Power distribution method and device for energy storage battery

Abstract

The invention provides a power distribution method and device for an energy storage battery, and the method comprises the steps: S1, calculating a state score corresponding to a state parameter based on the obtained state parameter of each battery cluster in the energy storage battery; s2, according to a preset weight coefficient, carrying out weighted calculation on the state scores to obtain a comprehensive score of each battery cluster; s3, sorting the battery clusters according to the comprehensive scores; step S4, grouping the sorted battery clusters; and S5, on the basis of a grouping result, theoretical power distribution calculation and verification adjustment are carried out, and a target grouped battery power distribution scheme is obtained. The power output is reasonably distributed according to the state of each group of batteries, and proper working conditions are matched to ensure that the battery groups with different performances operate under the optimal working condition, so that the overall service life of the energy storage battery is prolonged under the condition of maximizing the system efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a power distribution method and device for an energy storage battery. Background Art

[0002] Energy storage batteries play a key role in modern energy systems, especially in the storage and dispatch of renewable energy, smart grids, and electric vehicles. With the widespread development of these applications, the life, performance, and reliability of energy storage batteries have become key factors affecting system efficiency and economic benefits. In order to ensure that energy storage batteries can operate efficiently and safely, traditional battery management methods usually adopt a passive monitoring strategy, that is, to assess the health status and failure risk of the battery by regularly detecting its status. However, this approach has obvious limitations.

[0003] First, passive monitoring cannot prevent potential problems in a timely manner. Maintenance or replacement measures are often taken only when battery performance has significantly declined, resulting in energy waste and increased operating costs. Secondly, differences in performance within the battery cluster can also lead to excessive consumption of some batteries while other batteries are not fully utilized. This unbalanced utilization not only affects the efficiency of the overall system, but may also accelerate battery aging and shorten its service life. Therefore, how to use advanced battery management technology to monitor and optimize battery performance in real time has become the key to improving the overall efficiency and reliability of energy storage systems. Summary of the invention

[0004] The present invention provides a power distribution method and device for an energy storage battery, which are used to isolate and distribute the power output of each group of batteries according to the state of the energy storage battery, so as to prolong the overall service life of the energy storage battery.

[0005] In a first aspect, the present invention provides a power distribution method for an energy storage battery, comprising:

[0006] Step S1, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating the state score corresponding to the state parameters;

[0007] Step S2, performing weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster;

[0008] Step S3, sorting the battery clusters according to the comprehensive scores;

[0009] Step S4, grouping the sorted battery clusters;

[0010] Step S5: Based on the grouping result, theoretical power allocation calculation and verification adjustment are performed to obtain a target group battery power allocation solution.

[0011] Optionally, step S5 includes:

[0012] Step S51, based on the grouping result, performing theoretical power allocation calculation on each battery group composed of the battery clusters to obtain the group grid-connected power borne by each battery group;

[0013] Step S52, determine whether the group grid-connected power exceeds the set battery group transmission power range; if so, remove the battery group that exceeds the battery group transmission power range and return to step S51; if not, output the target group battery power allocation plan.

[0014] Optionally, the step S51 includes:

[0015] Determining the weight coefficient of each battery group by using a hierarchical analysis method;

[0016] Based on the weight coefficient, the group grid-connected power borne by each of the battery groups is calculated.

[0017] Optionally, the state parameters include: pressure difference, temperature difference, pressure difference consistency percentage and temperature difference consistency percentage; the state scores include: pressure difference state score, temperature difference state score, pressure difference consistency score and temperature difference consistency score; step S1 includes:

[0018] Using a preset pressure difference score determination formula and combining the pressure difference of the battery cluster, a pressure difference state score is calculated;

[0019] Using a preset temperature difference score determination formula and combining the temperature difference of the battery cluster, a temperature difference state score is calculated;

[0020] Using a preset pressure difference consistency score determination formula and combining the pressure difference consistency percentage of the battery cluster, a pressure difference consistency score is calculated;

[0021] The temperature difference consistency score is calculated using a preset temperature difference consistency score determination formula in combination with the temperature difference consistency percentage of the battery cluster.

[0022] Optionally, the state parameter further includes: battery health data; the state score further includes: health state score; and step S1 further includes:

[0023] Calculating a maximum number of cycles of the battery cluster based on the battery status data of the battery cluster;

[0024] The health status score of the battery cluster is obtained by using a preset life prediction model in combination with the maximum number of cycles.

[0025] Optionally, after step S5, the method further includes:

[0026] Step S6, when executing the battery power allocation plan for the target group, determine whether the discharge state of the battery group has reached the lower limit of the discharge state, and / or whether the charging state of the battery cluster has reached the upper limit of the charging state. If so, stop executing the battery power allocation plan for the target group, define the battery group as an abnormal group and eliminate it, and return to the step S51.

[0027] In a second aspect, the present invention provides a power distribution device for an energy storage battery, comprising:

[0028] A state score calculation module, used to calculate a state score corresponding to the state parameter based on the acquired state parameter of each battery cluster in the energy storage battery;

[0029] A battery cluster quantification module, used to perform weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster;

[0030] A battery cluster sorting module, used for sorting the battery clusters according to the comprehensive scores;

[0031] A battery cluster grouping module, used for grouping the sorted battery clusters;

[0032] The power allocation scheme determination module is used to perform theoretical power allocation calculation and verification adjustment based on the grouping results to obtain the target group battery power allocation scheme.

[0033] In a third aspect, the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are performed.

[0034] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, runs the steps of the method provided in the first aspect.

[0035] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps in the method provided in the first aspect are performed.

[0036] It can be seen from the above technical solutions that the present invention has the following advantages:

[0037] The present invention provides a power distribution method and device for energy storage batteries, the method comprising: step S1, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating the state score corresponding to the state parameter; step S2, according to the preset weight coefficient, weighted calculation of the state score to obtain the comprehensive score of each battery cluster; step S3, sorting the battery clusters according to the comprehensive score; step S4, grouping the sorted battery clusters; step S5, based on the grouping result, performing theoretical power distribution calculation and verification adjustment to obtain the target group battery power distribution plan. According to the state of each battery group, the power output is reasonably distributed, and the appropriate working conditions are matched to ensure that the battery groups with different performances operate under the best working conditions, thereby extending the overall life of the energy storage battery while maximizing the system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 A flowchart of a power distribution method for an energy storage battery according to a first embodiment of the present invention;

[0040] Figure 2 This is a flow chart of a second embodiment of a power distribution method for an energy storage battery according to the present invention;

[0041] Figure 3 It is a structural block diagram of an embodiment of a power distribution device for an energy storage battery of the present invention. DETAILED DESCRIPTION

[0042] The embodiment of the present invention provides a power distribution method and device for an energy storage battery, which is used to isolate and distribute the power output of each group of batteries according to the state of the energy storage battery, so as to extend the overall service life of the energy storage battery.

[0043] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] For example, see Figure 1 , Figure 1 This is a flow chart of a first embodiment of a power distribution method for an energy storage battery of the present invention, the method comprising:

[0045] Step S1, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating the state score corresponding to the state parameters;

[0046] In an embodiment of the present application, the state parameters of each battery cluster are obtained from an energy storage battery management system or a sensor, and then different types of state parameters are converted into comparable numerical values. After the conversion, the state score corresponding to each state parameter is calculated according to a predefined formula.

[0047] Step S2, performing weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster;

[0048] In the embodiment of the present application, the corresponding weight coefficient is set according to the importance of each state parameter, and then the score of each state score is multiplied by the corresponding weight coefficient, and the sum is calculated to obtain the comprehensive score of each battery cluster.

[0049] Step S3, sorting the battery clusters according to the comprehensive scores;

[0050] In the embodiment of the present application, a descending or ascending algorithm is used to sort the total scores of the battery clusters.

[0051] Step S4, grouping the sorted battery clusters;

[0052] In the embodiment of the present application, the sorted battery clusters may be allocated to different battery groups according to a pre-set grouping strategy.

[0053] Step S5: Based on the grouping result, theoretical power allocation calculation and verification adjustment are performed to obtain a target group battery power allocation solution.

[0054] In the embodiment of the present application, according to the comprehensive score of each battery group or the characteristics including weight, the grid-connected power that each battery group should bear is calculated and continuously checked and adjusted to obtain the target group battery power allocation plan.

[0055] In an optional embodiment, the step S5 includes:

[0056] Step S51, based on the grouping result, performing theoretical power allocation calculation on each battery group composed of the battery clusters to obtain the group grid-connected power borne by each battery group;

[0057] Step S52, determine whether the group grid-connected power exceeds the set battery group transmission power range; if so, remove the battery group that exceeds the battery group transmission power range and return to step S51; if not, output the target group battery power allocation plan.

[0058] In an embodiment of the present application, the verification process is implemented by checking whether the allocated power of each battery group is within its transmission power range. If the allocated power of a battery group exceeds its transmission power range, the battery group is proposed and the power allocation calculation is re-performed.

[0059] The power allocation method of an energy storage battery provided by an embodiment of the present invention includes: step S1, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating the state score corresponding to the state parameter; step S2, according to the preset weight coefficient, weighted calculation of the state score to obtain the comprehensive score of each battery cluster; step S3, sorting the battery clusters according to the comprehensive score; step S4, grouping the sorted battery clusters; step S5, based on the grouping result, performing theoretical power allocation calculation and verification adjustment to obtain the target group battery power allocation scheme. The power output is reasonably allocated according to the state of each battery group, and the appropriate working conditions are matched to ensure that the battery groups with different performances operate under the best working conditions, thereby extending the overall life of the energy storage battery while maximizing the system efficiency.

[0060] For example 2, please refer to Figure 2 , Figure 2 This is a flow chart of a second embodiment of a power distribution method for an energy storage battery of the present invention, the steps comprising:

[0061] S201, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating a state score corresponding to the state parameters;

[0062] In the embodiment of the present invention, the state parameters include: pressure difference, temperature difference, pressure difference consistency percentage, temperature difference consistency percentage and battery health data; the state scores include: pressure difference state score, temperature difference state score, pressure difference consistency score, temperature difference consistency score and health state score. And the state score determination steps corresponding to each state parameter are:

[0063] Using a preset pressure difference score determination formula and combining the pressure difference of the battery cluster, a pressure difference state score is calculated;

[0064] Using a preset temperature difference score determination formula and combining the temperature difference of the battery cluster, a temperature difference state score is calculated;

[0065] Using a preset pressure difference consistency score determination formula and combining the pressure difference consistency percentage of the battery cluster, a pressure difference consistency score is calculated;

[0066] Calculating the temperature difference consistency score by using a preset temperature difference consistency score determination formula and combining the temperature difference consistency percentage of the battery cluster;

[0067] Calculating a maximum number of cycles of the battery cluster based on the battery status data of the battery cluster;

[0068] The health status score of the battery cluster is obtained by using a preset life prediction model in combination with the maximum number of cycles.

[0069] In the embodiment of the present application, the preset pressure difference fraction determination formula is:

[0070]

[0071] Among them, S 压差 is the pressure difference state fraction, and x is the pressure difference.

[0072] In the specific implementation, when calculating the pressure difference state score, when the pressure difference is greater than 450mV, the score is directly set to 50 points; when the pressure difference is less than 250mV, the score is set to 100 points; when the pressure difference is between 450mV and 350mV, the score is linearly distributed between 50 and 70 points; when the pressure difference is between 250mV and 350mV, the score is linearly distributed between 70 and 100 points.

[0073] In the embodiment of the present application, the preset temperature difference score determination formula is:

[0074]

[0075] Among them, S 温差 is the temperature difference state fraction, and y is the temperature difference.

[0076] In the specific implementation, when calculating the temperature difference state score, when the temperature difference is greater than 15k, the score is directly set to 50 points; when the temperature difference is less than 10k, the score is set to 100 points; when the temperature difference is between 10k and 15k, the score is linearly distributed between 50 and 70 points; when the temperature difference is between 5k and 10k, the score is linearly distributed between 70 and 100 points.

[0077] In the embodiment of the present application, the preset pressure difference consistency score determination formula is:

[0078]

[0079] Among them, S 压差一致性 is the pressure difference consistency score, and z is the pressure difference consistency percentage.

[0080] In the specific implementation, when calculating the pressure difference consistency score, when the pressure difference consistency percentage exceeds 50%, the score is directly set to 50 points; when the pressure difference consistency percentage is lower than 5%, the score is set to 100 points; when the pressure difference consistency percentage is between 20% and 50%, the score is distributed between 50 and 70 points according to the linear principle; when the pressure difference consistency percentage is between 5% and 10%, the score is distributed between 70 and 100 points according to the linear principle.

[0081] In the embodiment of the present application, the preset temperature difference consistency score determination formula is:

[0082]

[0083] Among them, S 温差一致性 is the temperature difference consistency score, and v is the temperature difference consistency percentage.

[0084] In an embodiment of the present application, the health status score can be used to estimate the battery health status through a life prediction model of a lithium-ion battery. Specifically, the maximum number of cycles of the battery can be calculated. On this basis, the health status score can be predicted by combining advanced machine learning algorithms such as neural network models.

[0085] In the specific implementation, the life prediction model of lithium-ion batteries is:

[0086]

[0087] Where n represents the cumulative number of changes in the charge and discharge state during the operation of the battery. Whenever the battery switches from the charge state to the discharge state, or from the discharge state to the charge state, n increases by 1; n mod 2 refers to the remainder after n is divided by 2; A(0) is the initial lithium-ion battery aging cycle number, A(n) is the aging cycle number of the nth battery charge and discharge state change during the operation of the lithium-ion battery; ε(n) is the battery aging coefficient; N1 is the maximum cycle number of the lithium-ion battery at the rated charge and discharge current and the discharge depth is 100%; γ DOD is the battery discharge depth, which is expressed as γ DOD =100-S oc , S oc is the state of charge; N(n) is the maximum number of cycles. Different charge and discharge currents and discharge depths will result in different maximum number of cycles for the battery; H is the cycle constant; ε is the exponential factor of DOD; is the Arrhenius rate constant of the cycle number; T ref is the set ambient temperature; T a (n) is the actual ambient temperature of the battery; I ch_ave(n) is the average value of the battery current during the charging state; I dis_ave (n) is the average value of the battery current during the discharge state; and (n) is the exponential factor of the discharge current and the charge current, respectively.

[0088] S202, performing weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each of the battery clusters;

[0089] In the embodiment of the present application, based on the scores obtained in step S201, including the pressure difference state score, the temperature difference state score, the pressure difference consistency score and the temperature difference consistency score, the total score of the battery cluster is obtained by weighted calculation. The weight of each score in the total score is allocated according to its importance to ensure a comprehensive and accurate assessment of the health status of the battery cluster. The specific total score weighted calculation formula is as follows:

[0090] S 总 =ω1S 压差 +ω2S 温差 +ω3S 压差一致性 +ω4S 温差一致性 ;

[0091] Among them, S 总 is the comprehensive score of the battery cluster, ω1 is the pressure difference state score weight, ω2 is the temperature difference state score weight, ω3 is the pressure difference consistency score weight, and ω4 is the temperature difference consistency score weight.

[0092] S203, sorting the battery clusters according to the comprehensive scores;

[0093] In the embodiment of the present application, after calculating the comprehensive scores of all battery clusters, they are sorted in ascending order of the comprehensive scores. This sorting process will help quickly identify battery clusters with poor health status for priority attention. At the same time, battery clusters with good health status can also be effectively identified to fully utilize their remaining life and performance, thereby optimizing the overall operating efficiency and service life of the battery pack.

[0094] S204, grouping the sorted battery clusters;

[0095] In the embodiment of the present application, the battery clusters are grouped according to the sorting results, and the number of battery clusters and the number of groups in each group are determined. The basis for grouping is the actual input power of each battery group. In practical applications, the transmission power of each battery group has an upper and lower limit, which are respectively denoted as P min and P N , the actual power of the battery cluster should be kept within this range, expressed as:

[0096] P min ≤P≤P N .

[0097] P min As the lower limit of the battery group transmission power, to ensure the safety of grid connection, P N The rated power of the operation is also the upper limit of the battery pack transmission power. Therefore, the grouped battery pack will have corresponding upper and lower power limits.

[0098] In order to extend the battery life, the number of groups is determined based on the lower limit of the battery cluster transmission power. This method helps optimize the operating power of the battery cluster, avoid unnecessary power waste, and ensure system safety and extend the overall battery life.

[0099] The formula for calculating the number of groups is:

[0100]

[0101] Among them, m is the number of groups, P ref is the grid-connected power borne by the energy storage system, and floor is the rounding-down function.

[0102] S205, determining a weight coefficient of each battery group by using a hierarchy analysis method;

[0103] In the embodiment of the present application, after the battery groups are grouped, it is necessary to perform theoretical power allocation and management on each battery group. To ensure the rationality of power allocation, the Analytic Hierarchy Process (AHP) is used for decision analysis.

[0104] It should be noted that the AHP is a systematic and hierarchical decision analysis method, which is often used to solve complex multi-criteria decision-making problems. Its core idea is to decompose complex decision problems into multiple levels, gradually analyze and compare the impact of various factors on the overall goal, and finally help decision makers choose the best solution. This method first constructs the target layer by determining the overall goal of the decision. Next, according to the specific evaluation criteria related to the decision, the indicator layer is established to systematize and hierarchize the key factors affecting the decision. Finally, by analyzing the characteristics of different solutions, a solution layer is formed, which is used as the basis for final evaluation and selection. The key steps of the AHP include constructing a judgment matrix, and calculating the weight of each criterion relative to the previous layer goal by comparing the relative importance of each criterion in pairs. The data in the judgment matrix is ​​usually based on expert opinions or experience. The priorities of each level are hierarchically merged by the weighted summation method, and then the comprehensive weight of each alternative plan relative to the overall goal is obtained.

[0105] In power allocation decision-making, the hierarchical analysis method can effectively evaluate the power allocation scheme under multiple criteria. The target layer and indicator layer judgment matrix table is shown in the following table:

[0106] <![CDATA[S 总1 ]]> <![CDATA[S 总2 ]]> … <![CDATA[S 总m ]]> <![CDATA[S 总1 ]]> <![CDATA[a 11 ]]> <![CDATA[a 12 ]]> … <![CDATA[a 1m ]]> <![CDATA[S 总2 ]]> <![CDATA[a 21 ]]> <![CDATA[a 11 ]]> … <![CDATA[a 2m <!-- 7 -->]]> … … … … … <![CDATA[S 总m ]]> <![CDATA[a m1 ]]> <![CDATA[a m2 ]]> … <![CDATA[a mm ]]>

[0107] Specifically, the judgment matrix table of the target layer and the indicator layer shows the judgment matrix between the target layer and the indicator layer, quantifying the relative importance of different criteria in the overall power allocation. In this way, it can ensure that the contribution of each criterion to the goal is reasonably evaluated. Next, by further evaluating the alternative solution layer, we can allocate reasonable power to each battery group to ensure the optimal power allocation solution. In the end, the solution with the largest comprehensive weight is the optimal power allocation solution, which can better meet the requirements of the overall goal.

[0108] S206, calculating the group grid-connected power borne by each of the battery groups based on the weight coefficient;

[0109] In the embodiment of the present application, after the weight coefficient is obtained, the corresponding transmission power can be calculated according to the weight of each battery group. Specifically, it can be calculated by the following formula:

[0110]

[0111] Among them, P1 is the group grid-connected power borne by the first group of battery groups, P2 is the group grid-connected power borne by the second group of battery groups, and P m is the grid-connected power of the mth battery group, ω'1 is the weight of the first battery group, ω'2 is the weight of the second battery group, ω' m is the weight of the mth battery group.

[0112] S207, determine whether the group grid-connected power exceeds the set battery group transmission power range; if so, remove the battery group that exceeds the battery group transmission power range and return to step S205; if not, output the target group battery power allocation plan.

[0113] In an optional embodiment, after step S207, the method further includes:

[0114] When executing the target group battery power allocation plan, determine whether the discharge state of the battery group has reached the lower limit of the discharge state, and / or whether the charging state of the battery cluster has reached the upper limit of the charging state. If so, stop executing the target group battery power allocation plan, define the battery group as an abnormal group and eliminate it, and return to step S205.

[0115] In the embodiment of the present application, after the transmission power required by each battery group is obtained through theoretical calculation, the transmission power of each battery group needs to be further verified to ensure that it is within the set power upper and lower limits. The specific steps are as follows:

[0116] First, check whether the transmission power of each battery cluster is within the preset power range. If the transmission power allocated to a battery group is lower than the lower limit of the transmission power, the operation of the battery group is cancelled. At this time, the weight coefficient of the grid-connected power and the corresponding transmission power are recalculated according to the method of step S205 based on the remaining battery groups.

[0117] Furthermore, if after redistribution, the transmission power of a battery group exceeds the set transmission power upper limit, the power output of the battery group will be limited to the upper limit. For the remaining grid-connected power, the weight coefficients of the battery clusters and their transmission powers are redistributed again according to the method of step S205.

[0118] This process is iterated continuously, reducing the number of running battery groups in turn, until the transmission power of all participating battery storm eyes is within the set upper and lower limits. At this time, the power allocation process of the energy storage system ends, ensuring that the operation of each battery group is within a reasonable power load range.

[0119] In order to further prevent over-discharge or over-charge of battery groups, the system also needs to set upper and lower limits of the load state of each battery group in the energy storage system. Specifically, the lower limit of the discharge load state of the battery group is set to 20%, and the upper limit of the charging load state is set to 95%. When the SOC of the battery group reaches the lower limit of discharge or the upper limit of charge, its operation should be stopped in time to prevent excessive battery loss or a decrease in the efficiency of the energy storage system. Through this step-by-step verification and adjustment method, the rationality of the transmission power distribution of the battery group and the safe and stable operation of the system are ensured.

[0120] A power allocation method for an energy storage battery disclosed in an embodiment of the present invention scores status data including pressure difference, temperature difference, pressure difference consistency percentage, temperature difference consistency percentage and battery health data, accurately quantifies the status of the battery cluster, and further determines the objectivity of the target group battery power allocation scheme. At the same time, when executing the target group battery power allocation scheme, the charge state of the battery group is monitored in real time. If the discharge SOC reaches a lower limit or the charging SOC reaches an upper limit, the execution of the scheme is stopped, and the battery group is defined as an abnormal group and eliminated, and then the weight coefficient is returned to recalculate to ensure that the battery will not be over-discharged or over-charged, thereby protecting the battery life and system safety.

[0121] For example 3, please refer to Figure 3 , Figure 3 This is a structural block diagram of an embodiment of a power distribution device for an energy storage battery of the present invention, the device comprising:

[0122] A state score calculation module 301 is used to calculate a state score corresponding to the state parameter based on the acquired state parameter of each battery cluster in the energy storage battery;

[0123] A battery cluster quantification module 302, configured to perform weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster;

[0124] A battery cluster sorting module 303, used to sort the battery clusters according to the comprehensive scores;

[0125] A battery cluster grouping module 304, configured to group the sorted battery clusters;

[0126] The power allocation scheme determination module 305 is used to perform theoretical power allocation calculation and verification adjustment based on the grouping result to obtain a target group battery power allocation scheme.

[0127] In an optional embodiment, the power allocation scheme determining module 305 includes:

[0128] A grouping grid-connected power determination submodule, used to perform theoretical power allocation calculation on each battery group composed of the battery clusters based on the grouping result, to obtain the grouping grid-connected power borne by each battery group;

[0129] A judgment module is used to judge whether the group grid-connected power exceeds the set battery group transmission power range; if so, the battery group that exceeds the battery group transmission power range is eliminated, and the wind resistance grid-connected power determination submodule is returned to execute; if not, the target group battery power allocation plan is output.

[0130] In an optional embodiment, the state parameters include: pressure difference, temperature difference, pressure difference consistency percentage and temperature difference consistency percentage; the state score includes: pressure difference state score, temperature difference state score, pressure difference consistency score and temperature difference consistency score; the state score calculation module includes:

[0131] A pressure difference state score determination submodule, used to calculate the pressure difference state score by using a preset pressure difference score determination formula in combination with the pressure difference of the battery cluster;

[0132] A temperature difference state score determination submodule, used to calculate the temperature difference state score by using a preset temperature difference score determination formula in combination with the temperature difference of the battery cluster;

[0133] A pressure difference consistency score determination submodule, used to calculate the pressure difference consistency score by using a preset pressure difference consistency score determination formula in combination with the pressure difference consistency percentage of the battery cluster;

[0134] The temperature difference consistency score determination submodule is used to calculate the temperature difference consistency score by using a preset temperature difference consistency score determination formula in combination with the temperature difference consistency percentage of the battery cluster.

[0135] In an optional embodiment, the state parameter further includes: battery health data; the state score further includes: health state score; and step S1 further includes:

[0136] Calculating a maximum number of cycles of the battery cluster based on the battery status data of the battery cluster;

[0137] The health status score of the battery cluster is obtained by using a preset life prediction model in combination with the maximum number of cycles.

[0138] In an optional embodiment, the width value determination submodule includes:

[0139] A derivative analysis unit, configured to perform derivative analysis on the continuous grayscale change curve, and respectively determine a first boundary of the crack at the sub-pixel level of the first point set and a second boundary of the crack at the sub-pixel level of the second point set;

[0140] A width value calculation unit is used to calculate the width value of the crack image to be detected by using the first boundary and the second boundary.

[0141] In an optional embodiment, the extraction module 302 includes:

[0142] A boundary line determination submodule is used to determine two boundary lines from the crack image to be detected by using an edge detection algorithm;

[0143] The central axis and measuring point determination submodule is used to calculate the midpoints of the two central axes, determine the central axis, and determine the measuring point of the desired width from the central axis.

[0144] Embodiment 4, the embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a power distribution method for an energy storage battery in any embodiment.

[0145] Embodiment 5, the embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of a power distribution method for an energy storage battery in any embodiment are implemented.

[0146] Embodiment 6. The embodiment of the present invention further provides a computer program product, on which a computer program is stored. When the computer program is executed by the processor, the steps of a power distribution method for an energy storage battery in any embodiment are implemented.

[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0148] In the several embodiments provided in the present application, it should be understood that the methods, devices, electronic devices and storage media disclosed in the present invention can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a readable storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0152] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power distribution method for an energy storage battery, characterized in that: include: Step S1, based on the acquired state parameters of each battery cluster in the energy storage battery, calculating the state score corresponding to the state parameters; Step S2, performing weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster; Step S3, sorting the battery clusters according to the comprehensive scores; Step S4, grouping the sorted battery clusters; Step S5: Based on the grouping result, theoretical power allocation calculation and verification adjustment are performed to obtain a target group battery power allocation solution.

2. The power distribution method of the energy storage battery according to claim 1, characterized in that: The step S5 comprises: Step S51, based on the grouping result, performing theoretical power allocation calculation on each battery group composed of the battery clusters to obtain the group grid-connected power borne by each battery group; Step S52, determine whether the group grid-connected power exceeds the set battery group transmission power range; if so, remove the battery group that exceeds the battery group transmission power range and return to step S51; if not, output the target group battery power allocation plan.

3. The power distribution method of the energy storage battery according to claim 2, characterized in that: The step S51 comprises: Determining the weight coefficient of each battery group by using a hierarchical analysis method; Based on the weight coefficient, the group grid-connected power borne by each of the battery groups is calculated.

4. The power distribution method of the energy storage battery according to claim 1, characterized in that: The state parameters include: pressure difference, temperature difference, pressure difference consistency percentage and temperature difference consistency percentage; the state scores include: pressure difference state score, temperature difference state score, pressure difference consistency score and temperature difference consistency score; the step S1 includes: Using a preset pressure difference score determination formula and combining the pressure difference of the battery cluster, a pressure difference state score is calculated; Using a preset temperature difference score determination formula and combining the temperature difference of the battery cluster, a temperature difference state score is calculated; Using a preset pressure difference consistency score determination formula and combining the pressure difference consistency percentage of the battery cluster, a pressure difference consistency score is calculated; The temperature difference consistency score is calculated using a preset temperature difference consistency score determination formula in combination with the temperature difference consistency percentage of the battery cluster.

5. The power distribution method of the energy storage battery according to claim 4, characterized in that: The state parameter also includes: battery health data; the state score also includes: health state score; step S1 also includes: Calculating a maximum number of cycles of the battery cluster based on the battery status data of the battery cluster; The health status score of the battery cluster is obtained by using a preset life prediction model in combination with the maximum number of cycles.

6. The power distribution method of the energy storage battery according to claim 2, characterized in that: After step S5, the method further includes: Step S6, when executing the battery power allocation plan for the target group, determine whether the discharge state of the battery group has reached the lower limit of the discharge state, and / or whether the charging state of the battery cluster has reached the upper limit of the charging state. If so, stop executing the battery power allocation plan for the target group, define the battery group as an abnormal group and eliminate it, and return to the step S51.

7. A power distribution device for an energy storage battery, characterized in that: include: A state score calculation module, used to calculate a state score corresponding to the state parameter based on the acquired state parameter of each battery cluster in the energy storage battery; A battery cluster quantification module, used to perform weighted calculation on the state scores according to a preset weight coefficient to obtain a comprehensive score of each battery cluster; A battery cluster sorting module, used for sorting the battery clusters according to the comprehensive scores; A battery cluster grouping module, used for grouping the sorted battery clusters; The power allocation scheme determination module is used to perform theoretical power allocation calculation and verification adjustment based on the grouping results to obtain the target group battery power allocation scheme.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is executed.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is performed.

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

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