusts winding parameters in real-time, ensuring uniform stress distribution and reducing electrode sheet deformation. Key strategies include gradual tension reduction, automated monitoring, and sophisticated control algorithm optimization.
Electrode Alignment Optimization: A high-precision alignment mechanism incorporating machine vision technologies and automated control systems to ensure accurate positioning of electrode pads and separators during manufacturing.
Additionally, the research recommends advanced detection methods, particularly X-ray inspection, to provide high-resolution internal structure analysis and early defect identification.
Case studies examining specific failure events demonstrate the practical application of the Knowledge Graph approach. These examples illustrate how tension control instabilities and alignment errors directly contribute to electrode ear defects, validating the proposed analytical methodology.
The proposed Knowledge Graph framework represents a significant advancement in lithium battery quality control. By systematically integrating multidimensional data, applying sophisticated reasoning techniques, and developing targeted manufacturing interventions, the research offers a comprehensive strategy for reducing electrode ear defects.
Future developments will focus on further refining manufacturing processes, enhancing detection technologies, and expanding the Knowledge Graph's predictive capabilities. This approach promises to accelerate quality improvement, reduce production failures, and support the continued evolution of lithium battery technology.
In the manufacturing process of lithium batteries, poor electrode connection is an important quality issue, which usually leads to unstable battery connectivity and performance, thereby affecting the safety and service life of the entire battery pack. With the continuous improvement of battery production technology, although the frequency of poor electrode connection has decreased, it is still one of the important factors affecting battery performance. By applying Knowledge Graph technology, we can more systematically identify and analyze the failure causes of poor electrode connection, and then propose more accurate solutions to achieve lean production and continuous improvement.
The basis of the lithium battery electrode failure analysis method based on Knowledge Graph is to construct a Knowledge Graph of lithium battery failure modes. The system constructs a Knowledge Graph covering key factors such as electrode failure, material characteristics, manufacturing processes, testing equipment, cause analysis, and solutions through knowledge acquisition and extraction from multiple data sources. Through root cause analysis, failure prediction, and real-time monitoring, combined with inference mechanisms and Machine Learning models, it can effectively identify failure modes, predict risks, and provide intelligent support for design optimization and production process control. Ultimately, it is expected to significantly improve the accuracy of electrode failure prediction, quality control, and shorten the design optimization cycle.
Knowledge Graph Construction Steps
In the first step of constructing a Knowledge Graph, it is important to collect relevant multidimensional data sources in order to form a comprehensive knowledge foundation. The following are the main channels for acquiring knowledge.
- Production process records: quality monitoring data, operation records, equipment logs, etc. collected from the production line.
- Failure analysis report: Obtain data from past failure cases to analyze potential extreme ear problems.
- Expert experience interview: Extract technical difficulties and experiential knowledge through discussions with domain experts.
- Literature materials: theoretical knowledge provided by industry research reports, technical white papers, etc.
- Equipment monitoring data: Real-time monitoring data of equipment reveals the impact of process parameters and equipment status on the quality of the ear.
For these data sources, large-scale model technology is used to complete the analysis of knowledge and the mining of laws. Specifically, the process is shown in the following figure.
Knowledge Graph Schema Design
Knowledge Graph Schema design is the basis for conducting failure analysis based on Knowledge Graph and is the core of determining Knowledge Graph. Guided by the "Six Tao Methods" proposed in the book "Knowledge Graph: Cognitive Intelligence Theory and Practice", the following Knowledge Graph schema (example) is designed.
Knowledge Graph Instance
In the analysis of extreme ear defects, the core entities of Knowledge Graph include manufacturing processes, manufacturing equipment, testing equipment, and so on. By constructing such entities as Interrelationship Digraphs, it is possible to clearly understand the various influencing factors and their relationships of extreme ear defects, and further analyze the root causes of adverse failures.
Taking the failure analysis of extreme ear defects as an example, extreme ear defects usually refer to the deviation or deformation of the electrode ear position of the battery during the winding or stacking process, which causes the battery to be unable to contact the external circuit correctly. Extreme ear defects are a common defect that includes multiple failure modes, including electrode ear position deviation, electrode ear deformation or bending, electrode ear fracture, poor electrode ear welding, electrode ear material detachment or delamination, electrode ear corrosion, poor contact between the electrode ear and the shell, electrode ear overheating, electrode ear surface oxidation, electrode ear poor contact with the electrode sheet, inconsistent electrode ear size, electrode ear wrinkles, electrode ear contamination, electrode ear warping, electrode ear damage, electrode ear poor insulation, electrode ear burrs, and electrode ear polarity errors. This failure phenomenon not only affects the safety of the battery, but also may lead to a series of problems such as internal short circuit, battery overheating, and decreased charging and discharging efficiency. In severe cases, it may cause battery failure. The following figure is a partial example of the Knowledge Graph of lithium battery failure modes for pole ear failure analysis.
Techniques used in failure analysis based on Knowledge Graph
1. Failure mode correlation analysis
Through multi-dimensional correlation analysis of failure modes, potential failure paths can be identified. For example, certain specific electrode ear materials or process parameters may cause failure modes such as deviation, fracture, or deformation. Using graph algorithms, relevant influencing factors can be extracted from the Knowledge Graph to construct a causal chain of failure modes. Taking the above Knowledge Graph as an example, starting from the failure event (failure event FE-20241123001), the failure mode "electrode ear position deviation" to which the event belongs can be found, and then the two reasons that cause the failure mode, "click alignment" and "winding tension fluctuation", can be found. Furthermore, based on these two reasons, various possible solutions can be inferred, such as the cutting-edge "variable tension winding technology" for the cause of "winding tension fluctuation".
2. Critical Path Recognition
By identifying the critical failure path, the contribution of each factor to the failure of the pole ear can be analyzed. Here is an example of a simple root cause analysis function:
def identify_failure_paths(failure_mode):
paths = []
for entity in failure_mode.related_entities:
path = trace_causality(entity)
paths.append(path)
return analyze_path_probability(paths)
Through this method, problems can be accurately located and the likelihood and risk of their occurrence can be predicted.
3. Knowledge reasoning mechanism
The following figure summarizes the mechanism of knowledge reasoning.
Example of failure analysis of polar ear
According to the part exemplified by the Knowledge Graph mentioned above, the main reasons for the failure of extreme ear defects can be attributed to the following points:
- Winding tension fluctuation : Uneven or excessive tension fluctuation during the winding process can lead to uneven stress distribution of the electrode sheet. Especially, the diaphragm material may cause the electrode ear to shift or deform during excessive stretching or local contraction.
- Electrode alignment problem : In the winding or lamination process, the electrode sheet and the diaphragm are not accurately aligned, resulting in a shift in the position of the electrode sheet, further causing misalignment or deformation of the electrode ear position.
- Welding quality of electrode ears : The welding quality of electrode ears is a key factor affecting the performance and reliability of lithium batteries. The heat input, pressure control, welding time, and the state of the welding electrode during the welding process will directly affect the connection quality of the electrode ears. Improper welding process may lead to defects such as pores, virtual welding, and large heat-affected zones in the solder joints. These defects will reduce the contact resistance between the electrode ears and the current collector, increase the internal resistance of the battery, and may gradually deteriorate the welding interface due to thermal stress during the battery cycle, ultimately causing the electrode ears to fall off or break. Especially under high-rate charging and discharging or high-current discharging conditions, the defects of welding quality will more significantly affect the performance and safety of the battery.
Winding tension fluctuation
The fluctuation of winding tension is one of the key factors leading to poor electrode structure. In the manufacturing process of batteries, the tension control system of the winding machine needs to maintain stable tension to ensure uniform stress distribution of the electrode sheet. If the tension fluctuation is too large, the stretching degree of the electrode sheet will be inconsistent, resulting in uneven internal structure of the battery. Especially in the production of high-demand cylindrical and square batteries, the frequency of poor electrode structure is high.
We propose a variable tension winding technology to address this issue. This technology effectively controls the position of the battery's pole ears by dynamically adjusting the tension during the winding process. Specifically, the tension control system can be optimized through the following steps.
- Adjust the tension control system of the winding machine so that the tension gradually decreases as the number of layers increases during the winding process.
- Introduce an automated tension monitoring system to detect and adjust winding tension in real time to ensure even tension distribution.
- By optimizing the control algorithm through software, the rotation speed and tension coordination of the winding machine can be accurately adjusted to reduce tension fluctuations.
- Perform regular calibration to ensure that the tension control system of the winder is always in optimal working condition.
This solution can effectively reduce the stress unevenness of the electrode sheet due to tension fluctuations, improve the stretching and contraction problems of the diaphragm, thereby improving the winding quality and performance stability of the battery, and reducing the poor electrode ear and deformation rate.
Electrode alignment is not accurate
The alignment of electrode pads and diaphragms is another important factor affecting the position of the electrode ears. During the winding process, if the electrode pads are not accurately aligned, it may cause the electrode ears to be misaligned, which in turn affects the connection quality of the battery. In order to optimize this process, we propose an optimization scheme for the electrode pad alignment mechanism.
The specific steps of this plan include:
- Design and introduce a high-precision alignment system to ensure that the electrode pads and diaphragms are always in accurate positions during the winding process.
- Equipped with a visual positioning system (such as a machine vision camera), real-time monitoring of the alignment status of electrode pads and diaphragms, and dynamic adjustment.
- Use an automated control system to continuously adjust the feeding speed of the electrode sheet during the winding process to ensure the alignment of the electrode sheet and the diaphragm.
- Regularly check the alignment equipment to ensure its accuracy and stability, and avoid alignment errors caused by equipment aging.
- This scheme can significantly improve the alignment accuracy of electrode pads and separators, avoid electrode ear misalignment, and thus improve the performance stability of the battery.
Advanced detection methods
X-ray inspection is an efficient detection method for timely detecting and correcting problems with the electrode ear. By scanning the internal structure of the battery with X-rays, the position deviation and electrode contact quality of the electrode ear can be accurately detected. X-ray scanning can not only provide high-precision electrode ear position data, but also effectively identify other potential defects inside the battery, such as membrane damage and internal short circuits.
In practical applications, the operation steps of X-ray inspection are as follows:
- Place the battery in the X-ray scanner.
- Start scanning to check if the electrode ear is offset or defective.
- Record scan results, conduct data analysis, and adjust deviations.
Through this detection method, high-precision monitoring of the internal structure of the battery can be achieved, timely detection of electrode defects and other potential defects, and quality control in the battery production process can be ensured.
Case Examples: Adverse Failure Events of Extreme Ears
In order to better understand the causes of extreme ear failure, we can refer to the following typical failure events:
- Failure Event FE-20241123001: During the production of the battery, the electrode ears of some batteries were detected to be offset, resulting in poor external contact. This problem indicates that there may be tension control instability or electrode alignment errors during the winding process.
- Failure event FE-20241013006: During the winding process, the electrode ears of some batteries show obvious bending phenomenon, which cannot be in close contact with the battery shell. This problem may be related to excessive winding tension fluctuations or inaccurate equipment calibration.
- Failure event FE-20240525023: The misalignment of electrode pads and diaphragms and the deviation of the electrode ear position affect the packaging and sealing of the battery. This problem indicates that the accuracy of the electrode pad alignment device is insufficient or the control during the alignment process is not strict.
By analyzing these failure events, we can further verify the impact of tension fluctuations and electrode misalignment on electrode ear defects, and provide strong support for future process improvements.
Conclusion
As an important failure mode in the production of lithium batteries, the occurrence of pole ear defects involves multiple aspects, including winding tension fluctuations and inaccurate electrode alignment. By applying variable tension winding technology and optimizing the electrode alignment mechanism, the occurrence of pole ear defects can be effectively reduced, thereby improving the overall performance and safety of the battery. At the same time, combined with advanced detection methods such as X-ray inspection, real-time and accurate quality monitoring can be provided for the battery production process. In the future, with the further development of manufacturing processes and detection technology, the quality control of lithium batteries will be more refined, and the frequency of failure modes such as pole ear defects will also be greatly reduced, thereby promoting the wider application of lithium battery technology.