Battery state prediction method and system, computer equipment and storage medium

Abstract

The invention provides a battery state prediction method and system, computer equipment and a storage medium, and the method comprises the steps: randomly selecting a preset proportion of batteries from a target batch of batteries as a test sample set; performing current switching cycle life test on each battery sample to obtain a plurality of groups of current sudden change data comprising current time sequence data, voltage time sequence data and battery health state corresponding to the preset charge state segment point sequence; obtaining a current abrupt change point battery impedance spectrum according to the current time sequence data and the voltage time sequence data in each group of current abrupt change data, and constructing a battery state prediction model according to all the current abrupt change point battery impedance spectrums and the corresponding battery health states; and obtaining a battery state prediction value according to the obtained current mutation battery impedance spectrum of the to-be-evaluated battery and the battery state prediction model. According to the invention, the high efficiency, accuracy and operation convenience of online monitoring of the health state of the battery can be effectively improved under the low-cost condition of not additionally adding measuring equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of battery status management, and in particular to a battery status prediction method, system, computer equipment and storage medium. Background Art

[0002] The battery status is the basis for measuring the battery's storage capacity and power delivery capacity. It reflects the degree of battery aging and can be used for battery life assessment and battery optimization management to avoid safety hazards caused by battery failure.

[0003] Existing battery state prediction mainly includes state estimation methods based on battery equivalent circuit models and data-driven battery health state estimation methods. However, state estimation based on battery equivalent circuit models mainly estimates the battery state of charge and depends on the accuracy of the battery equivalent circuit model. The battery equivalent circuit model is difficult to directly reflect the battery capacity, and the battery capacity estimation is easily coupled with the state of charge estimation, which makes battery state estimation difficult. In addition, during the battery aging process, the battery model is prone to change, resulting in the gradual accumulation of estimation errors, and even causing internal circulation or short board effects in the energy storage system. At the same time, data-driven battery health status estimation mainly directly uses data such as current, voltage and temperature during battery operation to fit the current battery status data using various neural networks. However, the current and temperature in the actual battery operation data are highly coupled and the temperature itself cannot change suddenly, which makes neural network training prone to overfitting / underfitting problems, resulting in a lot of time and computing costs, and the estimation results are relatively divergent. Although the existing method of introducing impedance information can solve the application defects of the above-mentioned data-driven method to a certain extent, the battery impedance must be measured using pulse current, and impedance measurement can generally only be performed when the system is idle, which is difficult to perform during real-time operation of the system. There are major limitations, and it is difficult to meet the timeliness requirements of status detection, and it is not suitable for status monitoring in the current energy storage system energy scheduling scenario. Summary of the invention

[0004] The purpose of the present invention is to provide a battery state prediction method, which uses voltage data and current data before and after the current mutation point under different battery health states collected based on current switching cycle life test, and accordingly adopts a data-driven method combined with polynomial fitting to construct a battery state prediction model for analyzing the current mutation battery impedance spectrum of the same batch of batteries to be evaluated in actual use to obtain the corresponding battery state prediction value. It can not only avoid dependence on the battery equivalent circuit model, but also avoid the problems of high cost, poor accuracy and insufficient timeliness of the existing data-driven model application. It can effectively improve the efficiency, accuracy and operational convenience of online monitoring of battery health status without the need for additional low-cost measurement equipment.

[0005] In order to achieve the above objectives, it is necessary to provide a battery status prediction method, system, computer device and storage medium in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a battery status prediction method, the method comprising the following steps: Randomly select a preset proportion of batteries from the target batch of batteries as a test sample set; Performing a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current time series data and voltage time series data centered on the current mutation moment, and a battery health state corresponding to the current mutation moment; According to the current time series data and voltage time series data in each group of current mutation data, the corresponding battery impedance spectrum of the current mutation point is obtained; According to the battery impedance spectra of all current mutation points and the corresponding battery health status, a corresponding battery status prediction model is constructed; A current mutation battery impedance spectrum of a battery to be evaluated in the target batch of batteries is obtained, and a battery state prediction value corresponding to the battery to be evaluated is obtained according to the current mutation battery impedance spectrum and the battery state prediction model.

[0007] Furthermore, the step of performing a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence includes: According to the actual operating condition range of the battery sample, a first charge and discharge current, a second charge and discharge current and a minimum charge and discharge current are set; the first charge and discharge current is greater than the second charge and discharge current; According to the preset current switching charge and discharge rule, a cycle life test is performed on each battery sample and corresponding current mutation data and battery health status are collected until the total discharge charge in the cycle drops to a preset charge amount; the preset current switching charge and discharge rule is to use the first charge and discharge current and the second charge and discharge current alternately to perform current rate switching charging on each battery sample according to each charge state in the preset charge state segment point sequence until the cut-off voltage is reached, and then switch to constant voltage discharge until the current drops to the minimum charge and discharge current and then stand for a preset standstill time.

[0008] Furthermore, the step of obtaining the battery impedance spectrum of the corresponding current mutation point according to the current time series data and the voltage time series data in each group of current mutation data includes: Performing complex Morlet wavelet transforms at different frequencies on the current time series data and the voltage time series data respectively to obtain corresponding current wavelet coefficients and voltage wavelet coefficients; According to the current wavelet coefficient and voltage wavelet coefficient of different frequencies, the corresponding battery impedance of the current mutation point is obtained; The current mutation point battery impedance spectrum is generated according to the current mutation point battery impedance at different frequencies.

[0009] Furthermore, the step of constructing a corresponding battery state prediction model according to the battery impedance spectra at all current mutation points and the corresponding battery health states includes: Obtain the impedance spectrum real axis intersection value, inflection point impedance amplitude and inflection point impedance phase angle value corresponding to each current mutation point of the battery impedance spectrum in different cycle life test cycles; According to the real axis intersection value of the impedance spectrum, the inflection point impedance amplitude value and the inflection point impedance phase angle value, a sequence of real axis intersection value of the impedance spectrum, a sequence of inflection point impedance amplitude value and a sequence of inflection point impedance phase angle value corresponding to the preset state of charge segment point sequence in different cycle life test periods are obtained; The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude value sequence and the inflection point impedance phase angle value sequence are used as independent variables, and the corresponding battery health state is used as the dependent variable for polynomial fitting to obtain the battery state prediction model.

[0010] Furthermore, the step of obtaining the battery state prediction model by performing polynomial fitting using the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude value sequence and the inflection point impedance phase angle value sequence as independent variables and the corresponding battery health state sequence as the dependent variable includes: The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence of the previously preset test cycle number are used as independent variables, and polynomial fitting is performed with the corresponding battery health state as the dependent variable to obtain the corresponding first state prediction model, second state prediction model and third state prediction model; Using the second state prediction model and the third state prediction model as a temperature correction amount and a state of charge correction amount respectively to correct the first state prediction model, so as to obtain a polynomial fitting model; Iteratively adjusting the fitting coefficients of the polynomial fitting model by sequentially increasing the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence, the inflection point impedance phase angle value sequence and the battery health state of the test cycle; If the correlation coefficient of the fitting curve of the polynomial fitting model after each round of fitting coefficient adjustment iteration is greater than the preset correlation coefficient threshold, then when the preset number of iterations is reached, the iteration is stopped and the updated polynomial fitting model is used as the battery state prediction model; If the fitting curve correlation coefficient of the polynomial fitting model after iterative update of the fitting coefficient adjustment is less than the preset correlation coefficient threshold, the iteration is stopped, and all health states before the battery health state value corresponding to the latest added test cycle are taken as the same health state segment, and the polynomial fitting model corresponding to the previous round of iteration is taken as the piecewise polynomial fitting model of the same health state segment; The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude value sequence, the inflection point impedance phase angle value sequence and the corresponding battery health status of the latest added test cycle and each test cycle after the latest added test cycle are polynomially fitted to construct at least one corresponding piecewise polynomial fitting model until the preset number of iterations is reached, and all piecewise polynomial fitting models are aggregated to obtain the battery state prediction model.

[0011] Further, the piecewise polynomial fitting model is expressed as: In the formula, in, represents the battery health state prediction value of the i-th health state segment; Represents the intersection value of the real axis based on the anti-spectrum in the i-th health state segment Predicted value of health status; Indicates the impedance amplitude based on the inflection point in the i-th health state segment Predicted value of health status; Indicates the impedance phase angle value based on the inflection point in the i-th health state segment Predicted value of health status; and is a constant coefficient; express The fitting coefficients in ; express The fitting coefficients in ; express The fitting coefficients in ; , and The positive integer power representing the i-th health status segment.

[0012] Further, the step of obtaining the impedance spectrum real axis intersection value, the inflection point impedance amplitude value and the inflection point impedance phase angle value corresponding to each current mutation point battery impedance spectrum includes: Determine whether there is an impedance point with an impedance phase angle of 0 in the battery impedance spectrum at the current mutation point, and if so, obtain the real axis intersection value of the impedance spectrum according to the impedance point, and vice versa, obtain the first impedance point and the second impedance point corresponding to the impedance phase angle changing from a positive value to a negative value in the battery impedance spectrum at the current mutation point; According to the impedance amplitude and phase angle of the first impedance point and the second impedance point, based on the principle of similar triangle, the real axis intersection value of the impedance spectrum is obtained; the real axis intersection value of the impedance spectrum is expressed as in, Represents the intersection value of the real axis of the anti-spectrum; and Respectively represent the impedance amplitude and phase angle of the first impedance point; and represent the impedance amplitude and phase angle of the second impedance point respectively; Obtaining, in order from low to high frequency, a first impedance phase angle mutation point in the current mutation point battery impedance spectrum, and taking the first impedance phase angle mutation point as an impedance spectrum inflection point; The impedance amplitude and the impedance phase angle value of the inflection point of the impedance spectrum are respectively used as the corresponding inflection point impedance amplitude and inflection point impedance phase angle value.

[0013] In a second aspect, an embodiment of the present invention provides a battery state prediction method system, the system comprising: A sample acquisition module, used to randomly select a preset proportion of batteries from a target batch of batteries as a test sample set; A data acquisition module, used to perform a current switching cycle life test on each battery sample in the test sample set, and obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current time series data and voltage time series data centered on the current mutation moment, and a battery health state corresponding to the current mutation moment; An impedance spectrum acquisition module is used to obtain a battery impedance spectrum at a corresponding current mutation point according to the current time series data and voltage time series data in each group of current mutation data; A model building module is used to build a corresponding battery state prediction model based on the battery impedance spectra of all current mutation points and the corresponding battery health status; The state prediction module is used to obtain the current mutation battery impedance spectrum of the battery to be evaluated in the target batch of batteries, and obtain the battery state prediction value corresponding to the battery to be evaluated according to the current mutation battery impedance spectrum and the battery state prediction model.

[0014] In a third aspect, an embodiment of the present invention further 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 implements the steps of the above method when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0016] The present invention provides a battery state prediction method, system, computer device and storage medium. The method realizes randomly selecting a preset proportion of batteries from a target batch of batteries as a test sample set, performing a current switching cycle life test on each battery sample in the test sample set, and obtaining multiple groups of current timing data and voltage timing data corresponding to a preset state of charge segment point sequence, including current mutation data and voltage timing data centered on the current mutation moment, and current mutation data of the battery health state corresponding to the current mutation moment. According to the current timing data and voltage timing data in each group of current mutation data, the corresponding current mutation point battery impedance spectrum is obtained, and according to all current mutation point battery impedance spectra and the corresponding battery health state, a corresponding battery state prediction model is constructed, and the current mutation battery impedance spectrum of the battery to be evaluated in the target batch of batteries is obtained, and according to the current mutation battery impedance spectrum and the battery state prediction model, the battery state prediction value corresponding to the battery to be evaluated is obtained. Technical solution. Compared with the existing technology, the battery state prediction method, based on the voltage data and current data before and after the current mutation point under different battery health states collected based on the current switching cycle life test, adopts a data-driven method combined with polynomial fitting to construct a battery state prediction model for analyzing the current mutation battery impedance spectrum of the same batch of batteries to be evaluated in actual use to obtain the corresponding battery state prediction value. It can not only avoid the dependence on the battery equivalent circuit model, but also avoid the problems of high cost, poor accuracy and lack of timeliness of the existing data-driven model application. It can effectively improve the efficiency, accuracy and ease of operation of online monitoring of battery health status without the need for additional measurement equipment at a low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a battery state prediction method in an embodiment of the present invention; Figure 2 is a schematic diagram of a flow chart of performing a cycle life test on each battery sample according to a preset current switching charge and discharge rule in an embodiment of the present invention; Figure 3 is a schematic diagram of a battery impedance spectrum corresponding to a current mutation point in an embodiment of the present invention; Figure 4Schematic diagram of the fitting effect of the battery state prediction model in an embodiment of the present invention; Figure 5 is a structural schematic diagram of a battery state prediction method system in an embodiment of the present invention; Figure 6 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In one embodiment, Figure 1 As shown, a battery state prediction method is provided, comprising the following steps: S11. Randomly select a preset proportion of batteries from the target batch of batteries as a test sample set; wherein the target batch of batteries can be understood as any battery cells of the same type as the production batch of the battery cells to be evaluated; the corresponding preset proportion of random selection can be set according to actual application requirements and is not specifically limited here.

[0020] S12. Perform a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current timing data and voltage timing data centered on the current mutation moment, and the battery health status corresponding to the current mutation moment; wherein, the current switching cycle life test can be understood as a test process in which a switching charging current is introduced to generate a current mutation phenomenon according to each state of charge point in a preset state of charge segment point sequence during the charging and discharging process of a traditional battery cycle life test, so as to collect current timing data and voltage timing data centered on the current mutation moment under different battery health states.

[0021] In this embodiment, the current time series data and voltage time series data centered on the current mutation moment can be understood as the current data and voltage data in the constant current charging and discharging process for a certain period of time (such as 1 minute) before and after each current mutation moment. Specifically, the step of performing a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to the preset state of charge segment point sequence includes: According to the actual operating condition range of the battery sample, the first charge and discharge current, the second charge and discharge current and the minimum charge and discharge current are set; wherein the actual operating condition range can be understood as the charge and discharge conditions allowed under the normal operating state of the battery sample; in order to introduce a current mutation phenomenon during the charging process, the present embodiment preferably sets the first charge and discharge current I1 and the second charge and discharge current I2 to be within the current range between the maximum allowable charge and discharge current and the minimum charge and discharge current, the first charge and discharge current is greater than the second charge and discharge current, and the minimum charge and discharge current is 0.05C, where C is the battery charge and discharge rate.

[0022] According to the preset current switching charge and discharge rule, a cycle life test is performed on each battery sample, and the corresponding current mutation data and battery health status are collected until the total discharge charge in the cycle drops to the preset charge amount; the preset current switching charge and discharge rule is to alternately use the first charge and discharge current and the second charge and discharge current to perform current rate switching charging on each battery sample according to each charge state in the preset charge state segment point sequence until the cut-off voltage is reached, and then switch to constant voltage discharge until the current drops to the minimum charge and discharge current and stand for a preset standstill time; wherein the preset charge amount and the preset standstill time can be set according to actual application requirements, and the preset charge amount is preferably set to 0.7Q in this embodiment. nom (Q nom is the initial capacity of the battery), and the preset rest time is set to 3 hours.

[0023] The preset state of charge segment point sequence in this embodiment can be set according to actual application requirements, including multiple different SOC segment points, which can be expressed as SOC=[SOC0, SOC1, SOC2, ..., SOCn], and the spacing between adjacent state of charge points in the sequence needs to ensure that when the battery is charged and discharged according to the first charge and discharge current, the charge and discharge time is not less than a fixed time t1 (such as 2 minutes), and the constant voltage charge and discharge starting point in the spacing between adjacent state of charge points also needs to meet the interval requirement that the charge and discharge time is not less than a fixed time t1.

[0024] In practical applications, the cycle life test is performed on each battery sample according to the preset current switching charge and discharge rules, and the corresponding current mutation data and battery health status are collected until the total discharge charge of the cycle drops to the preset charge. The specific cycle test process is as follows: Figure 2 As shown, the following steps are included: 1) At the beginning of the cycle, starting from the fully charged state, constant current-constant voltage discharge is performed according to the first charge and discharge current. After the discharge reaches the cut-off voltage, constant voltage discharge is performed. After constant voltage discharge until the current decreases to the minimum charge and discharge current (0.05C), it is left to stand for a period of time (such as 3 hours). At this time, the SOH of this cycle is considered to be the ratio of the total charge discharged to the rated charge (SOH=Q / Qnom, used to indicate the actual capacity attenuation of the battery). Based on this SOH, the charge required for different SOC segmentation points is calculated.

[0025] 2) After the resting is completed, charging starts, and it is considered that SOC=0 at this time. The second charge and discharge current is used to charge to SOC=SOC0, and then the first charge and discharge current is switched to charge to SOC=SOC1, and then the second charge and discharge current is switched back to charge to SOC=SOC2; and so on. Each time the battery is charged to the SOC segment point in the preset state of charge segment point sequence, the charging current rate is switched once until the battery is charged to the battery charging cut-off voltage, and then the battery is switched to constant voltage charging until the current is less than the minimum charge and discharge current. It should be noted that during the cyclic charge and discharge process, each time the charge reaches an SOC segment point in the preset state of charge segment point sequence and the charging current rate is switched once (the first charge and discharge current and the second charge and discharge current are switched alternately at the SOC sequence point), it is necessary to collect the voltage and current during the constant current charge and discharge process for a period of t2 (e.g., 1 minute) before the SOC segment point, as well as the voltage and current during the constant current charge and discharge process for a period of t2 (e.g., 1 minute) after the SOC segment point, to obtain the current time series data and voltage time series data centered on the moment of current mutation under the required battery health state.

[0026] 3) After the charging current is less than the minimum charge and discharge current, stop charging and let the battery rest for a preset rest time, then return to step 1) to start the cycle, and continue to cycle charge and discharge until the total discharge charge of the battery in the cycle is reduced to the preset charge.

[0027] It should be noted that, considering that the battery capacity correction is uniformly performed according to the charging or discharging capacity in actual applications, the "constant current-constant voltage" charging and discharging process is adopted in this process, and no current mutation is performed. For example, when the charging current is switched during the charging process, the current is not switched during the discharging process, and the total discharge charge during the discharging process is used as the battery capacity, and the charging charge required for the next cycle corresponding to a specific SOC sequence is calculated; when the discharge current is switched during the discharging process, the current is not switched during the charging process, and the total charge during the charging process is used as the battery capacity, and the discharge charge required for the next cycle corresponding to a specific SOC sequence is calculated.

[0028] S13. Obtain the corresponding battery impedance spectrum at the current mutation point according to the current time series data and voltage time series data in each group of current mutation data; wherein, the battery impedance spectrum at the current mutation point can be understood as the battery electrochemical impedance spectrum obtained by drawing the battery impedance obtained by wavelet transforming the current time series data and voltage time series data in the current mutation data at different frequencies. Considering that the impedance spectrum also includes other information that affects the health status of the battery in addition to the DC internal resistance, it is more conducive to the identification of the actual battery state. In this embodiment, the battery impedance spectrum at the current mutation point is preferably obtained by analyzing the current time series data and voltage time series data in each group of current mutation data for the subsequent state prediction data-driven model construction, which can effectively improve the state prediction accuracy, and can also convert the energy storage system state identification problem into the problem of accurately obtaining the battery electrochemical impedance spectrum online, which is helpful for simple and efficient monitoring of the battery state.

[0029] Specifically, the step of obtaining the corresponding current mutation point battery impedance spectrum according to the current time series data and the voltage time series data in each group of current mutation data includes: The current time series data and the voltage time series data are respectively subjected to complex Morlet wavelet transform at different frequencies to obtain corresponding current wavelet coefficients and voltage wavelet coefficients; wherein the complex Morlet wavelet transform is used to analyze the amplitude and phase of the signal, and the filter corresponding to the symmetric wavelet basis function has the characteristic of linear phase, which can avoid phase distortion. The specific expression is: in, is the complex Morlet wavelet bandwidth parameter, is the center frequency of the complex Morlet wavelet, is the sampling frequency, is the number of sampling sequence points, is the total number of sampling points; is the complex Morlet wavelet scale factor, and the wavelet scale factor a is determined by the wavelet center frequency and the impedance frequency f to be measured, and the battery impedance frequency , change the complex Morlet wavelet scale factor Different frequencies can be measured The battery impedance under is the complex Morlet wavelet displacement factor. The wavelet displacement factor b corresponds to the impedance analysis moment. The wavelet bandwidth parameter and the wavelet center frequency are selected independently.

[0030] According to the wavelet transform algorithm, the voltage wavelet coefficient expression can be obtained as follows: in, represents the voltage wavelet coefficient; Represents the kth voltage in the voltage time series data; Represents the complex Morlet wavelet Take the conjugate; According to the wavelet transform algorithm, the current wavelet coefficient expression is: in, represents the current wavelet coefficient; Represents the kth current in the current time series data.

[0031] According to the current wavelet coefficients and voltage wavelet coefficients of different frequencies, the corresponding battery impedance of the current mutation point is obtained; wherein the battery impedance of the current mutation point is expressed as: in, Indicates the battery impedance at the current mutation point.

[0032] According to the battery impedance at the current mutation point of different frequencies, the battery impedance spectrum at the current mutation point is generated; that is, the battery impedance at the current mutation point at all frequencies is plotted to obtain the following: Figure 3 The battery impedance spectrum (EIS) corresponding to a current mutation point is shown.

[0033] S14. Constructing a corresponding battery state prediction model according to the battery impedance spectra at all current mutation points and the corresponding battery health status; wherein the battery state prediction model is preferably obtained by polynomial fitting; specifically, the step of constructing a corresponding battery state prediction model according to the battery impedance spectra at all current mutation points and the corresponding battery health status comprises: Obtain the impedance spectrum real axis intersection value, inflection point impedance amplitude and inflection point impedance phase angle value corresponding to each current mutation point battery impedance spectrum in different cycle life test cycles; wherein the step of obtaining the impedance spectrum real axis intersection value, inflection point impedance amplitude and inflection point impedance phase angle value corresponding to each current mutation point battery impedance spectrum includes: Determine whether there is an impedance point with an impedance phase angle of 0 in the battery impedance spectrum at the current mutation point. If so, obtain the real axis intersection value of the impedance spectrum according to the impedance point. Otherwise, obtain the first impedance point and the second impedance point corresponding to the impedance phase angle changing from a positive value to a negative value in the battery impedance spectrum at the current mutation point, and the first impedance point is expressed as , the second impedance point is expressed as , , .

[0034] According to the impedance amplitude and phase angle of the first impedance point and the second impedance point, the real axis intersection value of the impedance spectrum is obtained based on the principle of similar triangles; the acquisition process of the real axis intersection value of the impedance spectrum is to directly obtain the real axis intersection value based on the principle of similar triangles, considering that the impedance spectrum is densely arranged near the intersection with the horizontal axis and it can be considered that this section of the impedance spectrum is linear, and it is expressed as: in, Represents the intersection value of the real axis of the anti-spectrum; and Respectively represent the impedance amplitude and phase angle of the first impedance point; and represent the impedance amplitude and phase angle of the second impedance point respectively; Obtaining, in order from low to high frequency, a first impedance phase angle mutation point in the current mutation point battery impedance spectrum, and taking the first impedance phase angle mutation point as an impedance spectrum inflection point; The impedance amplitude and impedance phase angle value of the inflection point of the impedance spectrum are respectively used as the corresponding inflection point impedance amplitude and inflection point impedance phase angle value, respectively recorded as and .

[0035] According to the real-axis intersection value of the impedance spectrum, the inflection point impedance amplitude and the inflection point impedance phase angle value, the real-axis intersection value sequence of the impedance spectrum, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence corresponding to the preset state of charge segment point sequence in different cycle life test cycles are obtained; wherein, the real-axis intersection value sequence of the impedance spectrum can be understood as the time series data obtained by arranging in chronological order the real-axis intersection value of the impedance spectrum obtained by the sudden change current introduced by the charge state segment point of the preset charge state segment point sequence in a cycle life test cycle; similarly, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence can be respectively understood as the time series data obtained by arranging in chronological order the inflection point impedance amplitude and the inflection point impedance phase angle value obtained by the sudden change current introduced by the charge state segment point of the preset charge state segment point sequence in a cycle life test cycle.

[0036] The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence are used as independent variables, and the corresponding battery health state is used as the dependent variable for polynomial fitting to obtain the battery state prediction model; wherein, the polynomial fitting can use the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence as independent variables at the same time for fitting the battery health state, but considering that in actual applications, the impedance spectrum real axis intersection value is a direct influencing factor of the battery health state, while the inflection point impedance amplitude is more sensitive to the battery charge state and the inflection point impedance phase angle value is more sensitive to the battery temperature, that is, it can only be understood as an indirect influencing factor. In this embodiment, the impedance spectrum real axis intersection value is preferably used for state prediction, and then the state prediction values ​​based on the inflection point impedance amplitude and the inflection point impedance phase angle value are used as corrections for charge and temperature, respectively, to improve the accuracy of battery health state prediction.

[0037] Specifically, the step of obtaining the battery state prediction model by performing polynomial fitting using the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence as independent variables and the corresponding battery health state sequence as the dependent variable includes: The impedance spectrum real axis intersection value sequence, inflection point impedance amplitude sequence and inflection point impedance phase angle value sequence of the previously preset test cycle number are used as independent variables, and polynomial fitting is performed with the corresponding battery health status as the dependent variable to obtain the corresponding first state prediction model, second state prediction model and third state prediction model; wherein, the previously preset test cycle number can be understood as the first few test cycles corresponding to each battery sample cycle test. In practical applications, the first cycle or the first three cycles can be selected, which can be set according to actual experience. Correspondingly, the first state prediction model, the second state prediction model and the third state prediction model can be expressed as: in, Indicates the intersection value of the real axis based on the impedance spectrum corresponding to the preset test cycle number Predicted value of health status; Indicates the impedance amplitude based on the inflection point corresponding to the preset test cycle number before Predicted value of health status; Indicates the impedance phase angle value based on the inflection point in the i-th health state segment Predicted value of health status; express The fitting coefficients in ; express The fitting coefficients in ; express The fitting coefficients in ; n1, m1, and l1 represent positive integer powers.

[0038] The second state prediction model and the third state prediction model are used as the temperature correction amount and the state of charge correction amount respectively to correct the first state prediction model to obtain a polynomial fitting model; wherein the polynomial fitting model is expressed as: in, Indicates the predicted value of the battery health status corresponding to the preset number of test cycles; and is the correction factor.

[0039] The above steps obtained It can be understood as a model in which the correlation coefficient of the fitting curve obtained by fitting the real axis intersection value sequence of the impedance spectrum of the previous preset test cycle number, the inflection point impedance amplitude sequence, the inflection point impedance phase angle value sequence and the battery health status is greater than the preset correlation coefficient threshold; wherein, the preset correlation coefficient threshold can be determined according to the actual application requirements, for example, it is set to 0.9545, and it is believed that the model exceeding this threshold has a good fitting effect and can be used. It should be noted that if the data fitting based on the previous preset test cycle number cannot obtain a model in which the correlation coefficient of the fitting curve is greater than the preset correlation coefficient threshold, the battery sample with the largest data deviation can be selected, and it is believed that there is a large error in the manufacture of the battery sample, and the relevant measurement data is excluded and then re-fitted according to the above steps.

[0040] Taking into account that the actual fitting relationship presented by the data corresponding to different cyclic test periods may be very different, for example, the maximum powers and correction coefficients corresponding to the first state prediction model, the second state prediction model and the third state prediction model will be different, it is necessary to use the following method to combine the impedance spectrum real axis intersection value sequence, inflection point impedance amplitude sequence, and inflection point impedance phase angle value sequence corresponding to different test cycles (different health states) for fitting correction.

[0041] The fitting coefficients of the polynomial fitting model are iteratively adjusted by sequentially increasing the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence, the inflection point impedance phase angle value sequence and the battery health status of the test cycle; each time the fitting coefficients of a test cycle data are adjusted, it is necessary to consider whether the correlation coefficient of the fitting curve of the updated model after the new data is fitted can still be maintained at a level greater than the preset correlation coefficient threshold, so as to determine whether the current model framework is suitable for health status prediction under the new test cycle.

[0042] If the correlation coefficient of the fitting curve of the polynomial fitting model after each round of iterative update of the fitting coefficient adjustment is greater than the preset correlation coefficient threshold, then when the preset number of iterations is reached, the iteration is stopped and the updated polynomial fitting model is used as the battery state prediction model; that is, each time the data obtained by introducing a new round of current switching cycle life test cycle is used to fine-tune the fitting coefficient of the polynomial fitting model fitted based on the previous preset number of test cycles, it is necessary to confirm whether the correlation coefficient of the fitting curve corresponding to the model after the current coefficient update can be guaranteed to be greater than the preset correlation coefficient threshold. If it can be guaranteed, it is considered that the maximum power number and correction coefficient corresponding to the first state prediction model, the second state prediction model and the third state prediction model in the previous polynomial fitting model are still applicable to the data of the newly introduced cycle, and only the fitting coefficient is different; and so on, until the number of fitting coefficient adjustments for the new round of current switching cycle life test cycle introduced reaches the preset number of iterations, then the iteration can be stopped, and it is considered that all health states are applicable to the same polynomial model framework, and the only difference is that the specific fitting coefficient needs to be adjusted.

[0043] If the correlation coefficient of the fitting curve of the polynomial fitting model after iterative update of the fitting coefficient adjustment is less than the preset correlation coefficient threshold, the iteration is stopped, and all health states before the battery health state value corresponding to the latest added test cycle are taken as the same health state segment, and the polynomial fitting model corresponding to the previous round of iteration is taken as the piecewise polynomial fitting model of the same health state segment; that is, when the correlation coefficient of the fitting curve obtained by introducing the data of a new round of current switching cycle life test cycle for fitting coefficient adjustment is less than the preset correlation coefficient threshold, it is considered that the health state corresponding to this test cycle is no longer suitable for using the previously obtained model framework, and it is necessary to re-select the polynomial power and polynomial fitting coefficient starting from this test cycle.

[0044] Perform polynomial fitting on the real axis intersection value sequence of the impedance spectrum of the latest added test cycle and each test cycle after the latest added test cycle, the inflection point impedance amplitude sequence, the inflection point impedance phase angle value sequence and the corresponding battery health status, and construct at least one corresponding piecewise polynomial fitting model until the preset number of iterations is reached, and summarize all the piecewise polynomial fitting models to obtain the battery status prediction model; it should be noted that when the battery status is fitted for the latest added test cycle and its subsequent data according to the above method steps, a second or more health status segmentation points may appear, then two or more piecewise polynomial fitting models will be obtained, which will not be repeated here.

[0045] In this embodiment, the piecewise polynomial fitting model in the battery state model when there are health state segmentation points can be expressed as: In the formula, in, represents the battery health state prediction value of the i-th health state segment; Represents the intersection value of the real axis based on the anti-spectrum in the i-th health state segment Predicted value of health status; Indicates the impedance amplitude based on the inflection point in the i-th health state segment Predicted value of health status; Indicates the impedance phase angle value based on the inflection point in the i-th health state segment Predicted value of health status; and is a constant coefficient; express The fitting coefficients in ; express The fitting coefficients in ; express The fitting coefficients in ; , and The positive integer power representing the i-th health status segment.

[0046] The data-driven model provided in this embodiment is obtained by a polynomial fitting method, which is not only simple to operate and convenient for online training, but also effectively improves the accuracy of battery health state prediction by introducing the inflection point impedance amplitude and the inflection point impedance phase angle as correction values ​​for charge and temperature respectively to correct the state prediction value based on the real axis intersection value of the impedance spectrum. The fitting model is adaptively corrected by fine-tuning the fitting coefficients by successively adding data under different health states, effectively ensuring the reliability of battery state prediction corresponding to different health state stages.

[0047] S15, obtaining a current mutation battery impedance spectrum of a battery to be evaluated in the target batch of batteries, and obtaining a battery state prediction value corresponding to the battery to be evaluated according to the current mutation battery impedance spectrum and the battery state prediction model; wherein the battery to be evaluated in the target batch of batteries can be understood as a battery used online in an energy storage system; obtaining the current mutation battery impedance spectrum corresponding to the battery to be evaluated can be achieved by the following method: The power management system converter (PCS, Power Conversion System) connected to the battery system can be used to increase the active power required by the system, reduce the active power required by the system, start the system from shutdown to constant active power, or start the system from constant active power to shutdown to trigger a current mutation test; the current mutation test can also be triggered by controlling the active or passive balancing circuit inside the battery system from a non-working state to a working state, or the battery balancing circuit from a working state to a non-working state.

[0048] After a current mutation is induced by a converter or a balancing circuit, the corresponding current time series data and voltage time series data centered on the moment of the current mutation are collected, and the corresponding current mutation battery impedance spectrum is obtained according to the above-mentioned current mutation point battery impedance spectrum acquisition method, and the impedance spectrum real axis intersection value, inflection point impedance amplitude and inflection point impedance phase angle value obtained according to the current mutation battery impedance spectrum are substituted into the corresponding battery state prediction model to obtain the corresponding battery state prediction value.

[0049] It should be noted that the state identification method based on current mutation point data is particularly suitable for the current energy scheduling form of energy storage systems. The power scheduling instructions of existing energy storage systems are often measured in minutes, that is, the output power will be kept unchanged at least at the minute level. Since the voltage of lithium batteries is stable in the normal working area, it can be regarded as constant current charging and discharging. Therefore, when the power instruction changes, the current and voltage time series data corresponding to the corresponding current mutation can be obtained, which provides reliable data support for battery state evaluation and analysis, and has high practicality.

[0050] The embodiment of the present invention provides a method of randomly extracting a preset proportion of batteries from a target batch of batteries as a test sample set, performing a current switching cycle life test on each battery sample in the test sample set, and obtaining multiple groups of current time series data and voltage time series data corresponding to a preset state of charge segment point sequence, including current mutation data and voltage time series data centered on the current mutation moment, and current mutation data of the battery health state corresponding to the current mutation moment, obtaining the corresponding current mutation point battery impedance spectrum according to the current time series data and voltage time series data in each group of current mutation data, and constructing a corresponding battery state prediction model according to the battery impedance spectra of all current mutation points and the corresponding battery health state, and obtaining the current mutation battery impedance spectrum of the battery to be evaluated in the target batch of batteries, and constructing a corresponding battery state prediction model according to the current mutation battery impedance spectrum and the battery state prediction model. The technical solution for obtaining the battery state prediction value corresponding to the battery to be evaluated not only directly corresponds to the data-driven mode of the battery health state through statistical analysis based on battery sample test data (without full charging and discharging), ensuring the simplicity and convenience of state prediction, but also only needs to use the original converter or battery balancing circuit in the battery energy storage system in online applications to trigger the current mutation and collect relevant voltage and current data based on short constant current pulse measurement to obtain the current mutation battery impedance spectrum for evaluating the battery state, which can avoid increasing the testing cost due to the addition of additional test equipment, and also constructs the battery state prediction model by combining polynomial fitting with adaptive correction, which is efficient and accurate, convenient for online training and more practical, and effectively ensures the reliability of battery state prediction corresponding to different health state stages.

[0051] In addition, in order to verify the effectiveness of the battery state prediction method provided by the present invention, a 34Ah ternary lithium-ion battery cell (the positive electrode active material of the battery is Li(Ni0.5Co0.2Mn0.3)O2, and the negative electrode active material is graphite) is placed in a 45°C constant temperature box, and the BTS-Neware battery test system is used with two charging and discharging rates of 1C and 0.5C, a charge and discharge cut-off voltage of 2.75-4.2V, and SOC segmentation points are set to 80%SOC, 60%SOC, 40%SOC, 20%SOC and other test conditions. According to the aforementioned test method of the present invention, a constant current charge and discharge cycle life test is performed on it, and the first prediction model, the second prediction model and the third prediction model are obtained based on the collected current mutation data. The corresponding fitting effects are shown in FIG. Figure 4 As shown in the figure, the goodness-of-fit correlation coefficient (R 2 ) is as high as 97%, and the maximum estimated deviation does not exceed 4%.

[0052] In the formula, , and Represents the first prediction model, the second prediction model, and the third prediction model.

[0053] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0054] In one embodiment, Figure 5 As shown, a battery state prediction method system is provided, the system comprising: The sample acquisition module 1 is used to randomly select a preset proportion of batteries from a target batch of batteries as a test sample set; The data acquisition module 2 is used to perform a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current time series data and voltage time series data centered on the current mutation moment, and the battery health state corresponding to the current mutation moment; The impedance spectrum acquisition module 3 is used to obtain the battery impedance spectrum of the corresponding current mutation point according to the current time series data and voltage time series data in each group of current mutation data; Model building module 4, used to build a corresponding battery state prediction model according to the battery impedance spectra of all current mutation points and the corresponding battery health status; The state prediction module 5 is used to obtain the current mutation battery impedance spectrum of the battery to be evaluated in the target batch of batteries, and obtain the battery state prediction value corresponding to the battery to be evaluated according to the current mutation battery impedance spectrum and the battery state prediction model.

[0055] For the specific limitations of the battery status prediction method system, please refer to the limitations of the battery status prediction method above, and the corresponding technical effects can also be obtained equivalently, so they will not be repeated here. Each module in the above-mentioned battery status prediction method system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0056] Figure 6 FIG. 1 shows an internal structure diagram of a computer device in an embodiment, and the computer device may specifically be a terminal or a server. Figure 6As shown, the computer device includes a processor, a memory, a network interface, a display, a camera and an input device connected through a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the battery state prediction method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0057] It can be understood by those skilled in the art that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computing device may include more or less components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0058] In one 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 steps of the above method are implemented when the processor executes the computer program.

[0059] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0060] In summary, the battery state prediction method, system, computer device and storage medium provided by the embodiments of the present invention not only directly correspond to the data-driven mode of the battery health state through statistical analysis based on battery sample test data (without full charging and discharging), thereby ensuring the simplicity and convenience of state prediction, but also only need to use the original converter or battery balancing circuit in the battery energy storage system in online applications to trigger the current mutation and collect relevant voltage and current data based on short constant current pulse measurement to obtain the current mutation battery impedance spectrum for evaluating the battery state, thereby avoiding the increase of testing cost due to the addition of additional testing equipment, and constructing the battery state prediction model by polynomial fitting combined with adaptive correction, which is efficient and accurate, convenient for online training and more practical, and effectively ensures the reliability of battery state prediction corresponding to different health state stages.

[0061] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0062] The above-mentioned embodiments only express several preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principle of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the protection scope of the claims.

Claims

1. A battery status prediction method, characterized in that: The method comprises the following steps: Randomly select a preset proportion of batteries from the target batch of batteries as a test sample set; Performing a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current time series data and voltage time series data centered on the current mutation moment, and a battery health state corresponding to the current mutation moment; According to the current time series data and voltage time series data in each group of current mutation data, the corresponding battery impedance spectrum of the current mutation point is obtained; According to the battery impedance spectra of all current mutation points and the corresponding battery health status, a corresponding battery status prediction model is constructed; A current mutation battery impedance spectrum of a battery to be evaluated in the target batch of batteries is obtained, and a battery state prediction value corresponding to the battery to be evaluated is obtained according to the current mutation battery impedance spectrum and the battery state prediction model.

2. The battery status prediction method according to claim 1, characterized in that: The step of performing a current switching cycle life test on each battery sample in the test sample set to obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence includes: According to the actual operating condition range of the battery sample, a first charge and discharge current, a second charge and discharge current and a minimum charge and discharge current are set; the first charge and discharge current is greater than the second charge and discharge current; According to the preset current switching charge and discharge rule, a cycle life test is performed on each battery sample and corresponding current mutation data and battery health status are collected until the total discharge charge in the cycle drops to a preset charge amount; the preset current switching charge and discharge rule is to use the first charge and discharge current and the second charge and discharge current alternately to perform current rate switching charging on each battery sample according to each charge state in the preset charge state segment point sequence until the cut-off voltage is reached, and then switch to constant voltage discharge until the current drops to the minimum charge and discharge current and then stand for a preset standstill time.

3. The battery status prediction method according to claim 1, characterized in that: The step of obtaining the battery impedance spectrum at the corresponding current mutation point according to the current time series data and the voltage time series data in each group of current mutation data comprises: Performing complex Morlet wavelet transforms at different frequencies on the current time series data and the voltage time series data respectively to obtain corresponding current wavelet coefficients and voltage wavelet coefficients; According to the current wavelet coefficient and voltage wavelet coefficient of different frequencies, the corresponding battery impedance of the current mutation point is obtained; The current mutation point battery impedance spectrum is generated according to the current mutation point battery impedance at different frequencies.

4. The battery status prediction method according to claim 1, characterized in that: The step of constructing a corresponding battery state prediction model according to the battery impedance spectra of all current mutation points and the corresponding battery health status comprises: Obtain the impedance spectrum real axis intersection value, inflection point impedance amplitude and inflection point impedance phase angle value corresponding to each current mutation point of the battery impedance spectrum in different cycle life test cycles; According to the real axis intersection value of the impedance spectrum, the inflection point impedance amplitude value and the inflection point impedance phase angle value, a sequence of real axis intersection value of the impedance spectrum, a sequence of inflection point impedance amplitude value and a sequence of inflection point impedance phase angle value corresponding to the preset state of charge segment point sequence in different cycle life test periods are obtained; The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude value sequence and the inflection point impedance phase angle value sequence are used as independent variables, and the corresponding battery health state is used as the dependent variable for polynomial fitting to obtain the battery state prediction model.

5. The battery status prediction method according to claim 4, characterized in that: The step of performing polynomial fitting using the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence as independent variables and the corresponding battery health state sequence as the dependent variable to obtain the battery state prediction model comprises: The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence and the inflection point impedance phase angle value sequence of the previously preset test cycle number are used as independent variables, and polynomial fitting is performed with the corresponding battery health state as the dependent variable to obtain the corresponding first state prediction model, second state prediction model and third state prediction model; Using the second state prediction model and the third state prediction model as a temperature correction amount and a state of charge correction amount respectively to correct the first state prediction model, so as to obtain a polynomial fitting model; Iteratively adjusting the fitting coefficients of the polynomial fitting model by sequentially increasing the impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude sequence, the inflection point impedance phase angle value sequence and the battery health state of the test cycle; If the correlation coefficient of the fitting curve of the polynomial fitting model after each round of fitting coefficient adjustment iteration is greater than the preset correlation coefficient threshold, then when the preset number of iterations is reached, the iteration is stopped and the updated polynomial fitting model is used as the battery state prediction model; If the fitting curve correlation coefficient of the polynomial fitting model after iterative update of the fitting coefficient adjustment is less than the preset correlation coefficient threshold, the iteration is stopped, and all health states before the battery health state value corresponding to the latest added test cycle are taken as the same health state segment, and the polynomial fitting model corresponding to the previous round of iteration is taken as the piecewise polynomial fitting model of the same health state segment; The impedance spectrum real axis intersection value sequence, the inflection point impedance amplitude value sequence, the inflection point impedance phase angle value sequence and the corresponding battery health status of the latest added test cycle and each test cycle after the latest added test cycle are polynomially fitted to construct at least one corresponding piecewise polynomial fitting model until the preset number of iterations is reached, and all piecewise polynomial fitting models are aggregated to obtain the battery state prediction model.

6. The battery status prediction method according to claim 5, characterized in that: The piecewise polynomial fitting model is expressed as: In the formula, in, represents the battery health state prediction value of the i-th health state segment; Represents the intersection value of the real axis based on the anti-spectrum in the i-th health state segment Predicted value of health status; Indicates the impedance amplitude based on the inflection point in the i-th health state segment Predicted value of health status; Indicates the impedance phase angle value based on the inflection point in the i-th health state segment Predicted value of health status; and is a constant coefficient; express The fitting coefficients in ; express The fitting coefficients in ; express The fitting coefficients in ; , and The positive integer power representing the i-th health status segment.

7. The battery status prediction method according to claim 4, characterized in that: The steps of obtaining the impedance spectrum real axis intersection value, the inflection point impedance amplitude value and the inflection point impedance phase angle value corresponding to the battery impedance spectrum at each current mutation point include: Determine whether there is an impedance point with an impedance phase angle of 0 in the battery impedance spectrum at the current mutation point, and if so, obtain the real axis intersection value of the impedance spectrum according to the impedance point, and vice versa, obtain the first impedance point and the second impedance point corresponding to the impedance phase angle changing from a positive value to a negative value in the battery impedance spectrum at the current mutation point; According to the impedance amplitude and phase angle of the first impedance point and the second impedance point, based on the principle of similar triangle, the real axis intersection value of the impedance spectrum is obtained; the real axis intersection value of the impedance spectrum is expressed as in, Represents the intersection value of the real axis of the anti-spectrum; and Respectively represent the impedance amplitude and phase angle of the first impedance point; and represent the impedance amplitude and phase angle of the second impedance point respectively; Obtaining, in order from low to high frequency, a first impedance phase angle mutation point in the current mutation point battery impedance spectrum, and taking the first impedance phase angle mutation point as an impedance spectrum inflection point; The impedance amplitude and the impedance phase angle value of the inflection point of the impedance spectrum are respectively used as the corresponding inflection point impedance amplitude and inflection point impedance phase angle value.

8. A battery status prediction method system, characterized in that: The system comprises: A sample acquisition module, used to randomly select a preset proportion of batteries from a target batch of batteries as a test sample set; A data acquisition module, used to perform a current switching cycle life test on each battery sample in the test sample set, and obtain multiple sets of current mutation data corresponding to a preset state of charge segment point sequence; the current mutation data includes current time series data and voltage time series data centered on the current mutation moment, and a battery health state corresponding to the current mutation moment; An impedance spectrum acquisition module is used to obtain a battery impedance spectrum at a corresponding current mutation point according to the current time series data and voltage time series data in each group of current mutation data; A model building module is used to build a corresponding battery state prediction model based on the battery impedance spectra of all current mutation points and the corresponding battery health status; The state prediction module is used to obtain the current mutation battery impedance spectrum of the battery to be evaluated in the target batch of batteries, and obtain the battery state prediction value corresponding to the battery to be evaluated according to the current mutation battery impedance spectrum and the battery state prediction model.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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