Capacity automatic detection device and detection method for new energy capacitor

Abstract

The invention discloses an automatic capacity detection device and method for a new energy capacitor, and relates to the technical field of electrical variable measurement, and the device comprises a temperature analysis module which is used for building a capacity-temperature mapping model; the temperature compensation module is used for generating temperature coefficient compensation; the aging analysis module is used for capturing the slope change and relaxation time offset of the voltage platform; the aging compensation module is used for generating aging coefficient compensation of capacity detection; the coupling analysis module is used for generating multi-physics field joint parameters, constructing a nonlinear regression equation according to the multi-physics field joint parameters, and constructing an intelligent decoupling model according to the nonlinear regression equation; the parameter decoupling module is used for obtaining the dynamic coupling parameter for decoupling to obtain a decoupling capacity value; and the capacity calculation module is used for performing coefficient compensation on the decoupling capacity value according to the temperature coefficient compensation and the aging coefficient compensation to obtain a target capacity value. The method has the effect of improving the accuracy of capacity detection of the new energy capacitor.

Description

Technical Field

[0001] This application relates to the technical field of measuring electrical variables, and in particular to an automatic capacitance detection device and detection method for new energy capacitors. Background Art

[0002] New energy capacitors refer to capacitors used in the new energy field, mainly for energy storage and energy conversion. Common applications of new energy capacitors include energy storage systems, power balancing, etc. They are used to store and release energy, make up for the difference between energy generation and consumption, and improve the stability and reliability of the system. Therefore, in order to ensure that the designed value and actual value of the capacitor capacitance meet the requirements, it is necessary to detect the capacitance of new energy capacitors.

[0003] In the prior art, when testing the capacitance of new energy capacitors, the capacitance is directly calculated according to the circuit, lacking various influencing factors generated during the charging and discharging process of the capacitor. For example, in a non-constant temperature environment, the capacitance of the capacitor will change violently with temperature, resulting in misjudgment of the capacitance. At the same time, when existing detection equipment detects various parameters of the capacitor such as internal resistance, leakage current, polarization voltage, etc., there is dynamic coupling between the parameters, resulting in systematic errors. In addition, traditional capacitance detection will misjudge electrode aging as normal capacitance decline, resulting in inaccurate detection accuracy. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic capacitance detection device and method for new energy capacitors to solve the problems raised in the above background art.

[0005] In a first aspect, the present application provides an automatic capacitance detection device for new energy capacitors, and the device includes: A temperature analysis module: used to establish a gradient temperature environment, detect the capacitance of the capacitor according to the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model according to the capacitance change law; A temperature compensation module: used to obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution; An aging analysis module: used to obtain the charge and discharge data of the capacitor during the detection process, obtain the charge and discharge curve characteristics according to the charge and discharge data, and capture the change in the slope of the voltage platform and the shift of the relaxation time according to the charge and discharge curve characteristics; An aging compensation module: used to obtain the electrode aging parameters of the capacitor according to the change in the slope of the voltage platform and the shift of the relaxation time, and generate an aging coefficient compensation for capacitance detection according to the electrode aging parameters; Coupling analysis module: used to obtain voltage data, current data, temperature data, and frequency response during the charging and discharging process of the capacitor, generate multi-physical field combined parameters based on the voltage data, the current data, the temperature data, and the frequency response, construct a non-linear regression equation based on the multi-physical field combined parameters, and construct an intelligent decoupling model based on the non-linear regression equation; Parameter decoupling module: used to obtain the dynamic coupling parameters between the capacitance value and the internal resistance value, leakage current, and polarization voltage during the detection process of the capacitor, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value; Capacitance calculation module: used to perform coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value.

[0006] Preferably, the steps of performing capacitance detection on the capacitor according to the gradient temperature environment, obtaining the capacitance change law under different temperature conditions, and establishing a capacitance-temperature mapping model are specifically as follows: According to the gradient temperature environment, place the capacitor in preset different temperature environments for initial capacitance detection to obtain multiple initial capacitance data; Generate a temperature-capacitance correspondence table according to the gradient temperature environment and multiple initial capacitance data, and obtain the capacitance change law according to the temperature-capacitance correspondence table; Based on the capacitance change law, establish a capacitance-temperature curve, and obtain the curve change characteristics according to the capacitance-temperature curve; According to the curve change characteristics, obtain the change characteristics and change correspondence relationship of the capacitance changing with temperature, and establish a capacitance-temperature mapping model according to the change characteristics and the change correspondence relationship.

[0007] Preferably, the steps of obtaining the surface temperature distribution of the capacitor during the detection process and generating the temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution are specifically as follows: Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature distribution display diagram according to the surface temperature distribution; Based on the temperature distribution display diagram, extract the high-temperature region and low-temperature region on the surface of the capacitor, as well as the high-temperature value and low-temperature value; Based on the high-temperature region and the low-temperature region, obtain the corresponding regional temperature influence, and perform weighted processing on the high-temperature value and the low-temperature value according to the regional influence to obtain the weighted high-temperature value and the weighted low-temperature value; Based on the weighted high-temperature value and the weighted low-temperature value, generate a weighted average temperature value, and substitute the weighted average temperature value into the capacitance-temperature mapping model to generate the temperature coefficient compensation.

[0008] Preferably, the steps of obtaining the charge-discharge curve characteristics based on the charge-discharge data and capturing the voltage platform slope change and relaxation time shift according to the charge-discharge curve characteristics are specifically as follows: Generate a charge-discharge data curve based on the charge-discharge data, perform feature extraction on the charge-discharge data curve to obtain charge-discharge curve characteristics; According to the charge-discharge curve characteristics, capture the voltage platform data and relaxation time data of the capacitor obtained by capturing the charge-discharge curve characteristics; Obtain voltage platform slope data based on the voltage platform data, and obtain the voltage platform slope change based on the voltage platform slope data; Extract the offset of the relaxation time based on the relaxation time data to obtain the relaxation time shift.

[0009] Preferably, the steps of obtaining the electrode aging parameters of the capacitor according to the voltage platform slope change and the relaxation time shift, and generating the aging coefficient compensation for capacity detection according to the electrode aging parameters are specifically as follows: Perform first parameter identification on the aging degree of the capacitor according to the voltage platform slope data to obtain the first identified aging parameter of the capacitor; Perform second parameter identification on the aging degree of the capacitor according to the relaxation time shift to obtain the second identified aging parameter of the capacitor; Integrate the first identified aging parameter and the second identified aging parameter to obtain the electrode aging parameter of the capacitor; According to the electrode aging parameter, obtain the aging capacity influence value of the electrode aging on the capacity of the capacitor, and generate the aging coefficient compensation according to the aging capacity influence value.

[0010] Preferably, the steps of generating multi-physical field joint parameters according to the voltage data, the current data, the temperature data, and the frequency response, constructing a non-linear regression equation according to the multi-physical field joint parameters, and constructing an intelligent decoupling model according to the non-linear regression equation are specifically as follows: Generate voltage physical field parameters according to the voltage data, generate current physical field parameters according to the current data, generate temperature physical field parameters according to the temperature data, and generate frequency physical field parameters according to the frequency response; Combine the voltage physical field parameters, the current physical field parameters, the temperature physical field parameters, and the frequency physical field parameters to generate physical field joint parameters; According to the physical field joint parameters, obtain the coupling correlation curve relationship between each physical field, and construct a non-linear regression equation according to the coupling correlation curve relationship; Construct a decoupling model framework based on the coupling correlation curve relationship, and generate an intelligent decoupling model by combining the non-linear regression equation and the decoupling model framework.

[0011] Preferably, the steps of obtaining the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage of the capacitor during the detection process, substituting the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtaining the decoupled capacitance value are specifically as follows: Obtain the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage of the capacitor during the detection process; Substitute the dynamic coupling parameters into the intelligent decoupling model, and the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters; Obtain the coupling change trend according to the dynamic coupling changes, perform reverse coupling disassembly on the dynamic coupling parameters based on the coupling change trend, and generate an initial capacitance value and a coupling capacitance compensation coefficient; Perform coupling compensation on the initial capacitance value according to the coupling capacitance compensation coefficient to obtain the decoupled capacitance value.

[0012] Preferably, the steps of performing coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value are specifically as follows: Couple and correlate the temperature coefficient compensation and the aging coefficient compensation to generate an initial coupling coefficient compensation; Obtain the coupling correlation relationship between temperature and aging in the capacitor, and generate a coupling adjustment parameter according to the coupling correlation relationship; Adjust the initial coupling coefficient compensation according to the coupling adjustment parameter to generate a combined coupling coefficient compensation, and perform data compensation on the decoupled capacitance value according to the combined coupling coefficient compensation to generate the target capacitance value.

[0013] In a second aspect, the present application provides a method for automatically detecting the capacitance of a new energy capacitor, and the method includes: Establish a gradient temperature environment, perform capacitance detection on the capacitor according to the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model according to the capacitance change law; Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution; Obtain the charge and discharge data of the capacitor during the detection process, obtain the charge and discharge curve characteristics according to the charge and discharge data, and capture the voltage platform slope change and relaxation time shift according to the charge and discharge curve characteristics; Obtain the electrode aging parameter of the capacitor based on the change in the voltage platform slope and the relaxation time offset, and generate an aging coefficient compensation for capacitance detection according to the electrode aging parameter. Obtain the voltage data, current data, temperature data, and frequency response during the charge and discharge process of the capacitor, generate a multi-physical field combined parameter according to the voltage data, the current data, the temperature data, and the frequency response, construct a non-linear regression equation according to the multi-physical field combined parameter, and construct an intelligent decoupling model according to the non-linear regression equation. Obtain the dynamic coupling parameters between the capacitance value, internal resistance value, leakage current, and polarization voltage during the detection process of the capacitor, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value. Perform coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value.

[0014] In summary, the present application includes at least one of the following beneficial technical effects: By establishing a gradient temperature environment, perform basic capacitance detection on the capacitor to obtain the capacitance change law under different temperature conditions, then establish a capacitance-temperature mapping model according to the capacitance change law, and then collect the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the surface temperature distribution and the capacitance-temperature mapping model. By obtaining the charge and discharge data of the capacitor during the detection process, obtain the characteristics of the charge and discharge curve, capture the change in the voltage platform slope and the relaxation time offset according to the characteristics of the charge and discharge curve, obtain the electrode aging parameter of the capacitor according to the change in the voltage platform slope and the relaxation time offset, and generate an aging coefficient compensation according to the capacitance aging parameter. Then obtain the voltage data, current data, temperature data, and frequency response during the charge and discharge process of the capacitor, generate a multi-physical combined parameter, construct a non-linear regression equation according to the multi-physical combined parameter, and use the non-linear regression equation to construct an intelligent decoupling model. Obtain the dynamic coupling parameters between the capacitance and each parameter during the detection process of the capacitor, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value. Perform compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value. Improve the accuracy of the capacitance detection of new energy capacitors. Description of the Drawings

[0015] Figure 1 is a block diagram of a capacity automatic detection device for new energy capacitors provided by the present application.

[0016] Figure 2 is a step flow chart of a capacity automatic detection method for new energy capacitors provided by the present application.

[0017] Description of reference numerals: 1. Temperature analysis module; 2. Temperature compensation module; 3. Aging analysis module; 4. Aging compensation module; 5. Coupling analysis module; 6. Parameter decoupling module; 7. Capacity calculation module. Detailed implementation manners

[0018] The following is combined with Figure 1 - Figure 2 to further elaborate on this application in detail, but the implementation manners of the present invention are not limited thereto.

[0019] The embodiment of this application discloses a capacity automatic detection device and method for new energy capacitors.

[0020] In this embodiment, a capacity automatic detection device for new energy capacitors, the device includes: Temperature analysis module 1: used to establish a gradient temperature environment, detect the capacity of the capacitor according to the gradient temperature environment, obtain the capacity change law under different temperature conditions, and establish a capacity-temperature mapping model according to the capacity change law; Temperature compensation module 2: used to obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacity-temperature mapping model and the surface temperature distribution; Aging analysis module 3: used to obtain the charge and discharge data of the capacitor during the detection process, obtain the charge and discharge curve characteristics according to the charge and discharge data, and capture the change in the slope of the voltage platform and the relaxation time offset according to the charge and discharge curve characteristics; Aging compensation module 4: used to obtain the electrode aging parameters of the capacitor according to the change in the slope of the voltage platform and the relaxation time offset, and generate an aging coefficient compensation for capacity detection according to the electrode aging parameters; Coupling analysis module 5: used to obtain voltage data, current data, temperature data and frequency response during the charge and discharge process of the capacitor, generate multi-physical field joint parameters according to the voltage data, current data, temperature data and frequency response, construct a non-linear regression equation according to the multi-physical field joint parameters, and construct an intelligent decoupling model according to the non-linear regression equation; Parameter decoupling module 6: used to obtain the dynamic coupling parameters between the capacity value and the internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacity value; Capacity calculation module 7: used to perform coefficient compensation on the decoupled capacity value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value.

[0021] It should be noted that the above modules are only the basic modules of this embodiment. During the specific implementation process, without affecting the overall implementation effect, some modules can be appropriately added, reduced or modified.

[0022] Perform capacitance detection on the capacitor according to the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model according to the capacitance change law. The specific steps are as follows: According to the gradient temperature environment, place the capacitor in different preset temperature environments for initial capacitance detection to obtain multiple initial capacitance data; According to the gradient temperature environment and multiple initial capacitance data, generate a temperature-capacitance correspondence table, and obtain the capacitance change law according to the temperature-capacitance correspondence table; Based on the capacitance change law, establish a capacitance-temperature curve, and obtain the curve change characteristics according to the capacitance-temperature curve; According to the curve change characteristics, obtain the change characteristics and change correspondence relationship of the capacitance changing with temperature, and establish a capacitance-temperature mapping model according to the change characteristics and the change correspondence relationship.

[0023] In application, take a new energy capacitor of a certain brand used for two years as an example. According to the setting requirements of the gradient temperature environment, place the capacitor in a constant temperature oven at -20°C, 0°C, 25°C, 40°C, and 60°C in sequence. Keep it for 30 minutes in each temperature environment. After the capacitor temperature stabilizes, use a standard capacitance detection device to perform initial capacitance detection. The measured capacitance is 980 mAh at -20°C, 995 mAh at 0°C, 1020 mAh at 25°C, 1010 mAh at 40°C, and 990 mAh at 60°C. Generate a temperature-capacitance correspondence table based on these data, showing that the capacitance first increases and then decreases as the temperature rises. Draw a capacitance-temperature curve based on this table and find that the curve shows an upward slope of 0.5 mAh / °C in the range of 0°C to 40°C and a downward slope of -0.25 mAh / °C in the range of 40°C to 60°C. Extract the slope changes in these two ranges as the curve characteristics and establish a mapping relationship of the capacitance changing with temperature: when the temperature is below 40°C, the capacitance increases by 0.5 mAh for every 1°C increase, and when it is above 40°C, the capacitance decreases by 0.25 mAh for every 1°C increase.

[0024] Obtain the surface temperature distribution of the capacitor during the detection process, and generate the temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution. The specific steps are as follows: Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature distribution display diagram according to the surface temperature distribution; Based on the temperature distribution display diagram, extract the high-temperature area and low-temperature area on the capacitor surface, as well as the high-temperature value and low-temperature value; Based on the high-temperature area and low-temperature area, obtain the corresponding regional temperature influence, and perform weighted processing on the high-temperature value and low-temperature value according to the regional influence to obtain the weighted high-temperature value and weighted low-temperature value; Generate a weighted average temperature value based on the weighted high temperature value and the weighted low temperature value, and substitute the weighted average temperature value into the capacitance-temperature mapping model to generate temperature coefficient compensation.

[0025] In operation, taking a new energy capacitor used by a certain brand for two years as an example, during the detection process, an infrared thermal imager is used to scan its surface, and a surface temperature distribution map is generated, showing that the temperature of the left half of the capacitor is 48 °C (high temperature area), and the temperature of the right half is 32 °C (low temperature area). The area ratio of the high temperature area is 30%, and the low temperature area accounts for 70%. According to the equipment technical manual, the influence weight of the high temperature area on capacitance detection is 0.7, and the weight of the low temperature area is 0.3. Calculate the weighted high temperature value as 48 °C × 0.7 = 33.6, the weighted low temperature value as 32 °C × 0.3 = 9.6, and the weighted average temperature value is 43.2 °C. Substitute this temperature value into the capacitance-temperature mapping model established in claim 2 to calculate the temperature coefficient compensation: at 43.2 °C, the theoretical capacitance should be 1020 mAh - (43.2 - 40) × 0.25 = 1019.2 mAh, while the measured value is 1015 mAh, generating a temperature compensation coefficient of +4.2 mAh.

[0026] Steps for obtaining the charge-discharge curve characteristics based on the charge-discharge data and capturing the change in the voltage platform slope and the relaxation time offset according to the charge-discharge curve characteristics, specifically: Generate a charge-discharge data curve based on the charge-discharge data, perform feature extraction on the charge-discharge data curve to obtain the charge-discharge curve characteristics; According to the charge-discharge curve characteristics, capture the voltage platform data and relaxation time data of the capacitor obtained by capturing the charge-discharge curve characteristics; Obtain the voltage platform slope data based on the voltage platform data, and obtain the change in the voltage platform slope based on the voltage platform slope data; Extract the offset of the relaxation time based on the relaxation time data to obtain the relaxation time offset.

[0027] In operation, taking a new energy capacitor of a certain brand used for two years as an example, in the 1A constant current charge and discharge test, it was recorded that it took 120 seconds for the voltage to rise from 0V to 2.5V during the charging stage, and 95 seconds for the voltage to drop from 2.5V to 1.8V during the discharging stage. After generating the charge and discharge curve, the slope of the charging voltage platform (in the range of 2.3V - 2.5V) was extracted as 0.016V / s, which decreased by 20% compared with the standard value of 0.02V / s; the slope of the discharging voltage platform (in the range of 2.1V - 1.8V) was -0.028V / s, which deviated by 12% compared with the standard value of -0.025V / s. At the same time, the relaxation time (the time required for the voltage to fall back to 2.0V) after charging was detected as 45ms, which increased by 50% compared with the standard value of 30ms; the discharging relaxation time was 38ms, which increased by 52% compared with the standard value of 25ms. These data together indicate that there are problems of aging of the electrode active material and deterioration of the electrolyte in the capacitor.

[0028] Steps for obtaining the electrode aging parameters of the capacitor based on the change of the voltage platform slope and the deviation of the relaxation time, and generating the aging coefficient compensation for capacity detection according to the electrode aging parameters are as follows: Perform the first parameter identification on the aging degree of the capacitor according to the voltage platform slope data to obtain the first identified aging parameter of the capacitor; Perform the second parameter identification on the aging degree of the capacitor according to the relaxation time deviation to obtain the second identified aging parameter of the capacitor; Integrate the first identified aging parameter and the second identified aging parameter to obtain the electrode aging parameter of the capacitor; According to the electrode aging parameter, obtain the aging capacity influence value of the electrode aging on the capacity of the capacitor, and generate the aging coefficient compensation according to the aging capacity influence value.

[0029] In operation, taking a new energy capacitor of a certain brand used for two years as an example, according to the data that the slope of the charging voltage platform decreased by 20%, match the aging parameter comparison table, and determine that the loss rate of the electrode active material is 18%; according to the data that the discharging relaxation time increased by 50%, determine that the conductivity of the electrolyte decreased by 22%. After integrating the two types of parameters, calculate the comprehensive aging parameter as (18% + 22%) / 2 = 20%. According to the aging parameter - capacity loss comparison table, the capacity loss corresponding to the 20% aging parameter is 15% of the rated capacity (1000mAh), that is, 150mAh. Generate the aging coefficient compensation as +150mAh for correcting the detected capacity value.

[0030] Steps for generating the multi - physical - field joint parameters based on the voltage data, current data, temperature data, and frequency response, constructing the non - linear regression equation according to the multi - physical - field joint parameters, and constructing the intelligent decoupling model according to the non - linear regression equation are as follows: Generate voltage physical field parameters based on voltage data, generate current physical field parameters based on current data, generate temperature physical field parameters based on temperature data, and generate frequency physical field parameters based on frequency response; Generate combined physical field parameters by combining voltage physical field parameters, current physical field parameters, temperature physical field parameters, and frequency physical field parameters; Based on the combined physical field parameters, obtain the coupling correlation curve relationship between each physical field, and construct a non-linear regression equation according to the coupling correlation curve relationship; Construct a decoupling model framework based on the coupling correlation curve relationship, and generate an intelligent decoupling model by combining the non-linear regression equation and the decoupling model framework.

[0031] In application, taking a new energy capacitor used by a certain brand for two years as an example, during the charging and discharging process, it is collected that: the voltage fluctuation range is 2.8V - 3.2V (generate voltage field parameter V = 3.0 ± 0.2V), the current ripple coefficient is 0.15 (generate current field parameter I = 1.0 ± 0.15A), the surface hot spot temperature is 52°C (temperature field parameter T = 52°C), and the phase angle of the frequency response curve deviates by 8° at 1kHz (frequency field parameter F = 1kHz@ - 8°). Input the four types of parameters into the multi-physical field coupling model, and calculate the voltage-temperature coupling coefficient of 0.32 and the current-frequency coupling coefficient of -0.18. Establish an equation through polynomial regression: capacitance = 1020 - 0.32*(T - 25) + 0.18*(F - 1kHz), and construct a three-layer neural network decoupling model framework. The input layer contains 4 physical field parameters, the hidden layer is set with 6 nodes, and the output layer generates the decoupled capacitance reference value.

[0032] For the dynamic coupling parameters between the capacitance value and the internal resistance value, leakage current, and polarization voltage of the capacitor during the detection process, the steps of substituting the dynamic coupling parameters into the intelligent decoupling model to obtain the decoupled capacitance value are as follows: Obtain the dynamic coupling parameters between the capacitance value and the internal resistance value, leakage current, and polarization voltage of the capacitor during the detection process; Substitute the dynamic coupling parameters into the intelligent decoupling model, and the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters; Obtain the coupling change trend according to the dynamic coupling changes, and perform reverse coupling disassembly on the dynamic coupling parameters based on the coupling change trend to generate the initial capacitance value and the coupling capacitance compensation coefficient; Perform coupling compensation on the initial capacitance value according to the coupling capacitance compensation coefficient to obtain the decoupled capacitance value.

[0033] In operation, taking a new energy capacitor of a certain brand used for two years as an example, it is found during the detection process that when the internal resistance rises from 50 mΩ to 65 mΩ, the displayed capacity value drops from 980 mAh to 920 mAh (coupling coefficient -4 mAh / mΩ); when the leakage current increases from 10 μA to 25 μA, the displayed capacity value drops by 30 mAh (coupling coefficient -2 mAh / μA). Inputting these dynamic coupling parameters into the intelligent decoupling model, the model identifies that the change in internal resistance mainly affects the charging efficiency (weight 0.6), and the leakage current affects the self-discharge (weight 0.4). Through reverse calculation, the initial capacity value of 980 mAh is separated, and the coupling compensation coefficients are generated: internal resistance compensation +4×15 = 60 mAh, leakage current compensation +2×15 = 30 mAh, and the final decoupled capacity value = 920 + 60 + 30 = 1010 mAh.

[0034] The steps of performing coefficient compensation on the decoupled capacity value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacity value are specifically as follows: Couple and associate the temperature coefficient compensation and the aging coefficient compensation to generate an initial coupling coefficient compensation; Obtain the coupling correlation relationship between temperature and aging in the capacitor, and generate a coupling adjustment parameter according to the coupling correlation relationship; Adjust the initial coupling coefficient compensation according to the coupling adjustment parameter to generate a combined coupling coefficient compensation, and perform data compensation on the decoupled capacity value according to the combined coupling coefficient compensation to generate the target capacity value.

[0035] In operation, taking a new energy capacitor of a certain brand used for two years as an example, couple the temperature compensation coefficient +4.2 mAh with the aging compensation +150 mAh, and the initial compensation value is 154.2 mAh. It is detected that when the temperature rises, the aging speed of the electrode accelerates, and a temperature-aging correlation factor is established: for every 1°C increase in temperature, the aging rate increases by 0.5%. At a detection temperature of 43.2°C, adjust the aging compensation to 150×[1 + 0.5%×(43.2 - 25)] = 150×1.091 = 163.65 mAh. The combined compensation value is corrected to 4.2 + 163.65 = 167.85 mAh. Compensate the decoupled capacity value of 1010 mAh to obtain the target capacity value of 1010 + 167.85 = 1177.85 mAh, which is rounded to 1180 mAh, that is, the corrected actual capacity value.

[0036] The embodiment of the present invention provides a method for automatically detecting the capacity of a new energy capacitor, using a device for automatically detecting the capacity of a new energy capacitor as described in any one of the above, and the method includes the following: S100: Establish a gradient temperature environment, detect the capacity of the capacitor according to the gradient temperature environment to obtain the capacity change law under different temperature conditions, and establish a capacity-temperature mapping model according to the capacity change law; S200: Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution; S300: Obtain the charge and discharge data of the capacitor during the detection process, obtain the charge and discharge curve characteristics according to the charge and discharge data, and capture the change in the voltage platform slope and the relaxation time offset according to the charge and discharge curve characteristics; S400: Obtain the electrode aging parameters of the capacitor according to the change in the voltage platform slope and the relaxation time offset, and generate an aging coefficient compensation for capacitance detection according to the electrode aging parameters; S500: Obtain the voltage data, current data, temperature data, and frequency response of the capacitor during the charge and discharge process, generate multi-physical field joint parameters according to the voltage data, current data, temperature data, and frequency response, construct a non-linear regression equation according to the multi-physical field joint parameters, and construct an intelligent decoupling model according to the non-linear regression equation; S600: Obtain the dynamic coupling parameters between the capacitance value and the internal resistance value, leakage current, and polarization voltage of the capacitor during the detection process, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value; S700: Perform coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value.

[0037] The above are all preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. An automatic capacitance detection device for new energy capacitors, characterized in that, Including: A temperature analysis module: used to establish a gradient temperature environment, detect the capacitance of a capacitor according to the gradient temperature environment, obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model according to the capacitance change law; A temperature compensation module: used to obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution; An aging analysis module: used to obtain the charge and discharge data of the capacitor during the detection process, obtain the charge and discharge curve characteristics according to the charge and discharge data, and capture the voltage platform slope change and relaxation time shift according to the charge and discharge curve characteristics; An aging compensation module: used to obtain the electrode aging parameters of the capacitor according to the voltage platform slope change and the relaxation time shift, and generate an aging coefficient compensation for capacitance detection according to the electrode aging parameters; A coupling analysis module: used to obtain voltage data, current data, temperature data and frequency response during the charge and discharge process of the capacitor, generate multi-physical field joint parameters according to the voltage data, the current data, the temperature data and the frequency response, construct a non-linear regression equation according to the multi-physical field joint parameters, and construct an intelligent decoupling model according to the non-linear regression equation; A parameter decoupling module: used to obtain the dynamic coupling parameters between the capacitance value and the internal resistance value, leakage current and polarization voltage of the capacitor during the detection process, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value; A capacitance calculation module: used to perform coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value.

2. The automatic capacitance detection device for new energy capacitors according to claim 1, characterized in that, The steps of detecting the capacitance of the capacitor according to the gradient temperature environment, obtaining the capacitance change law under different temperature conditions, and establishing a capacitance-temperature mapping model are specifically as follows: According to the gradient temperature environment, place the capacitor in preset different temperature environments for initial capacitance detection to obtain a plurality of initial capacitance data; Generate a temperature-capacitance correspondence table according to the gradient temperature environment and a plurality of initial capacitance data, and obtain the capacitance change law according to the temperature-capacitance correspondence table; Based on the capacitance change law, establish a capacitance-temperature curve, and obtain the curve change characteristics according to the capacitance-temperature curve; According to the curve change characteristics, obtain the change characteristics and change correspondence relationship of the capacitance changing with temperature, and establish a capacitance-temperature mapping model according to the change characteristics and the change correspondence relationship.

3. The automatic capacitance detection device for new energy capacitors according to claim 2, characterized in that, The steps of obtaining the surface temperature distribution of the capacitor during the detection process and generating a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution are specifically as follows: Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature distribution display diagram according to the surface temperature distribution; Based on the temperature distribution display diagram, extract the high-temperature area and low-temperature area on the surface of the capacitor, as well as the high-temperature value and low-temperature value; Based on the high-temperature region and the low-temperature region, corresponding regional temperature effects are obtained, and according to the regional effects, the high-temperature value and the low-temperature value are weighted to obtain a weighted high-temperature value and a weighted low-temperature value; Based on the weighted high-temperature value and the weighted low-temperature value, a weighted average temperature value is generated, and the weighted average temperature value is substituted into the capacity-temperature mapping model to generate a temperature coefficient compensation.

4. An automatic capacitance detection device for new energy capacitors according to claim 1, characterized in that, The steps of obtaining the charge-discharge curve characteristics according to the charge-discharge data and capturing the change in the voltage platform slope and the relaxation time offset according to the charge-discharge curve characteristics are specifically as follows: A charge-discharge data curve is generated according to the charge-discharge data, and feature extraction is performed on the charge-discharge data curve to obtain charge-discharge curve characteristics; According to the charge-discharge curve characteristics, the voltage platform data and the relaxation time data of the capacitor are captured from the charge-discharge curve characteristics; Voltage platform slope data is obtained according to the voltage platform data, and the change in the voltage platform slope is obtained according to the voltage platform slope data; Based on the relaxation time data, the offset of the relaxation time is extracted to obtain the relaxation time offset.

5. The automatic capacitance detection device for new energy capacitors according to claim 4, characterized in that, The steps of obtaining the electrode aging parameters of the capacitor according to the change in the voltage platform slope and the relaxation time offset and generating an aging coefficient compensation for capacity detection according to the electrode aging parameters are specifically as follows: The aging degree of the capacitor is identified by a first parameter according to the voltage platform slope data to obtain the first identified aging parameter of the capacitor; The aging degree of the capacitor is identified by a second parameter according to the relaxation time offset to obtain the second identified aging parameter of the capacitor; The first identified aging parameter and the second identified aging parameter are integrated to obtain the electrode aging parameter of the capacitor; According to the electrode aging parameter, an aging capacity influence value of the influence of the electrode aging on the capacity of the capacitor is obtained, and an aging coefficient compensation is generated according to the aging capacity influence value.

6. The automatic capacitance detection device for new energy capacitors according to claim 1, wherein The steps of generating multi-physical field joint parameters according to the voltage data, the current data, the temperature data, and the frequency response, constructing a non-linear regression equation according to the multi-physical field joint parameters, and constructing an intelligent decoupling model according to the non-linear regression equation are specifically as follows: Voltage physical field parameters are generated according to the voltage data, current physical field parameters are generated according to the current data, temperature physical field parameters are generated according to the temperature data, and frequency physical field parameters are generated according to the frequency response; The voltage physical field parameters, the current physical field parameters, the temperature physical field parameters, and the frequency physical field parameters are combined to generate physical field joint parameters; According to the physical field joint parameters, the coupling correlation curve relationship between each physical field is obtained, and a non-linear regression equation is constructed according to the coupling correlation curve relationship; Based on the coupling correlation curve relationship, a decoupling model framework is constructed, and an intelligent decoupling model is generated by combining the non-linear regression equation and the decoupling model framework.

7. The automatic capacitance detection device for new energy capacitors according to claim 6, characterized in that, Steps for obtaining dynamic coupling parameters among the capacitance value, internal resistance value, leakage current, and polarization voltage during the detection of a capacitor, substituting the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtaining the decoupled capacitance value are as follows: Obtain the dynamic coupling parameters among the capacitance value, internal resistance value, leakage current, and polarization voltage during the detection of the capacitor; Substitute the dynamic coupling parameters into the intelligent decoupling model, and the intelligent decoupling model extracts the dynamic coupling changes of the dynamic coupling parameters; Obtain the coupling change trend according to the dynamic coupling changes, and perform reverse coupling decomposition on the dynamic coupling parameters based on the coupling change trend to generate an initial capacitance value and a coupling capacitance compensation coefficient; Perform coupling compensation on the initial capacitance value according to the coupling capacitance compensation coefficient to obtain the decoupled capacitance value.

8. An automatic capacitance detection device for new energy capacitors according to claim 7, characterized in that, Steps for performing coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value are as follows: Couple and correlate the temperature coefficient compensation and the aging coefficient compensation to generate an initial coupling coefficient compensation; Obtain the coupling correlation relationship between temperature and aging in the capacitor, and generate a coupling adjustment parameter according to the coupling correlation relationship; Adjust the initial coupling coefficient compensation according to the coupling adjustment parameter to generate a combined coupling coefficient compensation, and perform data compensation on the decoupled capacitance value according to the combined coupling coefficient compensation to generate the target capacitance value.

9. An automatic capacitance detection method for new energy capacitors, the method uses an automatic capacitance detection device for new energy capacitors according to any one of claims 1-8, characterized in that, The method includes: Establish a gradient temperature environment, perform capacitance detection on the capacitor according to the gradient temperature environment to obtain the capacitance change law under different temperature conditions, and establish a capacitance-temperature mapping model according to the capacitance change law; Obtain the surface temperature distribution of the capacitor during the detection process, and generate a temperature coefficient compensation according to the capacitance-temperature mapping model and the surface temperature distribution; Obtain the charge-discharge data of the capacitor during the detection process, obtain the charge-discharge curve characteristics according to the charge-discharge data, and capture the change in the voltage platform slope and the relaxation time shift according to the charge-discharge curve characteristics; Obtain the electrode aging parameters of the capacitor according to the change in the voltage platform slope and the relaxation time shift, and generate an aging coefficient compensation for capacitance detection according to the electrode aging parameters; Obtain the voltage data, current data, temperature data, and frequency response during the charge-discharge process of the capacitor, generate a multi-physical field joint parameter according to the voltage data, the current data, the temperature data, and the frequency response, construct a non-linear regression equation according to the multi-physical field joint parameter, and construct an intelligent decoupling model according to the non-linear regression equation; Obtain the dynamic coupling parameters among the capacitance value, internal resistance value, leakage current, and polarization voltage during the detection of the capacitor, substitute the dynamic coupling parameters into the intelligent decoupling model for decoupling, and obtain the decoupled capacitance value; Perform coefficient compensation on the decoupled capacitance value according to the temperature coefficient compensation and the aging coefficient compensation to obtain the target capacitance value.

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