Battery energy storage system SOC equalization method based on PFC

Abstract

The invention discloses a PFC-based battery energy storage system SOC equalization method, and relates to the technical field of electronic processing, a first power response instruction is calculated according to a PFC coefficient and a power grid frequency deviation, and when the power grid frequency deviation is greater than a preset disturbance threshold, the current of a battery module is regulated and controlled based on a differential power distribution coefficient and the first power response instruction; when the power grid frequency deviation is smaller than a preset disturbance threshold value and the imbalance factor is larger than a preset balance threshold value, calculating a second power response instruction according to the PFC coefficient, an adjustment factor of the battery energy storage system, the imbalance factor, the first power response instruction and the power grid frequency deviation, and regulating and controlling the current of the battery module based on the differential power distribution coefficient and the second power response instruction. According to the technical scheme, additional circuit hardware does not need to be arranged, and SOC fast equalization of the battery monomers and the battery modules in the battery energy storage system can be achieved; and hysteresis caused by equalization control can be avoided.

Description

Technical Field

[0001] This invention relates to the field of electronic processing technology, and more specifically to a SOC balancing method for battery energy storage systems based on PFC. Background Technology

[0002] In recent years, with the acceleration of global energy transition and the large-scale integration of renewable energy into the grid, the rotational inertia of the power system has decreased, posing a severe challenge to frequency stability. Compared to traditional synchronous generators, battery energy storage systems (BESS) have become a key resource for performing primary frequency control (PFC) and maintaining grid frequency stability due to their rapid response and precise control capabilities. However, most existing strategies for BESS participation in PFC treat the battery module as a whole, ignoring the manufacturing tolerances and operating environment differences that are common between individual battery cells. Under long-term and frequent PFC conditions, these differences can cause the state of charge (SOC) of individual cells to diverge, leading to the "weakest link" effect. This results in some cells being overcharged or over-discharged, which not only accelerates battery aging and reduces the effective capacity of the BESS, but in severe cases, may even trigger protection mechanisms, causing the BESS to shut down unexpectedly and threatening the safe and stable operation of the power grid.

[0003] With the large-scale integration of new energy systems and power equipment, the stability and security of the power grid frequency have received widespread attention. Traditional synchronous generators are unable to meet the stringent requirements of primary frequency regulation in terms of response speed and adjustment accuracy, making battery energy storage systems a critical frequency regulation resource. However, existing strategies for BESS (Battery Energy Storage System) to participate in PFC (Power Factor Control) typically ignore the heterogeneity of states among individual battery cells within the BESS. Long-term operation can lead to overcharging and over-discharging of some battery cells, accelerated BESS aging, and even shutdown. In addition, existing hardware balancing topologies suffer from high parasitic losses and high reliability risks. Furthermore, existing balancing control strategies often exhibit regulation lag or are limited to optimization within the battery management system (BMS), failing to fully consider the regulation needs of the power conversion system (PCS) and thus unable to simultaneously address both grid PFC requirements and internal state management. Summary of the Invention

[0004] The purpose of this invention is to provide a PFC-based SOC balancing method for battery energy storage systems to solve the technical problems existing in related technologies.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a PFC-based battery energy storage system SOC balancing method, comprising:

[0006] The grid frequency deviation of the battery energy storage system, the imbalance factor of the battery cells in the battery energy storage system, and the PFC coefficient are obtained. The imbalance factor is obtained by subtracting the minimum SOC prediction value of the battery cells from the maximum SOC prediction value of the battery cells.

[0007] The first power response command of the battery energy storage system is calculated based on the deviation between the PFC coefficient and the grid frequency.

[0008] When the grid frequency deviation is greater than the preset disturbance threshold, the differentiated power allocation coefficient of each battery module in the battery energy storage system is obtained, and the current of the battery module is regulated based on the differentiated power allocation coefficient and the first power response command. The battery energy storage system includes multiple battery modules connected in parallel, and the battery module includes any number of battery cells connected in series.

[0009] When the grid frequency deviation is less than the preset disturbance threshold and the imbalance factor is greater than the preset equilibrium threshold, the second power response command of the battery energy storage system is calculated based on the PFC coefficient, the adjustment factor of the battery energy storage system, the imbalance factor, the first power response command and the grid frequency deviation. The differentiated power allocation coefficient of each battery module in the battery energy storage system is obtained, and the current of the battery module is regulated based on the differentiated power allocation coefficient and the second power response command.

[0010] Optionally, the imbalance factor is obtained by the following method:

[0011] The real-time SOC data of each battery cell in the battery energy storage system is acquired, and the real-time SOC data of each battery cell is input into the target prediction model to obtain the SOC prediction value. The target prediction model is obtained by training an LSTM network with historical SOC data.

[0012] Determine the maximum and minimum SOC prediction values ​​among multiple SOC prediction values, and subtract the minimum SOC prediction value from the maximum SOC prediction value to obtain the imbalance factor.

[0013] Optionally, the current of the battery module can be adjusted based on the differentiated power allocation coefficient and the first power response command using the following calculation formula:

[0014] ;

[0015] in, For the current of the battery module, For differentiated power allocation coefficients, This is the first power response command. The first in the battery module The terminal voltage of each battery cell This represents the total number of individual battery cells in the battery module.

[0016] Optionally, the first power response command is expressed by the following calculation formula:

[0017] ;

[0018] in, This is the first power response command. PFC coefficient, This refers to the power grid frequency deviation.

[0019] Optionally, the current of the battery module can be adjusted according to the differentiated power allocation coefficient and the second power response command using the following calculation formula:

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] in, To regulate the current of the battery module, For differentiated power allocation coefficients, This is the second power response command. The first in the battery module The terminal voltage of each battery cell This refers to the total number of individual battery cells in the battery module. This is the first power response command. PFC coefficient, For power grid frequency deviation, As a regulating factor for battery energy storage systems, As an imbalance factor, This is the attenuation factor.

[0025] Optionally, when the battery energy storage system is in a charging state, the differentiated power allocation coefficient is expressed by the following formula:

[0026] ;

[0027] ;

[0028] in, For the first battery energy storage system Differential power allocation coefficients for each battery module For the first battery energy storage system Internal penalty factor for each battery module For the first battery energy storage system SOC prediction value for each battery module This refers to the total number of battery modules in the battery energy storage system. The imbalance penalty coefficient, Through the first battery energy storage system The maximum predicted SOC value of a single battery cell in a battery module is obtained by subtracting the minimum predicted SOC value from the maximum predicted SOC value.

[0029] When the battery energy storage system is in a discharged state, the differentiated power allocation coefficient is expressed by the following formula:

[0030] .

[0031] Optionally, when the battery energy storage system is in a charging state, the PFC coefficient is calculated using the following formula:

[0032] ;

[0033] in, PFC coefficient, This is the final value of the logistic function. This is the initial value for the logistic function. To regulate Adjustable factor;

[0034] When the battery energy storage system is in a discharged state, the PFC coefficient is calculated using the following formula:

[0035] .

[0036] Through the above technical solution, the first power response command of the battery energy storage system is calculated based on the PFC coefficient and the grid frequency deviation. When the grid frequency deviation is greater than a preset disturbance threshold, the current of the battery module is regulated based on the differentiated power allocation coefficient and the first power response command. At the same time, the SOC of the battery cell can be calculated based on the current of the battery module after regulation and the remaining capacity of each battery cell in the battery module, which can balance the SOC between battery modules and the SOC of the battery cells within the battery module. When the grid frequency deviation is less than the preset disturbance threshold and the imbalance factor is greater than the preset equilibrium threshold, the second power response command of the battery energy storage system is calculated based on the PFC coefficient, the adjustment factor of the battery energy storage system, the imbalance factor, the first power response command, and the grid frequency deviation. The current of the battery module is regulated based on the differentiated power allocation coefficient and the second power response command, which can balance the SOC between battery modules and the SOC between battery cells. Without requiring additional circuit hardware, it can achieve rapid SOC balancing between battery modules and between individual battery cells within the battery energy storage system. It can avoid accelerated aging of BESS caused by ignoring differences between individual battery cells and PFC failure caused by control conflicts. It can also avoid the lag caused by balancing control in related technologies, prevent premature shutdown of BESS due to imbalance between individual battery cells, effectively extend the cycle life of BESS and improve the economic efficiency of BESS operation.

[0037] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0038] Figure 1 This is a schematic diagram illustrating a PFC-based battery energy storage system SOC balancing method according to an exemplary embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram illustrating the structure of the BESS system according to an exemplary embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram of a target prediction model according to an exemplary embodiment of the present invention.

[0041] Figure 4 This is a schematic flowchart illustrating a PFC-based battery energy storage system SOC balancing method according to an exemplary embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram illustrating the performance comparison of power equalization according to an exemplary embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram illustrating the SOC equalization effect according to an exemplary embodiment of the present invention.

[0044] Figure 7 This is a schematic diagram of the BESS cycle life according to an exemplary embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, so as to provide a better understanding of the concept of the present invention, the technical problem solved, the technical features constituting the technical solution, and the technical effects brought about.

[0046] Currently, the following key technologies exist for BESS equalization control for PFC:

[0047] 1. Coordinated Control of BMS and PCS: The BMS focuses on internal monitoring and balancing, while the PCS focuses on responding to external grid dispatch commands. They typically operate independently, lacking coordinated control. Therefore, achieving real-time interaction and coordination between the BMS's internal status information and the PCS's external power flow is a prerequisite for ensuring the BESS's external frequency response while achieving internal balancing. 2. Balancing Strategy under Dynamic Operating Conditions: Unlike balancing during static or constant-current charging, power commands under PFC conditions are random and fluctuate rapidly. Therefore, it is necessary to develop a balancing strategy that can adapt to highly dynamic power changes, utilizing the adjustment margin during PFC's charging and discharging process to achieve BESS internal balancing.

[0048] 3. Accurate Battery Status Sensing: Effective balancing relies on accurate judgment of battery status. Utilizing long short-term memory (LSTM) networks to mine the temporal characteristics of historical battery data and accurately predict the future SOC change trend of individual battery cells is the foundation for improving proactive balancing.

[0049] When implementing the BESS multi-level collaborative SOC balancing strategy for PFC, the following main challenges are encountered:

[0050] One challenge is the conflict between the objectives of the BMS and PCS. The BESS faces dual objectives during operation: externally, it needs to strictly track the power grid's PFC commands to ensure frequency stability; internally, it needs to perform equalization operations. These two objectives may conflict. The core challenge is finding the optimal balance between maximizing the internal equalization effect while ensuring PFC performance.

[0051] Second, there is the issue of achieving equalization without additional hardware dependencies. Existing equalization technologies often rely on complex power electronic topologies, increasing costs and potential failure points. Achieving efficient equalization without adding extra hardware components, solely through improved control strategies and leveraging the power regulation capabilities of the PCS and the algorithm optimization of the BMS, presents a significant challenge in equalization control strategy design. Third, there is the issue of inter-level information interaction. Establishing efficient cross-level information interaction channels to achieve cross-level control from individual battery cell status to grid power commands, and preventing system oscillations or control failures caused by mismatches in inter-level information interaction, is a crucial problem that must be overcome in practical engineering applications.

[0052] In the first related technology, SOC balancing of the battery energy storage system is achieved by improving frequency droop control based on the battery's SOC or state of health (SOH). In the second related technology, reconfigurable balancing technology is used to maximize the utilization of battery capacity, thereby achieving SOC balancing of the battery energy storage system. In the third related technology, a balancing strategy based on threshold triggering and using a fuzzy logic controller to adjust the balancing current is used to achieve SOC balancing of the battery energy storage system. In the fourth related technology, internal balancing of the BESS is achieved by switching battery clusters.

[0053] However, the inventors discovered that when achieving SOC balancing in a battery energy storage system using the method in the first related technology, this method treats the Battery Energy Storage System (BESS) as an ideal lumped model participating in PFC, ignoring the objectively existing manufacturing tolerances and aging differences between individual battery cells. During long-term operation of the BESS, neglecting its internal imbalances can lead to overuse of some weaker cells, accelerating their aging and further widening the differences between cells, resulting in a decrease in the overall effective capacity of the BESS and ultimately shortening its lifespan. When achieving SOC balancing in a battery energy storage system using the method in the second related technology, although the reconfigurable balancing topology can achieve cell balancing, it relies on complex additional hardware circuitry, requiring this hardware condition to be present during the battery manufacturing stage. This not only increases the size, weight, and manufacturing cost of the BESS but also introduces additional losses. Furthermore, for BESS without such a pre-installed topology, it is difficult to achieve balancing functionality through hardware modifications, limiting its practical application scope. When SOC balancing in a battery energy storage system is achieved using methods from the third related technology, the threshold-triggered balancing strategy exhibits a lag. In dynamic conditions like PFC where power commands change rapidly, the lack of prediction of battery status means that balancing measures are only initiated after the imbalance between individual battery cells exceeds the threshold, potentially leading to protection tripping due to untimely adjustments. When SOC balancing is achieved using methods from the fourth related technology, the battery cluster switching-based balancing strategy is typically designed for slow scheduling cycles and is unsuitable for PFC conditions requiring rapid response. When the BESS performs PFC tasks, the power step disturbance generated by battery cluster switching conflicts with the rapidly changing PFC power commands executed by the PCS, weakening the PFC effect.

[0054] In view of this, the present invention provides a PFC-based SOC balancing method for battery energy storage systems to solve the technical problems existing in the above-mentioned related technologies.

[0055] like Figure 1 As shown, Figure 1 This is a schematic diagram illustrating a PFC-based battery energy storage system SOC balancing method according to an exemplary embodiment of the present invention, with reference to... Figure 1 The method includes;

[0056] S101: Obtain the grid frequency deviation of the battery energy storage system, the imbalance factor of the battery cells in the battery energy storage system, and the PFC coefficient, wherein the imbalance factor is obtained by subtracting the minimum SOC prediction value of the battery cells from the maximum SOC prediction value of the battery cells.

[0057] S102: Calculate the first power response command of the battery energy storage system based on the deviation between the PFC coefficient and the grid frequency;

[0058] S103: When the grid frequency deviation is greater than the preset disturbance threshold, obtain the differentiated power allocation coefficient of each battery module in the battery energy storage system, and regulate the current of the battery module based on the differentiated power allocation coefficient and the first power response command. The battery energy storage system includes multiple battery modules connected in parallel, and the battery module includes any number of battery cells connected in series.

[0059] S104: When the grid frequency deviation is less than the preset disturbance threshold and the imbalance factor is greater than the preset equilibrium threshold, calculate the second power response command of the battery energy storage system based on the PFC coefficient, the adjustment factor of the battery energy storage system, the imbalance factor, the first power response command and the grid frequency deviation, and obtain the differentiated power allocation coefficient of each battery module in the battery energy storage system. Based on the differentiated power allocation coefficient and the second power response command, regulate the current of the battery module.

[0060] Through the above technical solution, the first power response command of the battery energy storage system is calculated based on the PFC coefficient and the grid frequency deviation. When the grid frequency deviation is greater than a preset disturbance threshold, the current of the battery module is regulated based on the differentiated power allocation coefficient and the first power response command. At the same time, the SOC of the battery cell can be calculated based on the current of the battery module after regulation and the remaining capacity of each battery cell in the battery module, which can balance the SOC between battery modules and the SOC of the battery cells within the battery module. When the grid frequency deviation is less than the preset disturbance threshold and the imbalance factor is greater than the preset equilibrium threshold, the second power response command of the battery energy storage system is calculated based on the PFC coefficient, the adjustment factor of the battery energy storage system, the imbalance factor, the first power response command, and the grid frequency deviation. The current of the battery module is regulated based on the differentiated power allocation coefficient and the second power response command, which can balance the SOC between battery modules and the SOC between battery cells. Without requiring additional circuit hardware, it can achieve rapid SOC balancing between battery modules and between individual battery cells within the battery energy storage system. It can avoid accelerated aging of BESS caused by ignoring differences between individual battery cells and PFC failure caused by control conflicts. It can also avoid the lag caused by balancing control in related technologies, prevent premature shutdown of BESS due to imbalance between individual battery cells, effectively extend the cycle life of BESS and improve the economic efficiency of BESS operation.

[0061] To enable those skilled in the art to better understand the PFC-based battery energy storage system SOC balancing method provided by this invention, the above steps are illustrated in detail below.

[0062] For example, a battery energy storage system can be an integrated system capable of storing and releasing electrical energy and operating in coordination with the power grid or load. This system comprises multiple individual battery cells and multiple battery modules. Each battery module can be formed by connecting any number of individual battery cells in series, as shown in the specific structure below. Figure 2 As shown. A battery cell can be the smallest single cell that makes up a battery pack. Grid frequency deviation can be the difference between the actual operating frequency and the rated frequency of the battery energy storage system; it can be used to reflect the active power balance of the battery energy storage system. In this embodiment of the invention, the grid frequency deviation of the battery energy storage system and the imbalance factor between battery cells in the battery energy storage system are obtained.

[0063] In one possible manner, the imbalance factor is obtained by the following method:

[0064] The real-time SOC data of each battery cell in the battery energy storage system is acquired, and the real-time SOC data of each battery cell is input into the target prediction model to obtain the SOC prediction value. The target prediction model is obtained by training an LSTM network with historical SOC data.

[0065] Determine the maximum and minimum SOC prediction values ​​among multiple SOC prediction values, and subtract the minimum SOC prediction value from the maximum SOC prediction value to obtain the imbalance factor.

[0066] It should be understood that a battery energy storage system comprises multiple battery cells, each generating State of Charge (SOC) data during operation. Therefore, the real-time SOC data generated by each battery cell during operation can be used to predict its SOC value over a future time period. Specifically, for example... Figure 3 As shown, for each battery cell, the acquired real-time SOC data is input into the target prediction model to obtain the corresponding SOC prediction value. The target prediction model can be trained on an LSTM (Long Short-Term Memory) network using historical SOC data. The historical SOC data can be time-series data. The specific training and prediction processes are as follows: Figure 3 As shown, in Figure 3 In the diagram, each battery cell includes multiple SOC time series data points, Tanh represents the activation function, the Input gate is the input gate, the Output gate is the output gate, and the Forget gate is the forget gate. The input at time t, This is the output at time t-1. For time The cell status. During online operation, real-time SOC data is added to the end of the input sequence for single-step prediction, and the resulting SOC prediction value is then used for subsequent equilibrium management. Generating SOC prediction values ​​through a target prediction model can reduce the lag of real-time measurements under dynamic PFC conditions.

[0067] After inputting the real-time SOC data corresponding to multiple battery cells into the target prediction model, multiple SOC prediction values ​​can be obtained. Among these multiple SOC prediction values, the largest and smallest SOC prediction values ​​are found. Subtracting the smallest SOC prediction value from the largest SOC prediction value yields the imbalance factor.

[0068] For example, a preset disturbance threshold can be used as a key boundary value to determine whether a battery energy storage system has entered a disturbance, abnormal, or accident state. The Power Factor Correction (PFC) coefficient is a core parameter describing the unit's or energy storage's ability to respond to grid frequency deviations. When the grid frequency deviation exceeds the preset disturbance threshold, it indicates that the current active power imbalance of the battery energy storage system has exceeded the normal fluctuation range, entering a disturbance state, requiring the initiation of active control measures such as primary frequency regulation. In this embodiment of the invention, the PFC coefficient is obtained, and a first power response command is calculated based on the PFC coefficient.

[0069] In one possible manner, the first power response command is expressed by the following calculation:

[0070] ;

[0071] in, This is the first power response command. PFC coefficient, This refers to the power grid frequency deviation.

[0072] During a frequency regulation process in a battery energy storage system, heterogeneity exists between individual battery cells and battery modules. A model can be established to quantify the state heterogeneity between individual battery cells. The SOC value of a single battery cell can be estimated using the coulomb counting method, expressed as:

[0073] ;

[0074] in, It is the initial time; It is the first The remaining capacity of a battery after it has aged; Representing the Block battery in SOC at any moment For the first Block battery in SOC at any given moment; This is the battery pack charging and discharging control current; it is positive during discharging. The variable is the integral variable, representing the value from the initial value. Time's up The instantaneous time that changes continuously between moments is used to accumulate and calculate the current.

[0075] Differentiating the above formula yields the rate of change of SOC, from which a heterogeneity index can be defined. :

[0076] ;

[0077] ;

[0078] As can be seen from the above formula, different degrees of aging lead to differences in battery performance. and its corresponding Differences arise. Therefore, even if all batteries are initially in the same state, the batteries will exhibit differences during synchronous charging and discharging. It still shows a divergent trend, and heterogeneity accumulates over time.

[0079] Based on the aforementioned heterogeneity quantification model, a multi-level collaborative PFC framework comprising battery cells, battery modules, and a BESS system can be constructed. The functional configurations of each level are as follows: Battery Cell Level: Continuously monitors the state of battery cells and transmits the balance state information, including differences between battery cells, to the next higher level. Battery Module Level: Receives total power commands from the BESS system level, distributes power differentially among battery modules through the BMS, and transmits the balance state among battery modules to the BESS system level. BESS System Level: As the highest control level, issues total power commands that comprehensively consider adaptive PFC and SOC balance through the PCS.

[0080] Therefore, in battery energy storage systems, the heterogeneity between battery modules and between individual battery cells can be eliminated by regulating the current in the battery modules, thereby balancing the SOC of the battery modules and the SOC of the individual battery cells.

[0081] Specifically, a differentiated power allocation coefficient can be obtained for each battery module. This coefficient can be a weighting factor that assigns different charging and discharging power ratios to different battery modules based on their state differences. Then, for each battery module, the current can be adjusted according to the differentiated power allocation coefficient and the first power response command to balance the SOC of the battery module and the SOC of the individual battery cells.

[0082] In one possible manner, the current of the battery module can be adjusted based on the differential power allocation coefficient and the first power response command using the following calculation formula;

[0083] ;

[0084] in, For the current of the battery module, For differentiated power allocation coefficients, This is the first power response command. The first in the battery module The terminal voltage of each battery cell This represents the total number of individual battery cells in the battery module.

[0085] The above technical solutions can ensure full support of the grid frequency in the battery energy storage system.

[0086] For example, when the grid frequency deviation is less than a preset disturbance threshold and the imbalance factor is greater than a preset balancing threshold, it indicates that the inconsistency between battery modules in the battery energy storage system has exceeded the operating range, requiring the initiation of balancing control between battery modules to balance the SOC of the battery modules. In this embodiment of the invention, the first power response command can be adjusted by the adjustment factor of the battery energy storage system to obtain the second power response command. The adjustment factor of the battery energy storage system can be a comprehensive adjustment coefficient used in real time to correct the output power of the energy storage system during grid frequency regulation, power distribution, or balancing control. Then, the power circuit of the battery module is regulated according to the differentiated power distribution coefficient and the second power response command.

[0087] Specifically, the current of the battery module can be adjusted according to the differentiated power allocation coefficient and the second power response command using the following calculation formula;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] in, For the current of the battery module, For differentiated power allocation coefficients, This is the second power response command. The first in the battery module The terminal voltage of each battery cell This refers to the total number of individual battery cells in the battery module. This is the first power response command. PFC coefficient, For power grid frequency deviation, As a regulating factor for battery energy storage systems, As an imbalance factor, This is the attenuation factor.

[0093] In one possible approach, when the battery storage system is in a charging state, the differentiated power allocation coefficient is expressed by the following formula:

[0094] ;

[0095] ;

[0096] in, For the first battery energy storage system Differential power allocation coefficients for each battery module For the first battery energy storage system The internal penalty factor of the battery module, when added, can avoid the first... The risk of overcharging or over-discharging individual battery modules; For the first battery energy storage system SOC prediction value for each battery module This refers to the total number of battery modules in the battery energy storage system. The imbalance penalty coefficient, Through the first battery energy storage system The maximum predicted SOC value of a battery cell in a battery module is obtained by subtracting the minimum predicted SOC value from the maximum predicted SOC value.

[0097] When the battery energy storage system is in a discharged state, the differentiated power allocation coefficient is expressed by the following formula:

[0098] .

[0099] It should be understood that the differential power allocation coefficient of a battery energy storage system is different when it is charging and when it is generating. Therefore, the differential power allocation coefficient can be calculated based on the state of the battery energy storage system.

[0100] After regulating the current of the battery module in the battery energy storage system using the above method, the SOC of each battery cell in the battery module can be updated. Specifically, this can be expressed by the following calculation formula:

[0101] ;

[0102] in, For the update cycle, The first in the battery module SOC of a single battery cell The first in the battery module The remaining capacity of each battery cell To regulate the current of the battery module, This represents the total number of individual battery cells in the battery module.

[0103] In one possible manner, when the battery energy storage system is in a charging state, the PFC coefficient is calculated using the following formula:

[0104] ;

[0105] in, PFC coefficient, This is the final value of the logistic function. This is the initial value for the logistic function. To regulate Adjustable factor;

[0106] When the battery energy storage system is in a discharged state, the PFC coefficient is calculated using the following formula:

[0107] .

[0108] It should be understood that when balancing with a fixed PFC coefficient, the battery energy storage system may experience overcharging or over-discharging under extreme operating conditions. Therefore, different PFC coefficients can be calculated based on the state of charge or discharge of the battery energy storage system, thereby avoiding overcharging or over-discharging during SOC balancing.

[0109] In this embodiment of the invention, the PFC coefficient has an upper limit, which can be expressed by the following formula:

[0110] ;

[0111] in, This represents the upper limit of the PFC coefficient. This refers to the rated power of the battery energy storage system. The parameter tuning rate can be used to reflect the sensitivity of a battery energy storage system to primary leveling. This is the rated frequency of the power grid.

[0112] Therefore, when the SOC deviates from the limit operating range, the PFC coefficient can smoothly transition, achieving a balance between PFC effect and BESS condition protection.

[0113] In actual processing, such as Figure 4As shown, firstly, real-time SOC data of the grid frequency deviation and individual battery cells are collected. Then, based on adaptive droop control, the first power response command of the BESS system, the imbalance factor of the individual battery cells, and a multi-level collaborative framework of individual battery cells, battery modules, and the BESS system are calculated. Subsequently, an LSTM network is used to predict the SOC of individual battery cells and calculate the imbalance factor, while simultaneously formulating dynamic collaborative decisions based on the grid frequency deviation. Finally, decisions are made based on the real-time status. When it is determined that two-layer equalization is required and the grid operating conditions permit, active power regulation at the BESS system level is activated, and differentiated power allocation at the battery module level is always executed, thereby achieving dynamic collaboration between PFC and two-layer equalization.

[0114] In the specific implementation process, to verify the effectiveness of the strategy proposed in this invention, this embodiment builds a simulation model based on the parameters of a commercially available 5Ah soft-pack lithium-ion battery manufactured by DOW KOKAM. In the model settings, to simulate battery heterogeneity caused by battery aging, a battery module consisting of six battery cells connected in series is defined. The initial capacity of each battery is set to be distributed within the range of 5.00 Ah to 4.75 Ah to characterize different initial aging levels, and the initial SOC is set to 100%. Based on this, a 5.55 kWh BESS model is constructed, which contains 50 parallel battery modules, i.e., a BESS topology of 50P6S, to simulate the heterogeneous distribution within the BESS.

[0115] Selecting the UK power grid's history from 2024 The data is used as the PFC operating condition. The parameter settings for BESS when participating in PFC are as follows: BESS rated power. = 2.2 kW, frequency deviation threshold =±0.1Hz, PFC coefficient upper limit Km = 10 kW / Hz, parameter tuning rate = 0.5%, PFC-DB set to ±0.05 Hz.

[0116] SOC equalization method verification: First, the effectiveness of the power equalization strategy is verified. A preset equalization threshold is set. The standard deviation is set at 1%, and the standard deviation of SOC is introduced to evaluate the dispersion. The formula for its calculation is as follows:

[0117] ;

[0118] In the formula: for The standard deviation of the state of charge (SOC) within the battery pack at any given time. This refers to the total number of individual battery cells in the battery module. express Time of the first SOC of a single monomer represent The average SOC of the battery pack at any given time.

[0119] Figure 5 Demonstrates the process of regulating power equalization The dynamic changes and the corresponding current generated. For example... Figure 5 As shown in (a), The threshold is reached at t=6066s. This triggers the power equalization strategy, and the corresponding equalization current is as follows: Figure 5 As shown in (b), its function is to suppress the accumulation of SOC differences between monomers. Experimental data indicate that this strategy effectively reduces the accumulation of SOC differences between monomers. Maintaining the levels below a preset threshold, while simultaneously ensuring adequate spacing between individual battery cells within the group. The maximum reduction was 5.4%.

[0120] The effectiveness of the two-layer equilibrium strategy was further evaluated in a 5.55 kWh BESS model. Figure 6 The changes in various indicators after adopting a two-layer equilibrium strategy are shown. Among them, The SOC dispersion between battery modules was quantified. This represents the average of the standard deviations of the State of Charge (SOC) within each battery module. This represents the average imbalance factor across all battery modules. Figure 6 The results show that the proposed two-layer equilibrium strategy significantly suppresses index fluctuations in the high imbalance range. Quantitative analysis shows that, compared with the no-equilibrium strategy, this invention can... The root mean square (RMS) value decreased by 47.1%. For and The proposed strategy achieved a reduction of approximately 8.0% in RMS compared to the unbalanced approach, fully demonstrating the significant advantage of the two-layer balancing strategy in improving the internal consistency of large-scale BESS.

[0121] BESS operational capability and economic evaluation: When the internal imbalance of a BESS reaches a critical level, it must be shut down for maintenance. This embodiment selects the standard deviation coefficient (denoted as ). (This serves as an indicator for maintaining early warning.) The moment when the preset threshold is first exceeded is defined as the maintenance time point. Moment value It can be represented as

[0122] ;

[0123] in, for Average SOC of the battery module at any given time.

[0124] To evaluate the impact of this invention on the lifespan of a BESS and the levelized cost of storage (LCOS), this embodiment employs rainflow counting to process SOC data in the lifespan analysis. This extracts the average SOC and depth of discharge for each cycle, and then uses these values ​​to estimate the equivalent cycle number and capacity decay trend. The lifespan termination condition is defined as the BESS capacity decaying to 80% of its rated value. Furthermore, the LCOS analysis method is used to quantify the economic performance of the BESS over its entire lifespan; the calculation formula is as follows:

[0125] ;

[0126] In the formula: The initial investment cost of BESS For years of service, For the operating costs of BESS, This refers to the power consumption during the test. Capacity decay rate, For the energy conversion efficiency of BESS converters, This refers to the rated capacity of BESS.

[0127] Combined with Table 1 and Figure 7 Compared to strategies without balanced control, the multi-level collaborative balancing strategy proposed in this invention narrows the SOC operating range, extends the maintenance interval to 6.5 days, reduces LCOS by 0.5%, and extends cycle life by 30.1%. In summary, this invention significantly extends the service life of BESS and improves the economic benefits throughout its entire lifecycle while ensuring operational safety.

[0128] Table 1 Operational and Economic Indicators

[0129] .

[0130] Through the above technical solutions, a multi-level collaborative PFC architecture of battery cells, battery modules and BESS system is constructed. Based on this architecture, a two-layer SOC balancing strategy that coordinates PCS and BMS is designed. It integrates dynamic decision-making logic of SOC prediction, cross-level collaborative adjustment and frequency response priority principle to allocate the frequency response current of battery modules, realize the balance between BESS frequency control performance and SOC balancing, and improve battery utilization efficiency.

[0131] This strategy constructs a multi-level collaborative architecture, deeply integrating SOC predictions, cross-level collaborative regulation between the BMS and PCS, and frequency response-priority dynamic decision-making logic to form a complete two-layer balancing strategy. Secondly, compared to existing technologies, this invention, without increasing the cost of additional balancing hardware, fully leverages the BESS's inherent regulation potential through a two-layer balancing mechanism, effectively overcoming regulation lag and target conflict issues between the PCS and BMS under dynamic PFC conditions. Thirdly, the end-to-end dynamic collaboration achieved by this invention, through proactive suppression of BESS internal state heterogeneity, can significantly extend the BESS's full lifecycle lifespan while ensuring grid frequency safety response. The strategy proposed in this invention requires no hardware modification, has simple and effective control logic, and possesses extremely high engineering application value.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for SOC balancing in a PFC-based battery energy storage system, characterized in that, include: The grid frequency deviation of the battery energy storage system, the imbalance factor of the battery cells in the battery energy storage system, and the PFC coefficient are obtained. The imbalance factor is obtained by subtracting the minimum SOC prediction value of the battery cells from the maximum SOC prediction value of the battery cells. The first power response command of the battery energy storage system is calculated based on the deviation between the PFC coefficient and the grid frequency. When the grid frequency deviation is greater than the preset disturbance threshold, the differentiated power allocation coefficient of each battery module in the battery energy storage system is obtained, and the current of the battery module is regulated based on the differentiated power allocation coefficient and the first power response command. The battery energy storage system includes multiple battery modules connected in parallel, and the battery module includes any number of battery cells connected in series. When the grid frequency deviation is less than the preset disturbance threshold and the imbalance factor is greater than the preset equilibrium threshold, the second power response command of the battery energy storage system is calculated based on the PFC coefficient, the adjustment factor of the battery energy storage system, the imbalance factor, the first power response command and the grid frequency deviation. The differentiated power allocation coefficient of each battery module in the battery energy storage system is obtained, and the current of the battery module is regulated based on the differentiated power allocation coefficient and the second power response command.

2. The SOC balancing method for a PFC-based battery energy storage system according to claim 1, characterized in that, The imbalance factor is obtained through the following method: The real-time SOC data of each battery cell in the battery energy storage system is acquired, and the real-time SOC data of each battery cell is input into the target prediction model to obtain the SOC prediction value. The target prediction model is obtained by training an LSTM network with historical SOC data. Determine the maximum and minimum SOC prediction values ​​among multiple SOC prediction values, and subtract the minimum SOC prediction value from the maximum SOC prediction value to obtain the imbalance factor.

3. The SOC balancing method for a PFC-based battery energy storage system according to claim 1, characterized in that, The current of the battery module is adjusted based on the differentiated power allocation coefficient and the first power response command using the following calculation formula: ； in, For the current of the battery module, For differentiated power allocation coefficients, This is the first power response command. The first in the battery module The terminal voltage of each battery cell This represents the total number of individual battery cells in the battery module.

4. The SOC balancing method for a PFC-based battery energy storage system according to claim 3, characterized in that, The first power response command is expressed by the following formula: ； in, This is the first power response command. PFC coefficient, This refers to the power grid frequency deviation.

5. The SOC balancing method for a PFC-based battery energy storage system according to claim 1, characterized in that, The current of the battery module is adjusted based on the differentiated power allocation coefficient and the second power response command using the following calculation formula: ； ； ； ； in, To regulate the current of the battery module, For differentiated power allocation coefficients, This is the second power response command. The first in the battery module The terminal voltage of each battery cell This refers to the total number of individual battery cells in the battery module. This is the first power response command. PFC coefficient, For power grid frequency deviation, As a regulating factor for battery energy storage systems, As an imbalance factor, This is the attenuation factor.

6. The SOC balancing method for a PFC-based battery energy storage system according to claim 1, characterized in that, When the battery energy storage system is in a charging state, the differentiated power allocation coefficient is expressed by the following formula: ； ； in, For the first battery energy storage system Differential power allocation coefficients for each battery module For the first battery energy storage system Internal penalty factor for each battery module For the first battery energy storage system SOC prediction value for each battery module This refers to the total number of battery modules in the battery energy storage system. The imbalance penalty coefficient, Through the first battery energy storage system The maximum predicted SOC value of a single battery cell in a battery module is obtained by subtracting the minimum predicted SOC value from the maximum predicted SOC value. When the battery energy storage system is in a discharged state, the differentiated power allocation coefficient is expressed by the following formula: 。 7. The SOC balancing method for a PFC-based battery energy storage system according to any one of claims 1-6, characterized in that, When the battery energy storage system is in a charging state, the PFC coefficient is calculated using the following formula: ； in, PFC coefficient, This is the final value of the logistic function. This is the initial value for the logistic function. To regulate Adjustable factor; When the battery energy storage system is in a discharged state, the PFC coefficient is calculated using the following formula: 。

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