Method, system, device and medium for calculating health of sodium-ion energy storage battery

Abstract

This invention relates to the field of energy storage battery technology, specifically to a method, system, device, and medium for calculating the health status of a sodium-ion energy storage battery. The method includes: calculating several current states of charge (SOCs) of the energy storage battery at different times using the ampere-hour integral method; fitting the current SOCs and current terminal voltage to obtain a first correspondence; performing a Taylor expansion on the first correspondence to obtain the first coefficients of the nth-order Taylor expansion, forming a first coefficient matrix; pre-setting preset coefficients corresponding to different health statuses of the energy storage battery, forming a preset coefficient matrix; and comparing the first coefficient matrix with the preset coefficient matrix to obtain the health status corresponding to the preset coefficients that match the first coefficient matrix, which is the current health status of the energy storage battery. This invention effectively reduces the error in calculating the health status of energy storage batteries. When applied to sodium-ion energy storage batteries, it does not require a full charge-discharge cycle, making it more suitable for the application scenarios of sodium-ion energy storage batteries.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery technology, and more specifically, to a method, system, device, and medium for calculating the health of a sodium-ion energy storage battery. Background Technology

[0002] With the increasing emphasis placed on the energy storage industry by the government, the industry is also facing more and more opportunities and challenges. Battery health (SOH) describes the health status of a battery, specifically the ratio of the current capacity of a used battery to its initial capacity when unused, providing a comprehensive reflection of the battery's overall condition.

[0003] State of charge (SOC) refers to how much usable electrical energy a battery container still has at a given moment, while battery health (SOH) is generally understood as how much usable electrical energy is left in the battery container, that is, how much usable electrical energy it can still store. SOH can usually be calculated from SOC.

[0004] The ampere-hour integral method is currently the most widely used method for estimating the state of charge (SOC) in domestic battery management systems. Its principle is to estimate the SOC of the battery by accumulating the amount of electricity charged or discharged during the charging and discharging process. At the same time, the SOC of the battery is corrected according to the battery reaching full charge conditions or reaching static conditions for a certain period of time during the charging process.

[0005] However, the ampere-hour integration method for cumulative charge and discharge suffers from errors in current acquisition, conversion of analog signals into physical signals, and current integration during the charging and discharging process. These errors lead to a gradual increase in the final result's error over time. Furthermore, because sodium-ion energy storage battery systems operate under different conditions than conventional power batteries—sodium-ion energy storage batteries experience fewer full charge and discharge cycles—using the conventional ampere-hour integration method for SOC estimation also results in a significant error in the final SOH estimate. Summary of the Invention

[0006] The present invention aims to overcome at least one of the defects (deficiencies) of the prior art and provide a method, system, device and medium for calculating the health of sodium-ion energy storage batteries. When applied to sodium-ion energy storage batteries, it can solve the problem of large deviations when calculating the battery health of sodium-ion energy storage batteries using the ampere-hour integration method in the prior art.

[0007] The technical solution adopted in this invention is:

[0008] Firstly, a method for calculating the health status of sodium-ion energy storage batteries is provided, including:

[0009] Collect several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery;

[0010] Obtain the rated capacity and charge / discharge efficiency of the energy storage battery;

[0011] Based on several current currents, rated capacity, and charge / discharge efficiencies at different times, several current states of charge of the energy storage battery at different times are calculated using the ampere-hour integration method.

[0012] The first correspondence is obtained by fitting several current states of charge and current terminal voltages of the energy storage battery at different times.

[0013] Perform a Taylor expansion on the first correspondence to obtain the nth-order Taylor expansion of the first correspondence, where n is a natural number;

[0014] Obtain the first coefficients of the nth-order Taylor expansion of the first correspondence to form the first coefficient matrix;

[0015] Preset coefficients corresponding to different health levels of energy storage batteries are used to form a preset coefficient matrix;

[0016] The first coefficient matrix is ​​compared with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

[0017] Based on the original ampere-hour integration method for calculating the state of charge (SOC), this invention obtains a first correspondence by fitting the SOC with the current terminal voltage, thereby obtaining a first coefficient matrix. By calculating the matrix and matching the coefficients, the error incorporated into the calculation is smaller compared to the ampere-hour calculation of cumulative charge and discharge, and the final calculated SOH is more accurate.

[0018] Furthermore, the step of fitting several current states of charge and current terminal voltages of the energy storage battery at different times to obtain a first correspondence specifically includes: fitting the current state of charge of the energy storage battery as the independent variable and the current terminal voltage as the dependent variable to obtain the first correspondence.

[0019] The first correspondence established in this invention is the OCV curve composed of the current state of charge (SOC) and the current terminal voltage. When the battery is charged to the upper limit voltage with a specified current, the capacity is charged to the nominal capacity, at which point the SOC is 100%. Similarly, when discharged to the lower limit voltage with a specified current, the corresponding SOC is 0%. That is, each point on the OCV curve during the charging and discharging process has a correspondence between the current SOC and the current terminal voltage. For the same battery, different battery health levels will result in different OCV curves. Therefore, this invention uses the first correspondence to calculate battery health.

[0020] Furthermore, the step of performing a Taylor expansion on the first correspondence to obtain the nth-order Taylor expansion of the first correspondence, where n is a natural number; and obtaining the coefficients of the nth-order Taylor expansion of the first correspondence to form a first coefficient matrix, specifically includes:

[0021] Assuming the current state of charge of the energy storage battery is y, and the current terminal voltage is f(y), then the nth-order Taylor expansion of the first correspondence is: f(y) = b1y n +b2y (n-1) +b3y (n-2) +......+b n If y + b, then the first coefficient matrix is ​​[b1 b2 b3 ... b n b).

[0022] This invention achieves a higher degree of fit to the OCV curve and further reduces errors by performing an nth-order Taylor expansion on the first correspondence of the charging and discharging process. More specifically, the accuracy of the fit can be improved by increasing the value of n.

[0023] Furthermore, the preset coefficients corresponding to different health levels of the energy storage battery form a preset coefficient matrix, specifically including:

[0024] During the experimental phase, several corresponding current terminal voltages and currents were collected at different times during the charging and discharging process of the same type of energy storage battery.

[0025] The rated capacity and charge / discharge efficiency of the same type of energy storage battery under the same battery health condition during the experimental stage are obtained. Based on several currents, rated capacity and charge / discharge efficiency at different times, several current states of charge of the same type of energy storage battery at different times under the same battery health condition are calculated by the ampere-hour integration method.

[0026] By fitting several current states of charge and current terminal voltages at different times under the same battery health condition during the experimental phase, a second correspondence is obtained;

[0027] Based on the different battery health levels of the same type of energy storage battery, several sets of second correspondences were obtained for different battery health levels during the experimental phase.

[0028] Taylor expansion of several sets of second correspondences yields nth-order Taylor expansions of several second correspondences;

[0029] Obtain several sets of second coefficients from the nth-order Taylor expansion of several second correspondences to form a second coefficient matrix, which is the preset coefficient matrix.

[0030] During the experimental phase, several experiments were conducted on the same type of energy storage battery with known health levels, based on different health levels. The resulting second coefficient matrix served as experimental data and was used for subsequent comparison with the first coefficient matrix obtained during actual use. It is important to note that the energy storage batteries used in the experiments must be the same model as those used in actual applications.

[0031] This invention uses the first coefficient matrix of the same type of energy storage battery in actual use to match the corresponding preset coefficient matrix in the experimental stage, thereby reducing calculation errors. More specifically, the error can be further reduced through multiple experiments.

[0032] More specifically, the preset coefficient matrix obtained during the experimental phase can be pre-stored in the Battery Management System (BMS) used to manage the energy storage batteries in actual use. When the first coefficient matrix matches one of the preset coefficients in the preset coefficient matrix, the battery health corresponding to the preset coefficient is taken as the current battery health of the energy storage battery. This eliminates the need to store large amounts of data in the BMS, saving storage space.

[0033] Optionally, before comparing the first coefficient matrix with the preset coefficient matrix, the method further includes:

[0034] Obtain the current state of charge (SOC) status of the energy storage battery;

[0035] The preset coefficient matrix is ​​updated according to the state of charge condition to obtain the updated preset coefficient matrix.

[0036] Because energy storage batteries operate under different conditions in actual use—for example, the State of Charge (SOC) during discharge may not reach 0%, and the SOC during charging may not reach 100%—it is necessary to obtain the current state of charge (SOC) of the energy storage battery before comparing the first coefficient matrix with the preset coefficient matrix. This allows for a determination of whether the preset coefficient matrix needs updating. If an update is required, the preset coefficient matrix is ​​updated based on the SOC, and the updated preset coefficient matrix is ​​used as the current preset coefficient matrix for comparison with the first coefficient matrix. If no update is needed, the original preset coefficient matrix is ​​used for comparison with the first coefficient matrix.

[0037] For sodium-ion energy storage battery systems, this invention does not require the sodium-ion energy storage battery to undergo full charge and discharge conditions, which is more in line with the application scenarios of sodium-ion energy storage battery systems. At the same time, it takes into account the diversity of operating conditions in different stages of actual use of energy storage batteries, and adjusts the preset coefficient matrix according to different operating conditions to further reduce the error of the final calculated SOH.

[0038] Furthermore, the calculation of several current states of charge of the energy storage battery at different times using the ampere-hour integration method based on several currents, rated capacity, and charge / discharge efficiencies at different times specifically includes:

[0039] If the initial charge / discharge state is set to SOC0, then the current state of charge is:

[0040] If the initial charging / discharging state of the energy storage battery is set to SOC0, then the current state of charge of the energy storage battery is:

[0041] Where C N η is the rated capacity, η is the charge / discharge efficiency, t is the current time, and I is the current current.

[0042] In practical applications, the ampere-hour integral method uses the rated capacity of the energy storage battery at the time of manufacture. The current is obtained from the previous stage of data collection. If the current measurement is inaccurate, it will cause errors in the calculation of the State of Charge (SOC). Over time, these errors accumulate and become increasingly larger. This error problem can be solved by using a high-performance current sensor. The battery charge / discharge efficiency requires extensive prior experiments to establish an empirical formula, thus obtaining the battery's charge / discharge efficiency. The ampere-hour integral method can be used for most energy storage batteries. As long as the current measurement is accurate and there is sufficient data to estimate the initial state, the ampere-hour integral method is a simple and reliable SOC estimation method.

[0043] Secondly, a health calculation system for sodium-ion energy storage batteries is provided, including:

[0044] The data acquisition module is used to acquire several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery, as well as to obtain the rated capacity and charging and discharging efficiency of the energy storage battery.

[0045] The calculation module calculates several current states of charge of the energy storage battery at different times using the ampere-hour integration method, based on several current, rated capacity and charge / discharge efficiency at different times.

[0046] The fitting module is used to fit several current states of charge and current terminal voltages of the energy storage battery at different times to obtain the first correspondence.

[0047] The first coefficient matrix acquisition module is used to perform a Taylor expansion on the first correspondence to obtain the nth order Taylor expansion of the first correspondence, obtain the first coefficient of the nth order Taylor expansion of the first correspondence, and form the first coefficient matrix, where n is a natural number.

[0048] The preset coefficient acquisition module is used to preset the preset coefficients corresponding to different health levels of the energy storage battery, forming a preset coefficient matrix.

[0049] The health matching module is used to compare the first coefficient matrix with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

[0050] Furthermore, it also includes:

[0051] The state of charge (SOC) acquisition module is used to acquire the current SOC of the energy storage battery.

[0052] The preset coefficient matrix update module is used to update the preset coefficient matrix according to the state of charge condition to obtain the updated preset coefficient matrix.

[0053] Thirdly, an electronic device for calculating the health status of a sodium-ion energy storage battery is provided, the electronic device comprising:

[0054] At least one processor;

[0055] and a memory communicatively connected to the at least one processor;

[0056] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the energy storage battery health calculation method described in the first aspect.

[0057] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute and implement the health calculation method for sodium-ion energy storage batteries described in the first aspect.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] (1) Based on the original ampere-hour integration method for calculating SOC, this invention obtains a first correspondence relationship by fitting SOC and current terminal voltage, thereby obtaining a first coefficient matrix. By calculating the matrix and matching the coefficients, the error included in the calculation is smaller than that of the ampere-hour calculation of cumulative charging and discharging, and the final calculated SOH is more accurate.

[0060] (2) By performing an nth-order Taylor expansion on the first correspondence of the charging and discharging process, the present invention achieves a higher degree of fitting to the OCV curve and further reduces the error.

[0061] (3) When dealing with sodium-ion energy storage battery systems, the present invention can calculate the battery health without subjecting the sodium-ion energy storage battery to full charge and discharge conditions, which is more in line with the application scenarios of sodium-ion energy storage battery systems.

[0062] (4) The present invention takes into account the diversity of operating conditions in the actual use of energy storage batteries, and adjusts the preset coefficient matrix according to different operating conditions to further reduce the error of the final calculated SOH. Attached Figure Description

[0063] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention.

[0064] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention.

[0065] Figure 3 This is a structural diagram of the electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0066] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate the following embodiments, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0067] Example 1

[0068] like Figure 1 As shown in the figure, this embodiment provides a method for calculating the health of a sodium-ion energy storage battery, specifically including:

[0069] S1. Collect several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery;

[0070] S2. Obtain the rated capacity and charge / discharge efficiency of the energy storage battery;

[0071] S3. Based on several current currents, rated capacity, and charge / discharge efficiency at different times, calculate several current states of charge of the energy storage battery at different times using the ampere-hour integration method.

[0072] S4. Fit several current states of charge and current terminal voltage of the energy storage battery at different times to obtain the first correspondence;

[0073] S5. Perform a Taylor expansion on the first correspondence to obtain the nth-order Taylor expansion of the first correspondence, where n is a natural number.

[0074] S6. Obtain the first coefficient of the nth-order Taylor expansion of the first correspondence and form the first coefficient matrix;

[0075] S7. Preset coefficients corresponding to different health levels of the energy storage battery are used to form a preset coefficient matrix;

[0076] S8. Compare the first coefficient matrix with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

[0077] This embodiment, based on the original ampere-hour integration method for calculating SOC, obtains a first correspondence by fitting SOC and the current terminal voltage, thus yielding a first coefficient matrix. The matrix calculation, compared to the ampere-hour calculation based on cumulative charge and discharge, incorporates less error, resulting in a more accurate final SOH. For sodium-ion energy storage battery systems, this embodiment does not require full charge and discharge conditions, making it more suitable for the application scenarios of sodium-ion energy storage battery systems.

[0078] Since energy storage batteries typically undergo charging and discharging processes during use, and the ampere-hour integration method requires accumulating data from these processes, step S1 in this embodiment collects several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery for subsequent ampere-hour integration calculations. The data collection can be performed at preset time intervals.

[0079] Step S3 in this embodiment specifically includes:

[0080] If the initial charging / discharging state of the energy storage battery is set to SOC0, then the current state of charge of the energy storage battery is:

[0081] Where C N η is the rated capacity obtained in step S2, η is the charge / discharge efficiency obtained in step S2, t is the current time, and I is the current current collected in step S1.

[0082] In practical implementation, the rated capacity is the rated capacitance of the energy storage battery at the time of manufacture; the current is obtained by step S1. If the current measurement is inaccurate, it will cause an error in the calculation of SOC. Since the ampere-hour integration method requires the accumulation of the charging and discharging process, the error will become larger and larger over a long period of time. The battery charging and discharging efficiency needs to be obtained by establishing an empirical formula for battery charging and discharging efficiency through a large number of pre-experiments.

[0083] Step S4 in this embodiment specifically includes: fitting the current state of charge of the energy storage battery as the independent variable and the current terminal voltage as the dependent variable to obtain the first correspondence.

[0084] Step S5 in this embodiment specifically includes: Assuming the current state of charge of the energy storage battery is y, then the current terminal voltage is f(y), and the nth-order Taylor expansion of the first correspondence is: f(y) = b1y n +b2y (n-1) +b3y (n-2) +......+bn y+b.

[0085] Step S6 in this embodiment specifically includes: obtaining the first coefficient matrix as [b1 b2 b3......b n b).

[0086] In practice, the accuracy of the fit can be improved by increasing the value of n. In one embodiment, the value of n is 6, then the 6th order Taylor expansion of the first correspondence is: f(y) = b1y6 + b2y 5 +b3y 4 +b4y 3 +b5y 2 +b6y+b; the corresponding first coefficient matrix is ​​[b1 b2 b3 b4 b5 b6 b].

[0087] Step S7 in this embodiment specifically includes:

[0088] S701. Collect several corresponding current terminal voltages and currents at different times during the charging and discharging process of the same type of energy storage battery during the experimental phase.

[0089] S702. Obtain the rated capacity and charge / discharge efficiency of the same type of energy storage battery under the same battery health during the experimental stage. Based on several currents, rated capacity and charge / discharge efficiency at different times, calculate several current states of charge of the same type of energy storage battery at different times under the same battery health using the ampere-hour integration method.

[0090] S703. Fit several current states of charge and current terminal voltages at different times under the same battery health condition during the experimental phase to obtain a second correspondence.

[0091] S704. Based on the different battery health levels of the same type of energy storage battery, obtain several sets of second correspondence relationships corresponding to different battery health levels during the experimental stage.

[0092] S705. Perform Taylor expansion on several sets of second correspondences to obtain n-order Taylor expansions of several second correspondences;

[0093] S706. Obtain several sets of second coefficients from the nth-order Taylor expansion of several second correspondences to form a second coefficient matrix, which is the preset coefficient matrix.

[0094] In practical implementation, errors can be further reduced through multiple experiments. The SOC range of the preset coefficient matrix obtained in the experimental stage is generally 0%-100%. Based on the preset SOH value range and interval values, several n-order Taylor expansions under different battery health conditions are obtained, and thus the preset coefficient matrix is ​​established based on several sets of second coefficients for different battery health conditions.

[0095] For example, the SOH of an energy storage battery is typically between 80% and 100%, and the preset interval value can be set to 1%, which includes 21 different SOH values, that is, a total of 21 sets of second coefficients.

[0096] In the experimental phase, the relationship between state of charge and terminal voltage was first obtained under 100% battery health conditions. An nth-order Taylor expansion was then performed to obtain the Taylor expansion under 100% battery health conditions. For example, the 6th-order Taylor expansion obtained after a 6th-order Taylor expansion is: f(x) = a 11 x 6 +a 21 x 5 +a 31 x 4 +a 41 x 3 +a 51 x 2 +a 61 x+a1;

[0097] At a battery health rate of 99%, the relationship between the state of charge and the terminal voltage is obtained, and an nth-order Taylor expansion is performed. For example, after performing a 6th-order Taylor expansion, the 6th-order Taylor expansion at a battery health rate of 99% is: f(x) = a 12 x 6 +a 22 x 5 +a 32 x 4 +a 42 x 3 +a 52 x 2 +a 62 x+a2;

[0098] This process continues, obtaining a Taylor expansion at 1% intervals until the correspondence between the state of charge and terminal voltage is obtained at 80% battery health. An nth-order Taylor expansion is then performed; for example, a 6th-order Taylor expansion at 80% battery health yields the following formula: f(x) = a 121 x 6 +a 221 x 5 +a 321 x 4 +a 421 x 3 +a 521 x 2 +a 621 x+a 21 ;

[0099] Then, obtain the Taylor expansions corresponding to 21 different battery health conditions, extract their second coefficients, and form a 21*7 second coefficient matrix: This is the preset coefficient matrix; the matrix can be stored in the BMS's non-volatile memory (NVM) and called when the BMS is working.

[0100] In other implementations, to make the final fitted second coefficient matrix more accurate, the range of battery health can be expanded, the interval reduced, or the Taylor series increased during the raw data acquisition stage; or to reduce the computational load, the range of battery health can be narrowed, the interval increased, or the Taylor series decreased.

[0101] In practical implementation, since energy storage batteries will have different operating conditions during actual use, for example, under certain operating conditions, the SOC during discharge will not be reduced to 0%, and the SOC during charging will not be charged to 100%, but will be maintained within the range of 50%-80% or 30%-70% to work. Therefore, the preset coefficient matrix obtained in the experimental stage should also be updated according to the different operating conditions in the actual use stage.

[0102] Therefore, this embodiment also includes:

[0103] Obtain the current state of charge (SOC) status of the energy storage battery;

[0104] The preset coefficient matrix is ​​updated according to the state of charge condition to obtain the updated preset coefficient matrix.

[0105] In actual implementation, before comparing the first coefficient matrix with the preset coefficient matrix, it is necessary to obtain the current operating condition of the energy storage battery and determine whether the preset coefficient matrix needs to be updated. If an update is required, the preset coefficient matrix is ​​updated according to the state of charge condition, and the updated preset coefficient matrix is ​​used as the current preset coefficient matrix for comparison with the first coefficient matrix. If no update is required, the original preset coefficient matrix is ​​used for comparison with the first coefficient matrix.

[0106] The specific update steps include: selecting the terminal voltage data collected during the experimental phase within the current state of charge (SCC) range; refitting the reselected terminal voltage data with the current SCC to form a new correspondence; and then performing a Taylor expansion to obtain the Taylor expansion of the new correspondence, thereby obtaining the updated preset coefficient matrix.

[0107] For example, the SOC range of the preset coefficient matrix obtained in the experimental phase is generally 0%-100%. If the SOC in the actual use phase is in the range of 50%-80%, the preset coefficient matrix can be updated. That is, select the terminal voltage data in the experimental phase with an SOC range of 50%-80%, refit it to form a new correspondence, and then perform Taylor expansion to obtain the updated preset coefficient matrix. Therefore, the first coefficient matrix obtained during actual use can be matched with the updated preset coefficient matrix, which can better match the actual operating conditions of the energy storage battery and make the final battery health more accurate.

[0108] Step S8 in this embodiment specifically includes:

[0109] The preset coefficient matrix obtained during the experimental phase can be pre-stored in the battery management system (BMS). When the first coefficient matrix matches one of the preset coefficients in the preset coefficient matrix, the battery health corresponding to the preset coefficient is taken as the current battery health of the energy storage battery.

[0110] Assume that in the actual use phase, the first coefficient matrix obtained according to step S6 is [b1 b2 b3 b4 b5 b6 b], which is different from the preset coefficient matrix obtained in the experimental phase. Comparing the two matrices, we find that the first coefficient matrix [b1b2 b3 b4b5 b6 b] is similar to the second coefficient matrix [a] when the battery health is 90% during the experimental phase. 111 a 211 a 311 a 411 a 511 a 611 a 11 If the values ​​match, then the battery health of the energy storage battery in actual use is 90%.

[0111] In practice, the first coefficient matrix is ​​matched with the preset coefficients in the preset coefficient matrix. The difference can be calculated by taking the difference of the corresponding coefficients of each order. When the difference is the smallest or when the mean of the absolute values ​​of several differences is the smallest, it is considered to match the set of coefficients in the preset coefficient matrix.

[0112] For example, when the first coefficient matrix [b1 b2 b3 b4 b5 b6 b] is compared with the preset coefficient [a] at 90% battery health... 111 a 211 a 311 a 411 a 511 a 611 a 11The differences between the first coefficient matrix [b1 b2 b3 b4 b5 b6 b] and the preset coefficients for battery health levels other than 90% can be considered to be less than the differences between the first coefficient matrix [b1 b2 b3 b4 b5 b6 b] and the preset coefficients for battery health levels other than 90%. 111 a 211 a 311 a 411 a 511 a 611 a 11 Matching.

[0113] Alternatively, when the first coefficient matrix [b1 b2 b3 b4 b5 b6 b] is compared with the preset coefficient [a] at 90% battery health... 111 a 211 a 311 a 411 a 511 a 611 a 11 The average absolute value of the difference between the first coefficient matrix [b1 b2 b3 b4b5 b6 b] and the preset coefficients under other battery health conditions (excluding 90%) is less than the average absolute value of the difference between the first coefficient matrix [b1 b2 b3 b4b5 b6 b] and the preset coefficients under 90% battery health conditions. Therefore, it can be considered that the first coefficient matrix [b1 b2 b3 b4b5 b6 b] and the preset coefficients under 90% battery health conditions [a 111 a 211 a 311 a 411 a 511 a 611 a 11 Matching.

[0114] This embodiment stores a preset coefficient matrix in the BMS and updates it based on actual operating conditions. This eliminates the need for the BMS to store large amounts of data. Furthermore, compared to ampere-hour calculations based on cumulative charge and discharge, this embodiment incorporates less error and yields a more accurate State of Health (SOH). Building upon the original ampere-hour integration method for calculating State of Charge (SOC), this embodiment fits the SOC to the current terminal voltage to obtain a first correspondence, thus generating a first coefficient matrix. Through matrix calculation and matching coefficients from the actual usage phase with those from the experimental phase, the calculation error is further reduced compared to ampere-hour calculations based on cumulative charge and discharge, resulting in a more accurate SOH. For sodium-ion battery systems, this embodiment does not require full charge and discharge cycles, making it more suitable for the application scenarios of sodium-ion battery systems.

[0115] Example 2

[0116] like Figure 2As shown, this embodiment provides a health calculation system for a sodium-ion energy storage battery to implement the health calculation method for a sodium-ion energy storage battery provided in Embodiment 1, including:

[0117] The acquisition module 101 is used to acquire several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery, and to obtain the rated capacity and charging and discharging efficiency of the energy storage battery.

[0118] The calculation module 102 is used to calculate several current states of charge of the energy storage battery at different times using the ampere-hour integration method based on several current, rated capacity and charge-discharge efficiency at different times.

[0119] Specifically, this includes setting the initial charging / discharging state of the energy storage battery to SOC0, then the current state of charge of the energy storage battery is:

[0120] Where C N η is the rated capacity, η is the charge / discharge efficiency, t is the current time, and I is the current current.

[0121] The fitting module 103 is used to fit several current states of charge and current terminal voltages of the energy storage battery at different times to obtain a first correspondence.

[0122] Specifically, this includes fitting the current state of charge of the energy storage battery as the independent variable and the current terminal voltage as the dependent variable to obtain the first correspondence.

[0123] The first coefficient matrix acquisition module 104 is used to perform a Taylor expansion on the first correspondence to obtain the nth order Taylor expansion of the first correspondence, obtain the first coefficient of the nth order Taylor expansion of the first correspondence, and form the first coefficient matrix, where n is a natural number.

[0124] Specifically, assuming the current state of charge of the energy storage battery is y, and the current terminal voltage is f(y), then the nth-order Taylor expansion of the first correspondence is: f(y) = b1y n +b2y (n-1) +b3y (n-2) +......+b n y+b; the first coefficient matrix is ​​[b1b2b3......b n b).

[0125] The preset coefficient acquisition module 105 is used to preset the preset coefficients corresponding to different health levels of the energy storage battery, forming a preset coefficient matrix.

[0126] Specifically, this includes: collecting several corresponding current terminal voltages and currents at different times during the charging and discharging process of the same type of energy storage battery in the experimental phase;

[0127] The rated capacity and charge / discharge efficiency of the same type of energy storage battery under the same battery health condition during the experimental stage are obtained. Based on several currents, rated capacity and charge / discharge efficiency at different times, several current states of charge of the same type of energy storage battery at different times under the same battery health condition are calculated by the ampere-hour integration method.

[0128] By fitting several current states of charge and current terminal voltages at different times under the same battery health condition during the experimental phase, a second correspondence is obtained;

[0129] Based on the different battery health levels of the same type of energy storage battery, several sets of second correspondences were obtained for different battery health levels during the experimental phase.

[0130] Taylor expansion of several sets of second correspondences yields nth-order Taylor expansions of several second correspondences;

[0131] Obtain several sets of second coefficients from the nth-order Taylor expansion of several second correspondences to form a second coefficient matrix, which is the preset coefficient matrix.

[0132] The health matching module 106 is used to compare the first coefficient matrix with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

[0133] Specifically, this includes: matching the first coefficient matrix with the preset coefficients in the preset coefficient matrix. This can be done by taking the difference between the corresponding coefficients of each order. When the difference is the smallest or when the mean of the absolute values ​​of several differences is the smallest, it is considered to match the set of coefficients in the preset coefficient matrix.

[0134] This embodiment also includes:

[0135] The state of charge (SCC) acquisition module 107 is used to acquire the current SCC of the energy storage battery.

[0136] The preset coefficient matrix update module 108 is used to update the preset coefficient matrix according to the state of charge condition to obtain the updated preset coefficient matrix. The specific update steps include: selecting the terminal voltage data of the state of charge range under the current state of charge condition in the experimental stage, refitting to form a new correspondence, and then performing Taylor expansion to obtain the Taylor expansion of the new correspondence, thereby obtaining the updated preset coefficient matrix.

[0137] Since energy storage batteries may operate under different conditions during actual use, such as the SOC not dropping to 0% during discharge or charging to 100% during charging, it is necessary to obtain the current operating condition of the energy storage battery before comparing the first coefficient matrix with the preset coefficient matrix. If an update is needed, the preset coefficient matrix is ​​updated according to the state of charge condition, and the updated preset coefficient matrix is ​​used as the current preset coefficient matrix for comparison with the first coefficient matrix. If no update is needed, the original preset coefficient matrix is ​​used for comparison with the first coefficient matrix.

[0138] Example 3

[0139] This embodiment provides an electronic device for calculating the battery health of a sodium-ion energy storage battery, the electronic device comprising:

[0140] At least one processor;

[0141] and a memory communicatively connected to the at least one processor;

[0142] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform a method for calculating the health of a sodium-ion energy storage battery as described in Example 1.

[0143] This embodiment also provides a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute a method for calculating the health of a sodium-ion energy storage battery as described in Embodiment 1.

[0144] The electronic device described herein is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0145] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0146] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0147] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the health calculation method for sodium-ion energy storage batteries.

[0148] In practice, the battery health calculation method for the energy storage battery can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery health calculation method for the energy storage battery described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the health calculation method for the sodium-ion energy storage battery by any other suitable means (e.g., by means of firmware).

[0149] The various embodiments described above can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] Since the computer programs of this embodiment can be written in any combination of one or more programming languages, these computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, or as independent software packages, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0151] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0152] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0153] This embodiment can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0154] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for calculating the health status of a sodium-ion energy storage battery, characterized in that, include: Collect several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery; Obtain the rated capacity and charge / discharge efficiency of the energy storage battery; Based on several current currents, rated capacity, and charge / discharge efficiencies at different times, several current states of charge of the energy storage battery at different times are calculated using the ampere-hour integration method. The first correspondence is obtained by fitting several current states of charge and current terminal voltages of the energy storage battery at different times. Perform a Taylor expansion on the first correspondence to obtain the nth-order Taylor expansion of the first correspondence, where n is a natural number; Obtain the first coefficients of the nth-order Taylor expansion of the first correspondence to form the first coefficient matrix; Preset coefficients corresponding to different health levels of energy storage batteries are used to form a preset coefficient matrix; The first coefficient matrix is ​​compared with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

2. The method for calculating the health of a sodium-ion energy storage battery according to claim 1, characterized in that, The step of fitting several current states of charge and current terminal voltages of the energy storage battery at different times to obtain a first correspondence specifically includes: fitting the current state of charge of the energy storage battery as the independent variable and the current terminal voltage as the dependent variable to obtain the first correspondence.

3. The method for calculating the health of a sodium-ion energy storage battery according to claim 2, characterized in that, The first correspondence is expanded using Taylor to obtain the nth-order Taylor expansion of the first correspondence, where n is a natural number. Obtain the coefficients of the nth-order Taylor expansion of the first correspondence to form the first coefficient matrix, specifically including: Assuming the current state of charge of the energy storage battery is y, and the current terminal voltage is f(y), then the nth-order Taylor expansion of the first correspondence is: f(y) = b1y n +b2y (n-1) +b3y (n-2) +......+b n If y + b, then the first coefficient matrix is ​​[b1 b2 b3 ... b n b).

4. The method for calculating the health of a sodium-ion energy storage battery according to claim 3, characterized in that, The preset coefficients corresponding to different health levels of the energy storage battery form a preset coefficient matrix, specifically including: During the experimental phase, several corresponding current terminal voltages and currents were collected at different times during the charging and discharging process of the same type of energy storage battery. The rated capacity and charge / discharge efficiency of the same type of energy storage battery under the same battery health condition during the experimental stage are obtained. Based on several currents, rated capacity and charge / discharge efficiency at different times, several current states of charge of the same type of energy storage battery at different times under the same battery health condition are calculated by the ampere-hour integration method. By fitting several current states of charge and current terminal voltages at different times under the same battery health condition during the experimental phase, a second correspondence is obtained; Based on the different battery health levels of the same type of energy storage battery, several sets of second correspondence relationships were obtained for different battery health levels during the experimental phase. Taylor expansion of several sets of second correspondences yields nth-order Taylor expansions of several second correspondences; Obtain several sets of second coefficients from the nth-order Taylor expansion of several second correspondences to form a second coefficient matrix, which is the preset coefficient matrix.

5. The method for calculating the health of a sodium-ion energy storage battery according to claim 4, characterized in that, Also includes: Obtain the current state of charge (SOC) status of the energy storage battery; The preset coefficient matrix is ​​updated according to the state of charge condition to obtain the updated preset coefficient matrix.

6. A method for calculating the health of a sodium-ion energy storage battery according to any one of claims 1-5, characterized in that, The method of calculating several current states of charge of the energy storage battery at different times using the ampere-hour integration method based on several currents, rated capacity, and charge / discharge efficiencies at different times specifically includes: If the initial charging / discharging state of the energy storage battery is set to SOC0, then the current state of charge of the energy storage battery is: Where C N η is the rated capacity, η is the charge / discharge efficiency, t is the current time, and I is the current current.

7. A health calculation system for sodium-ion energy storage batteries, characterized in that, include: The data acquisition module is used to acquire several corresponding current terminal voltages and currents at different times during the charging and discharging process of the energy storage battery, as well as to obtain the rated capacity and charging and discharging efficiency of the energy storage battery. The calculation module is used to calculate several current states of charge of the energy storage battery at different times using the ampere-hour integration method based on several current, rated capacity and charge-discharge efficiency at different times. The fitting module is used to fit several current states of charge and current terminal voltages of the energy storage battery at different times to obtain the first correspondence. The first coefficient matrix acquisition module is used to perform a Taylor expansion on the first correspondence to obtain the nth order Taylor expansion of the first correspondence, obtain the first coefficient of the nth order Taylor expansion of the first correspondence, and form the first coefficient matrix, where n is a natural number. The preset coefficient acquisition module is used to preset the preset coefficients corresponding to different health levels of the energy storage battery, forming a preset coefficient matrix. The health matching module is used to compare the first coefficient matrix with the preset coefficient matrix to obtain the health level corresponding to the preset coefficient that matches the first coefficient matrix, which is the current health level of the energy storage battery.

8. The health calculation system for a sodium-ion energy storage battery according to claim 7, characterized in that, Also includes: The state of charge (SOC) acquisition module is used to acquire the current SOC of the energy storage battery. The preset coefficient matrix update module is used to update the preset coefficient matrix according to the state of charge condition to obtain the updated preset coefficient matrix.

9. An electronic device for calculating the health status of a sodium-ion energy storage battery, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform a method for calculating the health of a sodium-ion energy storage battery according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for calculating the health status of a sodium-ion energy storage battery according to any one of claims 1-6.

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