Online balancing method for batteries in series connection direction of energy storage system

Abstract

The invention discloses an energy storage system series direction battery online equalization method, and relates to the field of energy storage system battery equalization. In order to solve the defects that the existing battery equalization needs on-site manual intervention and the operation and maintenance cost is high since the battery charging machine can only be used for charging the outlier batteries in series after an energy storage system enters an off-line state, outlier information of SOC values of battery modules is obtained, and if the outlier information is a single outlier module and a plurality of alignment modules, the battery modules are aligned to the single outlier module and the plurality of alignment modules. If yes, executing a first charging strategy to charge the battery module; if the outlier information is a plurality of outlier modules and a plurality of alignment modules, executing a second power supply strategy to supply power to the battery module; according to the first electricity supplementing strategy, electricity supplementing is conducted on the outlier modules through the electricity supplementing strategy of a single outlier module and N-1 alignment modules, and according to the second electricity supplementing strategy, electricity supplementing is conducted on the m outlier modules through the electricity supplementing strategy of m outlier modules and N-m alignment modules. The method is mainly used for carrying out on-line balancing on the battery energy in the series connection direction of the energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of battery balancing in energy storage systems, and in particular to an online balancing method for batteries in series connection of energy storage systems based on a dynamic reconfigurable technology. Background Art

[0002] In a dynamically reconfigurable battery network, individual batteries in the series connection experience inconsistent SOC (State of Charge) values, leading to a weak link effect. This is primarily due to two factors: The batteries used during initial installation often exhibit significant variations; and even if initial consistency is good, subtle variations in materials and processes inherent in the batteries can lead to variations in operating temperature, self-discharge rates, and internal chemical reactions. Over long-term operation, these variations can compound and lead to a significant weak link effect. When the Digital Energy Switch System (DESS) detects low SOC consistency in the batteries, recharging is necessary to align the SOCs of the individual batteries and overcome the weak link effect.

[0003] In the parallel direction of batteries, since the DESS is constantly executing the dynamic reconfigurable strategy in real time during operation, the consistency of each battery in the parallel direction can be effectively controlled. Therefore, except for special circumstances such as faults, the batteries in the parallel direction can be regarded as a whole, and no additional differentiation processing is required when replenishing power in series.

[0004] Traditional fixed series-parallel energy storage systems cannot perform online battery recharge operations. The typical balancing operation method is: the energy storage system goes offline and recharges through special equipment such as recharge motors to achieve energy balance of the batteries in the string. This operation requires on-site manual intervention and high operation and maintenance costs.

[0005] Therefore, there is a need for an online balancing method for batteries in series in energy storage systems based on dynamic reconfigurable technology, which can achieve energy balancing of batteries in series remotely through a digital energy interaction system without the need for on-site manual operation, with fast recharging time, low operation and maintenance costs, and the ability to quickly recharge both single module outliers and multiple modules outliers. Summary of the Invention

[0006] In order to solve the defects of existing battery balancing that require on-site manual intervention, and the need to wait until the energy storage system enters an offline state before recharging the batteries in the string through the recharge generator, and the high operation and maintenance costs, the present invention provides an online balancing method for batteries in the series direction of energy storage systems based on dynamic reconfigurable technology, which can achieve energy balancing of batteries in the string remotely through a digital energy interaction system without the need for on-site manual operation, with fast recharging time, low operation and maintenance costs, and can quickly recharge both single module outliers and multi-module outliers.

[0007] The present invention provides an online balancing method for batteries in series connection of an energy storage system, comprising the following steps: Obtain outlier information of the SOC value of the battery module. If the outlier information is a single outlier module and multiple aligned modules, execute a first power replenishment strategy to replenish the battery module; if the outlier information is multiple outlier modules and multiple aligned modules, execute a second power replenishment strategy to replenish the battery module; The first power replenishment strategy: that is, a power replenishment strategy of a single outlier module and N-1 aligned modules is used to replenish power for the outlier module. The second power replenishment strategy: that is, the power replenishment strategy of m outlier modules and Nm aligned modules is used to replenish power for the m outlier modules.

[0008] Furthermore: the first power replenishment strategy includes the following steps: S11. Assume that the initial difference in SOC between the alignment module and the outlier module is ; The lower limit of SOC is , the upper limit of SOC is , the SOC working range of the alignment module is , the SOC working range of the outlier module is ; S12, if the initial SOC value of the current outlier module is less than , then execute S13; if the initial SOC value of the current outlier module ≥ , then execute S14; S13, if the initial SOC value of the current outlier module < , then the charging operation is performed to charge the SOC value of the outlier module to ; The current outlier module is always connected to the loop, and the alignment module is connected to the loop in a round-robin manner; S14, the initial SOC value of the current outlier module ≥ , perform the discharge operation; do not connect the current outlier module to the loop, and connect the multiple alignment modules to the loop; The duration of the first charging strategy is: when the initial SOC value of the outlier module is ≥ , the charging time is the discharge time of the discharge process; when the initial SOC value of the outlier module is < , the charging time includes the duration of the charging process and the discharging process.

[0009] Furthermore, in the first power replenishment strategy, it is necessary to regularly check for inconsistencies in the series connection direction. If an outlier module is found, power replenishment is immediately performed to always maintain consistency in the series connection direction.

[0010] Furthermore, the second power replenishment strategy includes the following steps: S21, grouping the batteries in the same cluster according to their SOCs, where the standard deviation of the SOCs of the batteries in each cluster is less than a given threshold value; S22, entering the discharge process, sorting the groups in descending order according to the average SOC value of each group; S23, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; S24, scheduling the time for connecting batteries of each group to the circuit within the scheduling time range using a given time slice, so that the SOC of the batteries in each group increases evenly; S25: When any battery reaches the lower discharge limit SOC, switch to the charging process; S26, entering the charging process, sorting the groups in ascending order according to the average SOC value of each group; S27, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; S28. Schedule the time for connecting the batteries of each group to the circuit within the scheduling time range using a given time slice, so that the SOC of the batteries in each group increases evenly; S29. When any battery reaches the upper limit SOC of discharge, switch to the discharge process and repeat the above charge and discharge process until the SOC standard deviation of all batteries meets the standard.

[0011] Further: In S21, it is assumed that the module in the string exists Group ,in The group of aligned modules is called the reference group. The coefficient of variation of the SOC of all modules in the reference group is less than the given threshold. , that is, it meets the SOC consistency condition; other groups are outliers relative to the benchmark group.

[0012] Furthermore: in S22 , during the discharge process, the average SOC of the reference group is greater than that of other groups. Since the reference group is always connected to the circuit, its discharge time is the longest.

[0013] Further: In S23, since all outlier groups will eventually be aggregated into the reference group, the reference group's power replenishment efficiency relative to other groups and The maximum is the optimization target, and the planned charging capacity difference between the benchmark group and each outlier group divided by the actual capacity difference between the benchmark group and each outlier group is the charging efficiency. The charging efficiency and Maximum, that is, tending towards the goal of shortest total charging time.

[0014] Furthermore: in S26 , during the charging process, the average SOC of the reference group is smaller than that of the other groups, the reference group is always connected to the circuit, and its charging time is the longest.

[0015] Further: In S28, after each scheduling execution, it is necessary to check whether the modules scheduled this time meet the consistency conditions for aggregation to other groups. If they meet the conditions, each group needs to be updated and the next scheduling duration is calculated. and charging time of the access circuit .

[0016] Furthermore: During the charging and discharging process, the constraints are specifically as follows: online real-time scheduling of the battery with the charging efficiency and maximum as the optimization objectives, and the access loop time, the upper limit of the SOC for battery charging and the lower limit of the SOC for battery discharging as the constraints, solving the optimization problem, and determining the strategy for each battery to access the loop within the scheduling cycle, thereby achieving rapid balancing of the batteries in the series direction.

[0017] The beneficial effects of the present invention are: The online balancing method for string batteries based on dynamic reconfigurable technology described in the present invention does not require on-site personnel operation compared to the traditional fixed string-parallel method. It can be completed online through a digital energy interaction system without the need for special instruments, and achieves rapid balancing of string batteries with the minimum recharging time as the optimization goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the digital energy system structure; Figure 2 It is a workflow diagram for online balancing of batteries in series connection based on a dynamically reconfigurable energy storage system. DETAILED DESCRIPTION

[0019] The following are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the scope of protection of the present invention. The embodiments described below are only used to explain the present invention and cannot be interpreted as limiting the present invention. The scope of protection of the present invention should be based on the scope of protection of the claims. The embodiments of the present invention are described in detail below. In order to facilitate the description of the present invention and simplify the description, the technical terms used in the description of the present invention should be interpreted broadly, including but not limited to conventional replacement schemes not mentioned in this application, and also including direct implementation and indirect implementation.

[0020] Example 1 Combine Figure 1 and Figure 2 This embodiment describes a method for online string battery balancing based on dynamically reconfigurable technology. The number of battery modules refers to the number of battery modules in the series direction. For example, a 3-parallel 14-string configuration has a total of 42 battery modules. Since the modules in the parallel direction can be considered as a whole, the 3-parallel 14-string configuration is analyzed and calculated as 14 modules. The basic charging scheduling object in this embodiment is the battery module. In practice, the scheduling object type should be determined based on the basic management object granularity of the DESS, including module or cell.

[0021] Optimization goal: This embodiment analyzes the time limits for recharging batteries in the series connection direction in various scenarios and formulates the most time-saving recharging strategy that meets safety constraints, so that the SOC of all batteries in the series connection direction is aligned.

[0022] Constraints: Due to the limited effective voltage range of the PCS (Power Conversion System), a typical 3-parallel, 14-string configuration of 280Ah or 314Ah battery modules generally only supports a minimum of 13 strings. Therefore, only 13 or 14 strings are supported for battery replenishment.

[0023] Recharging mode: The charging (or discharging) of batteries in series is essentially to align the SOC by allocating different charging times to different batteries.

[0024] Assuming that only one battery module has an outlier SOC value and needs to be recharged, and the SOC consistency of other modules meets the requirements, the module with the outlier SOC should always be connected to the loop for recharge to ensure the shortest recharge time. The other SOC-aligned modules need to ensure that the total amount of charging ampere-hours remains consistent throughout the recharge process. Since the aligned modules are also charging at the same time, the actual charging time should be the time required for the outlier module to catch up with the aligned modules. Assuming that the charging mode is a constant current charging mode, in order to ensure that the charging time of the aligned modules with aligned SOC is the same, the time for each aligned module to be connected to the loop should be consistent, otherwise it will lead to the consistency level of the aligned modules. The connection loop time of the SOC outlier module is set to , which is the charging time, then the number of alignment modules is 13, then according to the round-robin method, the time for each alignment module to access the circuit is , according to the constant current charging mode, the charging current is constant , then the total charging capacity of the outlier module is , the charging capacity of each alignment module is , if the battery capacity is , the SOC of the outlier module is smaller than that of the aligned module, and the initial SOC difference is , the total charging capacity of the outlier module is ; The charging time should meet ,Right now ; if , , the rate is 0.5C (C is used to indicate the battery charge and discharge capacity rate. Generally, the size of the charge and discharge current is often expressed by the charge and discharge rate. For example, the charge and discharge rate is 1C, which means that the energy storage battery can discharge all the power within 1 hour; 0.5C means that the energy storage battery can discharge all the power within 2 hours), charging current , the charging time is ; The maximum charging time at 0.5C is 2 hours. When the charging time is longer than 2 hours, it needs to be decomposed into multiple stages for charging-discharging cycle. Considering that the charging and discharging cycle should avoid the high SOC and low SOC ranges as much as possible, it is necessary to further determine the operable SOC working range. The upper and lower limits of the allowed SOC are and , considering the short board effect, the actual operable SOC range of the alignment module is , the actual operable SOC range of the outlier module is , the effective charging capacity of a charging process is , The upper limit of the number of times of replenishing power only through the charging process is .

[0025] Considering that the connection time of each battery module during the discharge process can be distributed, the alignment module can be connected to the circuit for a longer time than the outlier module, thus accelerating the alignment process. If the outlier module is not connected to the circuit at all during the discharge process, and the 13 alignment modules are always connected to the circuit, the SOC alignment can be completed as quickly as possible, meeting If the condition ,if , , rate is 0.5C, charging current ,but .

[0026] Based on the above analysis, for a single outlier module and 13 aligned modules, the recharging time during the discharge process is at least 1 / 13 of the recharging time during the charging process, and the recharging process can be completed in a single discharge process. Recharging during the discharge process should be preferred. If the initial SOC does not meet the requirements, a two-stage recharging process of first charging and then discharging can be adopted. For multiple outlier modules, a universal design scheme is used. The specific design scheme is broken down into design schemes for a single outlier module and multiple outlier modules.

[0027] First power replenishment strategy: a single outlier module and a power replenishment strategy for N-1 aligned modules: S11, the initial difference in SOC between the alignment module and the outlier module is , the battery capacity is , the battery charge and discharge rates are , the battery rate charge and discharge mode is constant current mode, the charging current is constant , the lower and upper limits of SOC are and , the SOC working range of the alignment module is , the SOC working range of the outlier module is .

[0028] S12, if the initial SOC value of the current outlier module Less than , then execute S13; if the initial SOC value of the current outlier module is Greater than or equal to , then execute S14; S13, initial SOC value of the current outlier module Less than , then the charging operation is performed to charge the SOC of the outlier module to The outlier modules are always connected to the loop, and the aligned modules are connected to the loop in a round-robin manner. At the same time, the number of modules connected to the loop is constant. The access loop time of the SOC outlier module is set to , which is the charging time. The time for aligning the module to access the circuit is The charging capacity of the outlier module is ( , charging time To meet ,Right now ,Charge Align the SOC value of the module after a certain period of time for: .

[0029] Charge After a certain period of time, the SOC value of the outlier module is , .

[0030] S14, perform the discharge operation, the outlier module is not connected to the circuit at all times, and the 13 aligned modules are always connected to the circuit. The SOC value before discharge determines the discharge time. For the case where the charging process is not performed, the SOC of the outlier module is , the SOC of the alignment module is , the discharge capacity is , discharge time for ; For the case where the charging process is performed, the SOC of the outlier module is , the SOC of the alignment module is , the discharge capacity is , the discharge time is .

[0031] The specific evaluation of the charging time is: when the initial SOC value of the outlier module Greater than or equal to , the charging time is the discharge time of the discharge process, which is equal to ; When the initial SOC value of the outlier module Less than , then the charging time includes the duration of the charging process and the discharging process, which is = .

[0032]

[0033] The above-mentioned recharging plan for a single outlier module requires regular checks on the inconsistencies in the series connection direction during actual operation and maintenance, timely detection of outlier modules, and immediate recharging when an outlier module is found. This ensures consistency in the series connection direction and avoids the situation where multiple outlier modules need to be recharged.

[0034] Second power replenishment strategy: Power replenishment strategy for 2m outlier modules and Nm aligned modules: In actual operation and maintenance, multiple outlier modules are inevitable. For a scenario with m outlier modules, a simple strategy is to recharge each outlier module sequentially, following the same recharge strategy for a single outlier module, in descending order of outlier severity. However, this recharge strategy fails to consider the situation where outlier modules may cluster during the recharge process, and lacks optimized recharge duration analysis and processing for this multi-cluster scenario.

[0035] S21, grouping the batteries in the same cluster according to their SOCs, where the standard deviation of the SOCs of the batteries in each cluster is less than a given threshold value; Assume that the module in series exists Group ,in The group of aligned modules is called the reference group. The coefficient of variation of the SOC of all modules in the reference group is less than the given threshold. , that is, the SOC consistency condition is met. The other groups are all outliers relative to the benchmark group. K is the total number of outlier groups, and K is adaptively determined based on the SOC consistency status of the outlier battery module. The coefficient of variation of SOC refers to the ratio of the standard deviation of the battery state of charge (State of Charge, referred to as SOC) to the average value, which is used to measure the degree of dispersion of the battery SOC. The smaller the coefficient of variation, the smaller the fluctuation of the battery SOC and the more stable the battery state; conversely, the larger the coefficient of variation, the greater the fluctuation of the battery SOC and the more unstable the battery state.

[0036] There are two cases of outlier groups, one is a single-module group composed of a single module, and the other is a multi-module group composed of multiple modules. The multi-module group meets the SOC consistency condition. During the charging process, any multiple single-module groups will be merged into a multi-module group when the SOC consistency condition is met. This process is called group aggregation. Similarly, the single-module group and the benchmark group will also aggregate. The charging process is the process of all modules aggregating into the benchmark group. The process of splitting a multi-module group or benchmark group into multiple groups is called disaggregation. Except for the fault of the battery itself, the charging strategy should ensure that disaggregation does not occur to avoid the situation where the charging process does not converge or converges too slowly. The average SOC of the benchmark group is greater than that of other groups. During the charging process, due to the existence of aggregation, is a variable that needs to be updated each time the battery module is scheduled to charge or discharge. Adaptive determination based on the SOC consistency state of the outlier module S22, entering the discharge process, sorting the groups in descending order according to the average SOC value of each group; During the discharge process, the average SOC of the reference group is greater than that of other groups. Since the reference group is always connected to the circuit, its discharge time is the longest, which is set as , the discharge durations of other groups are ,exist The SOC difference between the benchmark group and each group is , the initial SOC value of each group is , using constant current mode discharge.

[0037] S23, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; Since all outlier groups will eventually converge into the benchmark group, the power replenishment efficiency and The maximum is the optimization target, and the planned charging capacity difference between the benchmark group and each outlier group divided by the actual capacity difference between the benchmark group and each outlier group is the charging efficiency. The charging efficiency and Maximum, that is, tending towards the goal of shortest total charging time. The maximum charging efficiency is: (1) (2) In the above formula The weight factor of each module group needs to be supplemented with the distribution principle. for The module is The discharge duration of the circuit connected within the time, The number of modules is , The time length of each module connected to the loop is the same, which is During the discharge process, there is always one module not connected to the circuit, and the total time it is not connected to the circuit is , the sum of all modules except the reference module that are not connected to the circuit is , so the following constraints hold: (3) (4) The optimization problem formed by the combination of formulas (1), (2), (3), and (4) is a typical linear programming problem, which can be quickly solved by the simplex method and the interior point method. and According to formulas (2), (3), and (4), it is converted into the standard form of the simplex method ( )as follows: (5) exist The proportion of the time that each battery module in each outlier group cuts out of the circuit within the time is: (6) (7) S24, scheduling the battery access time of each group within the scheduling time range in a given time slice, so that the SOC of the batteries in each group increases evenly; the scheduling objects include the baseline group and the outlier group; If the scheduling time slice length is , then the number of times the battery module removal circuit is scheduled in each outlier group within the scheduling duration is: , (8) After each scheduling is completed, it is necessary to check whether the modules scheduled this time meet the consistency conditions for aggregation to other groups. If they meet the conditions, each group needs to be updated and the next scheduling duration needs to be calculated. and .

[0038] S25: When any battery reaches the lower discharge limit SOC, switch to the charging process; S26, entering the charging process, sorting the groups in ascending order according to the average SOC value of each group; During the charging process, the average SOC of the benchmark group is smaller than that of other groups. The benchmark group is always connected to the circuit and has the longest charging time, which is set as , the charging time of other groups are ,exist The differences between the SOC of each group and the benchmark group are , the initial SOC value of each group is , charging in constant current mode.

[0039] S27, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; Since all outlier groups will eventually converge into the benchmark group, the power replenishment efficiency and The maximum is the optimization target, and the planned charging capacity difference between the benchmark group and each outlier group divided by the actual capacity difference between each outlier group and the benchmark group is the charging efficiency. The charging efficiency and The maximum, that is, tends to the goal of the shortest total charging time. According to the above analysis, the charging process and the discharging process charging model are similar. The maximum charging efficiency is: (9) (10) In the above formula The weight factor of each module group needs to be supplemented with the distribution principle. for The module is The charging time of the circuit connected within the time, The number of modules is , The time length of each module connected to the loop is the same, which is During the charging process, there is always one module not connected to the circuit, and the total time of not connected to the circuit is , the sum of all modules except the reference module that are not connected to the circuit is , so the following constraints hold: (11) (12) The optimization problem formed by combining formulas (9), (10), (11), and (12) is a typical linear programming problem, which can be quickly solved by the simplex method and the interior point method. and Formulas (10), (11), and (12) are converted into the standard form of the simplex method ( )as follows: (13) S28. Schedule the time for connecting the batteries of each group to the circuit within the scheduling time range using a given time slice, so that the SOC of the batteries in each group increases evenly; After each scheduling is completed, it is necessary to check whether the modules scheduled this time meet the consistency conditions for aggregation to other groups. If they meet the conditions, each group needs to be updated and the next scheduling duration needs to be calculated. and .

[0040] S29. When any battery reaches the upper limit SOC of discharge, switch to the discharge process and repeat the above charge and discharge process until the SOC standard deviation of all batteries meets the standard.

[0041] Repeat the above charging and discharging process until all modules are aggregated into the reference group.

[0042] For detailed operation procedures, see Figure 2 .

[0043] Basic simulation conditions: Number of modules in series: 14; Magnification: 0.5; SOC standard deviation target for all batteries achieved during recharging: 0.005; Initial SOC distribution: Uniform; Number of test samples: 1000. Recharging duration does not include PCS reset time.

[0044] The simulation evaluation results are as follows:

Claims

1. A method for online balancing of batteries in series connection of an energy storage system, characterized in that: The steps include: Obtain outlier information of the SOC value of the battery module, and if the outlier information is a single outlier module and multiple aligned modules, execute a first power replenishment strategy to replenish power for the battery module; If the outlier information is a plurality of outlier modules and a plurality of aligned modules, executing a second power replenishment strategy to replenish power for the battery module; The first power replenishment strategy: that is, a power replenishment strategy of a single outlier module and N-1 aligned modules is used to replenish power for the outlier module. The second power replenishment strategy: that is, the power replenishment strategy of m outlier modules and Nm aligned modules is used to replenish power for the m outlier modules.

2. The method for online balancing of batteries in series connection of an energy storage system according to claim 1, characterized in that: The first power replenishment strategy includes the following steps: S11. Assume that the initial difference in SOC between the alignment module and the outlier module is ; The lower limit of SOC is , the upper limit of SOC is , the SOC working range of the alignment module is , the SOC working range of the outlier module is ; S12, if the initial SOC value of the current outlier module is less than , then execute S13; if the initial SOC value of the current outlier module ≥ , then execute S14; S13, if the initial SOC value of the current outlier module < , then the charging operation is performed to charge the SOC value of the outlier module to ; The current outlier module is always connected to the loop, and the alignment module is connected to the loop in a round-robin manner; S14, the initial SOC value of the current outlier module ≥ , perform the discharge operation; do not connect the current outlier module to the loop, and connect the multiple alignment modules to the loop; The duration of the first charging strategy is: when the initial SOC value of the outlier module is ≥ , the charging time is the discharge time of the discharge process; When the initial SOC value of the outlier module < , the charging time includes the duration of the charging process and the discharging process.

3. The method for online balancing of batteries in series connection of an energy storage system according to claim 2, characterized in that: In the first power replenishment strategy, it is necessary to regularly check the inconsistency of the batteries in the series connection direction. If an outlier module is found, power replenishment is immediately performed to always maintain consistency in the series connection direction.

4. The method for online balancing of batteries in series connection of an energy storage system according to claim 1, characterized in that: The second power replenishment strategy includes the following steps: S21, grouping the batteries in the same cluster according to their SOCs, where the standard deviation of the SOCs of the batteries in each cluster is less than a given threshold value; S22, entering the discharge process, sorting the groups in descending order according to the average SOC value of each group; S23, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; S24, scheduling the time for connecting batteries of each group to the circuit within the scheduling time range using a given time slice, so that the SOC of the batteries in each group increases evenly; S25: When any battery reaches the lower discharge limit SOC, switch to the charging process; S26, entering the charging process, sorting the groups in ascending order according to the average SOC value of each group; S27, using the maximum power replenishment efficiency of the reference group relative to other groups as the optimization objective function and the constraint conditions to solve the access loop time allocated to each group in each scheduling; S28. Schedule the time for connecting the batteries of each group to the circuit within the scheduling time range using a given time slice, so that the SOC of the batteries in each group increases evenly; S29. When any battery reaches the upper limit SOC of discharge, switch to the discharge process and repeat the above charge and discharge process until the SOC standard deviation of all batteries meets the standard.

5. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: In S21, assume that the module in series exists Group ,in The group of aligned modules is called the reference group. The coefficient of variation of the SOC of all modules in the reference group is less than the given threshold. , that is, the SOC consistency condition is met; The other groups are outliers relative to the benchmark group.

6. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: In S22 , during the discharge process, the average SOC of the reference group is greater than that of other groups. Since the reference group is always connected to the circuit, its discharge time is the longest.

7. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: In S23, since all outlier groups will eventually be aggregated into the reference group, the reference group is compared with the other groups in terms of power replenishment efficiency and The maximum is the optimization target, and the planned charging capacity difference between the benchmark group and each outlier group divided by the actual capacity difference between the benchmark group and each outlier group is the charging efficiency. The charging efficiency and Maximum, that is, tending towards the goal of shortest total charging time.

8. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: In S26 , during the charging process, the average SOC of the reference group is lower than that of the other groups, the reference group is always connected to the circuit, and its charging time is the longest.

9. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: In S28, after each scheduling is completed, it is necessary to check whether the modules scheduled this time meet the consistency conditions for aggregation to other groups. If they meet the conditions, each group needs to be updated and the next scheduling duration is calculated. and charging time of the access circuit .

10. The method for online balancing of batteries in series connection of an energy storage system according to claim 4, characterized in that: During the charging and discharging process, the constraints are specifically as follows: online real-time scheduling of batteries with charging efficiency and maximum as optimization objectives, and the access loop time, the upper limit of the SOC for battery charging cutoff, and the lower limit of the SOC for battery discharging cutoff as constraints. The optimization problem is solved to determine the strategy for each battery to access the loop within the scheduling cycle, thereby achieving rapid balancing of batteries in the series direction.

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