Battery manufacturing can be divided into three stages. The first stage is electrode production (including slurry mixing, electrode coating, drying, rolling, slitting and making electrode), the second stage is battery cell assembly (including winding/stacking, shelling, liquid injection and sealing), and the third stage is battery cell activation (including formation, capacity division, detection and sorting). As one of the key components of the battery, the battery electrode design, material selection and preparation process directly affect the comprehensive performance of the battery.
In the battery manufacturing process, the coating process plays a key role. The quality of pole piece coating, such as coating thickness uniformity, surface density distribution and defects, has a great impact on the consistency, cycle life, energy density, safety performance and other aspects of the battery.
In order to improve the process quality of pole piece coating and improve coating efficiency, we must first understand the development of coating and select a suitable coating method. Secondly, we must reduce the cost of experimental trial and error through process simulation, explore the factors affecting coating quality, and achieve the purpose of guiding production by optimizing and improving various parameters. Finally, based on online detection technology, the quality of coating is monitored online to avoid production defects caused by uncontrollable factors such as human and environmental factors.
This article will discuss the research status of coating process in battery manufacturing from three aspects: coating method, coating process simulation and coating detection, so as to promote the improvement of electrode coating process quality, coating efficiency and production quality control.
Related content:
- Lithium-ion battery electrode coating process research(1) | Battery Coating Method;
- Lithium-ion battery electrode coating process research(2) | Coating process simulation;
1.Electrode coating quality inspection
After coating, the coating needs to be inspected to ensure that it meets the design requirements. The main targets of quality inspection are coating thickness, surface density, surface defects, etc. Common inspection methods are shown in the figure below. The thickness, density and surface defects of the coating directly affect the electrochemical performance of the battery.
Studies have shown that an increase in coating thickness will cause the polarization to decrease first and then increase, while the specific capacity will increase first and then decrease. After comparing two electrodes with different surface densities, it was found that an increase in surface density will lead to an increase in the internal resistance of the battery, thereby reducing the rate performance by 1% to 2%. Mohanty et al. systematically studied the effects of different types of coating defects on the electrochemical performance of the battery and found that agglomerates and pore defects can reduce the Coulomb efficiency, and the battery capacity attenuation of pores, uneven coatings and metal particle defects is more obvious, while metal particle foreign matter can easily cause short circuits. The traditional pole piece detection method punches the sample and then measures its mass and thickness. However, this method has certain limitations because it destroys the electrode and can only measure local electrodes. In addition, this offline measurement method is not suitable for modern production needs. Currently, non-contact online detection methods are generally used for production line detection to avoid damage to the pole piece.
The coating thickness is mainly detected by laser triangulation and laser caliper measurement. The principle of laser measurement is based on the reflection of the laser beam and the imaging system to measure the coating thickness. The difference between the two measurement methods lies in the specific algorithms and implementation methods for calculating the coating thickness, as shown in the figure below.
Both measurement methods can measure thickness online with high accuracy, but laser triangulation detection is greatly affected by temperature. Uneven temperature will affect the optical signal and cause the error to increase. The measurement accuracy of the laser caliper based on dual laser triangulation: the thickness error of the positive electrode is 2.0% to 2.3%, and the thickness error of the negative electrode is 2.2% to 2.6%. The error range is controlled at about 2%, but the calibration of this method is difficult and the cost is high.
Based on laser detection of coating thickness, in addition to improving the accuracy of the detection equipment, the temperature compensation mechanism can be introduced to compensate the data by real-time detection of temperature changes, reduce the impact of temperature on the measurement results, and add constant temperature equipment to the production line to maintain the ambient temperature and improve the measurement accuracy.
The surface density of the coating can be quantified online by measuring the attenuation and backscattering of different forms of radiation. Common radiation detection methods include β-rays and X-rays. β-rays are widely used in coating surface density detection due to their high stability and penetration, but their measurement cost is high and harmful to the environment and human health. In contrast, although X-ray detection also has radiation, it has fewer limitations, lower costs, and can detect coating thickness and defects at the same time. Ultrasonic detection is another commonly used coating surface density detection method. It measures the surface density by measuring the change in the propagation speed of ultrasonic waves in the coating. Compared with radiation detection, this detection method is radiation-free and has higher sensitivity and accuracy. However, the detection area is limited, and in practical applications, it is impossible to perform comprehensive detection, and only “Z”-shaped detection can be performed. In addition to installing radiation protection devices based on radiation detection surface density, non-radiation detection technologies such as ultrasonic detection can also be used as substitutes. Although the detection area is limited, it can be improved by increasing detection points and optimizing detection paths, as well as targeting specific coating characteristics.
The coating surface defect detection methods include infrared thermal imaging detection [(a)] and optical CCD camera detection [(b)]. Infrared thermal imaging detection is based on infrared radiation emitted by infrared cameras or sensors. It detects coating surface defects based on the principle of temperature difference and can achieve online detection. However, it is not suitable for high-speed production environments because it has high requirements for detection equipment and detection environment and is easily affected by ambient temperature. Optical CCD cameras use optical imaging to capture images of the coating surface and identify corresponding defects through image processing algorithms.
By training a large number of defect samples through algorithms, online defect detection based on machine vision can be realized. This detection method has high accuracy and efficiency in the detection process, and can detect various defects in the coating during the production process of the pole piece, and is widely used in production lines. This detection method requires the input of a large amount of training data (the number of types of defects), combined with machine learning and deep learning algorithms, to improve the accuracy and generalization of defect recognition, thereby ensuring the reliability of optical camera detection.
Non-contact online detection technology is crucial in the large-scale production of pole pieces. It can not only detect whether the coating quality meets the design requirements, but also can quickly evaluate the coating thickness, surface density and surface defects through real-time monitoring, timely discover problems, make adjustments and corrections, and ensure the consistency of pole piece coating. The detection technology must first meet the characteristics of high precision and high efficiency, and be able to meet production needs. Compared with traditional offline detection, online detection technology can not only save time and cost, but also improve production efficiency and product quality control.
2 Summary and Outlook
The electrode coating process is one of the key processes in the entire battery manufacturing process. The thickness, surface density and defects of the coating during the coating process have a significant impact on the performance of the battery. In order to improve the quality of the coating and meet the current needs of battery manufacturing, the selection of coating processes and parameter optimization are key. To this end, it is urgent to use simulation methods to explore and optimize the mechanism and influence of structure and process parameters on coating quality, minimize coating defects under high-efficiency production conditions, and produce high-quality electrodes. In response to the above needs, this paper summarizes, analyzes and discusses the research progress of coating methods, simulation technology, and quality inspection technology.
However, existing research still has problems such as lack of intelligence in coating methods and no quantitative analysis of factors affecting coating quality and efficiency. Therefore, for future development, further exploration and optimization can be carried out from the following aspects:
(1) Development of intelligent coating methods: Facing the trend of intelligent manufacturing of lithium-ion batteries, how to make the coating method more intelligent is the future development direction of coating methods. Achieve efficient and accurate fully automatic coating, reduce manual parameter adjustment, and realize automatic parameter setting and automatic coating according to the designed pole piece requirements. The coating thickness, surface density and defect signals measured by sensors (laser, ultrasonic, optical camera) are fed back to the acquisition system. By processing and calculating the measurement signals, in addition, by introducing artificial intelligence and machine learning technology, the coating machine has the ability of self-learning and self-adaptation, further improving production efficiency and product quality, and realizing intelligent manufacturing of batteries.
(2) Establish a quantitative relationship between manufacturing parameters and coating process evaluation indicators: Establish a quantitative relationship between influencing factors such as feed speed/cavity structure/gasket structure/die head parameters/coating speed/coating gap on coating efficiency and coating quality, combine experimental and simulation data to establish a comprehensive and accurate relationship model, determine the degree of influence of different structural and process parameters on coating quality and efficiency, help to more accurately predict and control the coating process, provide a theoretical basis for coating process simulation, and provide guidance for process adjustment and optimization in actual production.
(3) Establish a multi-dimensional multi-field coupling model: From microscopic particle motion to macroscopic fluid dynamics and heat transfer, each scale needs to consider different temperature fields and the interactions between them. By establishing a multi-dimensional multi-field coupling model, key performance indicators such as coating thickness and porosity can be predicted. By adding an electrochemical model, the impact of coating process parameters on battery performance can be predicted. By comprehensively considering the coating process and battery performance, the coating can be made more efficient and reliable, achieving the goal of guiding industrial production. Adjustment can significantly improve the coverage of the detection area and ensure the comprehensiveness and accuracy of the detection results.
