A method, apparatus, device and storage medium for determining a balanced battery cell

Abstract

The present application relates to the technical field of battery equalization, and discloses a kind of equalization cell determination method, device, equipment and storage medium, method includes: equalization circuit attribute is parameterized model to the cell attribute and is expressed, and equalization variable vector is defined, and equalization variable vector is the vector that whether each cell participates in the independent variable group that equalization is characterized;Parameterized model and equalization variable vector are used to generate objective function based on, and objective function is used to make the capacity of equalization after cell reach target capacity and the energy of external power supply that equalization consumes is least;Equalization variable vector is optimized and solved based on objective function, and equalization vector is obtained, and equalization vector is used to determine whether each cell participates in equalization.The present application improves the identification accuracy of the equalization cell needed.

Description

Technical Field

[0001] This invention relates to the field of battery balancing technology, specifically to a method, apparatus, device, and storage medium for determining balancing battery cells. Background Technology

[0002] When batteries are used in groups, inconsistencies in the cells can easily occur during long-term operation, leading to problems such as overcharging, over-discharging, capacity decay, and shortened lifespan. The cell balancing methods of related technologies rely on the voltage deviation of individual cells to determine the balancing needs, which cannot reflect the actual capacity differences of the cells. The balancing strategy based on the SOC difference is affected by temperature, aging, and sampling noise, and the accuracy of SOC estimation is difficult to guarantee, resulting in inaccurate judgment of the cells that need to be balanced. Summary of the Invention

[0003] This invention provides a method, apparatus, device, and storage medium for determining equalized battery cells, in order to solve the problem of inaccurate identification of battery cells that need to be equalized.

[0004] In a first aspect, the present invention provides a method for determining balanced battery cells, the method comprising: representing the battery cell attributes and the balanced circuit attributes using a parameterized model, and defining a balanced variable vector, wherein the balanced variable vector is a vector composed of independent variables characterizing whether each battery cell participates in the balanced process; generating an objective function based on the parameterized model and the balanced variable vector, wherein the objective function is used to ensure that the capacity of all balanced battery cells reaches the target capacity and the energy consumed by the external power supply during the balanced process is minimized; and optimizing the balanced variable vector based on the objective function to obtain a balanced vector, wherein the balanced vector is used to determine whether each battery cell participates in the balanced process.

[0005] In some optional implementations, the objective function is generated based on a parameterized model and an equalization variable vector, including: constructing a first sub-function based on the parameterized model and the equalization variable vector to calculate the target capacity, the current remaining capacity of each cell, and the variance of the energy change after equalization of each cell; constructing a second sub-function based on the parameterized model and the equalization variable vector to calculate the energy consumption of each cell to the external power source after equalization; and weighted summing the first and second sub-functions to obtain the objective function.

[0006] In some optional implementations, the cell attributes and balancing circuit attributes are represented by parameterized models, including: the number of battery packs, the number of cells, the number of cells with balancing limits, the cell voltage, the remaining capacity of the cells, the target capacity, the balancing efficiency of the balancing circuit, the power supply of the balancing circuit, the cell balancing power, and the battery balancing current.

[0007] In some alternative implementations, the first sub-function is represented as:

[0008] In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The target capacity of each battery cell Indicates the first The remaining capacity of each battery cell Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to form the equilibrium variable vector Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Whether the discharge equalization of each battery cell is enabled.

[0009] In some alternative implementations, the second sub-function is represented as:

[0010] In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to form the equilibrium variable vector Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Is discharge equalization enabled for each battery cell? Indicates the first The charging efficiency coefficient of the energy-saving cell charging equalization circuit. Indicates the first The discharge efficiency coefficient of the energy-saving cell discharge equalization circuit.

[0011] In some optional implementations, the equilibrium variable vector is optimized based on the objective function, including: obtaining the equilibrium constraints; and optimizing the equilibrium variable vector using the Lagrange multiplier method based on the equilibrium constraints and the objective function.

[0012] In some optional implementations, the equalization constraints are obtained, including at least one of the following: a total upper limit condition for the number of equalization cells during charging, a total upper limit condition for the number of equalization cells during discharging, a constraint condition that the equalization circuit obtains energy only from an external power source, a constraint condition that the power of the equalization circuit is less than or equal to the power of the power source, a constraint condition that the number of equalization cells in each battery pack is limited, and a constraint condition that the cell state is unique.

[0013] Secondly, the present invention provides a battery cell equalization determination device, comprising: a model representation module for parametrically representing the battery cell attributes and equalization circuit attributes, and defining an equalization variable vector, wherein the equalization variable vector is a vector composed of independent variables characterizing whether each battery cell participates in equalization; an objective function module for generating an objective function based on the parametric model and the equalization variable vector, wherein the objective function is used to ensure that the capacity of all equalized battery cells reaches the target capacity and the energy consumed by the external power supply during equalization is minimized; and an optimization solution module for optimizing the equalization variable vector based on the objective function to obtain an equalization vector, wherein the equalization vector is used to determine whether each battery cell participates in equalization.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof.

[0016] The technical solution provided by this invention has the following advantages: This invention first establishes a unified parametric model for various attributes of the battery cells and balancing circuits, constructing a balancing variable vector. This fully quantifies multi-dimensional physical parameters such as cell capacity, balancing efficiency, and hardware power, solving the problem that traditional balancing strategies rely solely on single voltage or SOC data to determine the balancing target and cannot quantify the actual capacity differences of the cells. Based on this model, a dual-objective function is constructed, simultaneously considering two core indicators: cell balancing consistency and external power supply energy loss. The capacity variance quantifies the cell balancing effect, and the balancing circuit energy consumption quantifies operational losses. These are weighted to form a unified optimization objective, avoiding the shortcomings of existing solutions that only pursue cell consistency while ignoring energy consumption and generating numerous redundant balancing actions. By optimizing the balancing variable vector as a whole, a globally optimal balancing execution scheme can be output in one go, accurately selecting cells that need charging balancing, discharging balancing, or remaining idle. This balancing action matches the actual capacity difference of the cells, significantly reducing invalid balancing operations. The overall scheme does not rely on SOC difference thresholds or voltage deviation thresholds, which are susceptible to accuracy interference. While ensuring cell capacity consistency, it reduces energy consumption during the balancing process, effectively improving the balancing operation efficiency of the battery management system and reducing the daily maintenance costs of the battery pack. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a first method for determining a balanced battery cell according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a battery cell determination device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] According to an embodiment of the present invention, an embodiment of a method for determining equalized battery cells is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] This embodiment provides a method for determining equalized battery cells, which can be used in the aforementioned mobile terminals, such as mobile phones, tablets, etc. (the executing entity is described in conjunction with the actual situation). Figure 1This is a flowchart of a method for determining equalized battery cells according to an embodiment of the present invention. The process includes the following steps: Step S101: The cell attributes and equalization circuit attributes are represented by a parameterized model, and an equalization variable vector is defined. The equalization variable vector is a vector composed of independent variables that characterize whether each cell participates in the equalization process. Step S102: Generate an objective function based on the parameterized model and the equalization variable vector. The objective function is used to ensure that the capacity of the equalized cells reaches the target capacity and the energy consumed by the external power supply is minimized. Step S103: Based on the objective function, optimize the equilibrium variable vector to obtain the equilibrium vector, which is used to determine whether each cell participates in the equilibrium.

[0024] Specifically, after long-term charge and discharge cycles, the usable capacity of a single cell in a vehicle power battery pack will exhibit discrete deviations. The balancing determination of related technologies often relies on a single dimension of voltage or state of charge to select cells to be balanced, which is prone to judgment bias and excessive balancing energy consumption. The embodiments of the present invention solve this problem by globally optimizing the balancing determination.

[0025] First, the vehicle battery management system collects all hardware and cell static and dynamic parameters of the entire battery system and completes standardized parameter mapping modeling. Cell attributes refer to physical parameters that directly characterize the energy storage and operating status of a cell, including the remaining capacity of a single cell, the rated capacity of the cell, and the terminal voltage of the single cell. Balancing circuit attributes are the inherent hardware parameters of the bidirectional balancing topology, covering the charging and discharging balancing efficiency of each branch, the rated output power of the balancing circuit, and the balancing operating current of a single branch. After modeling, the balancing variable vector is defined. The balancing variable vector consists of a set of binary Boolean variables. Each set of variables in the vector corresponds to the charging balancing enable state and the discharging balancing enable state of a single cell. The variable values ​​are only 0 and 1, where 1 represents the corresponding balancing branch being on and 0 represents the branch remaining off. Taking a passenger vehicle power battery system with 96 cells as an example, the system arranges the two sets of Boolean variables corresponding to the 96 cells in an orderly manner, forming a balancing variable vector with a dimension of 192, completely covering the balancing operating state of all cells in the entire battery pack.

[0026] After completing all parameter modeling and variable definition, the next step is to construct the objective function for multi-objective fusion. There are two core objectives in vehicle battery balancing: firstly, to reduce the difference between the remaining capacity of all cells and the unified target capacity; and secondly, to reduce the total energy loss of the external auxiliary power supply during the balancing process. These two objectives have a natural trade-off; prioritizing optimization in one dimension will lead to performance degradation in the other. This invention transforms the two optimization objectives into quantifiable mathematical terms, then superimposes these two terms to obtain a uniquely solvable comprehensive objective function.

[0027] After the objective function is constructed, a constrained numerical solution process is initiated. Global optimization calculations are performed on all binary independent variables within the equilibrium variable vector, ultimately outputting the optimal equilibrium vector. The equilibrium vector perfectly matches the dimension of the previously defined equilibrium variable vector. Each set of values ​​within the vector directly corresponds to the equilibrium working mode of a single battery cell. Technicians can directly read the vector values ​​to complete the cell equilibrium task allocation. For the aforementioned 96-cell battery pack scenario, after the solution is completed, the charging equilibrium flag for some cells outputs 1, corresponding to the execution of supplementary energy equalization; the discharging equilibrium flag for some cells outputs 1, corresponding to the execution of energy release equalization; the remaining cells have both flags set to 0, indicating that no equalization operation is required.

[0028] Through the technical means provided in this invention, multi-dimensional performance improvements can be observed. All cell capacity differences are incorporated into a unified quantitative standard, avoiding the distortion problems caused by single voltage or state of charge indicators, significantly improving cell consistency optimization. The total energy consumption of the external power supply during the balancing process is included in the optimization boundary, and the calculation process actively avoids redundant balancing actions without gain, resulting in a significant reduction in system energy loss for the same degree of consistency improvement. The global synchronous solution mode outputs the balancing allocation scheme for all cells in the entire battery pack at once, eliminating the need for segmented iterative selection of cells to be balanced, reducing the balancing scheduling calculation time, and making it more suitable for the hardware computing power limits of real-time calculations in vehicle battery management systems.

[0029] In some optional implementations, step S101 above includes: Step a1 involves parametrically representing the number of battery packs, the number of cells, the number of cells with equalization limits, the cell voltage, the remaining capacity of the cells, the target capacity, the equalization efficiency of the equalization circuit, the power of the equalization circuit, the equalization power of the cells, and the equalization current of the battery.

[0030] Specifically, in large-scale series energy storage or vehicle power battery pack operation scenarios, the physical parameters of cells, equalization circuits, and battery packs are coupled with each other. The embodiments of the present invention complete the digital characterization of the physical quantities of the entire battery system, covering all key parameters of the battery pack cluster, individual cells, and bidirectional equalization branches, and complete the quantitative packaging one by one to prepare for subsequent optimization calculations.

[0031] This invention completes the assignment and modeling of macroscopic architecture parameters for the system. The complete battery management system consists of multiple cascaded battery packs, and the number of battery packs is defined as follows: m The number of battery cells includes the total number of battery cells and the number of battery cells included in each battery pack. N Record as the total number of battery cells. Indicates the first m The number of cells contained in a battery pack.

[0032] Regarding the limit on the number of cells for equalization, the maximum number of cells that the system allows to simultaneously enable charging equalization, the maximum number of cells that can simultaneously enable discharging equalization, and the upper limit of equalization cells that each independent battery pack hardware channel can handle are recorded, thereby anchoring the hardware boundaries for subsequent optimization calculations. This indicates the upper limit of the number of charging equalization cells in the battery management system. This indicates the upper limit of the number of discharge equalization cells in the battery management system. Indicates the first m The upper limit of the number of balanced batteries in the battery pack.

[0033] For each individual cell, a ternary mathematical structure is used to encapsulate the state parameters. , Indicates the first n The cell voltage of the energy-saving cell. Indicates the first n The remaining capacity of the battery cells in the energy-saving cells. Indicates the first n The target capacity for balancing the battery cells is determined by periodically sampling the cell voltage using the BMS analog front-end circuit. The remaining capacity is updated in real time based on the battery's ampere-hour integral combined with an aging correction coefficient. The target capacity can be selected as the average remaining capacity of all cells in the entire battery pack as a unified benchmark, providing a reference for subsequent capacity deviation quantification calculations.

[0034] For bidirectional active balancing hardware branches, a quadrupole structure is used to achieve a unified representation of circuit characteristics and control variables. The quaternion contains a charging equalization efficiency coefficient. Discharge equalization efficiency coefficient Two types of inherent hardware parameters, the coefficient of which was determined in advance by the power loss calibration test of the equalization circuit; at the same time, two sets of Boolean control variables were included, one of which The corresponding cell charging balancing branch enable state has a value of 1 indicating that the branch is on and replenishing energy, and a value of 0 indicating that the branch is off. Another Boolean variable... The enabling state of the corresponding discharge balancing branch follows the same value selection rules as the charging branch. By sequentially concatenating the two sets of Boolean variables corresponding to all cells, a complete balancing variable vector can be formed. B .

[0035] In addition, the power of the external power supply to the equalization circuit is recorded synchronously. Single cell branch balanced power Based on the power-voltage conversion relationship Obtain the balanced operating current on the battery side This completes the closed-loop characterization of the parameters in the power and current dimensions of the balanced branch. All parameters are digitally encapsulated and stored according to the above triplet and quadruple structures, providing a complete and standardized set of computational parameters for the subsequent construction of the objective function.

[0036] In some optional implementations, step S102 above includes: Step b1: Construct a first sub-function based on the parameterized model and the equalization variable vector to calculate the target capacity, the current remaining capacity of each cell, and the variance of the energy change after equalization of each cell; Step b2: Based on the parameterized model and the equalization variable vector, construct a second sub-function to calculate the energy consumption of the external power source after the equalization of each cell; Step b3: Weighted summation of the first and second sub-functions to obtain the objective function.

[0037] Specifically, after the aforementioned system-wide parametric modeling and definition of the equilibrium variable vector, the battery equilibrium optimization problem is transformed into a numerical solution problem involving the collaborative optimization of multiple indicators. Cell capacity consistency and external power consumption during the equilibrium process are two types of optimization indicators with a balancing relationship. Extremum processing of a single indicator will lead to the degradation of the performance of the other indicator. Therefore, this embodiment of the invention constructs sub-functions independently by splitting the two evaluation dimensions, and then introduces weight coefficients to complete the fusion, forming a comprehensive objective function that takes into account both types of demands, thereby guiding the global optimization of the equilibrium state.

[0038] First, a first sub-function is constructed based on the cell consistency evaluation. This first sub-function uses the system's preset equalization target capacity as a unified reference benchmark. Based on quantitative parameters stored in the parameterized model, such as the real-time remaining capacity of each cell, the power of the equalization branch, and the equalization duration, combined with the charge / discharge equalization start / stop status of each cell recorded by the equalization variable vector, it deduces the capacity change of a single cell after the equalization energy transfer operation. This quantifies the discrete deviation between the actual capacity and the target capacity after all cells are equalized. The first sub-function uniformly represents the dispersion of the entire battery pack's cell capacity in the form of variance. A higher variance value indicates a greater difference in energy storage levels between cells and a worse equalization consistency effect; conversely, a lower variance value indicates a higher degree of convergence in cell capacity. This forms a quantifiable and iteratively convergent consistency evaluation benchmark.

[0039] After completing the first sub-function, the second sub-function for the energy consumption dimension of the equalization circuit is constructed. The second sub-function focuses on the total energy loss generated by the external power supply during the operation of the bidirectional equalization circuit. It utilizes the inherent charging and discharging efficiency coefficients and single-branch equalization power of each equalization branch in the parameterized model, combined with the branch operating status identified by the equalization variable vector, to traverse all equalization branches corresponding to all cells and accumulate the energy loss output by the external power supply during the operating period of each branch. The output value of the second sub-function directly corresponds to the total energy consumption of the external power supply under a single complete equalization process. The magnitude of the value intuitively reflects the energy utilization level of the equalization operation, providing a quantitative evaluation basis for suppressing redundant equalization actions.

[0040] The two types of sub-functions differ significantly in their dimensions and numerical ranges, making direct superposition impossible. Therefore, two sets of independent weighting coefficients are introduced to scale the outputs of the two sub-functions. These weighting coefficients can be flexibly adjusted according to the emphasis of the battery system application scenario. If the scenario has stringent requirements for cell consistency, the weight of the first sub-function is increased; if the scenario has higher requirements for energy consumption control and maintenance cost management, the weight of the second sub-function is increased. The complete comprehensive objective function is obtained by linearly summing the values ​​of the two weighted and scaled sub-functions.

[0041] The embodiments of this invention can achieve decoupled analysis of consistency and energy consumption indicators through layered construction and weighted fusion modeling, which makes it easier for developers to adjust and optimize the bias separately for different application scenarios. The weighted fusion processing method does not require changes to the underlying parameter model and variable definition framework. By adjusting the weight parameters, it can be adapted to the operating conditions of various battery systems such as energy storage power stations, vehicle power batteries, and low-speed energy storage devices. While ensuring the convergence effect of cell equalization, it simultaneously constrains the power loss of the equalization process, avoiding the inherent defects of poor consistency or high energy consumption in traditional single-dimensional equalization strategies.

[0042] In some alternative implementations, the first sub-function is represented as:

[0043] In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The target capacity of each battery cell Indicates the first The remaining capacity of each battery cell Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to form the equilibrium variable vector Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Whether the discharge equalization of each battery cell is enabled.

[0044] The second sub-function is represented as:

[0045] In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to form the equilibrium variable vector Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Is discharge equalization enabled for each battery cell? Indicates the first The charging efficiency coefficient of the energy-saving cell charging equalization circuit. Indicates the first The discharge efficiency coefficient of the energy-saving cell discharge equalization circuit.

[0046] Therefore, the objective function can be expressed as:

[0047] In the formula, and This represents the weighting coefficient.

[0048] Specifically, the first sub-function provided in this embodiment of the invention is used to quantify the capacity dispersion of all cells in the entire battery pack after the equalization operation is completed. All parameters in the formula are taken from the system parameterization model built above. N This represents the total number of cells connected in series in the entire battery system. The summation operation covers all individual cells to achieve a globally consistent evaluation. and The difference characterizes the inherent deviation between the cell and the standard capacity before the equalization operation is initiated.

[0049] To achieve balanced and constant output power for a single branch, T The product of the two is the total duration of a single balancing process, and represents the total energy that can be transferred under the premise of no branch loss. , To form a binary Boolean identifier for the equilibrium variable vector, it only takes the value 1. It can calculate effective values.

[0050] Characterizing the first n The energy increase or decrease after cell balancing is calculated by subtracting the initial cell capacity from the energy increase or decrease after balancing, resulting in the remaining deviation between the cell capacity and a unified benchmark after balancing. The deviation is squared and summed across all cells, and the variance represents the overall battery capacity dispersion. Therefore, the smaller the value of the first sub-function, the higher the degree of convergence in cell capacity after balancing. The squaring operation amplifies the impact of cells with large capacity deviations on the overall performance. The optimization process prioritizes eliminating cells with larger deviations, avoiding the shortcomings of traditional threshold balancing strategies that only handle cells with differences exceeding the threshold and ignore the continuous accumulation of small-amplitude dispersions.

[0051] The second sub-function is used to fully calculate the total power loss of the external auxiliary power supply output during a single balancing process. This formula also traverses all cell branches to accumulate power consumption, and its core incorporates the inherent conversion efficiency coefficient of the bidirectional balancing circuit. , The energy conversion efficiencies corresponding to the charging and discharging equalization branches are pre-entered into the parameter model by offline power loss calibration tests.

[0052] When the battery cell performs charging equalization When the external power supply outputs electrical energy, it is delivered to the battery cell via a converter. Power loss occurs during the conversion process, and the total energy of the actual input power supply needs to be divided by the charging efficiency coefficient to compensate for the loss. When the battery cell performs discharge equalization... At that time, the energy released by the battery cell is fed back through the converter, only The energy consumed by the balancer can offset the external power output, with the remainder dissipated as heat. The difference between the energy consumption correction terms of the two types of balancing branches yields the net external power consumption corresponding to the balancing action of a single branch. The total power loss of the entire balancing system is obtained by summing the energy consumption of all cell branches. A lower value for the second sub-function indicates higher energy utilization efficiency during balancing, signifying that a non-redundant charging / discharging balancing branch is activated.

[0053] Finally, the two sets of sub-functions are multiplied by their independent weight coefficients and then linearly summed to obtain the global optimization objective function, where... and Manually configured weighting coefficients are used to adjust the priority of the two types of optimization indicators during the solution process, which can improve performance in the vehicle power battery scenario. Prioritize ensuring cell capacity consistency to improve overall vehicle capacity; the application scope for long-term energy storage power stations can be expanded. The numerical values ​​focus on suppressing balanced energy consumption and reducing long-term operation and maintenance electricity costs.

[0054] Based on the above composite objective function, which fully integrates the two evaluation dimensions of cell consistency and circuit energy consumption, the solution process simultaneously minimizes the capacity variance and the total external power loss. The output balance variable vector can select the optimal charge-discharge balance combination, avoiding the problems of insufficient consistency or excessive energy consumption that exist in traditional single-index balance strategies.

[0055] In some optional implementations, step S103 above includes: Step c1: Obtain the equilibrium constraints; Step c2: Based on the equilibrium constraints and objective function, the equilibrium variable vector is optimized using the Lagrange multiplier method.

[0056] Specifically, after constructing the objective function, the equilibrium optimization problem to be solved still belongs to an unconstrained optimization model. Directly carrying out numerical optimization will easily break through the physical boundaries of battery pack hardware channels, equilibrium power, and individual cell operating states, resulting in an equilibrium scheme that is only mathematically optimal and cannot adapt to the actual operating conditions of the hardware. Therefore, this implementation method completes the constrained global optimization solution in two steps. First, it fully collects the hardware and operational constraints at all levels of the system. Then, it integrates the constraints into the objective function to carry out iterative optimization and outputs the optimal equilibrium variable vector that satisfies all hardware constraints.

[0057] The constraints are derived from the inherent thresholds and operating specifications of the four levels of the battery management system, independent battery packs, individual cells, and bidirectional equalization power supply. All constraints can be digitally expressed using the quantitative parameters stored in the pre-parameterized model.

[0058] In this embodiment of the invention, the obtained equalization constraints include at least one of the following: the total upper limit of the number of equalization cells during charging, the total upper limit of the number of equalization cells during discharging, the constraint that the equalization circuit obtains energy only from an external power source, the constraint that the power of the equalization circuit is less than or equal to the power of the power source, the constraint that the number of equalization cells in each battery pack is limited, and the constraint that the cell state is unique.

[0059] The maximum limit on the number of charging equalization nodes is expressed as follows:

[0060] This indicates the upper limit of the number of charging equalization cells in the battery management system.

[0061] The maximum limit for the number of discharge equalization stages is expressed as follows:

[0062] This indicates the upper limit of the number of discharge equalization cells in the battery management system.

[0063] The limitation that the equalization circuit obtains energy only from an external power source is expressed as follows:

[0064] Specifically, , This indicates the power consumed by the external power source during charging equalization. This represents the power consumed by the external power supply during discharge equalization, therefore Indicates the first n The net power consumption of the cell balancing branch to the external balancing power supply.

[0065] After all the balancing branches of the entire battery system are superimposed, the external auxiliary power supply can only output electrical energy. The total energy released by the battery cells is not allowed to flow back or back into the balancing power supply. If the summation result is greater than 0, it means that the power supply outputs net electrical energy. If the summation result is less than 0, the energy fed back by the battery cells during discharge is more than the energy consumed during charging, resulting in energy backflow, which will damage the balancing power supply, DC-DC converter, and other hardware. Therefore, this inequality is used to impose a hard boundary limit.

[0066] Furthermore, the constraint that the power of the equalization circuit (i.e., the net power consumed by all equalization branches from the external equalization power supply) is less than or equal to the power of the power supply is expressed as follows:

[0067] Furthermore, embodiments of the present invention also constrain each cell to be located within the battery pack, and the balancing quantity of each battery pack must meet the balancing quantity limit of the corresponding battery pack. The constraint conditions for each cell are expressed as follows:

[0068] Indicates the first m The starting cell number of each battery pack.

[0069] Furthermore, embodiments of the present invention also constrain the cell state of each cell to be unique, as shown below:

[0070] This constraint is used to ensure that each cell can only be in a charging, discharging, or idle state at any given time.

[0071] Based on the above constraints, at the system level, there are upper limits on the maximum number of cells that can be simultaneously activated for charging equalization and discharging equalization. At the single battery pack level, the total number of cells that can be simultaneously activated for equalization operations in each battery pack's hardware channels is limited to prevent excessive parallel equalization branches in a single pack from causing bus voltage drop and device overheating. At the equalization power supply level, power boundaries are set, stipulating that the total output power of all equalization branches must not exceed the rated power of the external auxiliary power supply, while also constraining the equalization circuit to only obtain energy from the external power supply, prohibiting reverse energy flow from the cells to the power supply and preventing equipment damage. At the individual cell level, state mutual exclusion constraints are set, ensuring that any cell can only be in one of three states at any given time: charging equalization, discharging equalization, or idle, and that charging and discharging equalization branches are not allowed to be activated simultaneously. All of the above constraints are uniformly transformed into standardized numerical inequalities, serving as the set of boundary constraints for subsequent optimization solutions.

[0072] After obtaining all constraints, embodiments of the present invention can employ intelligent algorithms such as the Lagrange multiplier method, projected gradient and simplified gradient algorithms, constrained variable scaling algorithms, genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, and reinforcement learning to optimize and solve the problem.

[0073] This invention uses the Lagrange multiplier method as an example to iteratively solve the equilibrium variable vector. The core idea of ​​this algorithm is to transform the originally constrained optimization problem with multiple inequalities into an iteratively computeable augmented Lagrange unconstrained function by introducing Lagrange multipliers corresponding to the constraint terms, thereby eliminating the fragmented constraints of multiple types of boundary conditions on the optimization process.

[0074] The algorithm's iterative process has a clear progressive logic. It uses the multiplier method to solve the optimization problem, first supplementing the variable constraints:

[0075]

[0076] The augmented lagrange function of the original optimization problem is determined as follows:

[0077] The augmented Lagrangian function uses the equilibrium variable vector Lagrange multipliers corresponding to each set of constraints Iteration gain coefficient As input variables, the whole is composed of the original integrated objective function. It consists of three coupled parts: inequality constraint fitting terms, equality constraint fitting terms, and so on. It is a global sequential index for all constraints, used to uniformly and orderly label all inequality and equality constraints within the model, enabling the identification of each constraint residual in the augmented Lagrangian function. Corresponding Lagrange multipliers One-to-one binding; where the index range to All inequality constraints at the system, battery pack, and cell levels are addressed using the maximum value function. Constructing a quadratic penalty term can impose incremental penalties on solutions that violate inequalities such as hardware power, upper limits on the number of equalizers, and power flow direction during the iteration process, while also introducing... The multiplier is adjusted to update the benchmark. Index interval. to Corresponding to the constraints of mutual exclusion of cell charging and discharging, single-packet equalization capacity matching, etc., first pass through linear terms Establish the relationship between the multipliers and the constraint residuals, and then superimpose them. The convergence constraint is strengthened by doubling the squared residual penalty term through the gain coefficient. By uniformly controlling the penalty intensity of all penalties, the constrained equilibrium optimization problem, which originally contained multiple sets of inequalities and equality boundary restrictions, is completely transformed into an unconstrained iterative solution. During iterative optimization, the optimization objectives of cell capacity consistency and balanced energy consumption are taken into account simultaneously, continuously suppressing equilibrium scheduling solutions that exceed the hardware operating boundary, and ensuring that the equilibrium vector output after iterative convergence fully matches the various physical constraints of the battery system.

[0078] Then, unconstrained extremum solutions were performed on the augmented Lagrangian function to solve the unconstrained problem. Output the temporary optimal solution of the equilibrium variable vector in the current iteration round. .

[0079] The second step is to calculate the residual values ​​corresponding to all constraints for this set of temporary solutions. The residual is used to characterize the degree of deviation between the current solution and the hardware boundary.

[0080]

[0081] If the residual is less than the preset convergence threshold If k iterations are completed, the current equilibrium variable vector is output as the final optimal equilibrium vector. B If the residual does not meet the convergence criterion, the Lagrange multipliers and gain adjustment coefficients corresponding to each set of constraints are updated to correct the convergence speed. Then, the process jumps to the unconstrained extreme value solution step and repeats the iteration until the convergence criterion is met.

[0082] The specific steps are as follows: hour, , Indicates the convergence rate. The gain coefficient is used to adjust the convergence speed; The iteration count k and the Lagrange multipliers are updated synchronously: ; ; .

[0083] The equilibrium vector output by this solution process ensures that each set of Boolean identifiers synchronously conforms to the multi-level hardware constraints of the entire system, battery pack, cells, and equalization power supply. It avoids scheduling schemes that violate physical operating rules, such as exceeding the equalization channel limit, power overload, or simultaneous activation of bidirectional equalization for cells. It achieves globally optimal equalization allocation with both consistency and energy consumption objectives, exhibits stable computational convergence, and is suitable for the real-time equalization scheduling needs of various large-scale series battery systems, including vehicle-mounted power batteries and stationary energy storage power stations.

[0084] This embodiment also provides a cell equalization determination device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0085] This embodiment provides a device for determining the equalization of battery cells, such as... Figure 2 As shown, it includes: The model representation module 201 is used to perform parameterized model representation of cell attributes and equalization circuit attributes, and to define the equalization variable vector. The equalization variable vector is a vector composed of independent variables that characterize whether each cell participates in equalization. Objective function module 202 is used to generate an objective function based on the parameterized model and the equalization variable vector. The objective function is used to ensure that the capacity of the equalized cells reaches the target capacity and the energy consumed by the external power supply is minimized. The optimization solution module 203 is used to optimize the equilibrium variable vector based on the objective function to obtain the equilibrium vector, which is used to determine whether each cell participates in the equilibrium.

[0086] In some optional implementations, the model representation module 201 includes: parametric model representation of the number of battery packs, the number of battery cells, the number of battery cell balancing limits, the battery cell voltage, the remaining capacity of the battery cells, the target capacity, the balancing efficiency of the balancing circuit, the power supply of the balancing circuit, the battery cell balancing power, and the battery balancing current.

[0087] In some alternative implementations, the objective function module 202 includes: The first sub-function unit is used to construct a first sub-function based on the parameterized model and the equalization variable vector to calculate the target capacity, the current remaining capacity of each cell, and the variance of the energy change after equalization of each cell. The second sub-function unit is used to construct a second sub-function based on the parameterized model and the equalization variable vector to calculate the energy consumption of each cell to the external power supply after equalization. The objective function construction unit is used to perform a weighted summation of the first and second sub-functions to obtain the objective function. In some alternative implementations, the optimization solution module 203 includes: Constraint element, used to obtain equilibrium constraints; The optimization unit is used to optimize the equilibrium variable vector based on equilibrium constraints and objective function using the Lagrange multiplier method. The apparatus provided in this embodiment of the invention can execute the method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0088] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0089] The following is a detailed reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0090] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0091] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the methods of the embodiments of the present invention.

[0092] Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0093] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0094] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0095] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for determining balanced battery cells, characterized in that, The method includes: The attributes of the battery cells and the attributes of the balancing circuit are represented by a parameterized model, and a balancing variable vector is defined. The balancing variable vector is a vector composed of independent variables that characterize whether each battery cell participates in the balancing process. An objective function is generated based on the parameterized model and the equalization variable vector. The objective function is used to ensure that the capacity of the equalized cells reaches the target capacity and the energy consumed by the external power supply is minimized. The equilibrium variable vector is optimized based on the objective function to obtain the equilibrium vector, which is used to determine whether each cell participates in the equilibrium process.

2. The method according to claim 1, characterized in that, The generation of the objective function based on the parameterized model and the equilibrium variable vector includes: Based on the parameterized model and the equilibrium variable vector, a first sub-function is constructed to calculate the target capacity, the current remaining capacity of each cell, and the variance of the energy change after equilibrium of each cell. Based on the parameterized model and the equalization variable vector, a second sub-function is constructed to calculate the energy consumption of the external power source after the equalization of each cell. The target function is obtained by weighted summation of the first sub-function and the second sub-function.

3. The method according to claim 2, characterized in that, The parametric model representation of the cell attributes and equalization circuit attributes includes: A parameterized model is used to represent the number of battery packs, the number of cells, the number of cells with equalization limits, the cell voltage, the remaining capacity of the cells, the target capacity, the equalization efficiency of the equalization circuit, the power supply of the equalization circuit, the cell equalization power, and the battery equalization current.

4. The method according to claim 3, characterized in that, The first sub-function is represented as: In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The target capacity of each battery cell Indicates the first The remaining capacity of each battery cell Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to compose the equilibrium variable vector. Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Whether the discharge equalization of each battery cell is enabled.

5. The method according to claim 4 or 3, characterized in that, The second sub-function is represented as: In the formula, Indicates the total number of battery cells. Indicates the cell serial number. Indicates the first The balanced power of each battery cell Indicates the equilibrium time. and Used to compose the equilibrium variable vector. Indicates the first Is charging equalization enabled for each battery cell? Indicates the first Is discharge equalization enabled for each battery cell? Indicates the first The charging efficiency coefficient of the energy-saving cell charging equalization circuit. Indicates the first The discharge efficiency coefficient of the energy-saving cell discharge equalization circuit.

6. The method according to claim 1, characterized in that, The optimization solution of the equilibrium variable vector based on the objective function includes: Obtain the equilibrium constraints; Based on the equilibrium constraints and the objective function, the equilibrium variable vector is optimized using the Lagrange multiplier method.

7. The method according to claim 6, characterized in that, The conditions for obtaining the equilibrium constraints include: The conditions for obtaining the total upper limit of the number of charging equalization cells, the total upper limit of the number of discharging equalization cells, the limitation that the equalization circuit only obtains energy from the external power source, the limitation that the power of the equalization circuit is less than or equal to the power of the power source, the limitation of the number of equalization cells in each battery pack, and the limitation of the unique state of the battery cell are all considered.

8. A device for determining equalized battery cells, characterized in that, The device includes: The model representation module is used to perform parameterized model representation of cell attributes and equalization circuit attributes, and to define an equalization variable vector, which is a vector composed of independent variables that characterize whether each cell participates in equalization. The objective function module is used to generate an objective function based on the parameterized model and the equalization variable vector. The objective function is used to ensure that the capacity of the equalized cells reaches the target capacity and the energy consumed by the external power supply is minimized. The optimization solution module is used to optimize the equilibrium variable vector based on the objective function to obtain the equilibrium vector, which is used to determine whether each cell participates in the equilibrium process.

9. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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