Method, system, equipment and medium for evaluating internal temperature of energy storage lithium ion battery

Abstract

The invention belongs to the technical field of battery energy storage, and discloses a method, system, equipment and medium for evaluating the internal temperature of an energy storage lithium ion battery, and the method comprises the steps: obtaining the AC impedance data of the battery at a characteristic frequency, including a real part, an imaginary part, a module value and a phase angle; inputting the real part, the imaginary part, the module value and the phase angle into a battery internal temperature estimation model to obtain the internal temperature of the battery in the current state; the characteristic frequency is the frequency that the Pearson correlation coefficients of the real part, the imaginary part, the module value and the phase angle and the temperature meet correlation, and the frequency that the Pearson correlation coefficients of the real part, the imaginary part, the module value and the phase angle and the state of charge meet ircorrelation. According to the method, the real thermal state in the battery is reflected through the impedance data, and high-precision estimation of the internal temperature of the battery is achieved. Only the characteristic frequency is measured, so that the test time can be greatly shortened; the temperature data can be quickly estimated, the evaluation response speed is obviously improved, and the method has high engineering application value.

Description

Technical Field

[0001] The present invention belongs to the technical fields of electric vehicles and battery energy storage, and particularly relates to a method, a system, a device and a medium for evaluating the internal temperature of a lithium-ion battery for energy storage. Background Art

[0002] As a core component of a new energy system, the safety state of an energy storage battery has received wide attention. Among them, temperature is closely related to the safety state. At present, the temperature measurement of an energy storage battery mainly focuses on the surface or the tab. However, as the monomer capacity of the energy storage battery gradually increases, the internal and external temperature difference gradually increases, and the surface temperature is difficult to represent the actual temperature inside the battery. Therefore, a method for measuring or estimating the internal temperature of the battery is urgently needed.

[0003] The internal temperature of a lithium-ion battery refers to the temperature inside the battery, which reflects the heat generation, diffusion, exchange, etc. of the internal chemical reaction of the battery. At present, directly measuring the internal temperature of a lithium-ion battery mainly through methods such as built-in optical fiber sensors and thin film sensors. However, due to its complex process, high cost, and the mutual influence between the sensor life and the battery life, it is difficult to achieve engineering applications. With the improvement of computing power, the temperature estimation method based on a mathematical model has become the mainstream. However, in a complex environment, the model error increases. To improve the estimation accuracy, computational fluid dynamics (CFD) simulation has been applied to the thermal simulation of lithium-ion batteries. However, due to its complex calculation and dependence on accurate model parameters, it is difficult to be widely promoted and applied on a large scale. Summary of the Invention

[0004] In order to overcome the problem that it is difficult to test the internal temperature of a lithium-ion battery, and at the same time, to overcome the problem that the computational fluid dynamics simulation temperature estimation method used in the prior art for estimating the internal temperature of a lithium-ion battery is difficult to be widely promoted and applied on a large scale, the purpose of the present invention is to provide a method, a system, a device and a medium for evaluating the internal temperature of a lithium-ion battery for energy storage. This method reflects the true thermal state inside the battery through impedance data, without relying on traditional physical models and complex formulas. It uses a machine learning algorithm to establish a non-linear impedance-temperature mapping relationship to achieve the evaluation of the internal temperature of the battery. At the same time, only measuring the characteristic frequency can greatly shorten the test time, quickly estimate the temperature data, significantly improve the evaluation response speed, and has high engineering application value.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for evaluating the internal temperature of a lithium-ion battery for energy storage, comprising the following steps: Obtain the AC impedance data of the battery at the characteristic frequency, including the real part, the imaginary part, the modulus value and the phase angle; Input the real part, the imaginary part, the modulus value and the phase angle into the internal temperature estimation model of the battery to obtain the internal temperature of the current state of the battery; Among them, the characteristic frequency is the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with temperature satisfy the correlation, and the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with the state of charge satisfy the non-correlation.

[0006] Furthermore, the internal temperature estimation model of the battery is established by a machine learning algorithm.

[0007] Furthermore, the internal temperature estimation model of the battery is established through the following process: a regression model is established by a machine learning algorithm, the AC impedance data of the battery at the characteristic frequency is used as the input of the regression model, and the internal temperature of the battery is used as the output, and the regression model is trained to obtain the internal temperature estimation model of the battery.

[0008] Furthermore, the Pearson correlation coefficient is calculated by the following formula: (1) Among them, is the covariance of the AC impedance test data and the ambient temperature during the test or the covariance of the AC impedance test data and the state of charge; is the standard deviation of the AC impedance test data, is the standard deviation of the ambient temperature or the state of charge during the test, X is the AC impedance test data, Y is the ambient temperature or the state of charge during the test; E(X) is the sample expectation of the AC impedance test data, E(Y) is the sample expectation of the ambient temperature or the state of charge during the test, E(XY) is the expectation of the product of the AC impedance test data and the ambient temperature or the state of charge during the test, is the expectation of the square of the AC impedance test data, is the square of the expectation of the AC impedance test data, is the expectation of the square of the ambient temperature during the test, is the square of the expectation of the ambient temperature or the state of charge during the test, is the expectation of the product when the AC impedance test data and the ambient temperature deviate from their respective means simultaneously or the expectation of the product when the AC impedance test data and the state of charge deviate from their respective means simultaneously.

[0009] Furthermore, the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with temperature satisfy the correlation is the Pearson correlation coefficient satisfies 0.8 < ≤ 1.

[0010] Furthermore, the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with the state of charge satisfy the non-correlation is the Pearson correlation coefficient satisfies of the frequency.

[0011] Furthermore, the state of charge range is 0 to 100%, and the ambient temperature during testing is -20°C to 60°C.

[0012] A system for evaluating the internal temperature of a energy storage lithium-ion battery, comprising: An AC impedance data acquisition module for acquiring AC impedance data of the battery at characteristic frequencies, including real part, imaginary part, modulus value and phase angle; A battery internal temperature acquisition module for inputting the real part, imaginary part, modulus value and phase angle into an internal temperature estimation model of the battery to obtain the internal temperature of the current state of the battery; Wherein, the characteristic frequencies are the frequencies when both the real part and the modulus value satisfy the first set correlation with temperature and the frequencies when both the imaginary part and the phase angle satisfy the second set correlation with the state of charge.

[0013] An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for evaluating the internal temperature of the energy storage lithium-ion battery is implemented.

[0014] A computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the method for evaluating the internal temperature of the energy storage lithium-ion battery is implemented.

[0015] Compared with the prior art, the beneficial effects of the present invention are: Based on the differences in AC impedance characteristics of energy storage lithium-ion batteries at different temperatures, the present invention performs correlation analysis on the AC impedance data at characteristic frequencies with temperature and the AC impedance data with the state of charge through AC impedance test data at different temperatures, extracts the AC impedance data at frequencies that satisfy the correlation with the internal temperature of the battery and are not related to the state of charge as impedance characteristic parameters, and calculates through the internal temperature estimation model of the battery to obtain the internal temperature of the battery. Since AC impedance data is used, it can reflect the true thermal state inside the battery, without relying on traditional physical models and complex formulas, achieving high-precision estimation of the internal temperature of the battery and greatly reducing the test time cost. At the same time, only measuring the characteristic frequencies can greatly shorten the test time, quickly estimate the temperature data, significantly improve the evaluation response speed, and has high engineering application value. This method can better cope with the complex changes and unstable characteristics shown by the battery during actual use, and has the characteristics of fast evaluation and high accuracy. By real-time monitoring the internal temperature state of the battery, it can provide effective support for the battery management system and ensure the safe and stable operation of energy storage systems, electric vehicles and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1Flow chart of the method for evaluating the internal temperature of a lithium-ion energy storage battery; Figure 2 AC impedance spectra of the battery at different temperatures in the 0% SOC state; Figure 3 AC impedance spectra of the battery at different temperatures in the 30% SOC state; Figure 4 AC impedance spectra of the battery at different temperatures in the 50% SOC state; Figure 5 AC impedance spectra of the battery at different temperatures in the 70% SOC state; Figure 6 AC impedance spectra of the battery at different temperatures in the 100% SOC state; Figure 7 Pearson correlation coefficient graph between the real part and SOC; Figure 8 Pearson correlation coefficient graph between the imaginary part and SOC; Figure 9 Pearson correlation coefficient graph between the modulus value and SOC; Figure 10 Pearson correlation coefficient graph between the phase angle and SOC; Figure 11 Pearson correlation coefficient graph between the real part and temperature; Figure 12 Pearson correlation coefficient graph between the imaginary part and temperature; Figure 13 Pearson correlation coefficient graph between the modulus value and temperature; Figure 14 Pearson correlation coefficient graph between the phase angle and temperature; Figure 15 Comparison graph between the predicted value and the true value of the internal temperature estimation model of the battery; Figure 16 Schematic diagram of the system for evaluating the internal temperature of a lithium-ion energy storage battery. Detailed implementation manners

[0017] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] See Figure 1 , a method for evaluating the internal temperature of a lithium-ion energy storage battery according to the present invention, mainly for lithium-ion batteries used in new energy vehicles and electrochemical energy storage power stations, includes: Step S1, obtaining the AC impedance data of the battery at the characteristic frequency, including the real part, the imaginary part, the modulus value and the phase angle; Step S2, inputting the real part, the imaginary part, the modulus value and the phase angle into the internal temperature estimation model of the battery to obtain the internal temperature of the current state of the battery; Among them, the characteristic frequency is the frequency at which the Pearson correlation coefficients of the real part, the imaginary part, the modulus value and the phase angle with the temperature satisfy the correlation, and the frequency at which the Pearson correlation coefficients of the real part, the imaginary part, the modulus value and the phase angle with the state of charge satisfy the non-correlation. Specifically, it includes the following steps: (1) Data acquisition For multiple batteries, perform 3 charge and discharge tests on them at a specified rate (0.3 - 1.0C) current (the charge and discharge voltage range of the lithium iron phosphate battery is 2.5 - 3.65V), take the last discharge capacity as the battery calibration capacity, select the batteries with similar calibration capacities as parallel samples, and perform AC impedance tests at different states of charge (State of Charge, SOC) and different temperatures.

[0020] The state of the battery being completely discharged is recorded as 0% SOC, and the fully charged state is recorded as 100% SOC. When the battery SOC is 0%, the battery is placed in a high and low temperature chamber for a long enough time (more than 4 hours) to make the internal and external temperatures of the battery consistent. At this time, record the temperature of the high and low temperature chamber, that is, the ambient temperature during the test. Use current as the excitation signal for AC impedance testing. The test frequency is selected from 10 mHz to 1 kHz, and record the frequency (Frequency), real part, imaginary part, modulus value, and phase angle obtained from the test. Change the state of charge of the battery and the ambient temperature during the test to obtain the AC impedance data of the battery at different states of charge and different temperatures, where the range of the state of charge of the battery is 0 to 100%, and the range of the ambient temperature during the test is -20°C to 60°C.

[0021] (2)Impedance characteristic parameter extraction In order to clarify the variation relationship between the four parameters of the real part, imaginary part, modulus value, and phase angle and temperature at different frequencies, use the Pearson correlation coefficient to calculate the Pearson correlation coefficients between the four variables of the real part, imaginary part, modulus value, and phase angle and temperature at different frequencies , and the calculation formula is shown in Equation (1).

[0022] (1) (2) (3) (4) Among them, is the covariance of the AC impedance test data and the ambient temperature during the test or the covariance of the AC impedance test data and SOC; is the standard deviation of the AC impedance test data, is the standard deviation of the ambient temperature during the test or SOC, X is the AC impedance test data, Y is the ambient temperature during the test or SOC; E(X) is the sample expectation of the AC impedance test data, E(Y) is the sample expectation of the ambient temperature during the test or SOC, E(XY) is the expectation of the product of the AC impedance test data and the ambient temperature during the test or SOC, is the expectation of the square of the AC impedance test data, is the square of the expectation of the AC impedance test data, is the expectation of the square of the ambient temperature during the test, is the square of the expectation of the ambient temperature during the test or SOC, is the expectation of the product when the AC impedance test data and the ambient temperature during the test deviate from their respective means at the same time or the expectation of the product when the AC impedance test data and the state of charge deviate from their respective means at the same time.

[0023] When 0.8 < When ≤1, the AC impedance test data X and the ambient temperature Y during the test show a very strong correlation; when 0.6 < ≤0.8, the AC impedance test data X and the ambient temperature Y during the test show a strong correlation; when 0.4 < ≤0.6, the AC impedance test data X and the ambient temperature Y during the test show a medium correlation; when 0.2 < ≤0.4, the AC impedance test data X and the ambient temperature Y during the test show a weak correlation; when 0 ≤ ≤0.2, the AC impedance test data X and the ambient temperature Y during the test show a very weak correlation or no correlation.

[0024] Calculate the real part , imaginary part , modulus and phase angle of the i-th AC impedance test at different frequencies, respectively, and the correlation coefficients between them and the ambient temperature T i during the test. Select the AC impedance test data that is very strongly correlated with the ambient temperature T i () and very weakly correlated with the SOC () as the impedance characteristic parameters to exclude the influence of different state of charge, and the frequency corresponding to the impedance characteristic parameters as the characteristic frequency.

[0025] (3) Establishment of the battery internal temperature estimation model Normalize the impedance characteristic parameters at the characteristic frequency determined in step (2), namely the real part , imaginary part , modulus and phase angle , as well as the ambient temperature during the test in this state, to facilitate model calculation. Use the four parameters of the real part, imaginary part, modulus and phase angle as input parameters, and the battery temperature in this state as the output value. Use the gradient boosting tree algorithm or other algorithms to build the battery internal temperature estimation model, train the battery internal temperature estimation model, and obtain the optimal battery internal temperature estimation model parameter values.

[0026] (4) Perform an AC impedance test on the actually operating battery at the characteristic frequency to obtain the AC impedance data at the characteristic frequency, extract the real part, imaginary part, modulus and phase angle of the AC impedance data at the characteristic frequency, and input them into the battery internal temperature estimation model to obtain the internal temperature of the battery in the current state. Among them, the characteristic frequency is the frequency when both the real part and the modulus are very weakly correlated with the SOC and the frequency when both the imaginary part and the phase angle are very weakly correlated with the SOC.

[0027] The present invention realizes the real-time and accurate prediction of the internal temperature of the battery by collecting multi-dimensional data such as AC impedance, current and temperature, and constructing a non-linear model using machine learning algorithms. Compared with traditional heat conduction analysis, this method has strong adaptability and low prediction error.

[0028] Embodiment 1 A method for evaluating the internal temperature of a energy storage lithium-ion battery, comprising the following steps: (1) Experimental data collection In this embodiment, 5 lithium iron phosphate batteries with a capacity of 105 Ah are selected. First, three complete charge and discharge tests are carried out at a rate of 1 / 3C (35 A) within the voltage range of 2.5 - 3.65 V, and the last discharge capacity is used as the battery calibration capacity. Subsequently, AC impedance tests are carried out in a constant temperature chamber (i.e., a high and low temperature chamber) at -10°C, 0°C, 10°C, 25°C, 40°C and 55°C environments respectively. And before each AC impedance test, the battery is left standing in the constant temperature chamber for 4 h or more to ensure that the internal temperature of the battery is consistent with the temperature of the environmental chamber. The AC impedance test is carried out on the battery, the frequency is selected from 10 mHz to 1 kHz, and a 5 A AC current is used as the excitation signal. Subsequently, this step is repeated at 0% SOC, 30% SOC, 50% SOC, 70% SOC and 100% SOC states respectively, and the AC impedance spectra of the battery at different temperatures and different states of charge are obtained. As Figures 2 - 6 shown, when the SOC is the same, the impedance decreases significantly with the increase of temperature. Record the frequency, real part, imaginary part, modulus value and phase angle data obtained from each AC impedance test.

[0029] (2) Feature parameter extraction The AC impedance tests are carried out on 5 lithium iron phosphate batteries at the above 6 different temperatures and 5 different states of charge respectively. Select the data of the modulus value and phase angle with respect to the temperature change, a total of 150 groups, see Table 1, some data are omitted. The 150 groups of data obtained are normalized to facilitate better calculation of the subsequent model.

[0030] Taking the real part, imaginary part, modulus value and phase angle obtained from the AC impedance test as X respectively, and the temperature and state of charge as Y respectively, the Pearson correlation coefficient is calculated using Equation (1). When calculating the Pearson correlation between the parameter and the temperature, the SOC is controlled at 50%; when performing the Pearson correlation test between the parameter and the SOC, the temperature is controlled at 25°C to ensure the control of variables. The Pearson correlation coefficient is calculated and is as Figures 7 - 14 shown, Figure 7 is the Pearson correlation coefficient between the real part and the SOC; Figure 8 is the Pearson correlation coefficient between the imaginary part and the SOC; Figure 9 is the Pearson correlation coefficient between the modulus value and the SOC; Figure 10is the Pearson correlation coefficient between the phase angle and SOC; Figure 11 is the Pearson correlation coefficient between the real part and temperature; Figure 12 is the Pearson correlation coefficient between the imaginary part and temperature; Figure 13 is the Pearson correlation coefficient between the modulus value and temperature; Figure 14 is the Pearson correlation coefficient between the phase angle and temperature; It can be found from Figures 7 - 14 that at a frequency of 100 Hz, both the real part and the modulus value show a very weak correlation with SOC; at a frequency of 250 Hz, both the imaginary part and the phase angle show a very weak correlation with SOC; and at frequencies of 100 Hz and 250 Hz, the real part, the imaginary part, the modulus value, and the phase angle all show a very strong correlation with temperature. Therefore, 100 Hz and 250 Hz are selected as characteristic frequencies to eliminate the influence of SOC on impedance testing. The real part, the modulus value, the imaginary part, and the phase angle at these characteristic frequencies are respectively selected as impedance characteristic parameters for constructing the input of the battery internal temperature estimation model.

[0031] Table 1 Changes between the selected modulus value, phase angle and temperature

[0032] (3) Construction and verification of the battery internal temperature estimation model Divide 150 groups of data into a training set and a verification set in a ratio of 8:2 for establishing and verifying the battery internal temperature estimation model. Use the gradient boosting tree algorithm to construct the battery internal temperature estimation model; Take the impedance characteristic parameters in step (2) as input values, import them into the battery internal temperature estimation model, and take the battery temperature as the output value.

[0033] In the present invention, first, the impedance characteristic parameters and the corresponding ambient temperature during testing are normalized, and then a regression model is constructed based on XGBoost (eXtreme Gradient Boosting). The parameters of the regression model are set as: loss function reg:linear, maximum tree depth 10, total number of trees 30 (preset in this embodiment, which can be adjusted according to the actual situation). During the training process, the 1st to 30th boosting trees are constructed in sequence. Each round, a new tree is generated according to the current regression model residuals and incorporated into the existing regression model to continuously reduce the error. After completing 30 rounds of preset iterations, the training ends, and the battery internal temperature estimation model is obtained.

[0034] Verify the battery internal temperature estimation model through the verification set.

[0035] The comparison results between the predicted values and the true values of the verification set in the battery internal temperature estimation model are shown in Figure 15 , where the root mean square error is about 2°C.

[0036] Based on the data obtained from AC impedance tests at different temperatures, the present invention extracts the impedance parameters at characteristic frequencies that are highly correlated with the internal temperature of the battery and uncorrelated with the state of charge (SOC) through correlation analysis of impedance parameters at different frequencies with temperature and SOC, and uses them as impedance characteristic parameters. This method realizes high-precision estimation of the internal temperature of the battery and greatly reduces the test time cost, providing a fast and high-precision real-time monitoring solution for battery thermal management.

[0037] Based on the differences in AC impedance characteristics of lithium-ion batteries for energy storage at different temperatures, the present invention screens impedance characteristic parameters strongly correlated with temperature through correlation analysis, and constructs an internal temperature estimation model of the battery in combination with machine learning algorithms. This method reflects the true thermal state inside the battery through AC impedance data, without relying on traditional physical models and complex formulas, and uses machine learning algorithms to establish a non-linear mapping relationship between impedance and temperature to achieve the evaluation of the internal temperature of the battery. At the same time, only measuring the characteristic frequencies can greatly shorten the test time, quickly estimate the temperature data, significantly improve the evaluation response speed, and has high engineering application value. This method can better cope with the complex changes and unstable characteristics shown by the battery during actual use, and has the characteristics of fast evaluation and high accuracy. By real-time monitoring the internal temperature state of the battery, it provides effective support for the battery management system, ensuring the safe and stable operation of energy storage systems, electric vehicles and other fields, and this method has broad application prospects.

[0038] See Figure 16 , in an embodiment of the present invention, a system for evaluating the internal temperature of a lithium-ion battery for energy storage is provided, including: An AC impedance data acquisition module for acquiring AC impedance data of the battery at characteristic frequencies, including real part, imaginary part, modulus value and phase angle; An internal temperature acquisition module of the battery for inputting the real part, imaginary part, modulus value and phase angle into the internal temperature estimation model of the battery for calculation to obtain the internal temperature of the current state of the battery; Among them, the characteristic frequency is the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value and phase angle with temperature satisfy the correlation, and the Pearson correlation coefficients of the real part, imaginary part, modulus value and phase angle with the state of charge satisfy the non-correlation.

[0039] In an embodiment of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the battery multi-level safety performance level evaluation method is realized.

[0040] In an embodiment of the present invention, a computer-readable storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the battery multi-level safety performance level evaluation method in the above-mentioned embodiment.

[0041] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0042] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0043] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1The functions specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One process or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the internal temperature of a lithium-ion energy storage battery, characterized in that, Including the following steps: Obtain the AC impedance data of the battery at the characteristic frequency, including the real part, imaginary part, modulus value, and phase angle; Input the real part, imaginary part, modulus value, and phase angle into the internal temperature estimation model of the battery to obtain the internal temperature of the current state of the battery; Among them, the characteristic frequency is the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with temperature satisfy the correlation, and the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with the state of charge satisfy the non-correlation.

2. The method for evaluating the internal temperature of a storage lithium-ion battery according to claim 1, wherein The internal temperature estimation model of the battery is established by a machine learning algorithm.

3. The method for evaluating the internal temperature of a lithium-ion energy storage battery according to claim 1, wherein The internal temperature estimation model of the battery is established through the following process: establish a regression model by a machine learning algorithm, use the AC impedance data of the battery at the characteristic frequency as the input of the regression model, and the internal temperature of the battery as the output, and train the regression model to obtain the internal temperature estimation model of the battery.

4. The method for evaluating the internal temperature of a lithium-ion energy storage battery according to claim 1, characterized in that, Pearson correlation coefficient Calculated by the following formula: (1) wherein, is the covariance between the AC impedance test data and the ambient temperature during the test or the covariance between the AC impedance test data and the state of charge; is the standard deviation of the AC impedance test data, is the standard deviation of the ambient temperature or the state of charge during the test, X is the AC impedance test data, Y is the ambient temperature or the state of charge during the test; E(X) is the sample expectation of the AC impedance test data, E(Y) is the sample expectation of the ambient temperature or the state of charge during the test, and E(XY) is the expectation of the product of the AC impedance test data and the ambient temperature or the state of charge during the test, is the expectation of the square of the AC impedance test data, is the square of the expectation of the AC impedance test data, is the expectation of the square of the ambient temperature during the test, is the square of the expectation of the ambient temperature or the state of charge during the test, is the expectation of the product when the AC impedance test data and the ambient temperature deviate from their respective means simultaneously or the expectation of the product when the AC impedance test data and the state of charge deviate from their respective means simultaneously.

5. The method for evaluating the internal temperature of a lithium-ion energy storage battery according to claim 4, wherein The Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with temperature satisfy the frequency of correlation as the Pearson correlation coefficient Satisfying 0.8 < ≤ 1 frequency.

6. The method for evaluating the internal temperature of a storage lithium-ion battery according to claim 4, wherein, The frequencies at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with the state of charge satisfy non-correlation are the frequencies of the Pearson correlation coefficients satisfying of the frequencies 7. The method for evaluating the internal temperature of a storage lithium-ion battery according to claim 4, wherein The state of charge range is 0 to 100%, and the ambient temperature during testing is -20°C to 60°C.

8. A system for evaluating the internal temperature of a lithium-ion energy storage battery, characterized in that, Including: An AC impedance data acquisition module, configured to obtain the AC impedance data of the battery at the characteristic frequency, including the real part, imaginary part, modulus value, and phase angle; An internal temperature acquisition module of the battery, configured to input the real part, imaginary part, modulus value, and phase angle into the internal temperature estimation model of the battery, perform calculations, and obtain the internal temperature of the current state of the battery; Among them, the characteristic frequency is the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with temperature satisfy the correlation, and the frequency at which the Pearson correlation coefficients of the real part, imaginary part, modulus value, and phase angle with the state of charge satisfy the non-correlation.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the described processor executes the described computer program, it implements the method for evaluating the internal temperature of a storage lithium-ion battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the described computer program is executed by the processor, it implements the method for evaluating the internal temperature of a storage lithium-ion battery as described in any one of claims 1 to 7.

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