Lithium ion battery equalization strategy system

Abstract

The invention discloses a lithium ion battery equalization strategy system, and relates to the technical field of battery equalization strategies, and the system comprises a state monitoring module which obtains the state data of all single batteries in a battery pack in real time, carries out the analysis of the state data, and generates the time sequence data of the state of the single batteries; the imbalance identification module is used for identifying the imbalance state of the single batteries by adopting a clustering algorithm based on the time sequence data; the priority ranking module is used for calculating the priority ranking of the unbalanced battery pairs according to the unbalanced identification result; the dynamic energy distribution module is used for determining energy transmission current and speed by using a dynamic energy transmission strategy according to a priority ranking result, and generating an equalization strategy; and the equalization strategy execution module executes the equalization strategy to realize battery pack equalization. Through cooperative work of multiple modules, faster, more efficient and more stable equalization management of the lithium ion battery pack is realized, and the overall performance of the battery system is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery balancing strategies, and in particular to a lithium-ion battery balancing strategy system. Background Art

[0002] With the widespread application of lithium-ion batteries in electric vehicles, energy storage systems, consumer electronics and other fields, the performance requirements for battery pack management systems are constantly increasing. There are usually performance differences between the single cells of a lithium-ion battery pack, such as voltage, temperature and internal resistance. These differences will gradually increase during the use of the battery pack, resulting in a decrease in the overall performance of the battery pack, which may cause safety problems in severe cases. Therefore, battery pack balancing management has become a key issue in the application of lithium batteries.

[0003] Existing balancing management methods are mainly divided into two categories: passive balancing and active balancing. Passive balancing mainly consumes the electric energy of high-voltage single cells through the discharge circuit to balance the voltage of each single cell; while active balancing transfers energy from high-voltage single cells to low-voltage single cells through the energy transfer circuit. Passive balancing usually achieves balancing by consuming the electric energy of high-voltage single cells. Although this method has a simple structure, the balancing speed is extremely slow, especially when there is a large imbalance in the battery pack, it takes a long time to reach a balanced state, affecting the actual application efficiency of the battery pack; in the active balancing method, although the electric energy of the high-voltage single cell can be transferred to the low-voltage single cell, it is limited by the efficiency of the transfer circuit and the complex control algorithm. The actual balancing speed is still low and cannot quickly respond to the imbalance changes of the battery pack. In addition, traditional energy transfer methods (such as inductive coupling, transformer coupling, etc.) have high energy losses, especially when there are frequent transfers between multiple single cells, the loss is more significant. Summary of the invention

[0004] Based on the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a lithium-ion battery balancing strategy system to solve the above-mentioned technical problems.

[0005] To achieve the above object, the present invention provides the following technical solution: a lithium-ion battery balancing strategy system, comprising:

[0006] Status monitoring module: used to obtain status data of all single cells in the lithium-ion battery pack in real time, and analyze the status data to generate time series data of the status of the single cells, wherein the status data includes voltage data, temperature data and internal resistance data;

[0007] Unbalanced identification module: used to analyze the states of all single cells using a clustering algorithm according to the time series data, identify the unbalanced states of the single cells in the lithium-ion battery pack, and generate unbalanced identification results;

[0008] Priority sorting module: used to calculate the priority sorting between the unbalanced battery pairs using an optimization algorithm according to the unbalanced identification result, and generate a priority sorting result;

[0009] Dynamic energy allocation module: Based on the priority sorting results, the dynamic energy transmission strategy is used to determine the energy transmission current size and transmission rate between the unbalanced battery pairs and generate a balancing strategy;

[0010] Balancing strategy execution module: used to execute the balancing strategy to achieve lithium-ion battery balancing.

[0011] The present invention is further configured such that the state monitoring module includes a multidimensional feature extraction unit, a change rate calculation unit and a state timing generation unit; the multidimensional feature extraction unit is used to perform multidimensional feature extraction on the state data to obtain multi-scale components, wherein the state data is decomposed into intrinsic mode components using empirical mode decomposition; the change rate calculation unit is used to calculate the state change rate of the multi-scale components; and the state timing generation unit is used to construct a state time series of a single cell according to the multi-scale components and the state change rate.

[0012] The present invention is further configured to perform multi-dimensional feature extraction on the state data, and the calculation logic is: in, is the multi-scale component of the state data X of the i-th single cell at time t, X specifically refers to the voltage V, temperature T and internal resistance R, J is the number of intrinsic mode components, The state data of the ith single cell is decomposed into the sum of J intrinsic mode components through empirical mode decomposition, r i,X (t) is the trend component of the state data X of the i-th single cell at time t;

[0013] The state change rate of multi-scale components is calculated as follows: Where ΔX i (t) is the state change rate of the multi-scale components of the state data X of the i-th single battery at time t, ψ is a nonlinear transformation function used to emphasize the key features in the change rate, and τ is the independent variable of the integration;

[0014] The state time series of a single battery is constructed in the form of:

[0015] The present invention is further configured such that the imbalance identification module includes a similarity matrix construction unit, a cluster center determination unit and an imbalance index calculation unit; the similarity matrix construction unit is used to standardize the state time series of the single cell, and construct a similarity matrix between the single cells according to the standardized state time series; the cluster center determination unit is used to determine a cluster center according to the similarity matrix between the single cells by minimizing the state difference criterion, and the imbalance index calculation unit is used to calculate the imbalance index of the single cell according to the state time series of the single cell and the state time series of the cluster center.

[0016] The present invention is further configured such that the construction logic of the similarity matrix between the single cells is: ij =(S i (t)-S j (t)) T ∑ -1 (S i (t)-S j (t)), where D ij is the similarity between the ith single cell and the jth single cell, ∑ is the inverse matrix of the covariance matrix, which is used to adjust the weight of the state difference when calculating the distance;

[0017] A cluster center is determined by minimizing the state difference criterion. The calculation logic is: Among them, C is the cluster center, N is the total number of single batteries, argmin i () is to select the value of i so that the target Minimum;

[0018] The calculation logic of the imbalance index of a single cell is: Among them, U i is the imbalance index of the ith single cell, D iC is the similarity between the ith single cell and the cluster center C, κ and λ are adjustment parameters used to adjust the amplification and attenuation characteristics of the imbalance index.

[0019] The present invention is further configured such that the priority sorting module includes a cost function generating unit, an optimization objective function generating unit and a priority sorting unit; the cost function generating unit is used to define a transmission cost function between pairs of single cell batteries according to an imbalance index of the single cell batteries; the optimization objective function generating unit is used to construct an optimization objective function according to the transmission cost function to minimize the total cost of energy transmission; the priority sorting unit is used to generate a priority sorting result by sorting in ascending order according to the solution result of the optimization objective function according to the transmission cost function value.

[0020] The present invention is further configured such that the calculation logic of the transmission cost function is: Among them, Co ij is the transmission cost between the i-th single cell and the j-th single cell, the i-th single cell is a high-voltage battery, and the j-th single cell is a low-voltage battery;

[0021] The calculation logic of the optimization objective function is: Among them, F is the optimization objective function, x kl is a decision variable, indicating whether to select the single battery pair (k, l) for energy transmission, x kl =1 means selecting the single cell pair (k, l) for transmission, x kl =0 means that the single cell pair (k, l) is not selected for transmission, λ is the penalty coefficient, which is used to control the influence weight of the constraint penalty term on the optimization objective function, PT kl is the constraint penalty term, which represents the penalty value of the battery pair that does not transmit;

[0022] The priority sorting result is expressed as: Priority = Sort {(k, l) | x kl =1, by Co kl}, where Priority is the priority sorting result and Sort is the ascending function.

[0023] The present invention is further configured such that the dynamic energy allocation module includes a transmission rate calculation unit and a transmission current calculation unit; the transmission rate calculation unit is used to determine the energy transmission rate between unbalanced battery pairs using a dynamic energy transmission strategy; and the transmission current calculation unit is used to calculate the transmission current according to the determined energy transmission rate.

[0024] The present invention is further configured such that the calculation logic of the transmission rate is: Among them, r(t) is the transmission rate at time t, δ is the preset maximum allowable current, which is used to limit the upper limit of the transmission current, and α is the time constant, which is used to smooth the change of the transmission rate.

[0025] The present invention is further configured such that the calculation logic of the transmission current is: Among them, I(t+1) is the transmission current at time t+1, η is the learning rate, which is used to control the response speed of current adjustment, and λ is the temperature influence factor, which is used to slow down the transmission when the temperature rises.

[0026] The present invention provides a lithium-ion battery balancing strategy system, which includes a state monitoring module: used for real-time acquisition of state data of all single cells in a lithium-ion battery pack, and analysis of the state data to generate time series data of the state of the single cells, wherein the state data includes voltage data, temperature data and internal resistance data; an unbalanced identification module: used for analyzing the states of all single cells using a clustering algorithm according to the time series data, identifying the unbalanced state of the single cells in the lithium-ion battery pack, and generating an unbalanced identification result; a priority sorting module: used for calculating the priority sorting between unbalanced battery pairs using an optimization algorithm according to the unbalanced identification result, and generating a priority sorting result; a dynamic energy allocation module: used for determining the energy transmission current size and transmission rate between unbalanced battery pairs using a dynamic energy transmission strategy according to the priority sorting result, and generating a balancing strategy; and a balancing strategy execution module: used for executing the balancing strategy to achieve lithium-ion battery balancing, and the beneficial effects generated include:

[0027] 1. Improve the balancing speed: The present invention can quickly identify the state differences of each single battery in the battery pack through the real-time collaborative work of the state monitoring module and the imbalance identification module. Combined with the optimization sorting module, it can generate the best balancing sorting path, thereby significantly improving the balancing speed. Compared with traditional passive balancing and active balancing methods, the balancing process of the present invention is faster, effectively shortening the balancing time and improving the working efficiency of the battery pack;

[0028] 2. Improve energy utilization efficiency: The present invention adopts a dynamic energy allocation module, which intelligently adjusts the energy transmission path and transmission rate according to the optimized balancing sorting results, effectively reducing energy loss during transmission. Compared with the existing energy dissipation balancing and high-loss active balancing solutions, the present invention can achieve efficient energy transfer, greatly improve the overall energy utilization efficiency, and avoid unnecessary energy waste;

[0029] 3. Realize multi-dimensional precise balancing: The present invention can comprehensively analyze the multi-dimensional state data of the single battery, such as voltage, temperature, internal resistance, etc., and achieve more precise balancing control by establishing a multi-scale state feature extraction and recognition model. The multi-dimensional precise analysis enables the balancing strategy to not only respond to voltage differences, but also dynamically adjust the battery imbalance state caused by temperature and internal resistance, thereby significantly improving the accuracy of the balancing effect.

[0030] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0032] Figure 1 The figure is a schematic diagram of the structure of a lithium-ion battery balancing strategy system according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0034] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0035] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0036] A lithium-ion battery balancing strategy system, such as Figure 1 As shown, including:

[0037] Status monitoring module: used to obtain status data of all single cells in the lithium-ion battery pack in real time, and analyze the status data to generate time series data of the status of the single cells, wherein the status data includes voltage data, temperature data and internal resistance data;

[0038] Unbalanced identification module: used to analyze the states of all single cells using a clustering algorithm according to the time series data, identify the unbalanced states of the single cells in the lithium-ion battery pack, and generate unbalanced identification results;

[0039] Priority sorting module: used to calculate the priority sorting between the unbalanced battery pairs using an optimization algorithm according to the unbalanced identification result, and generate a priority sorting result;

[0040] Dynamic energy allocation module: Based on the priority sorting results, the dynamic energy transmission strategy is used to determine the energy transmission current size and transmission rate between the unbalanced battery pairs and generate a balancing strategy;

[0041] Balancing strategy execution module: used to execute the balancing strategy to achieve lithium-ion battery balancing.

[0042] Specifically, the state monitoring module collects and analyzes the state data of all single cells in the lithium-ion battery pack in real time, constructs a time series reflecting the operating state of the single cells, and provides basic information for subsequent imbalance identification and balancing strategies; the present invention is further configured such that the state monitoring module includes a multidimensional feature extraction unit, a change rate calculation unit and a state time series generation unit; the multidimensional feature extraction unit is used to perform multidimensional feature extraction on the state data to obtain multi-scale components, wherein the state data is decomposed into intrinsic mode components using empirical mode decomposition; the change rate calculation unit is used to calculate the state change rate of the multi-scale components; the state time series generation unit is used to construct a state time series of the single cell according to the multi-scale components and the state change rate.

[0043] The multi-dimensional feature extraction unit extracts multi-dimensional features from the state data, decomposes the complex state signal into multiple intrinsic mode components through a decomposition method, and extracts the multi-scale features of the battery operation. Specifically, the multi-dimensional feature extraction is performed on the state data, and the calculation logic is: in, is the multi-scale component of the state data X of the i-th single cell at time t, X specifically refers to the voltage V, temperature T and internal resistance R, J is the number of intrinsic mode components, The state data of the ith single cell is decomposed into the sum of J intrinsic mode components through empirical mode decomposition, r i,X (t) is the trend component of the state data X of the i-th single battery at time t; the complex battery state signal is decomposed into multiple intrinsic mode components (IMFs) through the empirical mode decomposition (EMD) method. Multiple intrinsic mode components reflect the characteristics of the battery at different frequencies and time scales. The number of intrinsic mode components J is related to the complexity of the signal and is adaptively determined by the decomposition process. More components can capture more complex signal characteristics. The value range is 3 to 10 and is automatically adjusted according to the actual signal characteristics; the trend component r i,X (t) represents the long-term change trend of the signal, which is a low-frequency component. By decomposing and extracting multi-scale features, it can comprehensively reflect the short-term fluctuations, periodic changes and long-term trends of the battery state.

[0044] The state change rate of multi-scale components is calculated as follows: Where ΔX i (t) is the state change rate of the multi-scale component of the state data X of the i-th single battery at time t, ψ is a nonlinear transformation function used to emphasize the key features in the change rate, and τ is the independent variable of the integration; the state time series of the single battery is constructed in the form of: The above calculation logic calculates the state change rate of multi-scale components of the battery state data, extracts the dynamic change characteristics of the state data by performing nonlinear transformation on the derivative of the state data, including calculating the change rate of each state component, and enhancing key features through nonlinear functions, thereby constructing a time series of the battery state. The overall logic is to capture the changes in the battery state at different time points and provide accurate dynamic information for subsequent analysis and balancing management; the nonlinear transformation function ψ depends on the characteristics of the data and the required enhancement effect, including exponential, logarithmic, hyperbolic tangent and other functions to enhance mutations or suppress noise; by calculating the rate of change, it can accurately capture the changing characteristics of the battery state at different time points and effectively identify different situations such as mutations and gradual changes.

[0045] The present invention is further configured such that the imbalance identification module includes a similarity matrix construction unit, a cluster center determination unit and an imbalance index calculation unit; the similarity matrix construction unit is used to standardize the state time series of the single cell, and construct a similarity matrix between the single cells according to the standardized state time series; the cluster center determination unit is used to determine a cluster center according to the similarity matrix between the single cells by minimizing the state difference criterion, and the imbalance index calculation unit is used to calculate the imbalance index of the single cell according to the state time series of the single cell and the state time series of the cluster center.

[0046] The present invention is further configured such that the construction logic of the similarity matrix between the single cells is: ij =(S i (t)-S j (t)) T ∑ -1 (S i (t)-S j (t)), where D ijis the similarity between the ith single cell and the jth single cell, ∑ is the inverse matrix of the covariance matrix, which is used to adjust the weight of the state difference when calculating the distance; specifically, the above calculation logic uses the Mahalanobis distance as the similarity metric. The Mahalanobis distance not only considers the numerical difference between each feature, but also combines the correlation between features, thereby providing a more accurate similarity assessment of the battery state. By calculating the distance between each pair of battery state time series, a similarity matrix is ​​formed for subsequent imbalance identification and cluster analysis; the inverse matrix ∑ of the covariance matrix is ​​obtained by calculating the covariance matrix of the state data and inverting it. Each element of the matrix reflects the relationship between the features. By using the Mahalanobis distance, the similarities and differences between the states of the batteries can be more accurately identified, providing important basic data support for the balancing strategy.

[0047] A cluster center is determined by minimizing the state difference criterion. The calculation logic is: Among them, C is the cluster center, N is the total number of single batteries, argmin i () is to select the value of i so that the target Minimum; Specifically, the above calculation logic determines a cluster center by minimizing the state difference. The cluster center represents the most representative battery in the battery pack, and its state is the smallest difference from all other batteries. The core of this process is to select a battery as the cluster center so that the sum of similarity metrics between this battery and all other batteries is minimized, which helps to balance management and optimize battery state evaluation.

[0048] The calculation logic of the imbalance index of a single cell is: Among them, U i is the imbalance index of the ith single cell, D iC is the similarity between the ith single cell and the cluster center C, κ and λ are adjustment parameters used to adjust the amplification and attenuation characteristics of the imbalance index; the above formula is used to calculate the imbalance index U of the single cell i , reflects the similarity between the battery and the cluster center. The larger the value, the greater the deviation of the battery state from the cluster center, and the more serious the imbalance. The formula introduces exponential functions and adjustment parameters to amplify or attenuate the deviation of the imbalance state, thereby quantifying the degree of imbalance; the adjustment parameter κ adjusts the amplification of the imbalance index, with a value range of [1,10], and the adjustment parameter λ adjusts the compression of small deviations, with a value range of [0.1,5]. This formula converts the deviation between the battery and the cluster center into a quantifiable imbalance index, providing a scientific basis for the balancing strategy.

[0049] The present invention is further configured such that the priority sorting module includes a cost function generating unit, an optimization objective function generating unit and a priority sorting unit; the cost function generating unit is used to define a transmission cost function between pairs of single cell batteries according to an imbalance index of the single cell batteries; the optimization objective function generating unit is used to construct an optimization objective function according to the transmission cost function to minimize the total cost of energy transmission; the priority sorting unit is used to generate a priority sorting result by sorting in ascending order according to the solution result of the optimization objective function according to the transmission cost function value.

[0050] The present invention is further configured such that the calculation logic of the transmission cost function is: Among them, Co ij is the transmission cost between the ith cell and the jth cell, the ith cell is a high-voltage battery, and the jth cell is a low-voltage battery; the above calculation logic calculates the transmission cost by combining the imbalance index and similarity distance of the battery state with exponential decay. The transmission cost is used to measure the "cost" or difficulty of energy transmission between two batteries. The greater the cost, the higher the resistance or cost of energy transmission between the two batteries; the smaller the cost, the easier the transmission. This calculation method can quantify the priority of energy transmission between batteries and provide a basis for the balancing control strategy; the transmission cost function concretizes the difficulty of energy transmission between batteries into a quantifiable value, providing a clear numerical basis for balancing management.

[0051] The calculation logic of the optimization objective function is: Among them, F is the optimization objective function, x kl is a decision variable, indicating whether to select the single battery pair (k, l) for energy transmission, x kl =1 means selecting the single cell pair (k, l) for transmission, x kl =0 means that the single cell pair (k, l) is not selected for transmission, λ is the penalty coefficient, which is used to control the influence weight of the constraint penalty term on the optimization objective function, PT kl is a constraint penalty term, which represents the penalty value of the battery pair that does not transmit. The calculation logic of the above optimization objective function is used to determine the best solution for energy transmission within the battery pack. This objective function combines the transmission cost and the constraint penalty term, and adjusts the decision variable x kl Optimize the energy transmission path and mode between battery pairs. The goal is to minimize the total transmission cost while considering the influence of constraints to achieve optimal control of energy transmission; the penalty coefficient λ is used to adjust the weight of the penalty term and control the impact of the penalty term on the overall optimization. The value range is [0.1,10]. The constraint penalty term PT klIt is used to punish the path selection that does not meet the conditions to ensure the rationality of the optimization results. The value range is non-negative, depending on the actual constraint requirements, and is usually a decimal or integer. Through the optimization of the objective function, the optimal path and solution for energy transmission within the battery pack can be determined to improve the balancing efficiency.

[0052] The priority sorting result is expressed as: Priority = Sort {(k, l) | x kl =1, by Co kl}, where Priority is the result of the priority sorting, and Sort is an ascending function; the above expression describes the logic of generating the priority sorting result, which aims to determine the priority of energy transmission for different battery pairs. The sorting is performed based on the transmission cost of each battery pair, thereby providing a sorting basis for subsequent balancing control. The sorting result directly affects the selection of the energy transmission path, so that energy is preferentially transmitted on the path with the lowest transmission cost; by giving priority to battery pairs with lower transmission costs for transmission, the overall cost of the energy transmission process can be effectively reduced and the system efficiency can be improved.

[0053] The present invention is further configured such that the dynamic energy allocation module includes a transmission rate calculation unit and a transmission current calculation unit; the transmission rate calculation unit is used to determine the energy transmission rate between unbalanced battery pairs using a dynamic energy transmission strategy; and the transmission current calculation unit is used to calculate the transmission current according to the determined energy transmission rate.

[0054] The present invention is further configured such that the calculation logic of the transmission rate is: Among them, r(t) is the transmission rate at time t, δ is the preset maximum allowable current, which is used to limit the upper limit of the transmission current, and α is the time constant, which is used to smooth the change of the transmission rate; the above formula describes the calculation logic of the transmission rate, and the transmission rate reflects the speed of change of the current during the transmission process between batteries. This formula combines the battery voltage difference and internal resistance, and adjusts the transmission rate by exponential decay to ensure that the transmission rate changes within a reasonable range to achieve stable and safe energy transmission; the time constant α is used to smooth the change of the transmission rate and adjust the response speed. The value range is [0.1,10]. According to the sensitivity of the system to the response of the transmission rate, the transmission rate can be adjusted smoothly through the time constant and exponential decay adjustment to avoid sudden changes during the transmission process and ensure the stability of the system.

[0055] The present invention is further configured such that the calculation logic of the transmission current is: Wherein, I(t+1) is the transmission current at time t+1, η is the learning rate, which is used to control the response speed of current adjustment, and λ is the temperature influence factor, which is used to slow down the transmission when the temperature rises. The above calculation logic adjusts the current to achieve smooth changes by controlling the difference between the current current and the expected transmission rate. The logic introduces the learning rate and the temperature influence factor to control the response speed of current adjustment and the sensitivity to temperature, thereby ensuring the stability and safety of the transmission process. The learning rate η is used to adjust the smoothness and sensitivity of current changes. If it is too large, the system may be unstable, and if it is too small, the response is too slow. The value range is [0.01, 1]. The temperature influence factor λ is used to control the inhibitory effect of temperature on current adjustment. The larger the value, the greater the influence of temperature on current change. The value range is [0.1, 5]. The introduction of the learning rate and the temperature factor makes the current change smoother, avoids the impact of too fast changes on the battery and the system, and improves the stability of the transmission process. By carefully controlling the current regulation, the uncertainty of the battery pack operating under complex working conditions is reduced, and the stability and safety of the overall system are improved.

[0056] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0057] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0058] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0059] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] Those of ordinary skill 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 to be beyond the scope of this application.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0062] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0065] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0066] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A lithium-ion battery balancing strategy system, characterized in that: include: Status monitoring module: used to obtain status data of all single cells in the lithium-ion battery pack in real time, and analyze the status data to generate time series data of the status of the single cells, wherein the status data includes voltage data, temperature data and internal resistance data; Unbalanced identification module: used to analyze the states of all single cells using a clustering algorithm according to the time series data, identify the unbalanced states of the single cells in the lithium-ion battery pack, and generate unbalanced identification results; Priority sorting module: used to calculate the priority sorting between the unbalanced battery pairs using an optimization algorithm according to the unbalanced identification result, and generate a priority sorting result; Dynamic energy allocation module: Based on the priority sorting results, the dynamic energy transmission strategy is used to determine the energy transmission current size and transmission rate between the unbalanced battery pairs and generate a balancing strategy; Balancing strategy execution module: used to execute the balancing strategy to achieve lithium-ion battery balancing.

2. A lithium-ion battery balancing strategy system according to claim 1, characterized in that: The state monitoring module includes a multidimensional feature extraction unit, a change rate calculation unit and a state time series generation unit; the multidimensional feature extraction unit is used to perform multidimensional feature extraction on the state data to obtain multi-scale components, wherein the state data is decomposed into intrinsic mode components using empirical mode decomposition; the change rate calculation unit is used to calculate the state change rate of the multi-scale components; the state time series generation unit is used to construct a state time series of a single cell according to the multi-scale components and the state change rate.

3. A lithium-ion battery balancing strategy system according to claim 2, characterized in that: Multi-dimensional feature extraction is performed on the status data, and the calculation logic is as follows: in, is the multi-scale component of the state data X of the i-th single cell at time t, X specifically refers to the voltage V, temperature T and internal resistance R, J is the number of intrinsic mode components, The state data of the ith single cell is decomposed into the sum of J intrinsic mode components through empirical mode decomposition, r i,X (t) is the trend component of the state data X of the i-th single cell at time t; The state change rate of multi-scale components is calculated as follows: Where ΔX i (t) is the state change rate of the multi-scale components of the state data X of the i-th single battery at time t, ψ is a nonlinear transformation function used to emphasize the key features in the change rate, and τ is the independent variable of the integration; The state time series of a single battery is constructed in the form of:

4. A lithium-ion battery balancing strategy system according to claim 1, characterized in that: The imbalance identification module includes a similarity matrix construction unit, a cluster center determination unit and an imbalance index calculation unit; the similarity matrix construction unit is used to standardize the state time series of the monomer battery, and construct a similarity matrix between the monomer batteries according to the standardized state time series; the cluster center determination unit is used to determine a cluster center according to the similarity matrix between the monomer batteries by minimizing the state difference criterion, and the imbalance index calculation unit is used to calculate the imbalance index of the monomer battery according to the state time series of the monomer battery and the state time series of the cluster center.

5. A lithium-ion battery balancing strategy system according to claim 4, characterized in that: The construction logic of the similarity matrix between single cells is: ij =(S i (t)-S j (t)) T ∑ -1 (S i (t)-S j (t)), where D ij is the similarity between the ith single cell and the jth single cell, ∑ is the inverse matrix of the covariance matrix, which is used to adjust the weight of the state difference when calculating the distance; A cluster center is determined by minimizing the state difference criterion. The calculation logic is: Among them, C is the cluster center, N is the total number of single batteries, argmin i () is to select the value of i so that the target Minimum; The calculation logic of the imbalance index of a single cell is: Among them, U i is the imbalance index of the ith single cell, D iC is the similarity between the ith single cell and the cluster center C, κ and λ are adjustment parameters used to adjust the amplification and attenuation characteristics of the imbalance index.

6. A lithium-ion battery balancing strategy system according to claim 1, characterized in that: The priority sorting module includes a cost function generating unit, an optimization objective function generating unit and a priority sorting unit; the cost function generating unit is used to define the transmission cost function between pairs of single cells according to the imbalance index of the single cells; the optimization objective function generating unit is used to construct an optimization objective function according to the transmission cost function to minimize the total cost of energy transmission; the priority sorting unit is used to generate a priority sorting result by sorting in ascending order according to the solution result of the optimization objective function according to the transmission cost function value.

7. A lithium-ion battery balancing strategy system according to claim 6, characterized in that: The calculation logic of the transmission cost function is: Among them, Co ij is the transmission cost between the i-th single cell and the j-th single cell, the i-th single cell is a high-voltage battery, and the j-th single cell is a low-voltage battery; The calculation logic of the optimization objective function is: Among them, F is the optimization objective function, x kl is a decision variable, indicating whether to select the single battery pair (k, l) for energy transmission, x kl =1 means selecting the single cell pair (k, l) for transmission, x kl =0 means that the single cell pair (k, l) is not selected for transmission, λ is the penalty coefficient, which is used to control the influence weight of the constraint penalty term on the optimization objective function, PT kl is the constraint penalty term, which represents the penalty value of the battery pair that does not transmit; The priority sorting result is expressed as: Priority = Sort {(k, l) | x kl =1, by Co kl }, where Priority is the priority sorting result and Sort is the ascending function.

8. A lithium-ion battery balancing strategy system according to claim 1, characterized in that: The dynamic energy allocation module includes a transmission rate calculation unit and a transmission current calculation unit; the transmission rate calculation unit is used to determine the energy transmission rate between the unbalanced battery pairs using a dynamic energy transmission strategy; The transmission current calculation unit is used to calculate the transmission current according to the determined energy transmission rate.

9. A lithium-ion battery balancing strategy system according to claim 8, characterized in that: The calculation logic of the transmission rate is: Among them, r(t) is the transmission rate at time t, δ is the preset maximum allowable current, which is used to limit the upper limit of the transmission current, and α is the time constant, which is used to smooth the change of the transmission rate.

10. A lithium-ion battery balancing strategy system according to claim 9, characterized in that: The calculation logic of the transmission current is: Among them, I(t+1) is the transmission current at time t+1, η is the learning rate, which is used to control the response speed of current adjustment, and λ is the temperature influence factor, which is used to slow down the transmission when the temperature rises.

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