A battery equalization charging control method and device, electronic equipment and storage medium

Abstract

The application relates to a battery equalization charging control method and device, electronic equipment and a storage medium, the method comprising: acquiring real-time state parameters of each single battery in a battery assembly; constructing a target function based on the real-time state parameters, the target function comprising a first evaluation item for evaluating the consistency of the battery assembly and a second evaluation item for evaluating the equalization energy efficiency, and the two are weighted and summed through a normalized weighting coefficient; dynamically adjusting the weighting coefficient based on the current equalization stage, and dividing the equalization stage into at least an equalization initial stage and an equalization final stage according to the comparison result of the maximum voltage difference between the single batteries and a preset period threshold; solving the target function by using a binary intelligent optimization algorithm to determine a target control state combination of a switching component; and controlling the switching component to be on or off according to the target control state combination to perform equalization between the single batteries.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, specifically relating to a battery equalization charging control method and device, electronic equipment, and storage medium. Background Technology

[0002] With the widespread application of energy storage technology, energy storage systems play a vital role in power dispatch and load balancing. Traditional monitoring methods focus only on single parameters, such as battery voltage and temperature, and cannot comprehensively analyze the system status. During battery pack charging, individual cells exhibit inherent differences in their State of Charge (SOC), voltage, and internal resistance. Long-term charging can easily lead to a "bottleneck effect," reducing battery pack capacity, cycle life, and even causing safety hazards. Active balancing technology achieves cell consistency regulation through energy transfer, which is the core means to solve this problem. However, existing active balancing control methods typically only make relatively simple conditional judgments, that is, balancing is initiated when the voltage of a certain cell deviates from the voltage of other cells by more than a fixed threshold. This does not consider global consistency and is prone to local balancing and global imbalance.

[0003] Existing active balancing control methods typically only make relatively simple condition judgments, that is, when the voltage of a certain battery deviates from the voltage of other cells by more than a fixed threshold, balancing is initiated. This does not consider global consistency and is prone to local balancing and global imbalance. Summary of the Invention

[0004] To address the problems raised in the background art, a battery equalization control method is provided in a first aspect of the present invention, applied to a battery assembly. The battery assembly includes multiple individual cells and multiple switching components for controlling energy transfer between the individual cells. The method includes: acquiring real-time state parameters of each individual cell in the battery assembly; constructing an objective function based on the real-time state parameters, the objective function including a first evaluation term for evaluating the consistency of the battery assembly and a second evaluation term for evaluating equalization energy efficiency, wherein the first evaluation term and the second evaluation term are weighted and summed using normalized weighting coefficients; dynamically adjusting the weighting coefficients based on the current equalization stage of the battery assembly, wherein the equalization stage is divided into at least an initial equalization stage and an end equalization stage based on a comparison of the maximum voltage difference between each individual cell and a preset period boundary threshold; solving the objective function using a binary intelligent optimization algorithm to determine a target control state combination of the switching components; and controlling the on / off state of the switching components according to the target control state combination to perform equalization between the individual cells.

[0005] In some embodiments of the present invention, the method further includes: determining mutual exclusion constraints based on the hardware topology of the battery assembly, the mutual exclusion constraints including: multiple switching components connected to the same energy transfer element are prohibited from being in a conducting state simultaneously, the energy transfer element being used to transfer energy between the individual cells to achieve the energy transfer.

[0006] In some embodiments of the present invention, the binary intelligent optimization algorithm is a binary particle swarm optimization algorithm; wherein, each particle is encoded as a binary bit string corresponding one-to-one with each of the switching components, and the value of each bit in the binary bit string corresponds to the on or off state of a switching component; after each iteration update, particles that do not meet the preset constraints are corrected or eliminated.

[0007] In some embodiments of the present invention, dynamically adjusting the weighting coefficients based on the current equilibrium stage of the battery assembly includes: calculating the maximum voltage difference of each individual cell in the battery assembly; comparing the maximum voltage difference with a preset period boundary threshold; when the maximum voltage difference is greater than the period boundary threshold, determining that the current stage is the initial stage of equilibrium, and dynamically reducing the weighting coefficient of the first evaluation item according to the maximum voltage difference and the rate of change of the maximum voltage difference to prioritize improving the consistency equilibrium speed; when the maximum voltage difference is less than or equal to the period boundary threshold, determining that the current stage is the final stage of equilibrium, and maintaining the weighting coefficient of the first evaluation item in a set low weight range to prioritize improving energy efficiency.

[0008] Furthermore, in the initial stage of equilibrium, the weighting coefficients are determined as follows: the weighting coefficients decrease exponentially as the maximum voltage difference at the current moment decreases compared to the maximum voltage difference at the initial moment; and are weighted and corrected in conjunction with the absolute value of the rate of change of the maximum voltage difference at the current moment; wherein, the smaller the absolute value of the rate of change, the slower the decay rate of the weighting coefficients.

[0009] Furthermore, at the end of the equilibrium period, the weighting coefficient is determined as follows: based on a preset minimum weight, a correction amount determined based on the maximum voltage difference at the current moment, a preset equilibrium stopping threshold, and an exponential decay factor is superimposed, so that the weighting coefficient is finely adjusted within the set low weight range with small changes in the maximum voltage difference.

[0010] In some embodiments of the present invention, the first evaluation term represents the ratio of the maximum voltage difference between individual cells in the battery module at the current moment to the permissible maximum voltage difference; the second evaluation term represents the ratio of the actual transferred energy to the theoretical maximum transferable energy during the current equilibrium cycle.

[0011] In a second aspect, the present invention provides a battery balancing control device, comprising: an acquisition module for acquiring real-time state parameters of each individual cell in a battery assembly; a construction module for constructing an objective function based on the real-time state parameters, the objective function including a first evaluation term for evaluating the consistency of the battery assembly and a second evaluation term for evaluating balancing energy efficiency, the first evaluation term and the second evaluation term being weighted and summed using normalized weighting coefficients; an adjustment module for dynamically adjusting the weighting coefficients based on the current balancing stage of the battery assembly, wherein the balancing stage is divided into at least an initial balancing stage and an end balancing stage based on a comparison of the maximum voltage difference between each individual cell and a preset period boundary threshold; a determination module for solving the objective function using a binary intelligent optimization algorithm to determine a target control state combination of a switching component; and a control module for controlling the on / off state of the switching component according to the target control state combination to perform balancing between the individual cells.

[0012] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the battery equalization control method provided in the first aspect of the present invention.

[0013] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the battery equalization control method provided in the first aspect of the present invention.

[0014] The beneficial effects of this invention are: This invention constructs a dual evaluation objective function that considers both consistency and energy efficiency. Traditional technologies often focus on a single consistency indicator, which can easily lead to excessive energy loss during the equalization process. This invention, through a normalized weighted summation method, can effectively balance energy utilization during the equalization process while ensuring the consistency of battery pack voltage or capacity, thus achieving the optimal balance between performance and energy consumption.

[0015] Secondly, the strategy of dynamically adjusting the weighting coefficients during the equilibrium phase greatly improves the targeting and efficiency of equilibrium control. In the initial stage of equilibrium, when the voltage difference between batteries is large, the system automatically adjusts the weights to prioritize rapid equilibrium; while in the final stage, when the voltage difference narrows, the system focuses on fine-tuning and maintaining efficiency. This phased adaptive control strategy effectively shortens the overall equilibrium time and avoids the inefficiency caused by a "one-size-fits-all" control approach.

[0016] Finally, a binary intelligent optimization algorithm is employed to solve for the switching states, ensuring the global optimality of the control strategy. Compared to traditional logical judgment or table lookup methods, this algorithm can accurately select the optimal solution from complex switching combinations, significantly improving the response speed and execution accuracy of the equalization circuit, thereby effectively extending the battery pack's lifespan and enhancing system safety. Attached Figure Description

[0017] Figure 1 This is a basic flowchart of the battery equalization control method in some embodiments of the present invention. Figure 2 This is a schematic diagram of the specific process of the battery balancing control method in some embodiments of the present invention; Figure 3 This is a schematic diagram of the battery balancing control device in some embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation

[0018] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0019] Example 1 refer to Figure 1 and Figure 2 In a first aspect, the present invention provides a battery balancing control method applied to a battery assembly, the battery assembly comprising multiple individual cells and multiple switching components for controlling energy transfer between the individual cells, comprising: S100. acquiring real-time state parameters of each individual cell in the battery assembly; S200. constructing an objective function based on the real-time state parameters, the objective function comprising a first evaluation term for evaluating the consistency of the battery assembly and a second evaluation term for evaluating balancing energy efficiency, the first evaluation term and the second evaluation term being weighted and summed using normalized weighting coefficients; S300. dynamically adjusting the weighting coefficients based on the current balancing stage of the battery assembly, wherein the balancing stage is divided into at least an initial balancing stage and an end balancing stage based on a comparison of the maximum voltage difference between each individual cell and a preset period boundary threshold; S400. solving the objective function using a binary intelligent optimization algorithm to determine a target control state combination of the switching components; S500. controlling the on / off state of the switching components according to the target control state combination to perform balancing between the individual cells.

[0020] In step S100 of some embodiments of the present invention, the real-time status parameters of each individual cell in the battery assembly are obtained. Specifically, this disclosure is applied to a battery equalization charging system with switched capacitor equalization, wherein switched capacitor equalization refers to connecting a capacitor and a switching transistor (or using a dedicated equalization chip) in series between adjacent individual cells to form an energy transfer path of cell-capacitor-cell, the core of which is to transfer energy by utilizing the charging and discharging characteristics of the capacitor.

[0021] For example: Suppose that the voltage of cell A (3.6V) is higher than that of cell B (3.4V), the controller controls the switch S1 to turn on and S2 to turn off, cell A charges capacitor C, and the capacitor voltage rises to 3.6V; then controls S1 to turn off and S2 to turn on, capacitor C discharges to cell B, transferring electrical energy to cell B, and the voltage of cell B rises; repeat the above process until the voltage difference between A and B is less than the equalization threshold (e.g., 10mV).

[0022] In some embodiments of the present invention, the method further includes: determining mutual exclusion constraints based on the hardware topology of the battery assembly, the mutual exclusion constraints including: multiple switching components connected to the same energy transfer element are prohibited from being in a conducting state simultaneously, the energy transfer element being used to transfer energy between the individual cells to achieve the energy transfer.

[0023] Specifically, the switching logic requires that Sia and Sib of the same capacitor cannot be 1 simultaneously to avoid short circuits, and requires separate control during the charging phase (capacitor charging from the high-voltage cell) and the discharging phase (capacitor discharging to the low-voltage cell). A hard constraint is the mutual exclusion constraint for the switches of the same capacitor; for any capacitor, the switches at its two ends cannot be turned on simultaneously.

[0024] In some embodiments of the present invention, step S200, the first evaluation item represents the ratio of the maximum voltage difference between individual cells in the battery module at the current moment to the permissible maximum voltage difference; the second evaluation item represents the ratio of the actual transferred energy to the theoretical maximum transferable energy during the current equilibrium cycle.

[0025] Specifically, the objective function (the fitness function of the intelligent optimization algorithm) is expressed as: ,in, Used to indicate the degree of voltage imbalance. Indicates the maximum allowable voltage difference of the battery pack. This represents the theoretical maximum efficiency of the switched capacitor. Indicates overall efficiency.

[0026] The efficiency can be determined in the following way: For capacitor Ci, which is connected to Celli and Cell(i+1); during its charging phase, the energy absorbed by the capacitor from the high-voltage cell Cellh is: ,in, This is the square of the individual cell voltage before charging. This is the square of the voltage after charging.

[0027] During its discharge phase, the effective energy released by the capacitor to the low-voltage cell Cel1 Energy loss is The efficiency of a single capacitor is: , m represents the number of capacitors participating in the equalization process.

[0028] Based on the above parameters and settings, the particle swarm optimization algorithm is iterated. The inertia weight, learning factor, etc. during the iteration process can be set based on the default values ​​of the particle swarm optimization algorithm until the preset number of iterations is reached or the difference between the fitness function values ​​of multiple consecutive iterations is less than the preset value, then the iteration stops.

[0029] In step S300 of some embodiments of the present invention, dynamically adjusting the weighting coefficients based on the current balancing phase of the battery assembly includes: S301. Calculate the maximum voltage difference between each individual cell in the battery assembly; compare the maximum voltage difference with a preset time boundary threshold; S302. When the maximum voltage difference is greater than the period boundary threshold, it is determined that the current stage is the initial stage of equilibrium. The weighting coefficient of the first evaluation item is dynamically reduced according to the maximum voltage difference and the rate of change of the maximum voltage difference, so as to prioritize improving the consistency equilibrium speed. S303. When the maximum voltage difference is less than or equal to the period boundary threshold, it is determined that the current period is at the end of the equilibrium period, and the weighting coefficient of the first evaluation item is maintained in the set low weight range to prioritize improving energy efficiency.

[0030] Furthermore, in the initial stage of equilibrium, the weighting coefficients are determined as follows: S304. The weighting coefficient decreases exponentially as the maximum voltage difference at the current moment decreases compared to the maximum voltage difference at the initial moment; and is weighted and corrected in conjunction with the absolute value of the rate of change of the maximum voltage difference at the current moment; wherein, the smaller the absolute value of the rate of change, the slower the decay rate of the weighting coefficient.

[0031] Specifically, if the current voltage difference (t) is greater than the preset time boundary threshold The formula is: , in, It is 0.8. This represents the voltage difference at the initial moment of equilibrium. Represents the exponential decay coefficient. This is the rate of change correction factor. , represents the rate of change of voltage difference, T is the sampling period, r(t) < 0, indicates that the voltage difference is decreasing, the smaller the value, the faster the equalization.

[0032] Furthermore, at the end of the equilibrium period, the weighting coefficients are determined in the following manner: S305. Based on the preset minimum weight, a correction amount determined by the maximum voltage difference at the current moment, the preset equalization stop threshold, and the exponential decay factor is superimposed to make the weighting coefficient fine-tuned within the set low weight range with small changes in the maximum voltage difference.

[0033] Specifically, if the current voltage difference (t) is less than or equal to the preset time boundary threshold. The formula is: , in, It equals 0.5. This indicates the equilibrium stopping threshold. For a minimum value, such as 1mV, Optionally, in the above formula, the initial stage of equilibrium is the current voltage difference. (t) is greater than the preset time boundary threshold The goal should be to rapidly reduce the voltage difference. The weight decreases exponentially with the voltage difference. The faster the equilibrium is reached, the smaller r(t) becomes, and the slower the weight decreases (the longer the high weight is maintained). The goal at the end of the equilibrium is to prioritize energy efficiency. The weight is kept stable in the low range and is adjusted only with slight changes in the voltage difference.

[0034] In step S400 of some embodiments of the present invention, the binary intelligent optimization algorithm is a binary particle swarm optimization algorithm; wherein, each particle is encoded as a binary bit string corresponding one-to-one with each of the switching components, and the value of each bit in the binary bit string corresponds to the on or off state of a switching component; after each iteration update, particles that do not meet the preset constraints are corrected or eliminated.

[0035] Specifically, since the switching state is discrete 0 / 1 (off / on), a binary PSO (BPSO) is required instead of a continuous PSO.

[0036] Particle encoding (solution space definition): Each particle corresponds to a certain stage of the switch control state, and the dimension is equal to the total number of switches, 2(n-1). Each dimension takes a value of 0 or 1. Particle position vector ; The fitness function is: ,in, Used to indicate the degree of voltage imbalance. Indicates the maximum allowable voltage difference of the battery pack. This represents the theoretical maximum efficiency of the switched capacitor. Indicates overall efficiency.

[0037] The efficiency can be determined in the following way: For capacitor Ci, it is connected to Celli and Cell(i+1). During its charging phase, the energy absorbed by the capacitor from the high-voltage cell is: ,in, This is the square of the individual cell voltage before charging. This is the square of the voltage after charging.

[0038] During its discharge phase, the effective energy released by the capacitor to the low-voltage cell Cel1 Energy loss is The efficiency of a single capacitor is: , m represents the number of capacitors participating in the equalization process.

[0039] Based on the above parameters and settings, the particle swarm optimization algorithm is iterated. The inertia weight, learning factor, etc. during the iteration process can be set based on the default values ​​of the particle swarm optimization algorithm until the preset number of iterations is reached or the difference between the fitness function values ​​of multiple consecutive iterations is less than the preset value, then the iteration stops.

[0040] There are also hard constraints during the iteration process. The hard constraints are the mutual exclusion constraints of the switches of the same capacitor. For any capacitor, the switches at both ends cannot be turned on at the same time.

[0041] Secondly, in the fitness function It is related to the equilibrium period, and is essentially positively correlated with the current voltage difference; the larger the voltage difference, the better. The larger the value, the more important it is to ensure balance.

[0042] In step S500 of some embodiments of the present invention, the on / off state of the switching component is controlled according to the target control state combination to perform equalization between the individual battery cells.

[0043] Specifically, when multiple cells are connected in series, multiple switching transistors are controlled in a time-sharing manner to achieve “shuttle charging” of the capacitor among all cells, ultimately making the voltage of all cells consistent.

[0044] Example 2 refer to Figure 3In a second aspect, the present invention provides a battery balancing control device 1, comprising: an acquisition module 11, configured to acquire real-time state parameters of each individual cell in the battery assembly; a construction module 12, configured to construct an objective function based on the real-time state parameters, the objective function including a first evaluation term for evaluating the consistency of the battery assembly and a second evaluation term for evaluating balancing energy efficiency, the first evaluation term and the second evaluation term being weighted and summed using normalized weighting coefficients; an adjustment module 13, configured to dynamically adjust the weighting coefficients based on the current balancing stage of the battery assembly, wherein the balancing stage is divided into at least an initial balancing stage and an end balancing stage based on a comparison of the maximum voltage difference between each individual cell and a preset period boundary threshold; a determination module 14, configured to solve the objective function using a binary intelligent optimization algorithm to determine a target control state combination of the switching component; and a control module 15, configured to control the on / off state of the switching component according to the target control state combination to perform balancing between the individual cells.

[0045] Example 3 refer to Figure 4 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect of the present invention.

[0046] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0047] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 4 Each box shown can represent a device or multiple devices as needed.

[0048] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0049] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to: Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0050] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A battery balancing control method, characterized in that, Applied to a battery assembly comprising a plurality of individual cells and a plurality of switching components for controlling energy transfer between the individual cells, the method includes: Obtain the real-time status parameters of each individual cell in the battery assembly; An objective function is constructed based on the real-time state parameters. The objective function includes a first evaluation term for evaluating the consistency of the battery module and a second evaluation term for evaluating the balanced energy efficiency. The first evaluation term and the second evaluation term are weighted and summed using normalized weighting coefficients. Based on the current equilibrium stage of the battery assembly, the weighting coefficient is dynamically adjusted. The equilibrium stage is divided into at least an initial equilibrium stage and an end equilibrium stage based on the comparison result of the maximum voltage difference between each individual battery cell and a preset period boundary threshold. The objective function is solved using a binary intelligent optimization algorithm to determine the target control state combination of the switching component; Based on the target control state combination, the on / off state of the switching component is controlled to perform equalization between the individual battery cells.

2. The battery equalization control method according to claim 1, characterized in that, The method further includes: determining mutual exclusion constraints based on the hardware topology of the battery assembly, wherein the mutual exclusion constraints include: multiple switching components connected to the same energy transmission element are prohibited from being in the on state simultaneously, and the energy transmission element is used to transfer energy between the individual cells to achieve the energy transfer.

3. The battery equalization control method according to claim 1, characterized in that, The binary intelligent optimization algorithm is a binary particle swarm optimization algorithm. Each particle is encoded as a binary bit string that corresponds one-to-one with each of the aforementioned switching components. The value of each bit in the binary bit string corresponds to the on or off state of a switching component. After each iteration update, particles that do not meet the preset constraints are corrected or removed.

4. The battery equalization control method according to claim 1, characterized in that, The step of dynamically adjusting the weighting coefficients based on the current equilibrium stage of the battery assembly includes: Calculate the maximum voltage difference between each individual cell in the battery assembly; The maximum voltage difference is compared with a preset period boundary threshold; When the maximum voltage difference is greater than the period boundary threshold, it is determined that the current stage is the initial stage of equilibrium. The weighting coefficient of the first evaluation item is dynamically reduced according to the maximum voltage difference and the rate of change of the maximum voltage difference, so as to prioritize improving the consistency equilibrium speed. When the maximum voltage difference is less than or equal to the period boundary threshold, it is determined that the current period is at the end of the equilibrium period, and the weighting coefficient of the first evaluation item is maintained in the set low weight range to prioritize improving energy efficiency.

5. The battery equalization control method according to claim 4, characterized in that, In the initial stage of equilibrium, the weighting coefficients are determined as follows: The weighting coefficients decrease exponentially as the maximum voltage difference at the current time decreases compared to the maximum voltage difference at the initial time; The weighted correction is then applied by combining the absolute value of the rate of change of the maximum voltage difference at the current moment; wherein, the smaller the absolute value of the rate of change, the slower the decay rate of the weighting coefficient.

6. The battery equalization control method according to claim 4, characterized in that, At the end of the equilibrium period, the weighting coefficients are determined as follows: Based on a preset minimum weight, a correction amount determined by the maximum voltage difference at the current moment, a preset equalization stop threshold, and an exponential decay factor is superimposed, so that the weighting coefficient is finely adjusted within the set low weight range as the maximum voltage difference changes slightly.

7. The battery equalization control method according to claim 1, characterized in that, The first evaluation item represents the ratio of the maximum voltage difference between individual cells in the battery module at the current moment to the maximum permissible voltage difference; The second evaluation term represents the ratio of the actual transferred energy to the theoretical maximum transferable energy within the current equilibrium period.

8. A battery balancing control device, characterized in that, Applied to a battery assembly comprising multiple individual cells and multiple switching components for controlling energy transfer between the individual cells, the device includes: The acquisition module is used to acquire the real-time status parameters of each individual cell in the battery assembly. A construction module is used to construct an objective function based on the real-time state parameters. The objective function includes a first evaluation term for evaluating the consistency of the battery module and a second evaluation term for evaluating the balanced energy efficiency. The first evaluation term and the second evaluation term are weighted and summed using normalized weighting coefficients. The adjustment module is used to dynamically adjust the weighting coefficient based on the current equilibrium stage of the battery assembly, wherein the equilibrium stage is divided into at least an initial equilibrium stage and an end equilibrium stage based on the comparison result of the maximum voltage difference between each individual cell and a preset period boundary threshold. The determination module is used to solve the objective function using a binary intelligent optimization algorithm to determine the target control state combination of the switching component; The control module is used to control the on / off state of the switching component according to the target control state combination, so as to perform equalization between the individual batteries.

9. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the battery equalization control method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the battery equalization control method as described in any one of claims 1 to 7.

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