Method, system and equipment for analyzing and processing operation data of power battery, medium and product

Abstract

The invention discloses an operation data analysis processing method, system and device of a power battery, a medium and a product, and relates to the technical field of new energy automobiles, and the method comprises the steps: obtaining operation data of a target power battery, carrying out the data cleaning and data fusion of the operation data of the target power battery, and obtaining the fusion data of the target power battery; segmenting the fusion data of the target power battery according to the time length and the sampling frequency in sequence to obtain low-frequency data of the target power battery; interpolating the low-frequency current of the target power battery by adopting a regression learning algorithm to obtain interpolated low-frequency current data of the target power battery; and inputting the interpolated low-frequency current data of the target power battery into the electro-thermal coupling optimization model to obtain interpolated voltage data and interpolated temperature data of the target power battery. According to the invention, the quality of the acquired power battery operation data is improved, and the complete acquisition of the full life cycle data of the power battery is realized.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, and in particular to a method, system, device, medium and product for analyzing and processing operating data of a power battery. Background Art

[0002] Under environmental and energy pressure, new energy electric vehicles have become the mainstream of industry development, and the accompanying electric vehicle safety issues have also received increasing attention. It has become a general consensus in the industry to use big data analysis methods to conduct power battery safety warnings and improve the safety performance of electric vehicle power batteries.

[0003] At present, there are many ways or scenarios for obtaining vehicle power battery related data around the operation scenarios of new energy vehicles, such as the vehicle-side operation monitoring platform, the vehicle charging data collection at the charging pile end when the vehicle is charging, the monitoring data obtained during vehicle transaction or daily use during vehicle evaluation and testing, etc. However, the vehicle power battery related data obtained in various scenarios often have more or less data anomalies due to interference in the data collection or transmission process, resulting in uneven quality of the original data, which brings great inconvenience to subsequent data mining and analysis. At the same time, due to the limitations of scenarios and technologies, the data in each scenario are relatively limited and the data quality is uneven, making it impossible to achieve complete acquisition of the data process of the entire life cycle of the power battery.

[0004] Therefore, there is an urgent need for a method for analyzing and processing the operating data of a power battery to solve the above problems. Summary of the invention

[0005] The purpose of this application is to provide a method, system, device, medium and product for analyzing and processing the operating data of a power battery, so as to improve the quality of the acquired power battery operating data and realize the complete acquisition of the power battery full life cycle data.

[0006] To achieve the above objectives, the present application provides the following solution: a method for analyzing and processing operating data of a power battery, comprising: Acquire the operation data of the target power battery; the operation data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and SOC under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC under charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection; Performing data cleaning on the operating data of the target power battery respectively to obtain the operating data of the target power battery after cleaning; Performing data fusion processing on the operation data of the target power battery after cleaning to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC; The fused data of the target power battery is divided in turn according to the time length and the sampling frequency to obtain low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC; A regression learning algorithm is used to interpolate the low-frequency current of the target power battery to obtain high-frequency current data of the target power battery; the high-frequency current data is current data after the low-frequency current data is interpolated; The interpolated low-frequency current data of the target power battery is input into the electro-thermal coupling optimization model to obtain the interpolated voltage data and the interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and an objective function; the initial electro-thermal coupling model is constructed based on battery development test data, an equivalent circuit model of a battery pack, and a thermal simulation model of a battery pack.

[0007] Optionally, the data cleaning method includes duplicate value processing and out-of-limit value processing.

[0008] Optionally, the fusion data of the target power battery is divided according to the time length and the sampling frequency in sequence to obtain the low-frequency data of the target power battery, specifically including: Segmenting the fusion data of the target power battery into segments according to a preset time length to obtain first-level segments of the fusion data of multiple target power batteries; Each first-level segment is divided according to different sampling frequencies to obtain second-level segments of fusion data of multiple target power batteries; For any secondary segment, the percentage of the secondary segments with the smallest sampling frequency in the corresponding primary segment is counted; It is determined whether the quantity ratio is less than a preset threshold value. If so, the secondary segment with the smallest sampling frequency is used as the low-frequency data of the target power battery.

[0009] Optionally, the equivalent circuit model of the battery pack is expressed as: ; in, express Derivative with respect to time; express Derivative with respect to time; Represents the electrochemical polarization resistance of the battery; Represents the electrochemical polarization capacitance of the battery; , Respectively represent two Voltage division of parallel networks; Indicates the battery current; Indicates the concentration polarization resistance of the battery; Represents the concentration polarization capacitance of the battery; Indicates the battery terminal voltage; is the battery's ohmic internal resistance, is the open circuit voltage of the battery; The thermal simulation model of the battery pack is expressed as: ; in, Indicates the specific heat capacity of the battery cell; Indicates the mass of the battery cell; Indicates i The temperature change of each battery cell, Indicates i The heat generated by each battery cell, Indicates i The heat dissipation of each battery cell.

[0010] Optionally, the optimization process of the electric-thermal coupling optimization model specifically includes: Acquire sample data; the sample data is the high-frequency current and high-frequency SOC of the sample power battery; Input the sample data into the initial electric-thermal coupling model, and output the voltage data prediction value and temperature data prediction value of the sample power battery; An objective function is constructed according to the predicted value of the voltage data of the sample power battery, the predicted value of the temperature data of the sample power battery, the actual value of the voltage data of the sample power battery, and the actual value of the temperature data of the sample power battery, and the model parameters of the initial electro-thermal coupling model are adjusted according to the objective function to obtain the electro-thermal coupling optimization model.

[0011] Optionally, the objective function The expression is: ; in, x is the sample data; j is the sample sequence; J Indicates the total number of sample data; p is the parameter to be optimized; p When indicating voltage, is the actual value of the voltage data of the sample power battery, is the predicted value of the voltage data of the sample power battery; when p When indicating temperature, is the true value of the temperature data of the sample power battery, is the predicted value of the temperature data of the sample power battery; P scale It is the value range of the model parameters in the electrical-thermal coupling optimization model.

[0012] In a second aspect, the present application provides a power battery operation data analysis and processing system, the power battery operation data analysis and processing system is used to implement the power battery operation data analysis and processing method, the power battery operation data analysis and processing system includes: A data acquisition unit, used to acquire operating data of a target power battery; the operating data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and SOC under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC during the charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection; A data cleaning unit, used to clean the operating data of the target power battery respectively, to obtain the cleaned operating data of the target power battery; A data fusion unit is used to perform data fusion processing on the operation data of the target power battery after cleaning, so as to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC; A data segmentation unit, used to segment the fused data of the target power battery according to the time length and the sampling frequency in sequence to obtain low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC; The interpolated low-frequency current data determination unit is used to interpolate the low-frequency current of the target power battery using a regression learning algorithm to obtain high-frequency current data of the target power battery; the high-frequency current data is the current data after the low-frequency current data is interpolated; The interpolated voltage data and interpolated temperature data determination unit is used to input the interpolated low-frequency current data of the target power battery into the electro-thermal coupling optimization model to obtain the interpolated voltage data and interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and an objective function; the initial electro-thermal coupling model is constructed based on battery development test data, an equivalent circuit model of a battery pack, and a thermal simulation model of a battery pack.

[0013] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for analyzing and processing operating data of a power battery.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for analyzing and processing operating data of a power battery.

[0015] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for analyzing and processing operating data of a power battery.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: The present application discloses a method, system, device, medium and product for analyzing and processing the operating data of a power battery. The data quality is initially improved by performing data cleaning and data fusion on the operating data of a target power battery. Then, the fused data is segmented to obtain the low-frequency data of the target power battery. Then, a regression learning algorithm is used to interpolate the low-frequency current of the target power battery to obtain the interpolated low-frequency current data of the target power battery. The data is cleaned and filled again to improve the data quality, and the complete acquisition of the data of the power battery throughout its life cycle is achieved. Finally, the interpolated low-frequency current data of the target power battery is input into an electric-thermal coupling optimization model to obtain the interpolated voltage data and interpolated temperature data of the target power battery, further improving the operating data quality of the target power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a method for analyzing and processing operating data of a power battery provided in one embodiment of the present application; Figure 2 A schematic diagram of the parameter coupling relationship between the electrothermal models provided in one embodiment of the present application; Figure 3 A schematic diagram of functional modules of a power battery operation data analysis and processing system provided in one embodiment of the present application; Figure 4A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0019] Reference numerals: Data acquisition unit-1, data cleaning unit-2, data fusion unit-3, data segmentation unit-4, interpolated low-frequency current data determination unit-5, interpolated voltage data and interpolated temperature data determination unit-6. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] In an exemplary embodiment, Figure 1 As shown, a method for analyzing and processing the operation data of a power battery is provided, comprising the following steps S1 to S6. Among them: Step S1, obtaining the operating data of the target power battery; the operating data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and state of charge (State of Charge, SOC) under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC during the charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection.

[0023] Specifically, vehicle monitoring data refers to the power battery operation data included in the vehicle monitoring data collected in accordance with the GB / T32960 standard, such as current, SOC, temperature, voltage, etc.; vehicle charging process monitoring data refers to the data collected by the charging pile management platform to monitor the charging process during the vehicle charging process, including charging current, charging voltage, SOC, temperature, etc.; power battery detection data refers to the process data of vehicle power battery status detection and evaluation performed by vehicle users in stages during use, including dynamic data of the detection process, such as current, voltage, temperature, etc., and static analysis data of the detection results including information such as the current capacity health of the battery and voltage difference.

[0024] Step S2, performing data cleaning on the operating data of the target power battery to obtain the cleaned operating data of the target power battery.

[0025] Specifically, data cleaning methods include duplicate value processing and out-of-limit value processing.

[0026] Step S3, performing data fusion processing on the operation data of the target power battery after cleaning to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC.

[0027] Specifically, the vehicle monitoring data, vehicle charging process monitoring data and power battery detection data are integrated. Each original operation data is usually time series data including voltage, current and SOC information. When integrating data, data integration is performed according to the data attribute field name (attribute fields such as voltage, current, SOC) definition and time label order. At the same time, if there are multiple data sources for the same attribute field and the values ​​of the multiple data sources are different, the average of each value is taken as the data value of the corresponding attribute field after fusion at the current time; if there is only one data source for the same attribute field at the same time, this data source is taken as the data value of the corresponding attribute field at the current time. For example, when the attribute field is current, at time T1, the current of the vehicle monitoring data is 1A, the current of the vehicle charging process monitoring data is 2A, and the current of the power battery detection data starts at 2.5A. The average is (1+2+2.5) / 3=1.83A, and the fused current at time T1 is 1.83A. If at time T1, the current of the vehicle monitoring data is 0A, the current of the vehicle charging process monitoring data is 2A, and the current of the power battery detection data starts at 0A, then the fused current at time T1 is recorded as 2A.

[0028] Step S4, dividing the fused data of the target power battery according to the time length and the sampling frequency in turn to obtain low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC.

[0029] As an optional implementation, step S4 specifically includes: Step S41, segmenting the fused data of the target power battery according to a preset time length to obtain the first-level segments of the fused data of multiple target power batteries. Specifically, segmenting the data according to the time tag of the power battery operation data, starting from the time point of the first frame of data, every half-year time interval (i.e., the preset time length is half a year) is divided into a segment, which is recorded as a first-level segment.

[0030] Step S42, each first-level segment is divided according to different sampling frequencies to obtain second-level segments of the fusion data of multiple target power batteries. Specifically, for each first-level segment, the data sampling frequency included in each segment is identified according to the data time tag. If there are more than two data sampling frequencies in the first-level segment, the first-level segment is further divided according to the different data sampling frequencies, and the continuous data with the same sampling frequency is divided into one segment, which is recorded as a second-level segment, thereby obtaining multiple second-level segments of the fusion data of the target power battery.

[0031] Step S43: for any secondary segment, count the proportion of the secondary segments with the smallest sampling frequency in the corresponding primary segment.

[0032] Step S44, determine whether the quantity ratio is less than a preset threshold, and if so, use the secondary segment with the smallest sampling frequency as the low-frequency data of the target power battery. The first preset condition is that the quantity ratio is less than the preset threshold. Specifically, if the quantity ratio of the secondary segment with the smallest sampling frequency in the corresponding primary segment is less than 70% (preset threshold), the corresponding secondary segment with the smallest sampling frequency is used as the low-frequency data of the target power battery.

[0033] Step S5, using a regression learning algorithm to interpolate the low-frequency current of the target power battery to obtain high-frequency current data of the target power battery; the high-frequency current data is current data after interpolation of the low-frequency current data; Among them, the current regression model is established by using regression learning algorithms such as artificial neural network regression and gradient boosting machine regression, and the interpolated low-frequency current data of the target power battery is interpolated according to the data frequency determined above by using the current regression model to obtain the interpolated low-frequency current data of the target power battery. The frequency of interpolation filling is determined by the sampling frequency that contains the highest proportion of the total amount of high-frequency data in the high-frequency data of the first-level segment and the data with different sampling frequencies.

[0034] Step S6, inputting the interpolated low-frequency current data of the target power battery into the electro-thermal coupling optimization model to obtain the interpolated voltage data and the interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and the objective function; the initial electro-thermal coupling model is constructed based on the battery development test data, the equivalent circuit model of the battery pack and the thermal simulation model of the battery pack.

[0035] Specifically, the battery development test data is used to construct a simulation model of the battery system's electrical, thermal and other external characteristic parameters (i.e., the construction of the initial electrical-thermal coupling model) to achieve the simulation output of the voltage, temperature and other monitoring data of the power battery cells. Among them, the battery development test data refers to the test process data generated by the power battery-related tests conducted during the development stage of the vehicle battery to ensure the performance of the power battery system, including performance testing, safety testing, HPPC test testing, life testing, thermal performance testing, etc. In addition, it also includes battery pack development parameter information, such as battery cell materials, battery pack structure layout, thermal management system design parameters, etc.

[0036] Based on the interpolated low-frequency current data of the target power battery, with the help of the electric-thermal coupling optimization model, the voltage data and temperature data of the low-frequency target power battery are simulated and fitted. The voltage sequence of the target power battery is formed by the simulation output of the equivalent circuit model of the battery pack. The temperature data sequence of the battery pack is generated by the simulation of the thermal simulation model of the battery pack, and finally the interpolated voltage data and interpolated temperature data of the target power battery are obtained.

[0037] Equivalent circuit model: This application takes the second-order RC equivalent circuit model as an example for detailed description. The second-order RC equivalent circuit model characterizes the relationship between the battery terminal voltage and current according to the circuit principle. The expression of the equivalent circuit model of the battery pack is as follows: (1) in, express Derivative with respect to time; express Derivative with respect to time; Represents the electrochemical polarization resistance of the battery; Represents the electrochemical polarization capacitance of the battery; , Respectively represent two Voltage division of parallel networks; Indicates the battery current; Indicates the concentration polarization resistance of the battery; Represents the concentration polarization capacitance of the battery; Indicates the battery terminal voltage; is the battery's ohmic internal resistance, is the battery open circuit voltage. The above parameters are usually determined by querying the battery SOC, temperature and battery health mapping table. The format of the mapping table is shown in Table 1: Table 1 Mapping relationship table

[0038] In Table 1, X1 represents SOC, X2 represents temperature, and the values ​​in Table 1 are the values ​​of the parameters to be queried by the model. The queryable parameters include , , , and , each parameter is queried through its corresponding mapping table. The mapping table is calculated based on the HPPC test data under different SOC and temperature conditions in the battery development test data; open circuit voltage It is a nonlinear function related to SOC and can usually be determined by fitting the SOC-OCV curve test data in the battery performance test.

[0039] Battery pack simulation model: It is determined by the series-parallel form of the battery pack system and the number of series-connected cells. If the battery cell is composed of multiple cells in parallel, the battery cell equivalent circuit model is processed in parallel according to the circuit principle; if the battery cells are composed of a single cell, and each cell is connected in series to form a battery pack system, the battery pack voltage and current are calculated according to the following formula: (2) (3) in, I p Indicates the current of the battery pack; U p Indicates the voltage of the battery pack; I j Indicates j The current of each battery cell; U j Indicates j The terminal voltage of each battery cell; n The number of battery cells connected in series in the battery pack.

[0040] Thermal simulation model of battery pack: The thermal simulation model of the battery pack includes the battery cell heat generation model and the battery heat dissipation model. The expression of the thermal simulation model of the battery pack is: (4) in, Indicates the specific heat capacity of the battery cell; Indicates the mass of the battery cell; Indicates i The temperature change of each battery cell, Indicates i The heat generated by each battery cell, Indicates i The heat dissipation of each battery cell.

[0041] According to the battery heat generation principle, the heat generated by the battery monomer Including: Joule heat, reaction heat, polarization heat and side reaction heat. Calculate the heat generated by the battery using the Bernardi battery heat generation formula , the formula is as follows: (5) in, q is the volume heat generation rate of the battery unit, I is the current, R is the internal resistance, T is the temperature, dU / dT is the derivative of the open circuit voltage with respect to temperature.

[0042] Heat dissipation of battery cells Including: conduction heat dissipation, convection heat dissipation, radiation heat dissipation, etc. Conduction heat dissipation occurs at the contact interface between the battery and other components, and its heat transfer expression is as follows: (6) in, q a is the heat conduction heat per unit area and per unit time; k It is a heat conduction system for objects; Indicates the temperature difference between the battery and the object being heated; Indicates the distance between the battery and the object to which heat is conducted.

[0043] Convective heat dissipation mainly refers to the heat exchange between the battery and the cooling system pipes. Its convection heat transfer follows Newton's law and the calculation formula is as follows: (7) in, q b The heat exchange between the battery surface and the cooling medium per unit area and per unit time; T c Indicates the battery surface temperature; T d Indicates the surface temperature of the cooling medium; h is the convective heat transfer coefficient.

[0044] Radiative heat dissipation, thermal radiation is the process of an object dissipating heat due to its own temperature through the movement of electromagnetic radiation. The higher the temperature, the greater the energy radiated. Under normal circumstances, the battery temperature will not be too high, and the heat dissipated by radiation is negligible. Therefore, the thermal radiation heat dissipation can be ignored when calculating the heat dissipation.

[0045] Based on the definitions of the above heat generation and heat dissipation formulas and the different positions of each battery cell in the battery system, the battery cell temperature change calculation formula is determined according to the principles of fluid mechanics and thermodynamics as follows: (8) in, It is a variable related to SOC and can be determined by developing test data; ohmic internal resistance R 0. Electrochemical polarization internal resistance R 1 and concentration polarization internal resistance R 2. It can be identified and determined through development test data. Indicates i The other adjacent battery cells have a conductive heat dissipation relationship. k The temperature difference of each battery cell; Indicates i The other adjacent battery cells have conduction heat dissipation relationships k The distance between the monomers; g Indicates i The number of other adjacent cells that have conduction heat dissipation relationship with each battery cell; Indicates i The current temperature of each battery cell; Indicates the ambient temperature of the battery cell; Indicates the surface temperature of the cooling medium; Indicates i The contact area between each battery cell and the ambient air; Indicates i The contact area between each battery cell and the cooling medium.

[0046] Battery pack electrical-thermal coupling model: The equivalent circuit model is established by coupling the equivalent circuit model and the thermal simulation model of the battery pack. The equivalent circuit model inputs the output terminal voltage, electromotive force, etc. into the thermal simulation model of the battery pack. At the same time, the thermal simulation model of the battery pack inputs its own simulated battery temperature into the equivalent circuit model. The equivalent circuit model determines the parameters of its equivalent circuit model based on the temperature input and the mapping relationship table, and generates voltage data through simulation. The schematic diagram of the parameter coupling relationship between the electrothermal models is shown in the figure. Figure 2 In addition, other equivalent circuit models, thermal simulation models, or machine learning simulation models such as neural networks can also be used to construct data simulation models to obtain a battery pack electrical-thermal coupling model.

[0047] As an optional implementation, in step S6, the optimization process of the electric-thermal coupling optimization model specifically includes: Step S61, obtaining sample data; the sample data is the high-frequency current and high-frequency SOC of the sample power battery.

[0048] Step S62: input the sample data into the initial electric-thermal coupling model, and output the voltage data prediction value and the temperature data prediction value of the sample power battery.

[0049] Step S63, construct an objective function based on the predicted value of the voltage data of the sample power battery, the predicted value of the temperature data of the sample power battery, the actual value of the voltage data of the sample power battery, and the actual value of the temperature data of the sample power battery, and adjust the model parameters of the initial electro-thermal coupling model according to the objective function to obtain the electro-thermal coupling optimization model. By extracting high-frequency data from the sample data, the parameters of the initial electro-thermal coupling model are corrected, and the optimization of the initial electro-thermal coupling model by high-frequency high-quality data is achieved, so that the electro-thermal coupling optimization model can output the voltage data and temperature data of the power battery more accurately. Extract all calibrable parameters in the initial electro-thermal coupling model to form a parameter vector to be optimized, and use the objective function to achieve parameter optimization.

[0050] As an optional implementation, in step S63, the objective function The expression is: (9) in, x is the sample data, here it is the current data; j is the sample sequence; J Indicates the total number of sample data; p is the parameter to be optimized; p When indicating voltage, is the actual value of the voltage data of the sample power battery, is the predicted value of the voltage data of the sample power battery; when p When indicating temperature, is the true value of the temperature data of the sample power battery, is the predicted value of the temperature data of the sample power battery, wherein the actual value and predicted value of the voltage data of the sample power battery and the actual value and predicted value of the temperature data of the sample power battery are the voltage data and temperature data calculated by the electric-thermal coupling optimization model; P scale It is the value range of the model parameters in the electrical-thermal coupling optimization model.

[0051] As an optional implementation, in step S63, a particle swarm optimization algorithm, a simulated degradation algorithm or a genetic algorithm is used to optimize the parameters to obtain a parameter sequence that minimizes the difference between the true value and the model output value within the parameter value range. p opt .

[0052] As an optional implementation, the method further includes step S7: The high-frequency data is integrated with the interpolated voltage data and interpolated temperature data of the target power battery, and integrated together according to the time label sequence to form a data set covering the entire life cycle of the battery.

[0053] Beneficial effects of this application: The present application preliminarily improves the data quality by performing data cleaning and data fusion on the operating data of the target power battery in multiple scenarios; then, the fused data is segmented to obtain the low-frequency data of the target power battery, and then, the low-frequency current of the target power battery is interpolated by a regression learning algorithm to obtain the interpolated low-frequency current data of the target power battery, and the data is cleaned and filled again to improve the data quality, thereby achieving complete acquisition of the power battery's full life cycle data for subsequent data analysis and mining applications; finally, the interpolated low-frequency current data of the target power battery is input into the electro-thermal coupling optimization model to obtain the interpolated voltage data and interpolated temperature data of the target power battery, wherein the electro-thermal coupling optimization model is obtained by optimizing and adjusting the initial electro-thermal coupling optimization model using the high-frequency high-quality data in the fused data of the target power battery, thereby further improving the operating data quality of the target power battery.

[0054] Based on the same inventive concept, the embodiment of the present application also provides a power battery operation data analysis and processing system for implementing the above-mentioned power battery operation data analysis and processing method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more power battery operation data analysis and processing systems provided below can refer to the limitations of the power battery operation data analysis and processing method above, and will not be repeated here.

[0055] In an exemplary embodiment, Figure 3 As shown, a power battery operation data analysis and processing system is provided, including: a data acquisition unit 1, a data cleaning unit 2, a data fusion unit 3, a data segmentation unit 4, an interpolated low-frequency current data determination unit 5, and an interpolated voltage data and interpolated temperature data determination unit 6.

[0056] The data acquisition unit 1 is used to acquire the operating data of the target power battery; the operating data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and SOC under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC during the charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection.

[0057] The data cleaning unit 2 is used to clean the operating data of the target power battery to obtain the cleaned operating data of the target power battery.

[0058] The data fusion unit 3 is used to perform data fusion processing on the operation data of the target power battery after cleaning, so as to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC.

[0059] The data segmentation unit 4 is used to segment the fused data of the target power battery according to the time length and the sampling frequency in sequence to obtain the low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC.

[0060] The interpolated low-frequency current data determining unit 5 is used to interpolate the low-frequency current of the target power battery using a regression learning algorithm to obtain high-frequency current data of the target power battery; the high-frequency current data is the current data after the low-frequency current data is interpolated.

[0061] The interpolated voltage data and interpolated temperature data determination unit 6 is used to input the interpolated low-frequency current data of the target power battery into the electro-thermal coupling optimization model to obtain the interpolated voltage data and interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and an objective function; the high-frequency data is fused data whose sampling frequency meets a second preset condition; the high-frequency data includes: high-frequency voltage, high-frequency current, high-frequency temperature and high-frequency SOC; the initial electro-thermal coupling model is constructed based on battery development test data, an equivalent circuit model of a battery pack and a thermal simulation model of a battery pack.

[0062] As an optional implementation, the power battery operation data analysis and processing system can be deployed as a whole in the cloud, integrating the high-frequency data with the interpolated voltage data and interpolated temperature data of the target power battery, and integrating them together according to the time tag sequence to form a data set covering the entire life cycle of the battery, and storing it in the cloud.

[0063] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for analyzing and processing operating data of a power battery.

[0064] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for analyzing and processing operating data of a power battery is implemented.

[0065] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements a method for analyzing and processing operating data of a power battery when executed by a processor.

[0066] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device 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 internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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 method for analyzing and processing the operating data of a power battery is implemented.

[0067] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0069] Those skilled 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 non-volatile 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 the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0070] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0071] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing and processing operating data of a power battery, characterized in that: The operating data analysis and processing method of the power battery includes: Acquire the operation data of the target power battery; the operation data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and SOC under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC under charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection; Performing data cleaning on the operating data of the target power battery respectively to obtain the operating data of the target power battery after cleaning; Performing data fusion processing on the operation data of the target power battery after cleaning to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC; The fused data of the target power battery is divided in turn according to the time length and the sampling frequency to obtain low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC; A regression learning algorithm is used to interpolate the low-frequency current of the target power battery to obtain high-frequency current data of the target power battery; the high-frequency current data is current data after the low-frequency current data is interpolated; The interpolated low-frequency current data of the target power battery is input into the electro-thermal coupling optimization model to obtain the interpolated voltage data and the interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and an objective function; the initial electro-thermal coupling model is constructed based on battery development test data, an equivalent circuit model of a battery pack, and a thermal simulation model of a battery pack.

2. The method for analyzing and processing the operation data of a power battery according to claim 1, characterized in that: Data cleaning methods include duplicate value processing and out-of-limit value processing.

3. The method for analyzing and processing the operation data of a power battery according to claim 1, characterized in that: The fusion data of the target power battery is divided according to the time length and sampling frequency to obtain the low-frequency data of the target power battery, including: Segmenting the fusion data of the target power battery into segments according to a preset time length to obtain first-level segments of the fusion data of multiple target power batteries; Each first-level segment is divided according to different sampling frequencies to obtain second-level segments of fusion data of multiple target power batteries; For any secondary segment, the percentage of the secondary segments with the smallest sampling frequency in the corresponding primary segment is counted; It is determined whether the quantity ratio is less than a preset threshold value. If so, the secondary segment with the smallest sampling frequency is used as the low-frequency data of the target power battery.

4. The method for analyzing and processing the operation data of a power battery according to claim 1, characterized in that: The expression of the equivalent circuit model of the battery pack is: ； in, express Derivative with respect to time; express Derivative with respect to time; Represents the electrochemical polarization resistance of the battery; Represents the electrochemical polarization capacitance of the battery; , Respectively represent two Voltage division of parallel networks; Indicates the battery current; Indicates the concentration polarization resistance of the battery; Represents the concentration polarization capacitance of the battery; Indicates the battery terminal voltage; is the battery's ohmic internal resistance, is the open circuit voltage of the battery; The thermal simulation model of the battery pack is expressed as: ； in, Indicates the specific heat capacity of the battery cell; Indicates the mass of the battery cell; Indicates i The temperature change of each battery cell, Indicates i The heat generated by each battery cell, Indicates i The heat dissipation of each battery cell.

5. The method for analyzing and processing the operation data of a power battery according to claim 3, characterized in that: The optimization process of the electro-thermal coupling optimization model includes: Acquire sample data; the sample data is the high-frequency current and high-frequency SOC of the sample power battery; Input the sample data into the initial electric-thermal coupling model, and output the voltage data prediction value and temperature data prediction value of the sample power battery; An objective function is constructed according to the predicted value of the voltage data of the sample power battery, the predicted value of the temperature data of the sample power battery, the actual value of the voltage data of the sample power battery, and the actual value of the temperature data of the sample power battery, and the model parameters of the initial electro-thermal coupling model are adjusted according to the objective function to obtain the electro-thermal coupling optimization model.

6. The method for analyzing and processing the operation data of a power battery according to claim 5, characterized in that: Objective Function The expression is: ； in, x is the sample data; j is the sample sequence; J Indicates the total number of sample data; p is the parameter to be optimized; p When indicating voltage, is the actual value of the voltage data of the sample power battery, is the predicted value of the voltage data of the sample power battery; when p When indicating temperature, is the true value of the temperature data of the sample power battery, is the predicted value of the temperature data of the sample power battery; P scale Optimize the range of model parameters in the electro-thermal coupling model.

7. A power battery operation data analysis and processing system, characterized in that: The power battery operation data analysis and processing system is used to implement the power battery operation data analysis and processing method according to any one of claims 1 to 6, and the power battery operation data analysis and processing system includes: A data acquisition unit, used to acquire operating data of a target power battery; the operating data includes: vehicle monitoring data, vehicle charging process monitoring data and power battery detection data; the vehicle monitoring data includes: voltage, current, temperature and SOC under vehicle monitoring; the vehicle charging process monitoring data includes: voltage, current, temperature and SOC during the charging process; the power battery detection data includes: voltage, current, temperature and SOC under power battery detection; A data cleaning unit, used to clean the operating data of the target power battery respectively, to obtain the cleaned operating data of the target power battery; A data fusion unit is used to perform data fusion processing on the operation data of the target power battery after cleaning, so as to obtain fusion data of the target power battery; the fusion data includes: fusion voltage, fusion current, fusion temperature and fusion SOC; A data segmentation unit, used to segment the fused data of the target power battery according to the time length and the sampling frequency in sequence to obtain low-frequency data of the target power battery; the low-frequency data is the fused data whose sampling frequency meets the first preset condition, and the low-frequency data includes: low-frequency voltage, low-frequency current, low-frequency temperature and low-frequency SOC; The interpolated low-frequency current data determination unit is used to interpolate the low-frequency current of the target power battery using a regression learning algorithm to obtain high-frequency current data of the target power battery; the high-frequency current data is the current data after the low-frequency current data is interpolated; The interpolated voltage data and interpolated temperature data determination unit is used to input the interpolated low-frequency current data of the target power battery into the electro-thermal coupling optimization model to obtain the interpolated voltage data and interpolated temperature data of the target power battery; the electro-thermal coupling optimization model is obtained by optimizing the initial electro-thermal coupling model using sample data and an objective function; the initial electro-thermal coupling model is constructed based on battery development test data, an equivalent circuit model of a battery pack, and a thermal simulation model of a battery pack.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for analyzing and processing operating data of a power battery according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the operating data analysis and processing method of the power battery according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the operating data analysis and processing method of the power battery according to any one of claims 1 to 6 is implemented.

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