In-situ characterization and evaluation method and system of pulse energy storage capacitor structure state

Abstract

The application relates to the technical field of energy storage device detection, in particular to a method and system for in-situ characterization and evaluation of the structure state of a pulse energy storage capacitor, an electronic device and a storage medium, wherein the method comprises the following steps: obtaining computer tomography data and wideband acoustic detection data of the pulse energy storage capacitor; based on the computer tomography data and the wideband acoustic detection data, performing in-situ nondestructive characterization on the internal structure morphology of the pulse energy storage capacitor to determine the micro morphology of the pulse energy storage capacitor; obtaining multi-frequency band data of the pulse energy storage capacitor; based on the multi-frequency band data, performing in-situ collaborative sensing on the electrical characteristics of the end part of the pulse energy storage capacitor to determine the end part characteristics of the pulse energy storage capacitor; calling a circuit simulation model of the pulse energy storage capacitor; inputting the micro morphology and the end part characteristics into the circuit simulation model; and outputting derived characteristic parameters of the pulse energy storage capacitor from the circuit simulation model. Thus, the problems of single measurement scheme parameter and insufficient monitoring precision in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of energy storage device testing technology, and in particular to an in-situ characterization and evaluation method, system, electronic device and storage medium for the structural state of a pulse energy storage capacitor. Background Technology

[0002] With the development of new energy, aerospace, and electromagnetic energy equipment, the demand for efficient and high-density energy storage under extreme impact conditions is increasing. Pulse energy storage capacitors, as components with the highest power density and fastest discharge rate, are widely used, and polypropylene materials have become the preferred dielectric due to their excellent field strength tolerance, self-healing ability, and cost advantages. However, during the manufacturing and service process, polypropylene capacitors are prone to "breakdown-self-healing" caused by initial defects and electro-thermal-mechanical coupling effects, resulting in reduced electrode area, decreased capacitance, and shortened lifespan.

[0003] Currently, most related technologies focus on two-dimensional film surfaces, relying on disassembly analysis, which makes it difficult to reveal the true degradation process under three-dimensional structures. Port status assessment often uses methods such as partial discharge, current, and capacitance detection, which are limited by the extremely low equivalent series resistance of low-pulse capacitors and the requirement for external signals, resulting in problems such as low detection accuracy and large interference. In summary, related technologies suffer from problems such as single measurement parameters and difficulty in accurately reflecting device degradation failure, self-healing behavior, and internal defect evolution mechanisms. Summary of the Invention

[0004] This application provides an in-situ characterization and evaluation method, system, electronic device, and storage medium for the structural state of a pulse energy storage capacitor, in order to solve the problems of single measurement parameters and insufficient monitoring accuracy in related technologies.

[0005] The first aspect of this application provides an in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor, comprising the following steps: acquiring computed tomography (CT) data and broadband acoustic detection data of the pulse energy storage capacitor; performing in-situ non-destructive characterization of the internal structural morphology of the pulse energy storage capacitor based on the CT data and broadband acoustic detection data to determine the microstructure of the pulse energy storage capacitor; acquiring multi-frequency data of the pulse energy storage capacitor; performing in-situ collaborative sensing of the electrical characteristics at the ends of the pulse energy storage capacitor based on the multi-frequency data to determine the end characteristics of the pulse energy storage capacitor; and calling a circuit simulation model of the pulse energy storage capacitor, inputting the microstructure and end characteristics into the circuit simulation model, and outputting derived characteristic parameters of the pulse energy storage capacitor from the circuit simulation model.

[0006] Optionally, in one embodiment of this application, obtaining computed tomography (CT) data and broadband acoustic detection data of a pulse energy storage capacitor includes: activating a CT scanner and scanning the pulse energy storage capacitor using the CT scanner to obtain CT data of the pulse energy storage capacitor; activating a broadband acoustic detection device and performing broadband acoustic detection on the pulse energy storage capacitor using the broadband acoustic detection device to obtain broadband acoustic detection data of the pulse energy storage capacitor.

[0007] Optionally, in one embodiment of this application, the computed tomography (CT) device includes a sample chamber, an X-ray source, a flat panel detector, a digital imaging assembly, a temperature control stage, and a mechanical testing stage.

[0008] Optionally, in one embodiment of this application, the broadband acoustic detection device includes an ultrasonic sensor, a transmitting transducer, a sound wave, and a receiving transducer.

[0009] Optionally, in one embodiment of this application, the in-situ collaborative sensing of the electrical characteristics at the ends of the pulse energy storage capacitor based on multi-band data to determine the end characteristics of the pulse energy storage capacitor includes: inputting multi-band data into a convolutional neural network, and outputting the end characteristics of the pulse energy storage capacitor from the convolutional neural network. The convolutional neural network performs regression training on the capacitor capacitance and equivalent series resistance based on the comprehensive features formed by the multi-band data to achieve collaborative sensing of the capacitor capacitance and equivalent series resistance.

[0010] Optionally, in one embodiment of this application, the convolutional neural network includes an input layer, a convolutional layer, an activation function, a pooling layer, a fully connected layer, and an output layer.

[0011] Optionally, in one embodiment of this application, the circuit simulation model includes an external circuit combining electric field, thermal field, and force field, and the derived characteristic parameters include the capacitor core temperature distribution, capacitor current distribution law, and capacitor stress distribution law.

[0012] A second aspect of this application provides an in-situ characterization and evaluation system for the structural state of a pulse energy storage capacitor, comprising: a computed tomography (CT) scanner for scanning the pulse energy storage capacitor to obtain CT scan data of the pulse energy storage capacitor; a broadband acoustic detection device for performing broadband acoustic detection on the pulse energy storage capacitor to obtain broadband acoustic detection data of the pulse energy storage capacitor; and an electronic device for acquiring data from the CT scanner and the broadband acoustic detection device, performing in-situ non-destructive characterization of the internal structural morphology of the pulse energy storage capacitor based on the CT scan data and the broadband acoustic detection data to determine the microstructure of the pulse energy storage capacitor, performing in-situ collaborative sensing of the end electrical characteristics of the pulse energy storage capacitor based on multi-band data to determine the end characteristics of the pulse energy storage capacitor, inputting the microstructure and end characteristics into a circuit simulation model, and outputting derived characteristic parameters of the pulse energy storage capacitor from the circuit simulation model.

[0013] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor as described in the above embodiments.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects:

[0016] First, computed tomography (CT) scan data and broadband acoustic detection data of the pulse energy storage capacitor are acquired. By combining these two types of data, the internal structural morphology of the capacitor is characterized in situ and non-destructively, thereby accurately determining its microstructure and internal details. Next, multi-band electrical signal data of the pulse energy storage capacitor are collected. Based on this data, the terminal electrical characteristics of the capacitor are collaboratively sensed, enabling real-time evaluation and monitoring of its terminal performance. Finally, the acquired microstructure information and terminal electrical characteristics are input into a pre-established circuit simulation model. Through calculation and analysis by the simulation model, derived characteristic parameters reflecting the capacitor's state are output, providing a scientific basis for subsequent performance evaluation and fault diagnosis. This solves the problems of single measurement parameters and insufficient monitoring accuracy in related technologies.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0019] Figure 1 This is a flowchart of an in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to an embodiment of this application;

[0020] Figure 2 This is a schematic diagram illustrating the principle of computed tomography according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram illustrating the broadband acoustic detection principle according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram illustrating the principle of multi-band information fusion according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram illustrating the principle of convolutional neural network regression training and end-feature perception according to an embodiment of this application;

[0024] Figure 6 This is a schematic diagram illustrating the overall principle of the in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to an embodiment of this application.

[0025] Figure 7 This is a block diagram of an in-situ characterization and evaluation system for the structural state of a pulse energy storage capacitor according to an embodiment of this application.

[0026] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] The following describes, with reference to the accompanying drawings, an in-situ characterization and evaluation method, system, electronic device, and storage medium for the structural state of a pulse energy storage capacitor according to embodiments of this application. Addressing the problems mentioned in the background art, this application provides an in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor. In this method, firstly, computed tomography (CT) data and broadband acoustic detection data of the pulse energy storage capacitor are acquired. By combining these two types of data, the internal structural morphology of the capacitor is characterized in-situ and non-destructively, thereby accurately determining its microstructure and internal details. Subsequently, multi-band electrical signal data of the pulse energy storage capacitor are collected. Based on this data, the terminal electrical characteristics of the capacitor are collaboratively sensed to achieve real-time evaluation and monitoring of its terminal performance. Finally, the acquired microstructure information and terminal electrical characteristics are input into a pre-established circuit simulation model. Through calculation and analysis by the simulation model, derived characteristic parameters reflecting the capacitor's state are output, providing a scientific basis for subsequent performance evaluation and fault diagnosis. This solves the problems of single measurement parameters and insufficient monitoring accuracy in related technologies.

[0029] Specifically, Figure 1 This is a flowchart illustrating an in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor provided in an embodiment of this application.

[0030] like Figure 1 As shown, the in-situ characterization and evaluation method for the structural state of the pulse energy storage capacitor includes the following steps:

[0031] In step S101, computed tomography (CT) data and broadband acoustic detection data of the pulse energy storage capacitor are acquired. Based on the CT data and broadband acoustic detection data, the internal structural morphology of the pulse energy storage capacitor is characterized in situ without damage to determine the microstructure of the pulse energy storage capacitor.

[0032] Pulse energy storage capacitors are capacitors that release a large amount of electrical energy in a short time, commonly used in high-power applications such as lasers and electromagnetic emissions. Computed tomography (CT) is a non-destructive testing technique that uses X-rays to scan an object from multiple angles and then reconstructs the results using a computer to obtain sliced ​​or three-dimensional images of the object's internal structure. Broadband acoustic testing is a method of non-destructive testing of materials or devices using sound waves across a wide frequency range. In-situ non-destructive characterization analyzes and identifies the internal structure of a device without moving or disassembling it, using non-destructive testing methods. Microstructure refers to the state characteristics of the internal materials and structures of a capacitor at the micrometer or even smaller scale, such as pores, cracks, and interface delamination.

[0033] It is understood that in-situ non-destructive characterization methods for the internal structure of capacitors include computed tomography and broadband acoustic testing, used to obtain information about their internal microstructure without damaging the device; in-situ collaborative sensing methods for the electrical characteristics of capacitor ends include multi-band information fusion, convolutional neural network regression training, and end characteristic sensing, used to achieve accurate identification of the state of the capacitor ends; based on the microstructure and end characteristics, embodiments of this application can further realize the deduction and evaluation of key performance indicators of capacitors by constructing a capacitor field-circuit collaborative simulation model and combining it with the derived feature parameter inversion method.

[0034] In one embodiment of this application, obtaining computed tomography (CT) scan data and broadband acoustic test data of a pulse energy storage capacitor includes: activating a CT scanner and scanning the pulse energy storage capacitor using the CT scanner to obtain CT scan data of the pulse energy storage capacitor; activating a broadband acoustic test device and performing broadband acoustic testing on the pulse energy storage capacitor using the broadband acoustic test device to obtain broadband acoustic test data of the pulse energy storage capacitor.

[0035] Understandably, computed tomography (CT) scans can provide three-dimensional image information of the internal microstructure of capacitors, which can be used to identify potential defects such as cracks, pores, and delamination; broadband acoustic testing can reflect changes in the acoustic impedance inside the material through multi-frequency acoustic excitation and response analysis, further revealing signs of structural anomalies or degradation. These two methods complement each other, and without affecting the normal use of the device, the embodiments of this application achieve accurate assessment of the device's health status.

[0036] In one embodiment of this application, the computed tomography (CT) device includes a sample chamber, an X-ray source, a flat panel detector, a digital imaging component, a temperature control stage, and a mechanical testing stage.

[0037] The sample chamber is the spatial area where the capacitor under test is placed, ensuring sample stability and fixed position. The X-ray source is a device that generates high-energy rays to penetrate the sample and obtain internal information. The flat panel detector is a device that receives the rays penetrating the sample and converts them into digital signals. The digital imaging component is a device that reconstructs and processes the signals acquired by the detector to generate a three-dimensional structural image. The temperature control stage is a device that controls the sample temperature to enable testing under different temperature environments. The mechanical testing stage is a testing platform that applies mechanical loads to the sample, aiding in the study of material or structural changes under stress.

[0038] Understandably, computed tomography (CT) devices are used to achieve high-precision tomographic imaging of pulsed energy storage capacitors. In one embodiment of this application, the rated parameters of the X-ray source are not less than 50 kV and 5 W, ensuring that the device has sufficient penetration capability and a spatial resolution of less than 500 nm, which can meet the resolution requirements for identifying microstructural defects.

[0039] Figure 2 The basic principle of computed tomography (CT) is illustrated. This device can achieve "transparent" observation of pulse energy storage capacitors, covering full device scanning, three-dimensional structural reconstruction and visualization, as well as cross-sectional slice analysis of fault points, thereby providing data support for device structure characterization and defect identification.

[0040] In one embodiment of this application, the broadband acoustic detection device includes an ultrasonic sensor, a transmitting transducer, a sound wave, and a receiving transducer.

[0041] An ultrasonic sensor is a device that detects changes in the internal structure or state of an object using ultrasonic signals. The transmitting transducer is responsible for converting electrical signals into ultrasonic signals and transmitting them; it is a key component in an ultrasonic testing system for generating sound waves. Sound waves are mechanical waves generated by the vibration of an object and propagate through a medium. The receiving transducer is responsible for receiving the sound waves reflected or transmitted after propagation through the object being measured and converting them into electrical signals for subsequent signal analysis and processing.

[0042] It is understood that the embodiments of this application, through a broadband acoustic detection device, can accurately identify microscopic defects in dielectric materials, such as micropores, cracks, and their size, distribution, and quantity, providing effective support for the internal condition assessment and quality control of capacitors.

[0043] In one embodiment of this application, the broadband acoustic detection device detects micropores and cracks in dielectric materials, including their size, distribution, and quantity. It can achieve high-frequency detection from 20kHz to 2MHz and has high-precision detection capabilities with a focusing diameter of no more than 1mm and a scanning resolution of less than 0.2mm. Figure 3 The working principle of the broadband acoustic testing device was demonstrated. The device mainly consists of ultrasonic sensors, transmitting transducers, sound wave propagation paths, and receiving transducers, and is used to perform non-destructive testing on the dielectric material inside pulse energy storage capacitors.

[0044] In step S102, multi-band data of the pulse energy storage capacitor is acquired, and the electrical characteristics of the pulse energy storage capacitor terminals are co-sensed in situ based on the multi-band data to determine the terminal characteristics of the pulse energy storage capacitor.

[0045] Multi-band data refers to electrical signal data collected across multiple frequency ranges, used to reflect the response characteristics of a device under different frequency excitations. Terminal electrical characteristics are the electrical performance parameters exhibited at the two ends of a capacitor, i.e., the input / output interface, mainly including capacitance, resistance, and impedance, reflecting the device's electrical behavior to the external environment after being connected to a circuit. Terminal characteristics are the specific manifestations of terminal electrical characteristics during operation, such as capacitance changes, increased losses, and abnormal current response, used to evaluate the capacitor's operating status and reliability.

[0046] It is understood that the embodiments of this application achieve comprehensive monitoring of the electrical behavior of the device under actual working conditions. Multi-band data can cover the dynamic response characteristics of the capacitor at different frequencies, enabling the capture of minute features such as capacitance changes, equivalent series resistance fluctuations, and uneven current response. At the same time, in-situ collaborative sensing can be performed during the normal operation of the capacitor without power interruption or disassembly, maintaining the integrity of the device and its actual operating conditions.

[0047] In one embodiment of this application, the terminal electrical characteristics of a pulse energy storage capacitor are co-sensed in situ based on multi-band data to determine the terminal characteristics of the pulse energy storage capacitor. This includes: inputting multi-band data into a convolutional neural network, and outputting the terminal characteristics of the pulse energy storage capacitor from the convolutional neural network. The convolutional neural network performs regression training on the capacitor capacitance and equivalent series resistance based on the comprehensive features formed by the multi-band data to achieve co-sensing of the capacitor capacitance and equivalent series resistance.

[0048] Convolutional Neural Networks (CNNs) are deep learning models adept at extracting spatial or local features from images, time-frequency graphs, or structured data. In this embodiment, a CNN is used to extract features from multi-band signals to aid in determining capacitor parameters. The comprehensive features are high-dimensional feature information extracted from multi-band data after transformation and fusion, serving as input to the CNN to express multi-faceted information about the capacitor's state. Regression training is a supervised learning method where the CNN learns the mapping relationship between input data and target parameters during the training phase, thereby effectively predicting the capacitor's capacitance and equivalent series resistance. Capacitance represents the capacitor's ability to store charge and is one of the core parameters for determining whether a capacitor has degraded or failed. Equivalent series resistance represents the unavoidable series resistance within the capacitor, reflecting its energy loss and heat generation level.

[0049] It is understood that the embodiments of this application support in-situ, non-invasive testing of capacitors under actual operating conditions, obtaining key electrical parameters without power interruption or disassembly, ensuring the authenticity and integrity of the testing process. By fusing dynamic response data such as voltage and current across multiple frequency bands, it can comprehensively capture the state characteristics of capacitors at different operating frequencies, making it more sensitive and accurate than traditional single-frequency measurement methods. Convolutional neural networks, through deep feature extraction and learning, make the perception results more robust and reliable, enabling simultaneous identification of capacitance and equivalent series resistance.

[0050] Figure 4 This paper demonstrates the principle and process of multi-band information fusion. First, the embodiments of this application perform wavelet transform on the acquired capacitor voltage and load current signals to extract time-frequency features; simultaneously, the energy distribution within the corresponding frequency band is calculated. By synchronously fusing the time-frequency features and frequency band energy, a multi-feature fused image is obtained. The fused multi-band information includes the time-frequency maps of capacitor voltage and load current and their corresponding frequency band energy, which can comprehensively reflect the dynamic response characteristics of the capacitor at different frequencies and times, thereby achieving an accurate and comprehensive characterization of the capacitor's operating state.

[0051] In one embodiment of this application, the convolutional neural network includes an input layer, a convolutional layer, an activation function, a pooling layer, a fully connected layer, and an output layer.

[0052] The neural network consists of several layers. The input layer is the first layer, receiving raw data such as images and signals and passing it to subsequent layers for processing. The convolutional layer extracts local features from the input data through convolution operations, capturing spatial or temporal patterns and forming the core structure of the convolutional neural network. The activation function performs a non-linear transformation on the output of the convolutional layer, enhancing the network's expressive power. The pooling layer reduces and abstracts the features extracted by the convolutional layer, typically using max pooling or average pooling to reduce the number of parameters and computational complexity, while enhancing the translation invariance of the features. The fully connected layer integrates the extracted features and is usually located at the end of the network, playing a role in classification or regression decisions. The output layer is the last layer of the neural network, outputting the final result, such as a regression value.

[0053] Understandable, Figure 5 This diagram illustrates the principle of convolutional neural network regression training and end-characteristic perception. End-characteristic perception includes sensing the capacitance and equivalent series resistance of a pulse capacitor. The convolutional neural network takes a time-frequency image generated by multi-band fusion as input, and after multiple convolutions and feature extraction, captures key features of changes in the internal electrical characteristics of the pulse energy storage capacitor. Finally, it outputs the corresponding variation patterns of the capacitor's capacitance and equivalent series resistance, achieving accurate regression prediction and dynamic sensing of the end-characteristic electrical properties.

[0054] In step S103, the circuit simulation model of the pulse energy storage capacitor is invoked, and the microstructure and end characteristics are input into the circuit simulation model. The circuit simulation model outputs the derived characteristic parameters of the pulse energy storage capacitor.

[0055] The circuit simulation model is a mathematical model that simulates the electrical behavior and performance of the capacitor and its connecting circuits, and can predict the capacitor's response under various operating conditions. Derived characteristic parameters are indirect physical quantities based on microstructure and end characteristics, obtained through calculation or inversion from the circuit simulation model. They are used to characterize the capacitor's performance and state in greater depth, such as the capacitor core temperature distribution, capacitor current distribution, and capacitor stress distribution.

[0056] Understandably, by calling the circuit simulation model of the pulse energy storage capacitor and using the capacitor's microstructure and end electrical characteristics as input, it is possible to achieve comprehensive simulation and analysis of the multi-physics behavior inside the capacitor, thereby accurately calculating and retrieving key derived characteristic parameters, such as temperature distribution, current distribution, and stress distribution, thus improving the understanding and quantitative assessment capabilities of the complex physical state inside the capacitor.

[0057] In one embodiment of this application, the circuit simulation model includes an external circuit combining electric field, thermal field, and force field, and the derived characteristic parameters include the capacitor core temperature distribution, capacitor current distribution law, and capacitor stress distribution law.

[0058] The electric field is the electrical distribution generated by voltage and charge within and around the capacitor. The thermal field is the temperature distribution in different regions inside the capacitor and the heat conduction paths. The force field is the distribution of mechanical stress and deformation generated in the capacitor material under thermal expansion, discharge impact, or structural loads, used to analyze structural reliability, material fatigue, and potential failure risks. The external circuit is the external electrical system connected to the capacitor, including components such as power supplies, loads, and switches; its dynamic response directly affects the capacitor's operating state and simulation boundary conditions.

[0059] Understandably, considering the multi-field coupling relationship between electric, thermal, and force fields can reveal the internal response characteristics of capacitors under complex conditions such as strong pulse excitation and high-temperature operation. Derived parameters, such as core temperature distribution, current distribution, and stress distribution, can provide deeper information on the structure-performance relationship, offering important evidence for identifying localized heating, abnormal current carrying, or potential structural fatigue.

[0060] The capacitor field-circuit co-simulation model is a comprehensive co-simulation model that encompasses the interaction of electric field, thermal field, force field, and external circuit. By simultaneously considering the coupling effects between multiple physical fields within the capacitor and their dynamic interaction with the external circuit, this model can comprehensively and accurately simulate the multi-physics response and circuit behavior of the capacitor under actual operating conditions, providing strong technical support for in-depth analysis of capacitor performance and reliability.

[0061] Figure 6 This illustration shows the overall principle of the in-situ characterization and evaluation method for the structural state of a pulsed energy storage capacitor according to an embodiment of this application. The computed tomography (CT) device includes an X-ray excitation source 1, a flat panel detector 3, and a controller 5. The broadband acoustic detection device includes an ultrasonic probe 2 and an ultrasonic generator and receiver 4. The CT scan and broadband acoustic detection are simultaneously applied to the pulsed capacitor sample under simulated actual working conditions. The electro-thermal-mechanical field experienced by the pulsed capacitor is realized by the working condition simulator 8, which mainly includes a resistor 6, an auxiliary capacitor 7, a high-voltage amplifier 9, and a function generator 10. Simultaneously, the terminal voltage, current, and other characteristics of the pulsed capacitor are tested during the working condition simulation, and the capacitance value of the pulsed capacitor is determined using information fusion sensing 11. C and equivalent series resistance ESR Finally, based on the inversion method of derived characteristic parameters of capacitor devices according to micromorphology and end characteristics, in-situ measurement and failure process characterization are realized.12

[0062] In one embodiment of this application, a capacitor internal structure characterization platform based on computed tomography (CT) imaging was established for in-situ non-destructive characterization of the capacitor's internal structure. A combination of a microfocus X-ray source and a flat panel detector was used to achieve sub-micron resolution microstructure measurement, enabling high-precision three-dimensional tomographic imaging and reconstruction performance testing of the capacitor. Based on YOLO (You Only Look Once, a one-time target detection algorithm), the morphological boundaries and spatial location information of defect areas in the scanned image can be automatically extracted. YOLO is a deep learning algorithm in the field of target detection, capable of locating and classifying multiple targets in a single image. Simultaneously, ultrasonic sensing technology was used to perform in-situ detection of micropores, cracks, and their size, distribution, and quantity within the dielectric material, and the accuracy and complementarity of the structural characterization results were verified through comparative analysis.

[0063] To address the in-situ collaborative sensing of the electrical characteristics at the capacitor terminals, this application constructs a circuit model of an energy storage system considering parameter aging effects. By simulating capacitor behavior under different aging states, the time-series waveforms of system voltage and load current are obtained, and the influence of changes in capacitance and equivalent series resistance on the spectral characteristics of the terminal signals is analyzed, thereby clarifying the key frequency range and its band energy distribution under the aging effect. Based on wavelet transform algorithms, the voltage and current time-series signals are converted to time-frequency and fused with the corresponding band energy information to form a comprehensive input containing multi-scale features. Finally, based on machine learning algorithms such as convolutional neural networks, regression training is performed on the fused features to achieve collaborative sensing and prediction of capacitance and equivalent series resistance.

[0064] In terms of inversion of capacitor-derived characteristic parameters, a multi-physics coupled capacitor field-circuit joint simulation model was established. Specifically, a coupled electro-magnetic-thermal-mechanical physical model was constructed using the Console multi-physics simulation software, and a circuit simulation model of the energy storage system was built using the MATLAB (Simulation and Model-Based Design Environment) Simulink platform. The two models were then used for collaborative simulation via the Livelink interface. The simulation model uses the measured microstructure of the capacitor as the health state input and the shell temperature and end electrical characteristics as boundary conditions, thereby inverting indirect measurable parameters such as core temperature, temperature gradient, and composite stress. Based on the simulation results, the physical property failure boundaries that may occur in the capacitor under extreme conditions can be characterized, and further characteristic data representing the relationship between multiple scales (micro-meso-macro) can be obtained, providing data-driven support for capacitor performance evaluation and state prediction under complex operating conditions.

[0065] According to the in-situ characterization and evaluation method for the structural state of a pulsed energy storage capacitor proposed in this application, firstly, computed tomography (CT) data and broadband acoustic detection data of the pulsed energy storage capacitor are acquired. By combining these two types of data, the internal structural morphology of the capacitor is characterized in-situ and non-destructively, thereby accurately determining its microstructure and internal details. Subsequently, multi-band electrical signal data of the pulsed energy storage capacitor are collected. Based on this data, the terminal electrical characteristics of the capacitor are collaboratively sensed to achieve real-time evaluation and monitoring of its terminal performance. Finally, the acquired microstructure information and terminal electrical characteristics are input into a pre-established circuit simulation model. Through calculation and analysis by the simulation model, derived characteristic parameters reflecting the capacitor state are output, providing a scientific basis for subsequent performance evaluation and fault diagnosis. This solves the problems of single measurement parameters and insufficient monitoring accuracy in related technologies.

[0066] Next, referring to the accompanying drawings, an in-situ characterization and evaluation system for the structural state of a pulse energy storage capacitor proposed according to an embodiment of this application is described.

[0067] Figure 7 This is a block diagram of an in-situ characterization and evaluation system for the structural state of a pulse energy storage capacitor according to an embodiment of this application.

[0068] like Figure 7 As shown, the in-situ characterization and evaluation system 100 for the structural state of the pulse energy storage capacitor includes: a computed tomography device 101, a broadband acoustic detection device 102, and an electronic device 103.

[0069] The system includes a computed tomography (CT) scanner 101 for scanning the pulse energy storage capacitor to obtain CT data; a broadband acoustic detection device 102 for performing broadband acoustic detection on the pulse energy storage capacitor to obtain broadband acoustic detection data; and an electronic device 103 for acquiring data from the CT scanner 101 and the broadband acoustic detection device 102, performing in-situ non-destructive characterization of the internal structure of the pulse energy storage capacitor based on the CT data and the broadband acoustic detection data to determine the microstructure of the pulse energy storage capacitor, performing in-situ collaborative sensing of the electrical characteristics at the ends of the pulse energy storage capacitor based on multi-band data to determine the end characteristics of the pulse energy storage capacitor, inputting the microstructure and end characteristics into a circuit simulation model, and outputting derived characteristic parameters of the pulse energy storage capacitor from the circuit simulation model.

[0070] It should be noted that the explanation of the above-mentioned embodiment of the in-situ characterization and evaluation method for the structural state of the pulse energy storage capacitor also applies to the in-situ characterization and evaluation system for the structural state of the pulse energy storage capacitor in this embodiment, and will not be repeated here.

[0071] The in-situ characterization and evaluation system for the structural state of a pulsed energy storage capacitor proposed in this application first acquires computed tomography (CT) scan data and broadband acoustic detection data of the pulsed energy storage capacitor. By combining these two types of data, the internal structural morphology of the capacitor is characterized in-situ and non-destructively, thereby accurately determining its microstructure and internal details. Subsequently, multi-band electrical signal data of the pulsed energy storage capacitor are collected. Based on this data, the terminal electrical characteristics of the capacitor are collaboratively sensed, enabling real-time evaluation and monitoring of its terminal performance. Finally, the acquired microstructure information and terminal electrical characteristics are input into a pre-established circuit simulation model. Through calculation and analysis by the simulation model, derived characteristic parameters reflecting the capacitor's state are output, providing a scientific basis for subsequent performance evaluation and fault diagnosis. This solves the problems of single measurement parameters and insufficient monitoring accuracy in related technologies.

[0072] Figure 8A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0073] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0074] When the processor 802 executes the program, it implements the in-situ characterization and evaluation method for the structural state of the pulse energy storage capacitor provided in the above embodiments.

[0075] Furthermore, electronic devices also include:

[0076] Communication interface 803 is used for communication between memory 801 and processor 802.

[0077] The memory 801 is used to store computer programs that can run on the processor 802.

[0078] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0079] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0080] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0081] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0082] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for in-situ characterization and evaluation of the structural state of a pulse energy storage capacitor.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0085] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0086] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0087] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0088] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor, characterized in that, Includes the following steps: Acquire computed tomography (CT) data and broadband acoustic detection data of the pulse energy storage capacitor. Based on the CT data and the broadband acoustic detection data, perform in-situ non-destructive characterization of the internal structure morphology of the pulse energy storage capacitor to determine the microstructure of the pulse energy storage capacitor. Multi-band data of the pulse energy storage capacitor is acquired, and the terminal electrical characteristics of the pulse energy storage capacitor are in-situ coordinated sensing based on the multi-band data to determine the terminal characteristics of the pulse energy storage capacitor, wherein the terminal characteristics are the performance of the terminal electrical characteristics during operation. The circuit simulation model of the pulse energy storage capacitor is invoked, and the microstructure and end characteristics are input into the circuit simulation model. The circuit simulation model outputs the derived characteristic parameters of the pulse energy storage capacitor. The circuit simulation model includes an external circuit consisting of electric field, thermal field and force field. The derived characteristic parameters include the temperature distribution of the capacitor core, the current distribution law of the capacitor, and the stress distribution law of the capacitor.

2. The in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to claim 1, characterized in that, The acquisition of computed tomography (CT) data and broadband acoustic detection data of the pulse energy storage capacitor includes: The computed tomography (CT) scanner is activated, and the pulse energy storage capacitor is scanned using the CT scanner to obtain the CT scan data of the pulse energy storage capacitor. Start the broadband acoustic testing device and use the broadband acoustic testing device to perform broadband acoustic testing on the pulse energy storage capacitor to obtain broadband acoustic testing data of the pulse energy storage capacitor.

3. The in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to claim 2, characterized in that, The computed tomography (CT) device includes a sample chamber, a radiation source, a flat panel detector, a digital imaging assembly, a temperature control stage, and a mechanical testing stage.

4. The in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to claim 2, characterized in that, The broadband acoustic detection device includes an ultrasonic sensor, a transmitting transducer, a sound wave, and a receiving transducer.

5. The in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to claim 1, characterized in that, The step of in-situ collaborative sensing of the electrical characteristics at the ends of the pulse energy storage capacitor based on the multi-band data to determine the end characteristics of the pulse energy storage capacitor includes: The multi-band data is input into a convolutional neural network, which outputs the end characteristics of the pulse energy storage capacitor. The convolutional neural network performs regression training on the capacitor capacitance and equivalent series resistance based on the comprehensive features formed by the multi-band data, thereby achieving the collaborative sensing of the capacitor capacitance and equivalent series resistance.

6. The in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor according to claim 5, characterized in that, The convolutional neural network includes an input layer, a convolutional layer, an activation function, a pooling layer, a fully connected layer, and an output layer.

7. An in-situ characterization and evaluation system for the structural state of a pulse energy storage capacitor, characterized in that, include: A computed tomography (CT) scanner is used to scan a pulse energy storage capacitor to obtain computed tomography data of the pulse energy storage capacitor. A broadband acoustic testing device is used to perform broadband acoustic testing on the pulse energy storage capacitor to obtain broadband acoustic testing data of the pulse energy storage capacitor. An electronic device is configured to acquire data from the computed tomography (CT) scanner and the broadband acoustic detection device, and based on the CT data and the broadband acoustic detection data, to perform in-situ non-destructive characterization of the internal structural morphology of the pulse energy storage capacitor to determine the microstructure of the pulse energy storage capacitor, and to perform in-situ collaborative sensing of the end electrical characteristics of the pulse energy storage capacitor based on multi-band data to determine the end characteristics of the pulse energy storage capacitor, wherein the end characteristics are the performance of the end electrical characteristics during operation; The microstructure and end characteristics are input into the circuit simulation model, and the circuit simulation model outputs the derived characteristic parameters of the pulse energy storage capacitor. The circuit simulation model includes an external circuit with electric field, thermal field and force field, and the derived characteristic parameters include the capacitor core temperature distribution, capacitor current distribution law and capacitor stress distribution law.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the in-situ characterization and evaluation method for the structural state of a pulse energy storage capacitor as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the in-situ characterization and evaluation method for the structural state of the pulse energy storage capacitor as described in any one of claims 1-6.

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