SOC (State of Charge) estimation method, device and equipment of lithium battery energy storage system and medium

Abstract

The invention relates to the technical field of lithium battery energy storage system management, in particular to an SOC estimation method, device and equipment for a lithium battery energy storage system and a medium, and the method comprises the steps: collecting the voltage of a single cell, the total voltage of a battery pack and the charging and discharging current of a battery; calculating the SOC of the single-string battery by adopting an ampere-hour integral method according to the voltage and the charging and discharging current of the single-string battery; calculating the SOC of the whole pack of batteries by adopting an ampere-hour integral method according to the total voltage and the charging and discharging current of the battery pack; membership functions are respectively designed for the single-string battery voltage and the total voltage of the battery pack; constructing a T-S fuzzy optimization decision table based on a membership function; and based on the T-S fuzzy optimization decision table, carrying out weighted average calculation on the membership degree to obtain an SOC optimal value. The result is optimized by means of the membership function and the T-S fuzzy optimization decision table, the SOC estimation precision is effectively improved, and the actual state of the lithium battery energy storage system can be reflected more accurately.

Description

Technical Field

[0001] The present application relates to the technical field of lithium battery energy storage system management, and in particular to a method, device, equipment and medium for estimating the SOC of a lithium battery energy storage system. Background Art

[0002] In today's energy sector, lithium battery energy storage technology is rapidly developing and widely used. From portable electronic devices to large-scale energy storage power stations, lithium batteries are indispensable. Due to their low cost, high energy density, and long cycle life, lithium batteries have become a highly sought-after energy storage device. However, the battery's state of charge (SOC) is a key indicator of the remaining charge in a lithium battery and is directly related to its safe, reliable, and efficient operation. Accurately estimating SOC is crucial during its use.

[0003] As the number of charge and discharge cycles increases, the performance of lithium batteries gradually declines, and the consistency of operating voltages between batteries deteriorates. Traditional SOC estimation methods are mostly performed on the entire battery pack. They cannot accurately distinguish the SOC of each battery string, making it difficult to evaluate battery consistency based on the SOC of each string. This results in the inability to promptly detect abnormal batteries within the battery pack in actual applications, which can easily lead to safety hazards and affect the stability and service life of the entire energy storage system. If the SOC of each battery string cannot be accurately grasped, it may lead to overcharging or over-discharging of the battery, shortening the battery life and even causing safety accidents. Poor battery consistency will reduce the overall performance of the energy storage system and increase operating costs. Summary of the Invention

[0004] In response to the problems existing in traditional SOC estimation methods, the present invention provides an SOC estimation method, device, equipment and medium for a lithium battery energy storage system to improve the overall estimation accuracy.

[0005] In a first aspect, the technical solution of the present invention provides a method for estimating the SOC of a lithium battery energy storage system, comprising: Collect the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; The SOC of a single battery string is calculated using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; the SOC of the entire battery pack is calculated using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; Membership functions are designed for the voltage of a single battery string and the total voltage of the battery pack, respectively. The membership functions are used to describe the fuzzy relationship between voltage and SOC. Based on the membership function, a TS fuzzy optimization decision table is constructed, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of the SOC under different voltage and current conditions; Based on the TS fuzzy optimization decision table, the SOC of a single battery cell and the SOC of the entire battery pack are fused and calculated through membership weighted average to obtain the optimal SOC value of the lithium battery energy storage system.

[0006] As a preferred embodiment of the technical solution of the present invention, the steps of designing the membership functions for the voltage of a single battery string and the total voltage of the battery pack include: The SOC of a single battery string and the SOC of a whole pack of batteries are used as input variables. Multiple fuzzy sets are defined for each input variable. Each fuzzy set corresponds to a linguistic variable, which is used to describe different voltage states. For each fuzzy set, a corresponding membership function is constructed; the membership function is used to describe the membership relationship between the voltage value and the fuzzy set; specifically, the membership function of the single-string battery SOC is based on the single-string battery voltage collected in real time; the membership function of the battery pack SOC constructed as a variable is based on the total battery pack voltage collected in real time.

[0007] By defining fuzzy sets and linguistic variables for the SOC of single-string batteries and whole-pack batteries, and constructing corresponding membership functions, the precise voltage value is converted into fuzzy membership, which can more flexibly and accurately describe the battery's SOC state, providing a basis for subsequent fuzzy reasoning and optimization calculations, and further improving the accuracy of SOC estimation.

[0008] As a preferred embodiment of the technical solution of the present invention, multiple fuzzy sets are defined for each input variable, each fuzzy set corresponds to a linguistic variable, and the steps include: Define language variables based on the SOC status of a single battery string and the SOC status of the entire battery pack; According to the defined linguistic variables, the SOC values ​​of a single battery cell and the SOC of the entire battery pack are fuzzy divided to generate a fuzzy set corresponding to each linguistic variable.

[0009] A reasonable fuzzy division is performed on the SOC status of single-string batteries and battery packs, and the corresponding linguistic variables are determined, so that the fuzzy set can better fit the actual SOC status of the battery, enhancing the accuracy and rationality of the fuzzy model's description of the battery status, thereby improving the reliability of the entire SOC estimation method.

[0010] As a preferred embodiment of the technical solution of the present invention, the step of constructing a TS fuzzy optimization decision table based on the membership function includes: The linguistic variables of the SOC of a single battery string and the SOC of a whole pack of batteries are combined in pairs to obtain different rule combinations; each rule combination represents a comprehensive situation of the SOC status of a single battery string and a whole pack of batteries; According to the calculation method of the TS fuzzy model, the output calculation formula corresponding to each rule combination is determined; the output calculation formula is a weighted average of the SOC of a single battery cell and the SOC of the entire battery pack, and the weight is determined by the value of the membership function; All rule combinations and their corresponding output calculation formulas are organized into a table to form a TS fuzzy optimization decision table; the TS fuzzy optimization decision table includes rule numbers, input variables, output calculation formulas and their weights.

[0011] By constructing a TS fuzzy optimization decision table, the SOC states of single-string batteries and whole-pack batteries are combined, and the output calculation formula for each combination is determined. This provides clear rules and basis for the subsequent calculation of the optimal SOC value, making the fuzzy reasoning and calculation process more standardized and systematic, which helps to improve the accuracy and efficiency of SOC estimation.

[0012] As a preferred embodiment of the technical solution of the present invention, the steps of obtaining the optimal SOC value by weighted average calculation of membership based on the TS fuzzy optimization decision table include: Substituting the collected single-string battery voltage and battery pack total voltage into the corresponding membership function, the membership of the single-string battery SOC and the whole pack battery SOC in each fuzzy set is obtained; According to each rule combination in the T-S fuzzy optimization decision table, determine the output calculation formula corresponding to each rule. The output calculation formula is a linear expression based on the SOC of a single battery cell and the SOC of the entire battery pack, and its weight is determined by the membership value of the current single battery cell SOC and the SOC of the entire battery pack; Calculate the output of each rule based on the output calculation formula of each rule and the corresponding membership value; The outputs of all rules are integrated through the weighted average method to obtain the optimal SOC value.

[0013] The optimal SOC value is calculated by weighted average based on the TS fuzzy optimization decision table and membership degree. It fully considers the battery status information under different rule combinations and integrates the SOC conditions of single-string batteries and whole-pack batteries. It effectively reduces the cumulative error caused by the ampere-hour integration method, improves the accuracy of SOC estimation, and makes the estimation result more reflective of the actual status of the battery.

[0014] As a preferred embodiment of the technical solution of the present invention, the outputs of all rules are integrated by a weighted average method to obtain the calculation formula of the step of obtaining the optimal SOC value:

[0015] Where n is the number of rule combinations, is the output of the i-th rule; Represents the activation degree of the i-th rule, activation degree is the smaller of the two membership degrees in the i-th rule combination.

[0016] The specific formula for calculating the optimal SOC value by weighted average is clarified, making the calculation process clearer and more accurate, providing a precise calculation basis for practical applications, helping to improve the accuracy and consistency of SOC estimation, and ensuring the reliability of the estimation results.

[0017] As a preferred embodiment of the technical solution of the present invention, the method further comprises: Compare the SOC of each battery string with the preset SOC threshold; Determine whether the difference between the preset SOC threshold and the SOC of each single string of batteries is greater than the set error; Output battery inconsistency prompt information and output the SOC of the single string of batteries whose difference is greater than the error.

[0018] By comparing the SOC of each single-string battery with the preset SOC threshold, single-string batteries with abnormal SOC in the battery pack can be discovered in a timely manner, and battery inconsistency prompt information can be output and the specific SOC difference can be provided, so that staff can take timely measures, such as balancing charging and battery replacement, etc., which helps to improve the consistency of the battery pack, extend the service life of the battery pack, and enhance the overall performance and safety of the lithium battery energy storage system.

[0019] In a second aspect, the technical solution of the present invention further provides an SOC estimation device for a lithium battery energy storage system, comprising an acquisition module, an SOC calculation module, and a fuzzy optimization module; The fuzzy optimization module includes a membership function creation unit, a fuzzy optimization decision table creation unit and an optimization calculation unit; Acquisition module, used to collect the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; The SOC calculation module is used to calculate the SOC of a single battery string using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; and to calculate the SOC of the entire battery pack using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; A membership function creation unit is used to design membership functions for the voltage of a single battery string and the total voltage of the battery pack, respectively, wherein the membership function is used to describe the fuzzy relationship between voltage and SOC; A fuzzy optimization decision table creation unit is used to construct a TS fuzzy optimization decision table based on the membership function, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of the SOC under different voltage and current conditions; The optimization calculation unit is used to obtain the optimal SOC value of the lithium battery energy storage system by fusing the SOC of a single battery cell and the SOC of the entire battery pack through membership weighted average based on the TS fuzzy optimization decision table.

[0020] As a preferred embodiment of the technical solution of the present invention, the fuzzy optimization module further includes a fuzzy set generation unit, which is used to take the SOC of a single string of batteries and the SOC of a whole pack of batteries as input variables, define multiple fuzzy sets for each input variable, and respectively determine different fuzzy sets described by linguistic variables; The membership function creation unit is specifically used to construct a membership function for each fuzzy set; specifically, the membership function of the single-string battery SOC is constructed with the real-time single-string battery voltage as the variable; and the membership function of the battery pack SOC is constructed with the real-time total battery pack voltage as the variable.

[0021] As a preferred embodiment of the technical solution of the present invention, the fuzzy set generation unit is specifically used to define language variables according to the status of the SOC of a single battery string and the SOC of a whole battery pack; based on the defined language variables, the values ​​of the SOC of a single battery cell and the SOC of the whole battery pack are fuzzily divided to generate a fuzzy set corresponding to each language variable.

[0022] As a preferred embodiment of the technical solution of the present invention, the fuzzy optimization decision table creation unit includes a fuzzy rule generation submodule, an output calculation formula determination submodule and a decision table creation submodule; The fuzzy rule generation submodule is used to combine the linguistic variables of the SOC of a single battery string with the linguistic variables of the SOC of the entire battery pack to obtain different rule combinations; each rule combination represents a comprehensive situation of the SOC status of a single battery string and the entire battery pack; The output calculation formula determination submodule is used to determine the output calculation formula corresponding to each rule combination according to the calculation method of the TS fuzzy model; The decision table creation submodule is used to organize all rule combinations and their corresponding output calculation formulas into a table form to form a TS fuzzy optimization decision table.

[0023] As a preferred embodiment of the technical solution of the present invention, the optimization calculation unit includes a membership calculation submodule, a rule output calculation submodule and an SOC optimal value calculation submodule; The membership calculation submodule is used to substitute the collected single-string battery voltage and battery pack total voltage into the corresponding membership function, and obtain the membership of the single-string battery SOC in the fuzzy set, as well as the membership of the whole pack battery SOC in the fuzzy set; The rule output calculation submodule is used to calculate the output of each rule based on the output calculation formula corresponding to each rule combination in the T-S fuzzy optimization decision table, combined with the current single-string battery SOC value and the entire pack battery SOC value and the corresponding membership degree; The SOC optimal value calculation submodule is used to synthesize the outputs of all rules through the weighted average method to obtain the optimal SOC value.

[0024] As a preferred embodiment of the technical solution of the present invention, the formula for calculating the optimal SOC value is:

[0025] Where n is the number of rule combinations, is the output of the i-th rule; Represents the activation degree of the i-th rule, activation degree is the smaller of the two membership degrees in the i-th rule combination.

[0026] As a preferred embodiment of the technical solution of the present invention, the device also includes a consistency judgment module for comparing the SOC of each single-string battery with a preset SOC threshold; judging whether the difference between the preset SOC threshold and the SOC of each single-string battery is greater than a set error; outputting battery inconsistency prompt information and outputting the SOC of the single-string battery for which the difference is greater than the error.

[0027] In a third aspect, the technical solution of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the SOC estimation method for the lithium battery energy storage system as described in the first aspect.

[0028] In a fourth aspect, the technical solution of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the SOC estimation method of the lithium battery energy storage system as described in the first aspect.

[0029] It can be seen from the above technical solution that this application has the following advantages: it comprehensively considers the relevant parameters of single-string batteries and whole-pack batteries, preliminarily calculates the SOC through the ampere-hour integration method, and optimizes the results with the help of membership functions and TS fuzzy optimization decision tables, effectively improving the accuracy of SOC estimation, and can more accurately reflect the actual state of the lithium battery energy storage system, providing strong guarantees for the safe and efficient operation of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 A schematic flow chart of a method provided in an embodiment of the present invention.

[0032] Figure 2 A schematic block diagram of an apparatus according to an embodiment of the present invention.

[0033] Figure 3 This is a circuit connection diagram of the acquisition module in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the application objectives, features, and advantages of this application more obvious and easy to understand, the technical solutions protected by this application will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this patent.

[0035] like Figure 1 As shown, an embodiment of the present invention provides a method for estimating the SOC of a lithium battery energy storage system, comprising: S1: Collects the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; In this embodiment, a voltage acquisition module (such as a 6830 chip) is used to collect the voltage of a single battery cell, a total voltage acquisition module (such as an ADX112AMADC sampling chip) is used to collect the total voltage of the battery pack, and a current acquisition module (using a high-speed AD module with dual channels) is used to obtain the battery charge and discharge current. Here, the current is collected through a Hall current sensor. The circuit connection for obtaining the current through the dual channels is as follows: Figure 3 As shown in the figure, HALL_CH1 and HALL_CH2 are the current signal input terminals of the two channels, connected to the Hall current sensor, used to collect current signals and convert them into voltage signals. Resistors R60 and R65 are current limiting resistors. They can prevent the input current from being too large, play a role in protecting the circuit, and prevent large currents from damaging subsequent circuit components. Capacitors C108 and C110 of the first channel and capacitors C107 and C111 of the second channel respectively constitute the filter circuit. Among them, the 10nF capacitor mainly filters out high-frequency interference signals, and the 100nF capacitor has a good inhibitory effect on low-frequency interference. This combination can effectively reduce noise interference in the input signal, making the collected current signal purer.

[0036] Operational amplifiers U34 and U35, model RS6331BPX, are used to amplify the input weak voltage signal to meet the AD conversion requirements of the subsequent microcontroller (MCU).

[0037] Resistors R66, R67, and R68 in the first channel and R62, R63, and R64 in the second channel form the operational amplifier's feedback circuit. R66 and R62 are feedback resistors, while R67 and R63, and R68 and R64, serve as matching resistors for the non-inverting and inverting inputs, respectively. Together, they determine the operational amplifier's gain, ensuring the appropriate amplification of the input signal.

[0038] The signals from MCU-HALL-AD1 and MCU-HALL-AD2, after operational amplification, are output here and connected to the analog-to-digital conversion (AD) pins of the microcontroller (MCU). This allows the MCU to digitize and analyze the current signals, enabling accurate current acquisition. Subsequent calculations and fuzzy optimization processing are all performed within the MCU.

[0039] S2: Calculate the SOC of a single battery string using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; calculate the SOC of the entire battery pack using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; S3: Designing membership functions for the voltage of a single battery string and the total voltage of the battery pack, respectively. The membership functions are used to describe the fuzzy relationship between voltage and SOC. S4: Based on the membership function, construct a TS fuzzy optimization decision table, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of the SOC under different voltage and current conditions; S5: Based on the TS fuzzy optimization decision table, the SOC of a single battery cell and the SOC of the entire battery pack are integrated and calculated by weighted average of the membership degree to obtain the optimal SOC value of the lithium battery energy storage system.

[0040] In some embodiments, the steps of designing membership functions for the voltage of a single battery string and the total voltage of the battery pack include: S31: Take the SOC of a single battery string and the SOC of a whole pack of batteries as input variables, and define multiple fuzzy sets for each input variable. Each fuzzy set corresponds to a linguistic variable, which is used to describe different voltage states. S32: For each fuzzy set, a corresponding membership function is constructed; the membership function is used to describe the membership relationship between the voltage value and the fuzzy set; specifically, the membership function of the single-string battery SOC is based on the single-string battery voltage collected in real time; the membership function of the battery pack SOC constructed as a variable is constructed based on the total voltage of the battery pack collected in real time.

[0041] It should be noted that multiple fuzzy sets are defined for each input variable, and each fuzzy set corresponds to a linguistic variable, specifically including: defining linguistic variables according to the status of the SOC of a single battery string and the SOC of the entire battery pack; and performing fuzzy division on the values ​​of the SOC of a single battery cell and the SOC of the entire battery pack based on the defined linguistic variables to generate a fuzzy set corresponding to each linguistic variable.

[0042] SOC_CLEE is used to represent the SOC value of the ampere-hour integration of a single battery string, and SOC_PACK is used to represent the SOC value of the ampere-hour integration of the entire battery pack. SOC_CELL and SOC_PACK domain: [0 100]; Language variables of SOC_CELL: C_L, C_M, C_H; Language variables of SOC_PACK: P_L, P_M, P_H; The membership function expression of SOC_CELL is: Designed for the voltage platform of a single lithium battery, using lithium iron phosphate battery, the voltage platform is 3.2V, x is the real-time cell voltage, μ C_L = exp(-(x – 3.0).^2 / (2*0.5^2)); used to measure the degree to which the cell voltage is at a low SOC level; μ C_M = exp(-(x – 3.2).^2 / (2*0.5^2)); measures the degree to which the cell voltage is at a medium SOC level; μ C_H = exp(-(x – 3.5).^2 / (2*0.5^2)); measures the degree to which the cell voltage is at a high SOC level. The membership function expression of SOC_PACK is: Regarding the total voltage of the battery pack PACK, each BCU battery box uses 52 battery strings with a total rated voltage of 166.4V. x is the total voltage collected in real time.

[0043] μ P_L = exp(-(x - 156).^2 / (2*15^2)); reflects the degree to which the total voltage is at a low SOC level; μ P_M = exp(-(x - 166).^2 / (2*15^2)); reflects the degree to which the total voltage is at a medium SOC level; μ P_H = exp(-(x - 182).^2 / (2*15^2)); reflects the degree to which the total voltage is at a high SOC level.

[0044] In an embodiment of the present invention, the steps of constructing a TS fuzzy optimization decision table based on the membership function include: S41: combining the linguistic variables of the SOC of a single battery string and the linguistic variables of the SOC of the whole battery pack in pairs to obtain different rule combinations; each rule combination represents a comprehensive situation of the SOC status of a single battery string and the whole battery pack; S42: Determine an output calculation formula corresponding to each rule combination based on the calculation method of the TS fuzzy model; the output calculation formula is a weighted average of the SOC of a single battery cell and the SOC of the entire battery pack, with the weight determined by the value of the membership function; S43: Arrange all rule combinations and their corresponding output calculation formulas into a table to form a TS fuzzy optimization decision table; the TS fuzzy optimization decision table includes rule numbers, input variables, output calculation formulas and their weights.

[0045] First, based on the above steps, determine the SOC language variables of single-string batteries and full-pack batteries: The language variable for the SOC of a single battery string (denoted as SOC_CELL) is defined in the document as: C_L (low SOC level), C_M (medium SOC level), and C_H (high SOC level). This is a fuzzy classification of the SOC state of a single battery string, used to more flexibly describe different battery SOC levels.

[0046] The linguistic variables for the SOC of the entire battery pack (denoted as SOC_PACK) are set as: P_L (low SOC level), P_M (medium SOC level), and P_H (high SOC level). This division corresponds to the linguistic variables for the SOC of a single battery string, facilitating the subsequent comprehensive consideration of the SOC states of both the single string and the entire pack.

[0047] The linguistic variables for the SOC of a single battery string (C_L, C_M, C_H) are paired with the linguistic variables for the SOC of a whole battery pack (P_L, P_M, P_H). This results in a total of 3 × 3 = 9 different rule combinations, each representing a comprehensive view of the SOC status of a single battery string and the whole battery pack.

[0048] For the above 9 rule combinations, the output calculation formula corresponding to each combination is determined according to the calculation method of the TS fuzzy model. The calculation is based on the membership function values ​​of SOC_PACK and SOC_CELL, for example: When SOC_CELL is C_L and SOC_PACK is P_L, the output calculation formula is SOC_PACK μ P_L +SOC_CELL μ C_L This calculation formula comprehensively considers the membership of the entire battery pack at a low SOC level and the membership of a single string of batteries at a low SOC level, and is used for subsequent calculation of the optimal SOC value.

[0049] According to the same logic, the output calculation formulas of the other 8 rule combinations are determined in turn. For example, when SOC_CELL is C_L and SOC_PACK is P_M, the output calculation formula is SOC_PACK μ P_M +SOC_CELL μ C_L wait.

[0050] The nine rule combinations and their corresponding output calculation formulas are organized into a table, forming the TS fuzzy optimization decision table shown in Table 1. The rows of the decision table represent the linguistic variables for the SOC of individual battery strings, the columns represent the linguistic variables for the SOC of the entire battery pack, and the corresponding output calculation formulas are displayed within the table. This table clearly demonstrates the calculation relationships for different combinations of individual battery strings and entire battery pack SOC states, facilitating the subsequent calculation of the optimal SOC value based on the weighted average of membership degrees.

[0051] Table 1: TS fuzzy optimization decision table

[0052] In the embodiment of the present invention, the step of obtaining the optimal SOC value by weighted average calculation of membership based on the TS fuzzy optimization decision table includes: Substituting the collected single-string battery voltage and battery pack total voltage into the corresponding membership function, the membership of the single-string battery SOC in the fuzzy set and the membership of the whole pack battery SOC in the fuzzy set were obtained; According to the output calculation formula corresponding to each rule combination in the T-S fuzzy optimization decision table, combined with the current SOC value of a single battery string and the SOC value of the entire battery pack and the corresponding membership degree, the output of each rule is calculated; The outputs of all rules are integrated through the weighted average method to obtain the optimal SOC value.

[0053] The calculation formula for obtaining the optimal SOC value is:

[0054] Where n is the number of rule combinations, is the output of the i-th rule; Represents the activation degree of the i-th rule, activation degree is the smaller of the two membership degrees in the i-th rule combination.

[0055] In some embodiments, the method further comprises: Compare the SOC of each battery string with the preset SOC threshold; Determine whether the difference between the preset SOC threshold and the SOC of each single string of batteries is greater than the set error; Output battery inconsistency prompt information and output the SOC of the single string of batteries whose difference is greater than the error.

[0056] After obtaining the SOC value for each battery string, the SOC of each battery string is compared with a preset SOC threshold. The preset SOC threshold can be set based on factors such as the battery type, operating environment, and performance requirements. For example, the middle value of the normal SOC range can be set as the preset threshold. A determination is made to see whether the difference between the preset SOC threshold and the SOC of each battery string is greater than a set error. The set error also needs to be determined based on actual conditions and can generally be set based on the battery's accuracy requirements and consistency standards, such as a set error of ±5%.

[0057] If a single string of batteries has a difference greater than the error, a battery inconsistency warning message is output, such as on a display screen or by sending an alarm signal to the monitoring system. The SOC of the battery string with the difference greater than the error is also output, allowing staff to quickly locate the abnormal battery and understand its specific SOC status for subsequent processing.

[0058] like Figure 2 As shown, an embodiment of the present invention further provides an SOC estimation device for a lithium battery energy storage system, comprising an acquisition module, an SOC calculation module, and a fuzzy optimization module; The fuzzy optimization module includes a membership function creation unit, a fuzzy optimization decision table creation unit and an optimization calculation unit; Acquisition module, used to collect the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; The SOC calculation module is used to calculate the SOC of a single battery string using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; and to calculate the SOC of the entire battery pack using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; A membership function creation unit is used to design membership functions for the voltage of a single battery string and the total voltage of the battery pack, respectively, wherein the membership function is used to describe the fuzzy relationship between voltage and SOC; A fuzzy optimization decision table creation unit is used to construct a TS fuzzy optimization decision table based on the membership function, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of the SOC under different voltage and current conditions; The optimization calculation unit is used to obtain the optimal SOC value of the lithium battery energy storage system by fusing the SOC of a single battery cell and the SOC of the entire battery pack through membership weighted average based on the TS fuzzy optimization decision table.

[0059] In some embodiments, the fuzzy optimization module further includes a fuzzy set generation unit for taking the SOC of a single string of batteries and the SOC of a whole pack of batteries as input variables, defining multiple fuzzy sets for each input variable, and determining linguistic variables to describe different fuzzy sets; The membership function creation unit is specifically used to construct a membership function for each fuzzy set; specifically, the membership function of the single-string battery SOC is constructed with the real-time single-string battery voltage as the variable; and the membership function of the battery pack SOC is constructed with the real-time total battery pack voltage as the variable.

[0060] In some embodiments, the fuzzy set generation unit is specifically used to define language variables according to the status of the SOC of a single battery string and the SOC of a whole battery pack; based on the defined language variables, the values ​​of the SOC of a single battery cell and the SOC of the whole battery pack are fuzzily divided to generate a fuzzy set corresponding to each language variable.

[0061] In some embodiments, the fuzzy optimization decision table creation unit includes a fuzzy rule generation submodule, an output calculation formula determination submodule, and a decision table creation submodule; The fuzzy rule generation submodule is used to combine the linguistic variables of the SOC of a single battery string with the linguistic variables of the SOC of the entire battery pack to obtain different rule combinations; each rule combination represents a comprehensive situation of the SOC status of a single battery string and the entire battery pack; The output calculation formula determination submodule is used to determine the output calculation formula corresponding to each rule combination according to the calculation method of the TS fuzzy model; The decision table creation submodule is used to organize all rule combinations and their corresponding output calculation formulas into a table form to form a TS fuzzy optimization decision table.

[0062] In some embodiments, the optimization calculation unit includes a membership calculation submodule, a rule output calculation submodule, and an SOC optimal value calculation submodule; The membership calculation submodule is used to substitute the collected single-string battery voltage and battery pack total voltage into the corresponding membership function, and obtain the membership of the single-string battery SOC in the fuzzy set, as well as the membership of the whole pack battery SOC in the fuzzy set; The rule output calculation submodule is used to calculate the output of each rule based on the output calculation formula corresponding to each rule combination in the T-S fuzzy optimization decision table, combined with the current single-string battery SOC value and the entire pack battery SOC value and the corresponding membership degree; The SOC optimal value calculation submodule is used to synthesize the outputs of all rules through the weighted average method to obtain the optimal SOC value.

[0063] The formula for calculating the optimal SOC value is:

[0064] Where n is the number of rule combinations, is the output of the i-th rule; Represents the activation degree of the i-th rule, activation degree is the smaller of the two membership degrees in the i-th rule combination.

[0065] In some embodiments, the device further includes a consistency judgment module for comparing the SOC of each single string of batteries with a preset SOC threshold; judging whether the difference between the preset SOC threshold and the SOC of each single string of batteries is greater than a set error; outputting a battery inconsistency prompt message and outputting the SOC of the single string of batteries for which the difference is greater than the error.

[0066] An embodiment of the present invention further provides an electronic device comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The communication bus can be used to transmit information between the electronic device and sensors. The processor can call logic instructions in the memory to execute the following method: S1: collecting the voltage of a single battery cell, the total voltage of the battery pack, and the charge and discharge current of the battery; S2: calculating the SOC of a single battery string using the ampere-hour integration method based on the voltage and charge and discharge current of the single battery string; and calculating the SOC of the entire battery pack using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; S3: designing membership functions for the voltage of the single battery string and the total voltage of the battery pack, respectively, wherein the membership functions are used to describe the fuzzy relationship between voltage and SOC; S4: constructing a TS fuzzy optimization decision table based on the membership functions, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of SOC under different voltage and current conditions; S5: based on the TS fuzzy optimization decision table, performing a fusion calculation of the SOC of the single battery cell and the SOC of the entire battery pack by weighted averaging the membership to obtain the optimal SOC value of the lithium battery energy storage system.

[0067] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0068] An embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute the method provided by the above method embodiment, for example, including: S1: collecting the voltage of a single battery cell, the total voltage of a battery pack, and the charge and discharge current of the battery; S2: calculating the SOC of a single battery string by using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; calculating the SOC of the entire battery pack by using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; S3: designing a membership function for the voltage of a single battery string and the total voltage of the battery pack respectively, and the membership function is used to describe the fuzzy relationship between voltage and SOC; S4: constructing a TS fuzzy optimization decision table based on the membership function, and the TS fuzzy optimization decision table is used to describe the optimization weight of SOC under different voltage and current conditions; S5: Based on the TS fuzzy optimization decision table, the SOC of a single battery cell and the SOC of the entire battery pack are fused and calculated by weighted average of the membership to obtain the optimal SOC value of the lithium battery energy storage system.

[0069] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating the SOC of a lithium battery energy storage system, characterized in that: include: Collect the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; The SOC of a single battery string is calculated using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; the SOC of the entire battery pack is calculated using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; Membership functions are designed for the voltage of a single battery string and the total voltage of the battery pack, respectively. The membership functions are used to describe the fuzzy relationship between voltage and SOC. Based on the membership function of the single-string battery voltage and the total battery pack voltage, a TS fuzzy optimization decision table is constructed. The TS fuzzy optimization decision table is used to describe the optimization weight of SOC under different voltage and current conditions; Based on the TS fuzzy optimization decision table, the SOC of a single battery cell and the SOC of the entire battery pack are fused and calculated through membership weighted average to obtain the optimal SOC value of the lithium battery energy storage system.

2. The SOC estimation method of the lithium battery energy storage system according to claim 1, characterized in that: The steps for designing the membership functions for the voltage of a single battery string and the total voltage of the battery pack include: The SOC of a single battery string and the SOC of a whole pack of batteries are used as input variables. Multiple fuzzy sets are defined for each input variable. Each fuzzy set corresponds to a linguistic variable, which is used to describe different voltage states. For each fuzzy set, a corresponding membership function is constructed; the membership function is used to describe the membership relationship between the voltage value and the fuzzy set; specifically, the membership function of the single-string battery SOC is based on the single-string battery voltage collected in real time; the membership function of the battery pack SOC constructed as a variable is based on the total battery pack voltage collected in real time.

3. The SOC estimation method of the lithium battery energy storage system according to claim 2, characterized in that: Define multiple fuzzy sets for each input variable, each fuzzy set corresponds to a linguistic variable, and the steps include: Define language variables based on the SOC status of a single battery string and the SOC status of the entire battery pack; According to the defined linguistic variables, the SOC values ​​of a single battery cell and the SOC of the entire battery pack are fuzzy divided to generate a fuzzy set corresponding to each linguistic variable.

4. The SOC estimation method of the lithium battery energy storage system according to claim 3, characterized in that: Based on the membership function, the steps of constructing the TS fuzzy optimization decision table include: The linguistic variables of the SOC of a single battery string and the SOC of a whole pack of batteries are combined in pairs to obtain different rule combinations; each rule combination represents a comprehensive situation of the SOC status of a single battery string and a whole pack of batteries; According to the calculation method of the TS fuzzy model, the output calculation formula corresponding to each rule combination is determined; the output calculation formula is a weighted average of the SOC of a single battery cell and the SOC of the entire battery pack, and the weight is determined by the value of the membership function; All rule combinations and their corresponding output calculation formulas are organized into a table to form a TS fuzzy optimization decision table; the TS fuzzy optimization decision table includes rule numbers, input variables, output calculation formulas and their weights.

5. The SOC estimation method of the lithium battery energy storage system according to claim 4, characterized in that: Based on the TS fuzzy optimization decision table, the steps of obtaining the optimal SOC value by weighted average calculation of membership degree include: Substituting the collected single-string battery voltage and battery pack total voltage into the corresponding membership function, the membership of the single-string battery SOC and the whole pack battery SOC in each fuzzy set is obtained; According to each rule combination in the T-S fuzzy optimization decision table, determine the output calculation formula corresponding to each rule. The output calculation formula is a linear expression based on the SOC of a single battery cell and the SOC of the entire battery pack, and its weight is determined by the membership value of the current single battery cell SOC and the SOC of the entire battery pack; Calculate the output of each rule based on the output calculation formula of each rule and the corresponding membership value; The outputs of all rules are integrated through the weighted average method to obtain the optimal SOC value.

6. The SOC estimation method of the lithium battery energy storage system according to claim 5, characterized in that: By combining the outputs of all rules using the weighted average method, we can obtain the calculation formula for the steps of the optimal SOC value: Where n is the number of rule combinations, is the output of the i-th rule; Represents the activation degree of the i-th rule, activation degree is the smaller of the two membership degrees in the i-th rule combination.

7. The SOC estimation method of the lithium battery energy storage system according to claim 1, characterized in that: The method further includes: Compare the SOC of each battery string with the preset SOC threshold; Determine whether the difference between the preset SOC threshold and the SOC of each single string of batteries is greater than the set error; Output battery inconsistency prompt information and output the SOC of the single string of batteries whose difference is greater than the error.

8. A SOC estimation device for a lithium battery energy storage system, characterized in that: Including acquisition module, SOC calculation module and fuzzy optimization module; The fuzzy optimization module includes a membership function creation unit, a fuzzy optimization decision table creation unit and an optimization calculation unit; Acquisition module, used to collect the voltage of a single battery string, the total voltage of the battery pack, and the charge and discharge current of the battery; The SOC calculation module is used to calculate the SOC of a single battery string using the ampere-hour integration method based on the voltage and charge and discharge current of a single battery string; and to calculate the SOC of the entire battery pack using the ampere-hour integration method based on the total voltage and charge and discharge current of the battery pack; A membership function creation unit is used to design membership functions for the voltage of a single battery string and the total voltage of the battery pack, respectively, wherein the membership function is used to describe the fuzzy relationship between voltage and SOC; A fuzzy optimization decision table creation unit is used to construct a TS fuzzy optimization decision table based on the membership function, wherein the TS fuzzy optimization decision table is used to describe the optimization weight of the SOC under different voltage and current conditions; The optimization calculation unit is used to obtain the optimal SOC value of the lithium battery energy storage system by fusing the SOC of a single battery cell and the SOC of the entire battery pack through membership weighted average based on the TS fuzzy optimization decision table.

9. An electronic device, 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 computer program instructions that can be executed by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to execute the SOC estimation method for the lithium battery energy storage system according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the SOC estimation method for the lithium battery energy storage system according to any one of claims 1 to 7.

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