Battery pack active equalization control method, device and equipment and storage medium

Abstract

The invention discloses a battery pack active equalization control method, device and equipment and a storage medium. Operation data of each battery cell in a battery pack are collected in real time; on the basis of the historical operation state data and the current operation data, predicting the operation trend of each battery cell in a future preset period; determining an energy flow path directed graph among the battery cells based on the running trend of each battery cell, and constructing an energy distribution graph model based on the energy flow path directed graph; analyzing the energy distribution diagram model based on a preset optimization rule, and determining energy transfer paths and an equilibrium energy value on each energy transfer path; and performing energy equalization on each battery cell based on the energy transfer path and the equalization energy value. Through active balance control of battery state prediction and energy flow optimization scheduling, the energy utilization rate of the system is effectively improved, and meanwhile the service life of the battery is prolonged.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, device, equipment and storage medium for active balancing control of a battery pack. Background Art

[0002] With the widespread adoption of lithium-ion batteries in electric vehicles, renewable energy storage systems, and mobile devices, the capacity, energy density, and scale of battery packs are continuously expanding. However, due to differences in production processes, changes in operating environments, and uneven aging during cycling, variations in parameters such as capacity, internal resistance, and voltage gradually develop between different battery cells, leading to increasingly prominent consistency issues within battery packs. This consistency discrepancy can not only cause some cells to overcharge or over-discharge, affecting the energy utilization of the entire system, but can also accelerate the aging of some cells, shortening the overall lifespan of the battery pack and even posing safety risks.

[0003] Currently, the industry generally uses passive balancing to alleviate battery consistency issues. Passive balancing typically dissipates excess power (for example, through resistor heat dissipation) to reduce the charge level of high-capacity cells, thereby aligning them with other cells. While this method has a relatively simple circuit structure and low cost, the balancing process results in significant energy waste and low system energy efficiency. This is especially true in scenarios with large battery capacities and significant variations, where the thermal management burden associated with this energy dissipation is significantly increased. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a battery pack active balancing control method, device, equipment and storage medium, which aims to effectively improve system energy utilization while extending battery life through active balancing control of battery status prediction and energy flow optimization scheduling.

[0005] A first aspect of an embodiment of the present application provides a battery pack active balancing control method, including: Real-time collection of operating data of each cell in the battery pack; Based on historical operating status data and current operating data, predict the operating trend of each battery cell in the future preset cycle; Determining a directed graph of energy flow paths between the battery cells based on the operating trends of the battery cells, and constructing an energy distribution graph model based on the directed graph of energy flow paths; Analyzing the energy distribution diagram model based on preset optimization rules to determine energy transfer paths and balanced energy values on each energy transfer path; Energy balancing is performed on each battery cell based on the energy transfer path and the balanced energy value.

[0006] In one embodiment, determining a directed graph of energy flow paths between the battery cells based on the operating trends of the battery cells includes: Based on the operating trends of each battery cell, the energy transfer conditions, voltage direction, current path and capacity redundancy between the battery cells are determined; Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, a directed graph of energy flow paths between the battery cells is determined.

[0007] In one embodiment, determining a directed graph of energy flow paths between the battery cells based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy includes: Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, the energy flow paths between the battery cells and the directions of the energy flow paths are determined to obtain the energy flow path directed graph.

[0008] In one embodiment, constructing an energy distribution graph model based on the energy flow path directed graph includes: Based on the state of charge and capacity estimation values of the interconnected batteries, the weight of each edge in the energy flow path directed graph is determined respectively to obtain the energy distribution graph model.

[0009] In one embodiment, determining the weight of each edge in the directed graph of the energy flow path based on the state of charge and capacity estimation value of each interconnected battery includes: For interconnected batteries i and j, the corresponding edge weights in the directed graph of the energy flow path are expressed as: ; in, represents the energy flow path weight from battery i to battery j, represents the state of charge of battery i, represents the state of charge of battery j, represents the estimated capacity of battery j, represents the estimated capacity of battery i, , Represents the weight adjustment coefficient.

[0010] In one embodiment, the operating trend of each battery cell in a future preset period includes: state of charge, estimated capacity, voltage, and state of charge change slope.

[0011] In one embodiment, the determining of energy transfer conditions between battery cells based on the operating trends of the battery cells includes: For each pair of battery cells i and j, if the difference between the state of charge of battery cell i in the future preset cycle and the state of charge of battery cell j in the future preset cycle is greater than the preset charge difference threshold, and the estimated capacity of battery cell j in the future preset cycle is greater than the preset battery capacity threshold, it is determined that the energy transfer condition is met between battery cells i and j; otherwise, the energy transfer condition is not met between battery cells i and j.

[0012] A second aspect of an embodiment of the present application provides a battery pack active balancing control device, comprising: The acquisition module is used to collect the operating data of each cell in the battery pack in real time; The prediction module is used to predict the operating trend of each battery cell in the future preset period based on historical operating status data and current operating data; A construction module is used to determine a directed graph of energy flow paths between the battery cells based on the operation trends of the battery cells, and to construct an energy distribution graph model based on the directed graph of energy flow paths; An analysis module, configured to analyze the energy distribution diagram model based on preset optimization rules to determine energy transfer paths and a balanced energy value on each energy transfer path; A balancing module is used to balance the energy of each battery cell based on the energy transfer path and the balanced energy value.

[0013] In one embodiment, the building block includes: A judgment unit, configured to judge the energy transfer conditions, voltage direction, current path, and capacity redundancy between the cells based on the operating trends of the cells; A determining unit is used to determine a directed graph of energy flow paths between the battery cells based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy.

[0014] In one embodiment, the determining unit is specifically configured to: Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, the energy flow paths between the battery cells and the directions of the energy flow paths are determined to obtain the energy flow path directed graph.

[0015] In one embodiment, the building block is specifically configured to: Based on the state of charge and capacity estimation values of the interconnected batteries, the weight of each edge in the energy flow path directed graph is determined respectively to obtain the energy distribution graph model.

[0016] In one embodiment, the building block is specifically configured to: For interconnected batteries i and j, the corresponding edge weights in the directed graph of the energy flow path are expressed as: ; in, represents the energy flow path weight from battery i to battery j, represents the state of charge of battery i, represents the state of charge of battery j, represents the estimated capacity of battery j, represents the estimated capacity of battery i, , Represents the weight adjustment coefficient.

[0017] In one embodiment, the operating trend of each battery cell in a future preset period includes: state of charge, estimated capacity, voltage, and state of charge change slope.

[0018] In one embodiment, the determining unit is specifically configured to: For each pair of battery cells i and j, if the difference between the state of charge of battery cell i in the future preset cycle and the state of charge of battery cell j in the future preset cycle is greater than the preset charge difference threshold, and the estimated capacity of battery cell j in the future preset cycle is greater than the preset battery capacity threshold, it is determined that the energy transfer condition is met between battery cells i and j; otherwise, the energy transfer condition is not met between battery cells i and j.

[0019] A third aspect of an embodiment of the present application provides a battery pack active balancing control device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0020] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in the first aspect above are implemented.

[0021] The beneficial effects of the embodiments of the present application are as follows: by collecting the operating data of each battery cell in the battery pack in real time; based on the historical operating status data and the current operating data, predicting the operating trend of each battery cell in the future preset period; determining the energy flow path directed graph between each battery cell based on the operating trend of each battery cell, and constructing an energy distribution graph model based on the energy flow path directed graph; analyzing the energy distribution graph model based on preset optimization rules to determine the energy transfer path and the balanced energy value on each energy transfer path; and balancing the energy of each battery cell based on the energy transfer path and the balanced energy value. Through active balancing control of battery status prediction and energy flow optimization scheduling, the aim is to effectively improve the system energy utilization while extending the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A flowchart of a battery pack active balancing control method according to an embodiment of the present application; Figure 2 A schematic diagram of a battery pack active balancing control device provided in one embodiment of the present application; Figure 3 A schematic diagram of a battery pack active balancing control device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0026] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0027] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0029] In the description of the embodiments of the present application, the term "multi-frame" refers to two or more (including two).

[0030] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0031] An embodiment of the present application provides a method for active balancing control of a battery pack, which aims to effectively improve system energy utilization while extending battery life through active balancing control of battery status prediction and energy flow optimization scheduling.

[0032] See also Figure 1 , Figure 1 This is a flow chart of a battery pack active balancing control method provided by an embodiment of the present application. The battery pack active balancing control method is applied to a wireless energy storage BMS system and is implemented by a battery pack active balancing control device. The battery pack active balancing control device includes but is not limited to a terminal or a server. Figure 1 It can be seen that the battery pack active balancing control method provided in the embodiment of the present application includes steps S110 to S150. The details are as follows: S110: Collecting operating data of each cell in the battery pack in real time.

[0033] For example, the operating data of the battery cell includes voltage, current, temperature, state of charge, capacity, etc. By combining the real-time collection of multimodal data, it provides a basis for the prediction of subsequent status and energy scheduling.

[0034] S120: Based on the historical operating status data and the current operating data, predict the operating trend of each battery cell in a future preset period.

[0035] To determine whether a battery cell has the ability to transfer or receive energy and can safely participate in energy exchange, this embodiment uses a pre-trained time series prediction model (such as LSTM / GRU) to analyze historical operating status data and current operating data collected in real time to predict the operating trend of each battery cell over a preset future period. In one embodiment, the operating trend of each battery cell over a preset future period includes: predicted state of charge value, estimated capacity value, voltage change trend, and state of charge change slope. This embodiment uses a time series model to predict future trends, thereby improving the accuracy of judging the energy exchange potential of battery cells and providing a dynamic forward-looking perspective for constructing a scheduling diagram rather than static scheduling.

[0036] S130: Determine a directed graph of energy flow paths between the battery cells based on the operation trends of the battery cells, and construct an energy distribution graph model based on the directed graph of energy flow paths.

[0037] The directed graph of energy flow paths not only indicates whether the energy transfer-out / in conditions are met between battery cells, but also needs to indicate the ability to control energy flow.

[0038] Exemplarily, a directed graph of energy flow paths between battery cells is determined based on the operating trends of each battery cell, including: judging the energy transfer conditions, voltage direction, current path and capacity redundancy between battery cells based on the operating trends of each battery cell; and determining the directed graph of energy flow paths between battery cells based on the energy transfer conditions, voltage direction, current path and capacity redundancy.

[0039] Based on the operating trend of each battery cell, the energy transfer conditions between the battery cells are judged separately, including: for each pair of battery cells i and battery cells j, if the difference between the charge state of battery cell i in the future preset cycle and the charge state of battery cell j in the future preset cycle is greater than the preset charge difference threshold, and the health state of battery cell j in the future preset cycle is greater than the preset battery health state threshold, it is determined that the energy transfer condition between battery cell i and battery cell j is met; otherwise, the energy transfer condition between battery cell i and battery cell j is not met.

[0040] Based on the operating trends of each battery cell, the system determines whether the voltage direction between the cells supports mutual transmission, whether the corresponding current path allows reverse flow, and whether the capacity redundancy is sufficient. The capacity redundancy is used to determine whether the current cell still has the ability to absorb energy.

[0041] Based on the energy transfer conditions, voltage direction, current path and capacity redundancy, a directed graph of energy flow paths between each battery cell is determined, including: based on the energy transfer conditions, voltage direction, current path and capacity redundancy, the energy flow path between each battery cell and the direction of each energy flow path are determined to obtain a directed graph of energy flow paths.

[0042] Specifically, for any cell pair (i, j), if all of the following conditions are met: the difference in state of charge between cells i and j within a preset future cycle is greater than a preset charge difference threshold, there is a sufficient state of charge difference, the voltage direction satisfies energy flow from high to low, the current path supports energy transfer in the corresponding direction, and cell j has capacity redundancy, i.e., remaining charging headroom; it can be determined that there is an energy flow path between cells i and j. A directed edge is established from cell i to cell j, forming a directed energy flow path between cells i and j. The directed energy flow paths between all cells constitute a directed energy flow path graph.

[0043] An energy distribution graph model is constructed based on an energy flow path directed graph, including: determining the weight of each edge in the energy flow path directed graph based on the state of charge and capacity estimation value of each interconnected battery; and obtaining the energy distribution graph model based on the energy flow path directed graph and the weight of each edge.

[0044] Based on the state of charge and capacity estimation of each interconnected battery, the weight of each edge in the energy flow path directed graph is determined separately, including: for interconnected battery i and battery j, the corresponding edge weight in the energy flow path directed graph is expressed as: ; in, represents the energy flow path weight from battery i to battery j, represents the state of charge of battery i, represents the state of charge of battery j, represents the estimated capacity of battery j, represents the estimated capacity of battery i, Prevent decimals with zero denominator, , Represents the weight adjustment coefficient, which is used to balance the impact of power difference and capacity ratio.

[0045] By predicting the future state trends of battery cells, a directed graph representing energy dispatchability is constructed. Dynamically adjustable weights are then assigned to each edge in the directed graph based on the cell's state of charge and capacity, enabling comprehensive and accurate modeling of each cell's future energy trends. In summary, the nodes of the energy flow path directed graph represent each battery cell, the edges represent paths that meet energy flow conditions, and the edge weights represent energy dispatchability.

[0046] S140: Analyze the energy distribution diagram model based on preset optimization rules to determine the energy transfer path and the balanced energy value on each energy transfer path.

[0047] The energy allocation diagram model is analyzed based on a preset optimization rule, including: analyzing the energy allocation diagram model based on a rule of minimizing overall energy difference or based on a rule of maximizing available energy utilization rate.

[0048] For example, the energy distribution diagram model is analyzed based on the rule of minimizing the overall energy difference, with the goal of balancing the state of charge values of all cells as much as possible and reducing the state of charge differences between cells. Specifically, an energy transfer objective function is constructed between any cells, and the energy transfer objective function is solved by pre-set constraints to obtain the energy transfer path and the balanced energy value on each energy transfer path. The constructed energy transfer objective function can be expressed as: ;in, Indicates the energy value planned to be transferred from any cell i to any cell j during the current balancing period. The constraints include: Less than or equal to the energy dispatchability between cell i and cell j in the graph model, as well as the maximum energy transfer allowed between cell i and cell j. By minimizing this energy transfer objective function, we can obtain the corresponding target node and the balanced energy value between the target nodes.

[0049] Alternatively, the purpose of analyzing the energy distribution graph model based on the rule of maximizing the available energy utilization rate is to give priority to charging high-energy cells to low-energy cells, thereby increasing the total available energy of the entire battery pack. Specifically, an energy utilization efficiency function is defined, and constraints are defined, including: selecting cells with a state of charge greater than a preset state of charge as energy transfer sources, cells with a state of charge less than or equal to a preset state of charge and a remaining capacity greater than a preset remaining capacity as selection targets, and the shortest energy flow path as the preferred path; the energy utilization efficiency function is solved based on the above constraints, and during the solution process, the paths of all cells are sorted by unit efficiency, and the dispatchable energy is allocated in sequence until the constraints are met, thereby obtaining the target nodes that maximize the available energy utilization rate (the edges between the target nodes serve as energy transfer paths) and the balanced energy values between the target nodes.

[0050] S150: Perform energy balance on each battery cell based on the energy transfer path and the balanced energy value.

[0051] Through the above analysis, it can be seen that the active balancing control method for battery packs provided in the embodiment of the present application collects the operating data of each battery cell in the battery pack in real time; predicts the operating trend of each battery cell in the future preset period based on the historical operating status data and the current operating data; determines the directed graph of the energy flow path between each battery cell based on the operating trend of each battery cell, and constructs an energy distribution graph model based on the directed graph of the energy flow path; analyzes the energy distribution graph model based on preset optimization rules to determine the energy transfer path and the balanced energy value on each energy transfer path; and balances the energy of each battery cell based on the energy transfer path and the balanced energy value. Active balancing control through battery status prediction and energy flow optimization scheduling aims to effectively improve system energy utilization while extending battery life.

[0052] See Figure 2 , Figure 2 Schematic diagram of a battery pack active balancing control device provided in one embodiment of the present application. The battery pack active balancing control device includes modules or units for executing Figure 1 Each step in the corresponding embodiment. Please refer to Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 2 The battery pack active balancing control device 200 includes: The acquisition module 210 is used to collect the operating data of each cell in the battery pack in real time; The prediction module 220 is used to predict the operating trend of each battery cell in a future preset period based on historical operating status data and current operating data; A construction module 230 is configured to determine a directed graph of energy flow paths between the battery cells based on the operating trends of the battery cells, and to construct an energy distribution graph model based on the directed graph of energy flow paths; An analysis module 240 is configured to analyze the energy distribution diagram model based on preset optimization rules to determine energy transfer paths and the balanced energy value on each energy transfer path; The balancing module 250 is configured to balance the energy of each battery cell based on the energy transfer path and the balanced energy value.

[0053] In one embodiment, the building module 230 includes: A judgment unit, configured to judge the energy transfer conditions, voltage direction, current path, and capacity redundancy between the cells based on the operating trends of the cells; A determining unit is configured to determine a directed graph of energy flow paths between the battery cells based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy.

[0054] In one embodiment, the determining unit is specifically configured to: Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, the energy flow paths between the battery cells and the directions of the energy flow paths are determined to obtain the energy flow path directed graph.

[0055] In one embodiment, the construction module 230 is specifically configured to: Based on the state of charge and capacity estimation values of the interconnected batteries, the weight of each edge in the energy flow path directed graph is determined respectively to obtain the energy distribution graph model.

[0056] In one embodiment, the construction module 230 is specifically configured to: For interconnected batteries i and j, the corresponding edge weights in the directed graph of the energy flow path are expressed as: ; in, represents the energy flow path weight from battery i to battery j, represents the state of charge of battery i, represents the state of charge of battery j, represents the estimated capacity of battery j, represents the estimated capacity of battery i, , Represents the weight adjustment coefficient.

[0057] In one embodiment, the operating trend of each battery cell in a future preset period includes: state of charge, estimated capacity, voltage, and state of charge change slope.

[0058] In one embodiment, the determining unit is specifically configured to: For each pair of battery cells i and j, if the difference between the state of charge of battery cell i in the future preset cycle and the state of charge of battery cell j in the future preset cycle is greater than the preset charge difference threshold, and the estimated capacity of battery cell j in the future preset cycle is greater than the preset battery capacity threshold, it is determined that the energy transfer condition is met between battery cells i and j; otherwise, the energy transfer condition is not met between battery cells i and j.

[0059] See Figure 3 , Figure 3 A schematic diagram of a battery pack active balancing control device provided in one embodiment of the present application. Figure 3 It can be seen that the battery pack active balancing control device 300 includes: a processor 310, a memory 320, and a computer program 330 stored in the memory 320 and executable on the processor 310; when the processor 310 executes the computer program 330, the steps in the above-mentioned embodiments of the battery pack active balancing control method are implemented, such as Figure 1Alternatively, when the processor 310 executes the computer program 330, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 2 The functions of modules 210 to 250 are shown.

[0060] Exemplarily, computer program 330 may be divided into one or more modules / units, one or more of which are stored in memory 320 and executed by processor 310 to implement the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of computer program 330 in a battery pack active balancing control device. For example, computer program 330 may be divided into an acquisition module, a prediction module, a construction module, an analysis module, and a balancing module.

[0061] The battery pack active balancing control device provided in this embodiment may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 3 The present invention is merely an example of a battery pack active balancing control device and does not constitute a limitation on the battery pack active balancing control device. The present invention may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the battery pack active balancing control device may also include input and output devices, network access devices, buses, etc.

[0062] The processor 310 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0063] The memory 320 can be an internal storage unit of the battery pack active balancing control device, such as a hard drive or memory of the battery pack active balancing control device. The memory 320 can also be an external storage device of the battery pack active balancing control device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the battery pack active balancing control device can include both an internal storage unit and an external storage device. The memory 320 is used to store computer programs and other programs and data required by the battery pack active balancing control device. The memory 320 can also be used to temporarily store data that has been output or is about to be output.

[0064] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0065] An embodiment of the present application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0066] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0067] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0068] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0069] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0070] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0072] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0073] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A battery pack active balancing control method, characterized in that: include: Real-time collection of operating data of each cell in the battery pack; Based on historical operating status data and current operating data, predict the operating trend of each battery cell in the future preset cycle; Determining a directed graph of energy flow paths between the battery cells based on the operating trends of the battery cells, and constructing an energy distribution graph model based on the directed graph of energy flow paths; Analyzing the energy distribution diagram model based on preset optimization rules to determine energy transfer paths and balanced energy values on each energy transfer path; Energy balancing is performed on each battery cell based on the energy transfer path and the balanced energy value.

2. The battery pack active balancing control method according to claim 1, wherein: The step of determining a directed graph of energy flow paths between the battery cells based on the operating trends of the battery cells includes: Based on the operating trends of each battery cell, the energy transfer conditions, voltage direction, current path and capacity redundancy between the battery cells are determined; Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, a directed graph of energy flow paths between the battery cells is determined.

3. The battery pack active balancing control method according to claim 2, wherein: The determining, based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, a directed graph of energy flow paths between the battery cells includes: Based on the energy transfer condition, the voltage direction, the current path, and the capacity redundancy, the energy flow paths between the battery cells and the directions of the energy flow paths are determined to obtain the energy flow path directed graph.

4. The battery pack active balancing control method according to claim 3, wherein: The constructing of an energy distribution graph model based on the energy flow path directed graph includes: Based on the state of charge and capacity estimation values of the interconnected batteries, the weight of each edge in the energy flow path directed graph is determined respectively to obtain the energy distribution graph model.

5. The battery pack active balancing control method according to claim 4, wherein: The step of determining the weight of each edge in the directed graph of the energy flow path based on the state of charge and the estimated capacity of each interconnected battery includes: For interconnected batteries i and j, the corresponding edge weights in the directed graph of the energy flow path are expressed as: ； in, represents the energy flow path weight from battery i to battery j, represents the state of charge of battery i, represents the state of charge of battery j, represents the estimated capacity of battery j, represents the estimated capacity of battery i, , Represents the weight adjustment coefficient.

6. The battery pack active balancing control method according to claim 1, wherein: The operating trend of each battery cell in a future preset period includes: state of charge, estimated capacity, voltage, and state of charge change slope.

7. The battery pack active balancing control method according to claim 6, wherein: The energy transfer conditions between the battery cells are determined based on the operating trends of the battery cells, including: For each pair of battery cells i and j, if the difference between the state of charge of battery cell i in the future preset cycle and the state of charge of battery cell j in the future preset cycle is greater than the preset charge difference threshold, and the estimated capacity of battery cell j in the future preset cycle is greater than the preset battery capacity threshold, it is determined that the energy transfer condition is met between battery cells i and j; otherwise, the energy transfer condition is not met between battery cells i and j.

8. A battery pack active balancing control device, characterized in that: include: The acquisition module is used to collect the operating data of each cell in the battery pack in real time; The prediction module is used to predict the operating trend of each battery cell in the future preset period based on historical operating status data and current operating data; A construction module is used to determine a directed graph of energy flow paths between the battery cells based on the operation trends of the battery cells, and to construct an energy distribution graph model based on the directed graph of energy flow paths; An analysis module, configured to analyze the energy distribution diagram model based on preset optimization rules to determine energy transfer paths and a balanced energy value on each energy transfer path; A balancing module is used to balance the energy of each battery cell based on the energy transfer path and the balanced energy value.

9. A battery pack active balancing control device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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