Testing method of FDC integrated busbar

Abstract

The invention relates to the technical field of circuit board testing, in particular to an FDC integrated busbar testing method, which comprises the steps of simulating working conditions of a busbar in different load states based on a standard voltage source to obtain resistance change data; acquiring an amplitude resonance point of each battery cell according to the voltage acquisition unit, and correcting the amplitude resonance point according to the resistance change data; dividing the busbar into a plurality of partitions based on the strain energy density, performing heat effect mapping test on the plurality of partitions, and constructing a heat conductivity coefficient matrix based on the temperature data acquired by the temperature acquisition unit; and performing simulation analysis on the stress response data and the heat conductivity coefficient matrix based on the alternating stress test sequence, evaluating the residual life and potential fault points of each battery cell based on the life distribution curve, and generating a performance test report of the integrated busbar. According to the invention, the precision of the busbar performance test is improved, and the safety, stability and reliability of the battery system are effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of circuit board testing, and in particular to a testing method for an FDC integrated busbar. Background Art

[0002] With the rapid development of new energy vehicles and energy storage systems, the design requirements for integrated power battery busbars are becoming increasingly stringent. As a key component in the battery module that carries large currents, the FDC (Fused Disconnect Connector) integrated busbar not only needs to have excellent conductivity, but also the material aging problem caused by the coupling of electrical, thermal and force fields during long-term operation has become the core bottleneck restricting system reliability.

[0003] Existing detection technologies mainly focus on the static electrical parameter measurement of the busbar, such as obtaining the steady-state resistance value through a standard load box, or monitoring the surface temperature field with the help of an infrared thermal imager. Although this discrete testing method can reflect the immediate working status of the busbar, it ignores the coupled transmission process of the electrical-thermal-mechanical multi-physical fields under dynamic conditions. Especially in actual scenarios such as frequent charging and discharging of electric vehicles and rapid temperature changes, the local stress concentration phenomenon of the busbar structure and the heat accumulation effect reinforce each other, making the prediction deviation rate of the single parameter detection system as high as more than 35%.

[0004] The biggest flaw of the existing technical system is the lack of a time-domain-spatial domain joint analysis method for multi-dimensional physical parameters, which makes it impossible to build an effective reliability evaluation system. The accelerated aging experiments in the standard inspection procedures mostly use a linear loading mode with constant temperature and constant current, which is significantly different from the alternating impact load in actual working conditions. Although some improvement schemes have emerged in recent years, such as burying FBG fiber optic sensors in specific locations to obtain strain data, or using ultrasonic flaw detection technology to detect crack propagation, these isolated technical enhancements have not broken through the framework limitations of single-field analysis, resulting in failures in the battery system that cannot be predicted in time during actual use, reducing the safety and reliability of the battery system. Summary of the invention

[0005] The main purpose of the present invention is to provide a testing method for an FDC integrated busbar, aiming to overcome the problem that the prior art cannot comprehensively evaluate the reliability of the busbar under complex working conditions.

[0006] In order to achieve the above-mentioned invention problem, the present invention proposes a test method for an FDC integrated busbar, wherein the FDC integrated busbar includes a voltage acquisition unit and a temperature acquisition unit, and the test method includes: Simulating the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions; Measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, acquiring the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain stress response data; Acquire the strain energy density of the stress response data on the busbar, and divide the busbar into a plurality of partitions based on the strain energy density; Performing a thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; Constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; Based on the life distribution curve, the remaining life and potential failure points of each battery cell are evaluated, and a performance test report of the integrated busbar is generated.

[0007] Furthermore, the step of simulating the working conditions of the busbar under different load conditions based on the standard voltage source to obtain the resistance change data of the busbar under different voltage and current conditions includes: Constructing an N-dimensional electrical parameter space of the busbar, performing M-level load configuration on each coordinate axis of the N-dimensional electrical parameter space, and generating an M×N-dimensional load parameter matrix; Based on the discrete cosine transform algorithm, the load parameter matrix is ​​decomposed into multiple frequency bands, a phase offset is applied to each frequency band according to a preset spectrum ratio, and a multi-band load signal with a dynamic phase shift relationship is generated; A time series control model is established in a standard voltage source, the multi-band load signal is split into K time windows for alternate injection, and three-dimensional voltage-current-temperature synchronous time domain data of each time period is synchronously collected; Performing eigenvalue decomposition on the synchronized time domain data in a phase space coordinate system, calculating the power spectrum density weight factor of each dominant mode, and generating a frequency-impedance mapping table; A family of variable operating condition resistance characteristic curves including a four-dimensional relationship among frequency, temperature, current and voltage is established according to the frequency-impedance mapping table to obtain the resistance change data.

[0008] Further, the step of measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, acquiring the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain the stress response data includes: Perform multi-channel segmented sampling and time-domain synchronous processing on the voltage signal collected by the voltage collection unit to generate a voltage fluctuation sequence with time alignment for each battery cell; According to the time domain waveform of the voltage fluctuation sequence, a multi-scale frequency domain transform is performed on the voltage signal of each battery cell, characteristic points of the frequency band whose amplitude-frequency response exceeds a preset threshold are extracted, and a frequency domain amplitude distribution curve is generated; Based on the peak-trough topological relationship of the frequency domain amplitude distribution curve, a characteristic frequency interval that satisfies amplitude monotonicity and continuous phase change is selected, and the frequency point corresponding to the maximum amplitude in the characteristic frequency interval is marked as an initial amplitude resonance point; Constructing a sliding window with a preset number of bits, controlling the sliding window to slide at the initial amplitude resonance point, and generating a denoised steady-state amplitude resonance point sequence; According to the resistance slope corresponding to each voltage-current combination condition in the resistance change data, the steady-state amplitude resonance point sequence is corrected by piecewise linear interpolation to generate a corrected resonance point coordinate set dynamically matching the busbar load state; The frequency offset in the modified resonance point coordinate set is cross-correlatedly calculated with the equivalent mechanical stiffness parameter of the corresponding battery cell point by point to generate multi-dimensional stress response data.

[0009] Furthermore, the step of obtaining the strain energy density of the stress response data on the busbar and dividing the busbar into a plurality of partitions based on the strain energy density includes: Performing time-frequency phase coherence processing on the stress response data, extracting the phase shift spectrum of the strain pulse, and generating a multi-band correlation spectrum including the main frequency band energy distribution; Taking the busbar surface grid vertices as nodes, generating node weights according to the energy values ​​of each main frequency band in the multi-band association spectrum, and generating a topological network model based on the node weights; Calculating the amplitude change rate of the stress gradient tensor along the normal vector direction of each grid unit in the topological network model, and generating a parametric line cluster based on the amplitude change rate; Constructing a rectangular bounding box based on the parametric line cluster, calculating the average strain energy density in the rectangular bounding box, and taking the rectangular bounding box with an average strain energy density higher than a threshold as a preliminary partition boundary; Connecting the boundaries of the adjacent preliminary partitions to form a continuous closed area, and merging the adjacent closed areas whose spacing is less than a preset minimum gap to form a plurality of preliminary partitions; Scan the number of intersections of each of the preliminary partitions. If the number of intersections is an odd number, split the corresponding preliminary partition into sub-partitions until there are no abnormal intersections, thereby forming multiple partitions.

[0010] Furthermore, the step of performing thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data includes: Based on a pulse current source, a step current load is periodically injected into the surface and internal reference points of the partitions, and a temperature acquisition unit synchronously acquires a temperature time series of the surface and internal reference points of each partition at a preset sampling period; Performing frequency domain feature decomposition on the temperature fluctuation of each partition according to the temperature time series to generate a frequency domain feature vector of each partition; Calculating the phase correlation coefficient in each of the frequency domain feature vectors, and adding an attenuation mark to those with a correlation coefficient lower than a preset coefficient; The frequency domain marked with the attenuation mark is low-pass filtered to obtain a temperature propagation curve, and a thermal conductivity matrix matching the physical structure of the busbar is generated based on the temperature propagation curve.

[0011] Furthermore, the step of constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell includes: Acquire phase information of each frequency component in the stress response data, and acquire a basic frequency range of the alternating stress load according to the phase information; Selecting a plurality of alternating stress cycles according to the basic frequency range, setting parameters for the alternating stress loads of different cycles respectively, and constructing an alternating stress test sequence; Calculating the temperature change of the battery cell in each cycle based on the alternating stress test sequence to obtain a periodic temperature change curve; Combining the temperature change curve with the stress response data to generate dynamic composite stress data, and calculating the damage evolution path of each battery cell according to the thermal conductivity matrix of the dynamic composite stress data; The cycle fatigue life of each battery cell is predicted based on the damage evolution path, and a life distribution curve is generated.

[0012] Furthermore, the step of evaluating the remaining life and potential failure points of each battery cell based on the life distribution curve and generating a performance test report of the integrated busbar includes: Acquire cycle fatigue life data of each battery cell in the life distribution curve, and determine the remaining life of each battery cell according to the cycle fatigue life data; Based on a clustering algorithm, the cyclic fatigue life data is divided into multiple categories to obtain fatigue degree sets of different categories; Propose a variation trend of fatigue degree in each category, and identify an abnormal fluctuation area in the life distribution curve based on the variation trend; The battery cells corresponding to the abnormal fluctuation area are marked as potential failure points, and a performance test report including the remaining life of each battery cell and the potential failure points is generated.

[0013] The present invention also provides a testing device for an FDC integrated busbar, comprising: A simulation module, used to simulate the working conditions of the busbar under different load conditions based on a standard voltage source, and obtain resistance change data of the busbar under different voltage and current conditions; A measurement module, configured to measure the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, obtain the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correct the amplitude resonance point according to the resistance change data to obtain stress response data; An acquisition module, configured to acquire the strain energy density of the stress response data on the busbar, and divide the busbar into a plurality of partitions based on the strain energy density; A construction module, used for performing a thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; An analysis module, used for constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; A generation module is used to evaluate the remaining life and potential failure points of each battery cell based on the life distribution curve and generate a performance test report of the integrated busbar.

[0014] The present invention further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0016] Beneficial effects: The present application proposes a test method for an FDC integrated busbar. By simulating the working conditions of the busbar under different load conditions based on a standard voltage source, the resistance change data is obtained, and the amplitude resonance point of the battery cell is obtained according to the fluctuation information of the voltage signal, and the accurate stress response data is further corrected and obtained. This process can fully consider the dynamic response of the battery busbar under different voltage and current conditions. The strain energy density calculated based on the stress response data can reasonably partition the busbar structure, thereby realizing the accurate mapping of the thermal effect in the local area, and constructing a thermal conductivity matrix through temperature data to effectively evaluate the heat transfer characteristics of each partition. The construction of an alternating stress test sequence can simulate the alternating load and temperature changes encountered by the battery busbar during actual use, and comprehensively simulate the coupling effects of factors such as stress, thermal effect, and thermal conductivity under actual working conditions, making up for the defect of the prior art that the dynamic working conditions cannot be fully reflected. Through the evaluation of the life distribution curve, the remaining life and potential fault points of the battery cell are predicted, and a detailed performance test report is generated, which not only provides a more accurate reliability evaluation basis for the battery management system, but also provides a scientific basis for early fault diagnosis and maintenance of the battery system.

[0017] In general, the testing method of the present application not only improves the accuracy of busbar performance testing through the joint analysis of multi-dimensional physical parameters, but also effectively improves the safety, stability and reliability of the battery system, filling the gap in the existing technology and having broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the steps of a method for testing an FDC integrated busbar in one embodiment of the present invention; Figure 2 It is a structural schematic diagram of an FDC integrated busbar according to an embodiment of the present invention; Figure 3 It is a structural schematic block diagram of a FDC integrated busbar testing device according to an embodiment of the present invention; Figure 4 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0019] Among them, Figure 2 The markings are: 10, busbar; 20, voltage collection element; 30, temperature collection element; 40, fixed structure; 50, FDC; 60, connector.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0023] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0024] Reference Figure 1 , an embodiment of the present invention provides a method for testing an FDC integrated busbar, the method comprising: S1: simulating the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions; In step S1, the working conditions of the busbar under different load conditions are simulated, and different voltage and current conditions can be applied to the busbar through a standard voltage source. The function of the standard voltage source is to provide a controllable and stable voltage output, which can simulate the various load conditions that the busbar may encounter in actual work by adjusting the voltage and current intensity. Under these load conditions, the resistance of the busbar will change because resistance is closely related to current, temperature and material properties. By simulating different load conditions and measuring the changes in voltage and current in real time, a series of resistance change data can be obtained, reflecting the electrical performance characteristics of the busbar under specific working conditions. During the experiment, the standard voltage source needs to be calibrated to cover various possible load conditions. The voltage source can be an adjustable power supply that can adjust the output voltage and current according to the test requirements to simulate different working conditions. By adjusting the combination of voltage and current, the working state of the busbar in different application scenarios, such as high load, high current, etc., can be simulated to ensure the comprehensiveness and accuracy of the data.

[0025] S2: measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, obtaining the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain stress response data; In step S2, the voltage signal of each cell in the busbar is monitored in real time during the test based on the voltage acquisition unit. The voltage acquisition unit is composed of a high-precision voltage sensor, which can provide accurate voltage data under different loads and working conditions. The measured voltage signal will show a fluctuation characteristic, which reflects the response of the cell under load changes, temperature changes or current fluctuations. After measuring the voltage signal, the vibration characteristics of the cell are analyzed by the fluctuation information. The cell will produce certain mechanical vibrations during operation, especially under high current or heavy load conditions. The fluctuation of the voltage signal is not only related to the electrical state of the cell, but also reflects the physical changes caused by factors such as thermal effects and current pulses. In this process, the frequency, amplitude and other frequency domain characteristics of the fluctuation are used to determine the amplitude resonance point of the cell. The amplitude resonance point is the frequency point where the cell may resonate under the working load. By obtaining the information of these amplitude resonance points, the stress of the cell under specific working conditions can be further inferred. Generally speaking, when the cell reaches the resonance point, large stress fluctuations will occur, which will cause temperature changes and even fatigue or damage to the material. However, it should be noted that the amplitude resonance point data alone cannot fully describe the stress distribution of the battery cell under specific working conditions, and the amplitude resonance point needs to be corrected in combination with the resistance change data obtained in step S1. The change in resistance reflects the physical properties of the battery cell such as thermal effects and contact resistance under different loads. Correcting the amplitude resonance point based on the resistance change data can more accurately reflect the stress distribution of the battery cell under actual working conditions. Through the correction, a corrected stress response data can be obtained, which describes the stress condition of the battery cell under different workloads. In particular, when the battery cell is under high load or impacted, stress concentration may occur, which is crucial for the health assessment and life prediction of the battery cell.

[0026] S3: obtaining the strain energy density of the stress response data on the busbar, and dividing the busbar into a plurality of partitions based on the strain energy density; In step S3, the stress response of each cell in the busbar is determined based on the voltage signal fluctuation and resistance change data measured by the voltage acquisition unit. The stress response data is a physical quantity that describes the stress state of the cell under the working load. The stress response data may include the influence of multiple factors such as load state, current change, temperature fluctuation, etc. on the cell, and can accurately reflect the stress condition of the busbar under actual working conditions. The stress response data is obtained and converted into strain energy density. The strain energy density is a physical quantity that describes the amount of energy accumulated by an object in a unit volume due to external stress. It is closely related to the relationship between stress and strain. The strain energy density can be obtained by the integral or calculation formula in the stress-strain curve. In a specific implementation, the strain energy density of the area can be calculated by the stress change of each cell in the stress response data. Through this process, the strain energy distribution of different areas in the busbar can be obtained, which can reflect the energy concentration area of ​​the busbar under different working loads. According to the obtained strain energy density data, the busbar is divided into multiple partitions, that is, according to the strain energy density distribution of different areas, the busbar is divided into areas with different working characteristics for more accurate analysis. The specific division method can be to set up multiple partitions based on the size of the strain energy density, for example, the area with higher strain energy density is divided into a high stress area, and the area with lower strain energy density is divided into a low stress area. The cells in each partition will have similar stress and working characteristics, which will help to conduct independent thermal effect analysis and life prediction for each partition later.

[0027] S4: performing a thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; In step S4, temperature data is acquired in each partition by a temperature acquisition unit. The temperature acquisition unit may include a thermocouple, a temperature sensor or an infrared imaging device, etc., which can monitor the temperature change of the busbar in real time during operation. By arranging these temperature sensors at different positions of the busbar, the temperature change of each partition can be acquired. For example, in a specific test, it is assumed that the busbar is divided into three main partitions: a high load zone, a medium load zone and a low load zone. The real-time temperature data of each partition can be obtained by the temperature sensor. For example, the temperature of the high load zone may reach a higher value, such as 90°C, while the temperatures of the medium load zone and the low load zone are 70°C and 50°C, respectively. Based on these temperature data, a thermal conductivity matrix is ​​constructed. The thermal conductivity matrix is ​​an important parameter that describes the ability of heat to spread in an object and reflects the efficiency of heat conduction between different regions. In different partitions of the busbar, the thermal conductivity will vary depending on the material, structure and load state. When constructing the thermal conductivity matrix, the thermal conduction characteristics of the entire busbar are calculated in combination with the heat conduction paths between different partitions. Specifically, the construction of the thermal conductivity matrix can be achieved through numerical methods such as finite element analysis (FEA), simulating the heat conduction process and performing accurate heat distribution calculations in each partition. Through these calculations, the thermal conductivity matrix of each partition in the busbar can be obtained, and the heat flow path inside the busbar can be further analyzed.

[0028] S5: constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; In step S5, the alternating stress test sequence refers to generating a series of constantly changing stress conditions by simulating the load changes such as current and voltage that the busbar may encounter in actual work, simulating the response of the battery cell under various load and environmental conditions, and these load changes are periodic, increasing or decreasing. They can include alternating changes in factors such as voltage, temperature, and load, simulating the actual load fluctuations of the battery cell during long-term use. Through the alternating stress test sequence, the fatigue response of the battery cell under different stresses and the performance degradation process in different time periods can be observed, so as to accurately evaluate the life. Based on these stress response data and the thermal conductivity matrix, simulation analysis is performed, and the behavior of the battery cell under these stress conditions can be simulated by using a simulation algorithm. For example, the thermal conductivity matrix is ​​used to know the thermal conduction effect of each partition of the busbar under thermal load, and it is combined with the stress response data of the battery cell to analyze how the stress, deformation, and thermal expansion of the battery cell affect its performance under the alternating action of temperature and load. The goal of this process is to evaluate the fatigue and aging process of the battery cell, and then predict its life and possible failure modes. Through simulation analysis, we can obtain the life distribution curve of the battery cell under different alternating stress conditions. The life distribution curve is a graph showing the cyclic stress that the battery cell is expected to withstand under specific conditions, with time or number of cycles as the horizontal axis and life or the degree of performance degradation of the battery cell as the vertical axis. The shape of the life distribution curve is usually an exponential or logarithmic curve, indicating that the performance of the battery cell gradually decreases as the load increases or the usage cycle increases. Through this curve, the remaining life of each battery cell in actual work can be accurately predicted.

[0029] S6: Evaluate the remaining life and potential failure points of each battery cell based on the life distribution curve, and generate a performance test report for the integrated busbar.

[0030] In step S6, based on the life distribution curve, the remaining life of the battery cell can be evaluated. First, the current health status of the battery cell is obtained, including its current voltage, capacity, internal resistance and other basic parameters. By comparing these parameters with the different points in the life distribution curve, the remaining life of the battery cell can be estimated. For example, if a battery cell has gone through 300 charge and discharge cycles, and according to the life distribution curve, the remaining life of the battery cell at this number of cycles is about 50%, it can be predicted that the battery cell will experience capacity decay or performance degradation in the next cycle. In this way, a reasonable remaining life estimate can be provided for each battery cell. The fault point refers to the critical point where the battery cell may fail or degrade during use. The identification of potential fault points depends on the life distribution curve of the battery cell and the stress response data of the battery cell under various load conditions. When the load conditions (such as temperature, current, etc.) of the battery cell are close to their limits, the life of the battery cell will drop sharply. By analyzing the life distribution curve and the working environment of the battery cell, those conditions that are prone to failure can be identified. For example, when the battery cell works under high temperature and high load for a long time, its life decay rate may accelerate. Based on the evaluation of the remaining life of the battery cells and potential failure points, a performance test report of the integrated busbar is generated. The report records the life prediction, potential failure points and corresponding optimization suggestions for each battery cell.

[0031] In one embodiment, reference Figure 2The FDC50 integrated busbar includes a busbar 10, a voltage acquisition element 20, a temperature acquisition element 30, a fixed structure 40, an FDC50 and a connector 60. The busbar 10 is responsible for connecting multiple battery cells into a battery pack system, providing a current conduction path, and ensuring efficient power transmission between batteries through materials with good conductivity. The voltage acquisition element 20 and the temperature acquisition element 30 are arranged on the busbar 10. The voltage acquisition element 20 is responsible for real-time acquisition of the voltage data of the battery cell and monitoring the voltage change of each battery. The temperature acquisition element 30 is to collect the temperature data of the busbar and the battery cell. FDC50 (functional design component) is the processing unit of the integrated busbar, which aggregates the data of the voltage acquisition element 20 and the temperature acquisition element 30, and analyzes the health status, performance changes and potential fault points of the battery through the built-in algorithm. FDC50 can process data such as voltage and temperature in real time, and can also make intelligent predictions on the working conditions of the battery, generate a battery life distribution curve, and then help evaluate the remaining life of the battery cell and optimize the battery management strategy. The fixed structure 40 is the supporting frame of the FDC50 integrated busbar, ensuring that the FDC50 busbar can be firmly fixed in the battery module and enhancing the stability of the overall structure. The connector 60 is used to make electrical connections with other parts of the battery module, supporting fast and reliable access and ensuring easy operation when the module is replaced or repaired. In practical applications, the FDC50 integrated busbar is tightly combined with the busbar 10 through the die-cut circuit to form a compact electrical connection structure. When current passes through, the conductor efficiently transfers electrical energy to various parts of the battery module. At the same time, the blister fixing structure ensures the stability of the entire component in a dynamic environment and reduces potential mechanical failures. This design greatly improves the integration and work efficiency of the battery management system (BMS) and meets the high requirements of modern battery modules for electrical performance and reliability.

[0032] In one embodiment, the step of simulating the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions includes: Constructing an N-dimensional electrical parameter space of the busbar, performing M-level load configuration on each coordinate axis of the N-dimensional electrical parameter space, and generating an M×N-dimensional load parameter matrix; Based on the discrete cosine transform algorithm, the load parameter matrix is ​​decomposed into multiple frequency bands, a phase offset is applied to each frequency band according to a preset spectrum ratio, and a multi-band load signal with a dynamic phase shift relationship is generated; A time series control model is established in a standard voltage source, the multi-band load signal is split into K time windows for alternate injection, and three-dimensional voltage-current-temperature synchronous time domain data of each time period is synchronously collected; Performing eigenvalue decomposition on the synchronized time domain data in a phase space coordinate system, calculating the power spectrum density weight factor of each dominant mode, and generating a frequency-impedance mapping table; A family of variable operating condition resistance characteristic curves including a four-dimensional relationship among frequency, temperature, current and voltage is established according to the frequency-impedance mapping table to obtain the resistance change data.

[0033] In the above embodiment, in order to simulate the resistance change of the busbar under different working conditions, an N-dimensional electrical parameter space of the busbar is constructed, which can fully describe the various electrical states and parameters that may appear in the busbar during operation. Each dimension in the N-dimensional electrical parameter space represents a different electrical parameter, such as current, voltage, temperature, load, etc. By comprehensively modeling the electrical characteristics of the busbar, it can be ensured that all possible working states are covered during the simulation test, so that more accurate resistance change data can be obtained. After constructing the parameter space, M-level load configurations are performed on each coordinate axis of the space. The load configuration on each coordinate axis represents a different working condition. The purpose of each load configuration is to simulate a different working environment, and to obtain the performance changes of the busbar under these conditions through these load configurations. By configuring the load on each coordinate axis, an M×N-dimensional load parameter matrix is ​​generated, which contains all possible load configuration combinations to form a comprehensive test matrix. The load parameter matrix is ​​decomposed in multiple frequency bands based on the discrete cosine transform (DCT) algorithm. The purpose of this process is to convert signals under different load configurations into signals of multiple frequency bands, so that the electrical responses under different frequencies can be analyzed separately. The DCT algorithm is a signal transformation technology that can convert complex signals into frequency components. In this process, it can effectively reduce redundant information and improve computational efficiency. Through discrete cosine transform, the signal of the load parameter matrix can be decomposed into multiple frequency bands, each of which contains electrical characteristic information within a different frequency range, and a phase offset is applied to each frequency band. The application of phase offset is to simulate the dynamic changes of electrical signals in the actual working environment. By applying a phase offset to each frequency band according to a preset spectrum ratio, a multi-band load signal with a dynamic phase shift relationship can be generated. In the standard voltage source, the purpose of establishing a time series control model is to control the injection process of the load signal. Through this model, the multi-band load signal can be decomposed into a time series and split into K time windows for alternating injection. The signals in these time windows will be injected into the busbar in different time periods. Each time window represents a specific working state, and the signals in these time periods will be collected synchronously. The parameters collected synchronously include three-dimensional voltage, current and temperature data, which will serve as a direct reflection of the electrical characteristics of the busbar under different load conditions. By collecting synchronous time domain data, eigenvalue decomposition is performed on the synchronous time domain data in the phase space coordinate system. Eigenvalue decomposition can represent complex signals as a combination of different modes, and the power spectrum density of different modes can be extracted, thereby identifying the electrical characteristics of the busbar at different frequencies. The results of eigenvalue decomposition can be used to calculate the power spectrum density weight factors of each dominant mode. These weight factors reflect the contribution of each frequency band to the overall signal. The calculated weight factors can be used to generate a frequency-impedance mapping table, which describes the resistance characteristics of the busbar at different frequencies.By using the frequency-impedance mapping table, a family of variable operating condition resistance characteristic curves including the four-dimensional relationship among frequency, temperature, current and voltage can be established, thereby obtaining the resistance variation data.

[0034] In one embodiment, the voltage acquisition unit measures the voltage signal of each battery cell in the busbar, obtains the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and corrects the amplitude resonance point according to the resistance change data to obtain the stress response data, including: Perform multi-channel segmented sampling and time-domain synchronous processing on the voltage signal collected by the voltage collection unit to generate a voltage fluctuation sequence with time alignment for each battery cell; According to the time domain waveform of the voltage fluctuation sequence, a multi-scale frequency domain transform is performed on the voltage signal of each battery cell, characteristic points of the frequency band whose amplitude-frequency response exceeds a preset threshold are extracted, and a frequency domain amplitude distribution curve is generated; Based on the peak-trough topological relationship of the frequency domain amplitude distribution curve, a characteristic frequency interval that satisfies amplitude monotonicity and continuous phase change is selected, and the frequency point corresponding to the maximum amplitude in the characteristic frequency interval is marked as an initial amplitude resonance point; Constructing a sliding window with a preset number of bits, controlling the sliding window to slide at the initial amplitude resonance point, and generating a denoised steady-state amplitude resonance point sequence; According to the resistance slope corresponding to each voltage-current combination condition in the resistance change data, the steady-state amplitude resonance point sequence is corrected by piecewise linear interpolation to generate a corrected resonance point coordinate set dynamically matching the busbar load state; The frequency offset in the modified resonance point coordinate set is cross-correlatedly calculated with the equivalent mechanical stiffness parameter of the corresponding battery cell point by point to generate multi-dimensional stress response data.

[0035] In the above embodiment, the voltage acquisition unit measures the voltage signals of each battery cell in the busbar, and these voltage signals reflect the electrical behavior and response of the battery cell under different load conditions. By analyzing the fluctuation information of these voltage signals, the amplitude resonance point of each battery cell can be obtained. The amplitude resonance point refers to the specific amplitude fluctuation exhibited by the battery cell voltage signal during the oscillation process. At these points, the electrical response of the battery cell will fluctuate greatly, and then the correction processing is performed. The correction processing first performs multi-channel segmented sampling and time domain synchronization processing on the voltage signal collected by the voltage acquisition unit to make the voltage signals from each battery cell consistent in time. Through segmented sampling, the signal can be divided into multiple small intervals. The time domain synchronization processing ensures that these signals can be correctly aligned in time, and generates a time-aligned voltage fluctuation sequence for each battery cell. Based on these time-aligned voltage fluctuation sequences, a multi-scale frequency domain transformation method is used to process the voltage signal of each battery cell, which can analyze the frequency components of the voltage signal from different scales and identify the amplitude-frequency response feature points in different frequency bands. By extracting these characteristic points, the frequency domain amplitude distribution curve can be obtained, which shows the amplitude changes in each frequency band. By analyzing the topological relationship between the peaks and troughs of the frequency domain amplitude distribution curve, the characteristic frequency intervals that meet the amplitude monotonicity and the continuous phase change can be screened out. By selecting these frequency intervals, the frequency points with the largest amplitude or significant changes in the frequency domain amplitude distribution curve can be marked. A sliding window with a preset number of bits is constructed, and a more stable amplitude resonance point sequence is obtained by sliding near the initial amplitude resonance point and denoising the data, which can reduce the interference components in the signal, so that the obtained steady-state amplitude resonance point more truly reflects the resonance characteristics of the battery cell under different load conditions. After sliding window processing, a denoised steady-state amplitude resonance point sequence is generated. By calculating the resistance slope under the resistance change data, the dynamic law of resistance change with voltage and current can be obtained. By combining these resistance slopes with the steady-state amplitude resonance point sequence, the amplitude resonance point can be corrected by piecewise linear interpolation to generate a corrected resonance point coordinate set that dynamically matches the busbar load state. The mechanical stiffness of a battery cell is a physical property of the battery cell during the stress process, which can reflect the stress response characteristics of the battery cell. By cross-calculating the frequency offset and the mechanical stiffness parameters, multi-dimensional stress response data can be obtained.

[0036] In one embodiment, the step of obtaining the strain energy density of the stress response data on the busbar and dividing the busbar into a plurality of partitions based on the strain energy density comprises: Performing time-frequency phase coherence processing on the stress response data, extracting the phase shift spectrum of the strain pulse, and generating a multi-band correlation spectrum including the main frequency band energy distribution; Taking the busbar surface grid vertices as nodes, generating node weights according to the energy values ​​of each main frequency band in the multi-band association spectrum, and generating a topological network model based on the node weights; Calculating the amplitude change rate of the stress gradient tensor along the normal vector direction of each grid unit in the topological network model, and generating a parametric line cluster based on the amplitude change rate; Constructing a rectangular bounding box based on the parametric line cluster, calculating the average strain energy density in the rectangular bounding box, and taking the rectangular bounding box with an average strain energy density higher than a threshold as a preliminary partition boundary; Connecting the boundaries of the adjacent preliminary partitions to form a continuous closed area, and merging the adjacent closed areas whose spacing is less than a preset minimum gap to form a plurality of preliminary partitions; Scan the number of intersections of each of the preliminary partitions. If the number of intersections is an odd number, split the corresponding preliminary partition into sub-partitions until there are no abnormal intersections, thereby forming multiple partitions.

[0037] In the above embodiment, after obtaining the stress response data, the data is processed for time-frequency phase coherence, the phase shift information of the strain pulse is extracted, and a multi-band correlation spectrum is constructed based on this information. The spectrum shows the distribution of energy in different frequency bands, reflecting the stress response characteristics of the busbar in different frequency ranges. Through the energy values ​​of each main frequency band in the multi-band correlation spectrum, a weight value is assigned to each grid vertex. The node weight reflects the energy response size of each grid point in different frequency bands. The entire busbar surface is constructed as a topological network model. Each node in the network represents a grid vertex on the busbar surface, and the node weight represents the energy response of the grid vertex. The stress gradient tensor describes the rate of stress change on the busbar surface, and the amplitude change rate represents the degree of stress change between different grid units. The stress distribution characteristics of different areas on the busbar surface are analyzed, and it is determined which areas have a large stress change. Based on the calculated amplitude change rate, a parametric cluster can be generated. A rectangular bounding box is constructed based on the parametric cluster to divide and enclose the areas with higher strain energy density on the busbar surface. By calculating the average strain energy density in each rectangular bounding box, it can be determined which areas have a higher strain energy density. If the average strain energy density of a region exceeds the preset threshold, the region is considered to have a large strain energy. The rectangular bounding boxes containing the higher strain energy density are used as preliminary partition boundaries, and the adjacent preliminary partition boundaries are connected to form a continuous closed area. If the distance between two preliminary partitions is less than the preset minimum gap, the two adjacent closed areas are merged to form a larger partition. Scan the number of intersections of each partition. If the number of intersections of a preliminary partition is an odd number, it means that there are irregular intersections in the region, and the partition needs to be further split into sub-partitions to ensure the stability and rationality of the partition. This method can avoid abnormal partitions caused by uneven distribution of intersections, and finally form multiple reasonable partitions.

[0038] In one embodiment, the step of performing thermal effect mapping tests on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data includes: Based on a pulse current source, a step current load is periodically injected into the surface and internal reference points of the partitions, and a temperature acquisition unit synchronously acquires a temperature time series of the surface and internal reference points of each partition at a preset sampling period; Performing frequency domain feature decomposition on the temperature fluctuation of each partition according to the temperature time series to generate a frequency domain feature vector of each partition; Calculating the phase correlation coefficient in each of the frequency domain feature vectors, and adding an attenuation mark to those with a correlation coefficient lower than a preset coefficient; The frequency domain marked with the attenuation mark is low-pass filtered to obtain a temperature propagation curve, and a thermal conductivity matrix matching the physical structure of the busbar is generated based on the temperature propagation curve.

[0039] In the above embodiment, a step current load is periodically injected into the surface and internal reference points of the partition based on a pulse current source to simulate the thermal effect reaction of the busbar under actual working conditions. When the current passes through the busbar, due to the resistance effect, the current will be converted into heat. At this time, the change in temperature distribution will reflect the thermal conductivity of the busbar. Therefore, in this way, the busbar can be stimulated to produce temperature changes in different areas. The temperature acquisition unit is controlled to synchronously collect the temperature data of the surface and internal reference points of each partition at a preset sampling period to generate a corresponding temperature time series. Frequency domain feature decomposition is a method of converting temperature data in a time series into a frequency domain expression through methods such as Fourier transform. By frequency domain decomposition of the temperature time series, the frequency domain feature vector of each partition can be generated. These feature vectors contain the response characteristics of each partition at different frequencies. The phase correlation coefficient can describe the phase relationship between different frequency components, that is, whether the temperature fluctuations in different frequency bands are synchronous. If the phase relationship of some frequency components is relatively consistent, it indicates that these components may represent a relatively consistent mode of the busbar during the heat conduction process. On the contrary, if the phase relationship is relatively loose, it may indicate that these frequency components have a low correlation with the overall heat conduction response of the busbar. By calculating the phase correlation coefficient, we can determine which frequency components are more important in the heat conduction process and which components have a smaller effect. By setting a preset correlation coefficient threshold, those frequency components below the threshold are marked as attenuation components, and the frequency domain of these attenuation marks is low-pass filtered to retain only the low-frequency components that play a major role in temperature propagation. The temperature propagation curve obtained by low-pass filtering can describe the contribution of different frequency components to temperature changes and reveal the propagation characteristics of temperature from one area to another. The thermal conductivity matrix is ​​an important parameter for describing the thermal conductivity of materials. It reflects the efficiency of heat transfer between the various partitions of the busbar. By matching the physical structure of the busbar, the thermal conductivity matrix can accurately describe the thermal conduction characteristics of each partition.

[0040] In one embodiment, the step of constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell includes: Acquire phase information of each frequency component in the stress response data, and acquire a basic frequency range of the alternating stress load according to the phase information; Selecting a plurality of alternating stress cycles according to the basic frequency range, setting parameters for the alternating stress loads of different cycles respectively, and constructing an alternating stress test sequence; Calculating the temperature change of the battery cell in each cycle based on the alternating stress test sequence to obtain a periodic temperature change curve; Combining the temperature change curve with the stress response data to generate dynamic composite stress data, and calculating the damage evolution path of each battery cell according to the thermal conductivity matrix of the dynamic composite stress data; The cycle fatigue life of each battery cell is predicted based on the damage evolution path, and a life distribution curve is generated.

[0041] In the above embodiment, the phase information of each frequency component is extracted from the stress response data. Through this phase information, the changing law of each frequency component in the stress response can be understood, and the basic frequency range of the alternating stress load can be determined. In other words, it can be clear in which frequency range the stress fluctuation will have a more significant impact on the battery cell. According to this information, multiple alternating stress cycles are selected, and for each selected cycle, the parameters of the alternating stress load are set according to its specific characteristics. These parameter settings include factors such as the amplitude, cycle, and action time of the stress to ensure that the test sequence can truly reproduce the actual working conditions faced by the battery cell. By setting these parameters, an alternating stress test sequence suitable for different working environments can be constructed. Based on the load conditions in the alternating stress sequence, by establishing a heat conduction model, the temperature change of the battery cell in each cycle can be simulated to obtain a periodic temperature change curve, which reflects the temperature change law of the battery cell over time under the action of alternating stress. The temperature change curve is combined with the stress response data to generate dynamic composite stress data. The composite data contains dual information of temperature change and stress change, which can fully reflect the comprehensive response of the battery cell under different loads and thermal effects. The thermal conductivity matrix is ​​a matrix that characterizes the thermal conductivity of materials. It can reflect the efficiency of heat transfer between different parts. By combining with temperature change and stress data, more accurate simulation results can be provided for the damage evolution path of the battery cell. The damage evolution path refers to the evolution of the damage degree of the internal material of the battery cell over time after the battery cell has experienced a series of stresses and heat loads. This path can help predict the health state of the battery cell at a certain moment in the future and determine whether it will fail prematurely due to fatigue damage. Cyclic fatigue life refers to the number of cycles that the battery cell can continue to work during repeated loading and unloading. Through the simulation of the damage evolution path, the life of the battery cell can be accurately predicted and a life distribution curve can be generated. The life distribution curve is a curve obtained after statistical analysis of the life of multiple battery cells, which can reflect the life differences of different battery cells under the same working conditions.

[0042] In one embodiment, the step of evaluating the remaining life and potential failure points of each battery cell based on the life distribution curve and generating a performance test report of the integrated busbar includes: Acquire cycle fatigue life data of each battery cell in the life distribution curve, and determine the remaining life of each battery cell according to the cycle fatigue life data; Based on a clustering algorithm, the cyclic fatigue life data is divided into multiple categories to obtain fatigue degree sets of different categories; Propose a variation trend of fatigue degree in each category, and identify an abnormal fluctuation area in the life distribution curve based on the variation trend; The battery cells corresponding to the abnormal fluctuation area are marked as potential failure points, and a performance test report including the remaining life of each battery cell and the potential failure points is generated.

[0043] In the above embodiment, the life distribution curve is generated by the above process, reflecting the fatigue characteristics and durability of each battery cell under the action of alternating stress. By extracting these data, the specific cycle fatigue life of each battery cell can be understood, that is, the maximum number of cycles they can continue to work under repeated stress. According to the cycle fatigue life data obtained from the life distribution curve, based on the difference between the current working condition and the maximum cycle fatigue life of the battery cell. For example, if a battery cell has worked for a certain number of cycles, its remaining life can be obtained by subtracting the number of cycles used from the maximum life. The accuracy of this data directly depends on the prediction result of fatigue life, so the accuracy and reliability of the above life curve must be ensured. The cycle fatigue life data is classified by clustering algorithm, and the cycle fatigue life data of each battery cell is divided into multiple categories, each category represents the performance of a class of batteries in fatigue tolerance. The clustered results can form multiple fatigue sets, and the batteries in each set have a certain degree of similarity in cycle fatigue life. In each category, based on the change trend of fatigue data, its changes over time or use cycle are analyzed. These changing trends can usually reveal the aging process of the battery cell or its performance degradation under stress environment. For example, in a certain category, if the fatigue of most battery cells shows a rapid upward trend, the battery cells in this category will face a higher risk of failure at some point in the future. Therefore, based on these trend changes, abnormal fluctuation areas in the life distribution curve can be identified. These abnormal fluctuation areas indicate that the battery cells have abnormal fatigue or performance degradation under specific working conditions. Through in-depth analysis of these changing trends, the areas with abnormal fluctuations in the life distribution curve can be marked, and the battery cells corresponding to these abnormal fluctuation areas can be used as potential failure points. Finally, based on the results of the above analysis, a performance test report containing the remaining life and potential failure points of each battery cell can be generated. The report lists the remaining life of each battery cell, and also marks and analyzes the potential failure points, providing users with comprehensive information about the health status of the battery cell, which helps decision makers take appropriate maintenance or replacement measures, thereby ensuring the reliability and stability of the FDC integrated busbar system in long-term use. Through such a test method, it can help manufacturers or users more accurately predict the performance degradation trend of the battery cell, optimize the maintenance cycle, reduce the failure rate, and improve the overall quality of the product.

[0044] Reference Figure 3 , a test device for an FDC integrated busbar, comprising: The simulation module 100 is used to simulate the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions; The measuring module 200 is used to measure the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, obtain the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correct the amplitude resonance point according to the resistance change data to obtain stress response data; An acquisition module 300 is used to acquire the strain energy density of the stress response data on the busbar, and divide the busbar into a plurality of partitions based on the strain energy density; A construction module 400 is used to perform a thermal effect mapping test on the plurality of partitions, and obtain temperature data of each partition based on the temperature acquisition unit, and construct a thermal conductivity matrix based on the temperature data; The analysis module 500 is used to construct an alternating stress test sequence, and simulate and analyze the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; The generation module 600 is used to evaluate the remaining life and potential failure points of each battery cell based on the life distribution curve, and generate a performance test report of the integrated busbar.

[0045] In this embodiment, by simulating the working conditions of the busbar under different load conditions based on a standard voltage source, the resistance change data is obtained, and the amplitude resonance point of the battery cell is obtained according to the fluctuation information of the voltage signal, and the accurate stress response data is further corrected and obtained. This process can fully consider the dynamic response of the battery busbar under different voltage and current conditions. The strain energy density calculated based on the stress response data can reasonably partition the busbar structure, thereby realizing the accurate mapping of the thermal effect in the local area, and constructing a thermal conductivity matrix through temperature data to effectively evaluate the heat transfer characteristics of each partition. The construction of an alternating stress test sequence can simulate the alternating load and temperature changes encountered by the battery busbar during actual use, and comprehensively simulate the coupling effects of factors such as stress, thermal effect, and thermal conductivity under actual working conditions, making up for the defect of the prior art that the dynamic working conditions cannot be fully reflected. Through the evaluation of the life distribution curve, the remaining life and potential fault points of the battery cell are predicted, and a detailed performance test report is generated, which not only provides a more accurate reliability evaluation basis for the battery management system, but also provides a scientific basis for early fault diagnosis and maintenance of the battery system.

[0046] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data and other information related to this application. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a test method for an FDC integrated busbar is implemented.

[0047] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a test method for an FDC integrated busbar is implemented, including the following steps: simulating the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions; measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, obtaining the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain stress response data; obtaining the strain energy density of the stress response data on the busbar, and dividing the busbar into a plurality of partitions based on the strain energy density; performing thermal effect mapping tests on the plurality of partitions, and obtaining the temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; constructing an alternating stress test sequence, and simulating and analyzing the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; evaluating the remaining life and potential failure points of each battery cell based on the life distribution curve, and generating a performance test report for the integrated busbar.

[0048] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for testing an FDC integrated busbar, characterized in that: The FDC integrated busbar includes a voltage acquisition unit and a temperature acquisition unit, and the testing method includes: Simulating the working conditions of the busbar under different load conditions based on a standard voltage source to obtain resistance change data of the busbar under different voltage and current conditions; Measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, acquiring the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain stress response data; Acquire the strain energy density of the stress response data on the busbar, and divide the busbar into a plurality of partitions based on the strain energy density; Performing a thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; Constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; Based on the life distribution curve, the remaining life and potential failure points of each battery cell are evaluated, and a performance test report of the integrated busbar is generated.

2. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of simulating the working conditions of the busbar under different load conditions based on the standard voltage source to obtain the resistance change data of the busbar under different voltage and current conditions includes: Constructing an N-dimensional electrical parameter space of the busbar, performing M-level load configuration on each coordinate axis of the N-dimensional electrical parameter space, and generating an M×N-dimensional load parameter matrix; Based on the discrete cosine transform algorithm, the load parameter matrix is ​​decomposed into multiple frequency bands, a phase offset is applied to each frequency band according to a preset spectrum ratio, and a multi-band load signal with a dynamic phase shift relationship is generated; A time series control model is established in a standard voltage source, the multi-band load signal is split into K time windows for alternate injection, and three-dimensional voltage-current-temperature synchronous time domain data of each time period is synchronously collected; Performing eigenvalue decomposition on the synchronized time domain data in a phase space coordinate system, calculating the power spectrum density weight factor of each dominant mode, and generating a frequency-impedance mapping table; A family of variable operating condition resistance characteristic curves including a four-dimensional relationship among frequency, temperature, current and voltage is established according to the frequency-impedance mapping table to obtain the resistance change data.

3. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of measuring the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, acquiring the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correcting the amplitude resonance point according to the resistance change data to obtain the stress response data includes: Perform multi-channel segmented sampling and time-domain synchronous processing on the voltage signal collected by the voltage collection unit to generate a voltage fluctuation sequence with time alignment for each battery cell; According to the time domain waveform of the voltage fluctuation sequence, a multi-scale frequency domain transform is performed on the voltage signal of each battery cell, characteristic points of the frequency band whose amplitude-frequency response exceeds a preset threshold are extracted, and a frequency domain amplitude distribution curve is generated; Based on the peak-trough topological relationship of the frequency domain amplitude distribution curve, a characteristic frequency interval that satisfies amplitude monotonicity and continuous phase change is selected, and the frequency point corresponding to the maximum amplitude in the characteristic frequency interval is marked as an initial amplitude resonance point; Constructing a sliding window with a preset number of bits, controlling the sliding window to slide at the initial amplitude resonance point, and generating a denoised steady-state amplitude resonance point sequence; According to the resistance slope corresponding to each voltage-current combination condition in the resistance change data, the steady-state amplitude resonance point sequence is corrected by piecewise linear interpolation to generate a corrected resonance point coordinate set dynamically matching the busbar load state; The frequency offset in the modified resonance point coordinate set is cross-correlatedly calculated with the equivalent mechanical stiffness parameter of the corresponding battery cell point by point to generate multi-dimensional stress response data.

4. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of obtaining the strain energy density of the stress response data on the busbar and dividing the busbar into a plurality of partitions based on the strain energy density comprises: Performing time-frequency phase coherence processing on the stress response data, extracting the phase shift spectrum of the strain pulse, and generating a multi-band correlation spectrum including the main frequency band energy distribution; Taking the busbar surface grid vertices as nodes, generating node weights according to the energy values ​​of each main frequency band in the multi-band association spectrum, and generating a topological network model based on the node weights; Calculating the amplitude change rate of the stress gradient tensor along the normal vector direction of each grid unit in the topological network model, and generating a parametric line cluster based on the amplitude change rate; Constructing a rectangular bounding box based on the parametric line cluster, calculating the average strain energy density in the rectangular bounding box, and taking the rectangular bounding box with an average strain energy density higher than a threshold as a preliminary partition boundary; Connecting the boundaries of the adjacent preliminary partitions to form a continuous closed area, and merging the adjacent closed areas whose spacing is less than a preset minimum gap to form a plurality of preliminary partitions; Scan the number of intersections of each of the preliminary partitions. If the number of intersections is an odd number, split the corresponding preliminary partition into sub-partitions until there are no abnormal intersections, thereby forming multiple partitions.

5. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of performing thermal effect mapping tests on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data includes: Based on a pulse current source, a step current load is periodically injected into the surface and internal reference points of the partitions, and a temperature acquisition unit synchronously acquires a temperature time series of the surface and internal reference points of each partition at a preset sampling period; Performing frequency domain feature decomposition on the temperature fluctuation of each partition according to the temperature time series to generate a frequency domain feature vector of each partition; Calculating the phase correlation coefficient in each of the frequency domain feature vectors, and adding an attenuation mark to those with a correlation coefficient lower than a preset coefficient; The frequency domain marked with the attenuation mark is low-pass filtered to obtain a temperature propagation curve, and a thermal conductivity matrix matching the physical structure of the busbar is generated based on the temperature propagation curve.

6. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell includes: Acquire phase information of each frequency component in the stress response data, and acquire a basic frequency range of the alternating stress load according to the phase information; Selecting a plurality of alternating stress cycles according to the basic frequency range, setting parameters for the alternating stress loads of different cycles respectively, and constructing an alternating stress test sequence; Calculating the temperature change of the battery cell in each cycle based on the alternating stress test sequence to obtain a periodic temperature change curve; Combining the temperature change curve with the stress response data to generate dynamic composite stress data, and calculating the damage evolution path of each battery cell according to the thermal conductivity matrix of the dynamic composite stress data; The cycle fatigue life of each battery cell is predicted based on the damage evolution path, and a life distribution curve is generated.

7. The FDC integrated busbar testing method according to claim 1, characterized in that: The step of evaluating the remaining life and potential failure points of each battery cell based on the life distribution curve and generating a performance test report of the integrated busbar includes: Acquire cycle fatigue life data of each battery cell in the life distribution curve, and determine the remaining life of each battery cell according to the cycle fatigue life data; Based on a clustering algorithm, the cyclic fatigue life data is divided into multiple categories to obtain fatigue degree sets of different categories; Propose a variation trend of fatigue degree in each category, and identify an abnormal fluctuation area in the life distribution curve based on the variation trend; The battery cells corresponding to the abnormal fluctuation area are marked as potential failure points, and a performance test report including the remaining life of each battery cell and the potential failure points is generated.

8. A testing device for an FDC integrated busbar, applied to the method according to any one of claims 1 to 7, characterized in that: include: A simulation module, used to simulate the working conditions of the busbar under different load conditions based on a standard voltage source, and obtain resistance change data of the busbar under different voltage and current conditions; A measurement module, configured to measure the voltage signal of each battery cell in the busbar according to the voltage acquisition unit, obtain the amplitude resonance point of each battery cell based on the fluctuation information of the voltage signal, and correct the amplitude resonance point according to the resistance change data to obtain stress response data; An acquisition module, configured to acquire the strain energy density of the stress response data on the busbar, and divide the busbar into a plurality of partitions based on the strain energy density; A construction module, used for performing a thermal effect mapping test on the plurality of partitions, acquiring temperature data of each partition based on the temperature acquisition unit, and constructing a thermal conductivity matrix based on the temperature data; An analysis module, used for constructing an alternating stress test sequence, and performing simulation analysis on the stress response data and the thermal conductivity matrix based on the alternating stress test sequence to obtain a life distribution curve of each battery cell; A generation module is used to evaluate the remaining life and potential failure points of each battery cell based on the life distribution curve and generate a performance test report of the integrated busbar.

9. A computer device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images