A method for SOE processing of an energy storage battery stack, an energy storage system and a computer program

Abstract

The present application belongs to the technical field of battery management, and particularly relates to a kind of energy storage battery stack SOE processing method, energy storage system and computer program.A kind of energy storage battery stack SOE processing method, the method includes data acquisition input, calculation under multiple application modes and result output, the multiple application modes are battery stack access multi-branch energy storage converter PCS application mode, battery cluster is high voltage direct hanging type energy storage application mode and battery cluster parallel access single-path energy storage converter PCS application mode.The present application realizes the precise scheduling of energy under different energy storage scenarios and different energy storage architectures through the precise and friendly evaluation of battery stack SOE, avoids problems such as system power supply anomaly, battery overcharge and overdischarge caused by battery stack error or rapid change, and further improves the energy use efficiency of the entire energy storage system and reduces the safety risk of the system.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology, specifically relating to a method for SOE processing of energy storage battery stacks, an energy storage system, and a computer program. Background Technology

[0002] Large-scale energy storage power stations have a large number of batteries. Their battery systems generally consist of individual cells, battery clusters, battery stacks, and battery systems, ranging from small to large and from few to many. Among them, battery clusters (i.e., battery packs, a term used in the energy storage industry) are generally composed of individual cells connected in series, or multiple individual cells connected in parallel and then in series; battery stacks are composed of multiple battery clusters connected in parallel; and battery systems are composed of multiple battery stacks, each of which generally operates independently.

[0003] There are several operating modes for battery stacks, such as: multi-branch mode: within the battery stack, multiple branch energy storage converters (PCS) refer to each PCS having multiple branches. Each branch is connected to a single battery cluster via high-voltage control devices such as contactors. Each branch can charge and discharge the battery cluster, or it can remain inactive. High-voltage direct connection mode: within the battery stack, a single energy storage converter (PCS) is connected to a single battery cluster via high-voltage control devices such as contactors. Adjacent energy storage converters (PCS) are connected in series via switching devices, or they can be connected in series across gaps to ultimately form a higher voltage. Single-branch mode: within the battery stack, all battery clusters are first connected in parallel and then connected to a single energy storage converter (PCS) via high-voltage control devices such as contactors. In this mode, the energy storage converter (PCS) can charge and discharge the battery.

[0004] During the operation of an energy storage system, the system needs to display the SOE (State of Energy) value of each battery stack. Currently, the SOE of a battery stack is generally the average value of the SOE of each battery cluster within the stack. However, due to the operating mode of the battery stack, which contains multiple battery clusters, and these clusters may operate synchronously or asynchronously, simply using an average value is insufficient to adequately represent the energy storage system. Summary of the Invention

[0005] The purpose of this invention is to solve the problems existing in the prior art and provide a method for SOE processing of energy storage battery stacks. This method is designed to better adapt to energy storage systems than the current simple average calculation method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for SOE processing of energy storage battery stacks includes data acquisition input, calculation and result output under multiple application modes, wherein the multiple application modes are: battery stack connected to a multi-branch energy storage converter PCS application mode, battery cluster as a high-voltage direct-connected energy storage application mode, and battery cluster connected in parallel to a single-path energy storage converter PCS application mode.

[0008] The data acquisition inputs include: individual battery voltage, expressed in VOL. ij [t] represents the battery cluster SOE, represented by SOE. i [t] represents the calculation period, denoted by T; the battery cluster current is denoted by I. i [t] represents the rated capacity, denoted by Cap; the battery cluster temperature is denoted by Temp. i [t] represents: the lower limit of the SOE interval for operation scheduling, denoted by SOEL; the upper limit of the SOE interval for operation scheduling, denoted by SOEH; the current error value, denoted by Curdif; where i represents the battery cluster number and j represents the individual battery number; where t represents the time.

[0009] The output includes: SOE of the battery stack, represented by GSOE_D[t]; intermediate quantities of the SOE calculation of the battery stack, represented by GSOE_old[t]; where t represents time.

[0010] The calculations under the various application modes include the following steps:

[0011] 1) Obtain the set of operational battery clusters B

[0012] Obtain the battery cluster set B for each of the various application modes;

[0013] 2) Obtain the calibration deviation object GSOE'[t], where t represents time;

[0014] 2.1) First, calculate the data for each data point within the battery cluster set B. The average value of the "SOE data" for each cluster is denoted as SOE. avg [t]; The minimum value in the "SOE data" of each cluster is denoted as SOE. min [t]; The maximum value in each cluster's "SOE data" is denoted as SOE. max [t]; The minimum value of the voltage of all individual cells in each cluster is denoted as VOL. min [t]; The maximum value of the voltage of all individual cells in each cluster is denoted as VOL. max [t]; the average value of the current in each cluster is denoted as I. avg [t] and the multiple of this average value are denoted as I. avg [t] / Cap, the average temperature of each cluster is denoted as Temp. avg [t]; where t represents time.

[0015] 2.2) Obtain the calibration deviation object GSOE'[t] for each of the various application modes;

[0016] 3) Obtain the battery stack status. When the status is discharging:

[0017] 3.1) When GSOE’[t] ≥ GSOE_old[t - 1], GSOE_D[t] = GSOE_old[t - 1];

[0018] 3.2) When GSOE’[t] < GSOE_old[t - 1], calculate the single - calibration deviation d[t]:

[0019] d[t] = T×I avg [t]×(GSOE_old[t - 1] - GSOE’[t]) / ((SOE min [t] - SOEL)×Cap);

[0020] When s[t]×d[t] < threshold D:

[0021] GSOE_D[t] = max{GSOE_old[t - 1] - D, GSOE’[t]};

[0022] If s[t]×d[t] ≥ threshold D:

[0023] GSOE_D[t] = GSOEold[t - 1] - s[t]×d[t];

[0024] The range of threshold D is [0.1, 0.5], s[t] is the threshold - calibration parameter, and the range of s[t] is [0.5, 5];

[0025] 3.3) GSOE_old[t] = GSOE_D[t];

[0026] 4) Obtain the battery - stack state. When the state is charging:

[0027] 4.1) When GSOE’[t] ≤ GSOE_old[t - 1], GSOE_D[t] = GSOE_old[t - 1];

[0028] 4.2) When GSOE’[t] > GSOE_old[t - 1], calculate the single - calibration deviation d[t]:

[0029] d[t] = T×I avg [t]×(GSOE’[t] - GSOE_old[t - 1]) / ((SOEH - SOE max [t])×Cap);

[0030] When s[t]×d[t] < threshold D:

[0031] GSOE_D[t] = min{GSOE_old[t - 1] + D, GSOE’[t]};

[0032] If s[t]×d[t]≥threshold D:

[0033] GSOE_D[t]=GSOEold[t-1]+s[t]×d[t];

[0034] 4.3) GSOE_old[t]=GSOE_D[t];

[0035] 5) Output the results GSOE_D[t] and GSOE_old[t].

[0036] Preferably, step 1) obtaining the operational battery cluster set B includes the following steps:

[0037] The average voltage of each individual cell in each battery cluster within the battery stack at time t is denoted as VOL. avgi [t], all VOL avgi The intermediate value in the [t] data is denoted as VOL. mid [t], Current value of all battery clusters I i The intermediate value of [t] is denoted as I. mid [t];

[0038] For the parallel connection of battery clusters to a single-branch energy storage converter (PCS) application mode: obtain the set of battery clusters in operation, when |VOL avgi [t]-VOL mid [t]|<threshold DSOE and|I i [t]-I mid [t]| / Cap<threshold D1, the i-th cluster is in operation, and thus the set of operational clusters B is obtained; where: threshold DSOE ranges [5mV, 30mV], threshold D1 ranges [0.05, 0.15];

[0039] For the application mode of battery stack connected to multi-branch energy storage converter PCS and the application mode of battery cluster being high-voltage direct-connected energy storage: the set of battery clusters in operation within the battery stack is B1, the contactor status of each cluster is detected, the set of all closed battery clusters is B2, and the set of currently operating clusters B is the intersection of sets B1 and B2.

[0040] As a preferred embodiment, in step 2.2), for the application mode of parallel connection of battery clusters to a single-branch energy storage converter (PCS), the calibration deviation object GSOE'[t] is calculated as follows:

[0041] When SOE avg When [t]>(SOEH-threshold DD), GSOE'[t]=SOE max [t];

[0042] When SOE avgWhen [t] < (SOEL + threshold DD), GSOE'[t] = SOE min [t];

[0043] When SOE avg [t]≤(SOEH-thresholdDD)orSOE avg When [t]≥(SOEL+thresholdDD),

[0044]

[0045] Wherein, GSOE'[t] is the calibration deviation object calculated by "cell stack SOE", and the threshold DD range is [3%, 10%];

[0046] For the application mode of battery stack connected to multi-branch energy storage converter PCS and the application mode of battery cluster as high-voltage direct-connected energy storage, the calibration deviation object GSOE'[t] is calculated as follows:

[0047] GSOE'[t]=SOE avg [t].

[0048] Preferably, SOEL is 10, SOEH is 90, and the calculation period T ranges from 100ms to 1000ms. Most preferably, the calculation period T is 200ms.

[0049] Preferably, in step 3.2), the corresponding threshold calibration parameter s[t] is obtained by querying the discharge characteristic table based on the minimum single-cell voltage VOLmin[t], the average cluster current rate Iavg[t] / Cap, and the average cluster temperature Tempavg[t] in the current battery cluster set B; in step 4.2), the maximum single-cell voltage VOLmin[t] in the current battery cluster set B3 is used as the threshold calibration parameter s[t]. max [t], average cluster current multiplier I avg [t] / Cap, average cluster temperature Temp avg [t], query the discharge characteristic table to obtain the corresponding threshold calibration parameter s[t].

[0050] As a preferred method, the charge / discharge state of the battery stack is determined as follows:

[0051] 1) When I_B[t] > Curdif × CN_B[t], it is determined to be charging;

[0052] 2) When I_B[t] < -1 × Curdif × CN_B[t], it is determined to be a discharge;

[0053] 3) When I_B[t]≤Curdif×CN_B[t] or I_B[t]≥-1×Curdif×CN_B[t], it is determined to be an open circuit;

[0054] Where I_B[t] represents the cumulative current of all battery clusters in the set B of battery clusters in operation within the battery stack at time t; Curdif represents the current error value; CN_B[t] represents the number of all battery clusters in the set B of battery clusters in operation within the battery stack at time t.

[0055] As a preferred option, when the energy storage system is running for the first time, GSOE_D[1]=GSOE'[1], GSOE_old[1]=GSOE_D[1], where 1 represents the initial first moment; when the current battery stack state is open circuit, GSOE_D[t]=GSOE_old[t-1], GSOE_old[t]=GSOE_D[t].

[0056] Furthermore, the present invention also discloses an energy storage system that uses the method described above to process the SOE of the battery stack.

[0057] Furthermore, the present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method.

[0058] Furthermore, the present invention also discloses a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the method.

[0059] Furthermore, the present invention also discloses a computer program product, including a computer program or instructions that, when executed by a processor, implement the method.

[0060] By adopting the above-mentioned technical solution, this invention achieves precise energy scheduling under different energy storage scenarios and different energy storage architectures through accurate and user-friendly evaluation of battery stack SOE, avoiding problems such as abnormal system power supply and battery overcharging and over-discharging caused by battery stack errors or rapid changes, thereby improving the energy utilization efficiency of the entire energy storage system and reducing system safety risks. Attached Figure Description

[0061] Figure 1 A schematic diagram of a battery stack connected to a multi-branch energy storage converter (PCS).

[0062] Figure 2 The diagram shows a battery stack used in a high-voltage direct-connected energy storage application.

[0063] Figure 3 A schematic diagram of a battery cluster connected in parallel to a single-channel energy storage converter (PCS).

[0064] Figure 4 The SOE processing methods for energy storage systems are divided into different application modes.

[0065] Figure 5 The set of battery clusters that are in operation within the battery stack. Detailed Implementation

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] like Figure 4 As shown, a method for SOE processing of energy storage battery stacks is presented. This method includes a data input section and calculations under various application modes. These various application modes include: a battery stack connected to a multi-branch energy storage converter (PCS); a battery cluster (pile) as a high-voltage direct-connected energy storage application mode; and a battery cluster connected in parallel to a single-path energy storage converter (PCS).

[0068] like Figure 1 The various application modes shown include the battery stack connected to a multi-branch energy storage converter (PCS) application mode. In the battery stack, the multi-branch energy storage converter (PCS) refers to a device with multiple branches inside. Each branch is connected to a single battery cluster through a high-voltage control device such as a contactor. Each branch can charge and discharge the battery cluster, or it can be inactive.

[0069] like Figure 2 The battery cluster (pile) shown is a high-voltage direct-connected energy storage application mode. Within the battery stack, a single energy storage converter (PCS) is connected to a single battery cluster through high-voltage control devices such as contactors. Adjacent energy storage converters (PCS) are connected in series through some switching devices, or they can be connected in series across each other to ultimately form a higher voltage.

[0070] like Figure 3 The battery clusters shown are connected in parallel to a single-channel energy storage converter (PCS) in the application mode. Within the battery stack, all battery clusters are first connected in parallel and then connected to a single energy storage converter (PCS) through high-voltage control devices such as contactors.

[0071] in:

[0072] Data input: Individual cell voltage, expressed in VOL ij [t] represents the battery cluster SOE, represented by SOE. i [t] represents the calculation period, denoted by T; the battery cluster current is denoted by I. i [t] represents the rated capacity, denoted by Cap; the battery cluster temperature is denoted by Temp. i[t] represents the SOE interval for operation scheduling, which is within the range of [SOEL, SOEH]; and the current error value, represented by Curdif.

[0073] Output results: Battery stack SOE, represented by GSOE_D[t]; intermediate computational quantities of battery stack SOE, represented by GSOE_old[t].

[0074] Where i represents the battery cluster number within the battery stack; j represents the individual cell number within the battery cluster; t represents the time; SOEL is generally 10, SOEH is generally 90; the calculation period T is preferably in the range of 100ms~1000ms, with a period of 200ms.

[0075] 1. Obtain the set of operational battery clusters and calibration deviation objects.

[0076] 1.1 Obtain the set of operational battery clusters B

[0077] The average voltage of each individual cell in each battery cluster within the battery stack at time t is denoted as VOL. avgi [t], all VOL avgi The intermediate value in the [t] data is denoted as VOL. mid [t], Current value of all battery clusters I i The intermediate value of [t] is denoted as I. mid [t];

[0078] 1) For: Battery clusters connected in parallel to a single-branch energy storage converter (PCS) application mode:

[0079] Obtain the set of battery clusters in operation, when |VOL avgi [t]-VOL mid [t]|<threshold DSOE and|I i [t]-I mid [t]| / Cap<threshold D1, the i-th cluster is in operation, and thus the set of operational clusters B is obtained.

[0080] 2) Application mode of battery stack connected to multi-branch energy storage converter (PCS) and application mode of battery cluster as high-voltage direct-connected energy storage:

[0081] The set of operational battery clusters within the battery stack is B1. The current contactor status of each cluster is monitored (the battery cluster can only operate after the contactor is closed). The set of all closed battery clusters is B2. The currently operational cluster set B is the intersection of sets B1 and B2. Therefore... Figure 5 .

[0082] 1.2 Obtaining the calibration deviation object

[0083] 1) Calculate the data within the battery cluster set B at time t:

[0084] The average value of the SOE data for each cluster is denoted as SOE. avg [t]; The minimum value in the "SOE data" of each cluster is denoted as SOE. min [t]; The maximum value in each cluster's "SOE data" is denoted as SOE. max [t]; The minimum value of the voltage of all individual cells in each cluster is denoted as VOL. min [t]; The maximum value of the voltage of all individual cells in each cluster is denoted as VOL. max [t]; the average value of the current in each cluster is denoted as I. avg [t] and the multiple of this average value are denoted as I. avg [t] / Cap, the average temperature of each cluster is denoted as Temp. avg [t]; where t represents time.

[0085] 2) For: Battery clusters connected in parallel to a single-branch energy storage converter (PCS) application mode:

[0086] When SOE avg When [t]>(SOEH-threshold DD), GSOE'[t]=SOE max [t];

[0087] When SOE avg When [t] < (SOEL + threshold DD), GSOE'[t] = SOE min [t];

[0088] When SOE avg [t]≤(SOEH-thresholdDD)orSOE avg When [t]≥(SOEL+thresholdDD),

[0089]

[0090] Wherein, GSOE'[t] is the calibration deviation object calculated by "battery stack SOE";

[0091] Threshold DSOE range [5mV, 30mV], preferably 10mV;

[0092] The threshold D1 ranges from [0.05, 0.15], with 0.05 being preferred;

[0093] The threshold DD range is [3%, 10%], preferably 5%.

[0094] 3) Application mode of battery stack connected to multi-branch energy storage converter (PCS) and application mode of battery cluster as high-voltage direct-connected energy storage:

[0095] GSOE'[t]=SOE avg[ t];

[0096] Among them, GSOE’[t] is the calibrated deviation object for calculating the "battery stack SOE".

[0097] 2. Judgment of the charge and discharge state of the battery stack:

[0098] When I_B[t] > Curdif × CN_B[t], it is judged as charging;

[0099] When I_B[t] < -1 × Curdif × CN_B[t], it is judged as discharging;

[0100] When I_B[t] ≤ Curdif × CN_B[t] or I_B[t] ≥ -1 × Curdif × CN_B[t], it is judged as open circuit;

[0101] Among them, I_B[t] represents the cumulative value of the currents of all battery clusters in the battery cluster set B put into operation in the battery stack at time t; Curdif represents the current error value; CN_B[t] represents the number of all battery clusters in the battery cluster set B put into operation in the battery stack at time t.

[0102] 3. At the initial operation, that is, at the initial first moment:

[0103] GSOE_D[1] = GSOE’[1], GSOE_old[1] = GSOE_D[1].

[0104] 4. When the current state of the battery stack is open circuit:

[0105] GSOE_D[t] = GSOE_old[t - 1], GSOE_old[t] = GSOE_D[t]. <000(0297>

[0106] 5. When the state of the battery stack is discharging:

[0107] [[ID={33}]]5.1. When GSOE’[t] ≥ GSOE_old[t - 1], GSOE_D[t] = GSOE_old[t - 1]; <00(0301>

[0108] 5.2. When GSOE’[t] < GSOE_old[t - 1],

[0109] 1) Calculate the single - time calibration deviation d[t]:

[0110] d[t] = T × I avg [t] × (GSOE_old[t - 1] - GSOE’[t]) / ((SOE min [t] - SOEL) × Cap);

[0111] 2) Based on the minimum single - cell voltage VOL in the current battery cluster set B min[t], average cluster current multiplier I avg [t] / Cap, average cluster temperature Temp avg [t], query the discharge characteristic table to obtain the corresponding threshold calibration parameter s[t] (range [0.5, 5]);

[0112] Example of a discharge characteristic table for a certain type of lithium battery:

[0113]

[0114] 3) When s[t]×d[t]<threshold D (preferably 0.2, range [0.1, 0.5]):

[0115] GSOE_D[t]=max{GSOE_old[t-1]-D, GSOE'[t]};

[0116] 4) If s[t]×d[t]≥threshold D (preferably 0.2, range [0.1, 0.5]):

[0117] GSOE_D[t]=GSOEold[t-1]-s[t]*d[t];

[0118] 5.3, GSOE_old[t]=GSOE_D[t];

[0119] 6. When the battery stack is in the charging state:

[0120] 6.1, when GSOE'[t]≤GSOE_old[t-1], GSOE_D[t]=GSOE_old[t-1];

[0121] 6.2, when GSOE'[t] > GSOE_old[t-1],

[0122] 1) Calculate the single calibration deviation d[t]:

[0123] d[t]=T×I avg [t]×(GSOE'[t]-GSOE_old[t-1]) / ((SOEH-SOE max [t]) ×Cap);

[0124] 2) Based on the maximum single-cell voltage VOLmax[t], the average cluster current rate Iavg[t] / Cap, and the average cluster temperature Tempavg[t] in the current battery cluster set B3, the corresponding threshold calibration parameter s[t] (range [0.5, 5]) is obtained by querying the discharge characteristic table;

[0125] Example of a charging characteristic table for a certain type of lithium battery:

[0126]

[0127] 3) When s[t]*d[t] < threshold D (preferably 0.2, range [0.1, 0.5]):

[0128] GSOE_D[t]=min{GSOE_old[t-1]+D, GSOE'[t]};

[0129] 4) If s[t]*d[t] ≥ threshold D (preferably 0.2, range [0.1, 0.5]):

[0130] GSOE_D[t]=GSOEold[t-1]+s[t]*d[t];

[0131] 6.3, GSOE_old[t]=GSOE_D[t];

[0132] 7. Output results GSOE_D[t] and GSOE_old[t].

[0133] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

Claims

1. A method for SOE processing of an energy storage battery stack, comprising data acquisition input, calculation in multiple application modes, and result output, the multiple application modes being a battery stack connected to a multi-branch PCS application mode, a battery cluster directly connected to a high-voltage energy storage application mode, and a battery cluster connected to a single-branch PCS application mode in parallel; the data acquisition input comprising: Monomer cell voltage, in VOL ij [t] denotes; Battery cluster SOE, with SOE i [t] denotes; calculation period, with T denotes; Battery cluster current, denoted by I i [t] represents; rated capacity, denoted by Cap; battery cluster temperature, denoted by Temp i [t] represents; lower limit of operation schedule SOE interval, denoted by SOEL, upper limit of operation schedule SOE interval, denoted by SOEH; current error value, denoted by Curdif; wherein i represents battery cluster serial number, j represents monomer battery serial number; wherein t represents time; the result output comprising: a battery stack SOE, denoted as GSOE_D[t]; and a battery stack SOE calculation intermediate quantity, denoted as GSOE_old[t]; characterized in that the calculation in multiple application modes comprises the following steps: 1) obtaining a set of operating battery clusters B obtaining a set of operating battery clusters B for multiple application modes respectively; 2) obtaining a calibrated deviation object GSOE'[t] 2.1) First, calculate the average value of each data in the battery cluster set B, the average value of each cluster "SOE data" is recorded as SOE avg [t]; the minimum value in each cluster "SOE data" is recorded as SOE min [t]; the maximum value in each cluster "SOE data" is recorded as SOE max [t]; the minimum value of all single battery voltages in each cluster is recorded as VOL min [t]; the maximum value of all single battery voltages in each cluster is recorded as VOL max [t]; the average value of the current in each cluster is recorded as I avg [t]; the average value of the current in each cluster is recorded as I avg [t] / Cap, the average value of the temperature in each cluster is recorded as Temp avg [t]; wherein t represents the time 2.2) obtaining a calibrated deviation object GSOE'[t] for multiple application modes respectively; 3) obtaining a battery stack state, when the state is discharging: 3.1) when GSOE'[t]≥GSOE_old[t-1], GSOE_D[t]=GSOE_old[t-1]; 3.2) when GSOE'[t]<GSOE_old[t-1], calculating a single calibration deviation d[t]: d[t] = T x I avg [t] x (GSOE_old[t-1] - GSOE'[t]) / ((SOE min [t] - SOEL) x Cap); when s[t]×d[t]<threshold D: GSOE_D[t]=max{GSOE_old[t-1]-D, GSOE'[t]}; if s[t]×d[t]≥threshold D: GSOE_D[t]=GSOE_old[t-1]-s[t]×d[t]; threshold D ranges from 0.1 to 0.5, s[t] is a threshold calibration parameter, and ranges from 0.5 to 5; 3.3) GSOE_old[t]=GSOE_D[t]; 4) obtaining a battery stack state, when the state is charging: 4.1) when GSOE'[t]≤GSOE_old[t-1], GSOE_D[t]=GSOE_old[t-1]; 4.2) when GSOE'[t]>GSOE_old[t-1], calculating a single calibration deviation d[t]: d[t] = T x I avg [t] x (GSOE'[t] - GSOE_old[t-1]) / ((SOEH - SOE max [t]) x Cap); when s[t]×d[t]<threshold D: GSOE_D[t]=min{GSOE_old[t-1]+D, GSOE'[t]}; if s[t]×d[t]≥threshold D: GSOE_D[t]=GSOE_old[t-1]+s[t]×d[t]; 4.3) GSOE_old[t]=GSOE_D[t]; 5) outputting results GSOE_D[t] and GSOE_old[t].

2. The method of claim 1, wherein, Step 1) obtaining a set of operating battery clusters B comprises the following steps: The average value of the single cell voltage of each battery cluster in the battery stack at time t is denoted as VOL avgi [t], all VOL avgi The middle value in the [t] data is denoted as VOL mid [t], all battery cluster current values I i The middle value of [t] is denoted as I mid [t]; For battery cluster parallel access single branch energy storage converter PCS application mode: obtain the set of operating battery clusters, when |VOL avgi [t]-VOL mid [t]|<threshold DSOE and |I i [t]-I mid [t]| / Cap<threshold D1, the i-th cluster is in an operating state, and then the set of operating clusters B is obtained; wherein: the threshold DSOE ranges from 5 mV to 30 mV, and the threshold D1 ranges from 0.05 to 0.

15. for the battery stack connected to a multi-branch PCS application mode and the battery cluster directly connected to a high-voltage energy storage application mode: a set of operating battery clusters in the battery stack is set as B1, a contactor state of each cluster is detected, a set of all closed battery clusters is B2, and a current operating cluster set B is an intersection of the set B1 and the set B2.

3. The method of claim 1, wherein, In step 2.2), the calculation of the calibrated deviation object GSOE'[t] for the battery cluster parallelly connecting single-branch energy storage converter PCS application mode is as follows: When SOE avg [t] > (SOE H-threshold DD), GSOE'[t] = SOE max [t]; When SOE avg [t] < (SOEL + threshold DD), GSOE'[t] = SOE min [t]; When SOE avg [t] ≤ (SOEH - threshold DD) or SOE avg [t] ≥ (SOEL + threshold DD) ； Wherein, GSOE'[t] is the calibrated deviation object of the "battery stack SOE" calculation, the threshold DD range is [3%, 10%]; For the battery stack connecting multi-branch energy storage converter PCS application mode and the battery cluster being a high-voltage direct-hanging energy storage application mode, the calculation of the calibrated deviation object GSOE'[t] is as follows: GSOE'[t] = SOE avg [t].

4. The method of claim 1, wherein, SOEL is 10, SOEH is 90, and the calculation period T ranges from 100 ms to 1000 ms.

5. The method of claim 1, wherein, Step 3.2) based on the minimum single battery voltage VOLmin[t] in the current battery cluster set B, the average cluster current rate Iavg[t] / Cap, the average cluster temperature Tempavg[t], the corresponding threshold calibration parameter s[t] is obtained by querying the discharge characteristic table; in step 4.2) based on the maximum single battery voltage VOLmax[t] in the current battery cluster set B3, the average cluster current rate Iavg[t] / Cap, the average cluster temperature Tempavg[t], the corresponding threshold calibration parameter s[t] is obtained by querying the discharge characteristic table. max [t] in the current battery cluster set B, the average cluster current rate I avg [t] / Cap, the average cluster temperature Temp avg [t], the corresponding threshold calibration parameter s[t] is obtained by querying the discharge characteristic table.

6. The method of claim 1, wherein, The judgment of the battery stack charging and discharging state is as follows: 1) When I_B[t]>CurdifCN_B[t], it is judged as charging; 2) When I_B[t]<-1xCurdifCN_B[t], it is judged as discharging; 3) When I_B[t]≤CurdifCN_B[t] or I_B[t]≥-1xCurdifCN_B[t], it is judged as open circuit; Wherein, I_B[t] represents the current cumulative value of all battery clusters in the battery cluster set B in the battery stack at time t; Curdif represents the current error value; CN_B[t] represents the number of all battery clusters in the battery cluster set B in the battery stack at time t.

7. The method of claim 6, wherein, If the energy storage system is first operated, GSOE_D[1]=GSOE'[1], GSOE_old[1]=GSOE_D[1], 1 represents the initial first time; When the current battery stack state is open circuit, GSOE_D[t]=GSOE_old[t-1], GSOE_old[t]=GSOE_D[t].

8. An energy storage system characterized by, The system uses the method of any one of claims 1-7 to process the SOE of the battery stack.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to realize the method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions are executed by the processor to realize the method of any one of claims 1-7.

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