Battery pack capacity equalization method and device, terminal equipment and storage medium

Abstract

The application is suitable for the technical field of battery management, and provides a capacitor equalization method and device of a battery pack, a terminal equipment and a storage medium. The method comprises: acquiring current state data of a plurality of battery cells in the battery pack. A quantum random number generator generates a true random number by using quantum vacuum fluctuation as a source of randomness. According to the current state data of the plurality of battery cells and the true random number, a target battery cell that needs to be subjected to capacitor equalization processing and an equalization processing operation type of the plurality of battery cells are determined. According to the equalization processing operation type, capacitor equalization processing is performed on the target battery cell, and balanced state data of the target battery cell is obtained. The quantum random number generator generates unpredictable true random numbers, and the equalization weight value and the operation type are dynamically calculated in combination with the real-time state data of the battery cells, so that the problem of overuse of a specific battery cell caused by a fixed equalization strategy is effectively avoided, the randomness and fairness of capacitor equalization are improved, and the service life of the battery pack is prolonged.

Description

Technical Field

[0001] This application belongs to the field of battery management technology, and in particular relates to a method, apparatus, terminal equipment and storage medium for capacitor balancing of a battery pack. Background Technology

[0002] In battery pack management practices, conventional capacitor balancing methods mainly rely on fixed balancing strategies, such as polling mechanisms or balancing methods based on preset priorities. Such fixed strategies often result in certain cells being repeatedly selected for charging and discharging operations, while other cells participate less in the balancing process. This leads to overuse of some cells, significantly accelerating their aging rate and reducing the overall battery pack's lifespan.

[0003] Meanwhile, existing technologies generally adopt a static capacity balancing mode, which predicts battery status and formulates balancing schemes by analyzing historical operating data. However, the actual working environment of battery packs has highly dynamic characteristics, including temperature fluctuations, load changes, and real-time adjustments to usage patterns. This makes it impossible for prediction models based on historical data to accurately capture current state changes, resulting in balancing strategies that are difficult to adapt to the dynamically evolving battery pack environment, leading to an overall decline in battery pack lifespan and a decrease in system stability. Summary of the Invention

[0004] This application provides a battery pack capacitance balancing method, apparatus, terminal device, and storage medium, which can solve the technical problem that existing battery pack capacitance balancing methods often use fixed balancing strategies or static capacity balancing modes, resulting in greater differences in cell aging within the battery pack and shorter battery pack lifespan.

[0005] In a first aspect, embodiments of this application provide a capacitor balancing method for a battery pack, the method comprising:

[0006] Obtain the current status data of multiple cells in the battery pack;

[0007] A truly random number is generated by using quantum vacuum fluctuations as the source of randomness through a quantum random number generator;

[0008] Based on the current status data of the multiple battery cells and the true random number, determine the target battery cells that need to undergo capacitor balancing and the type of capacitor balancing operation.

[0009] According to the capacitor equalization processing operation type, the target cell is subjected to capacitor equalization processing to obtain the equalized state data of the target cell.

[0010] In one possible implementation of the first aspect, determining the target battery cell requiring capacitor balancing and the capacitor balancing operation type based on the current state data of the plurality of battery cells and the true random number includes:

[0011] Based on the current state data of each battery cell, the balanced weight value of each battery cell and the total weight value of the candidate battery cell set of the same type are calculated.

[0012] The capacitor equalization processing operation type is determined based on the total weight value of the candidate cell set of the same type; wherein, the capacitor equalization processing operation type includes discharging operation and charging operation;

[0013] The equilibrium weight value of each battery cell is converted into a probability distribution value to obtain the cumulative probability interval corresponding to each battery cell;

[0014] The target cell among the plurality of cells is determined based on the capacitor equalization processing operation type, the cumulative probability interval corresponding to each cell, and the true random number.

[0015] In one possible implementation of the first aspect, the current state data includes: current battery level, current health status, current internal resistance, and current temperature.

[0016] The step of calculating the balanced weight value of each battery cell and the total weight value of the candidate battery cell set of the same type based on the current state data of each battery cell includes:

[0017] Based on the current power value of each battery cell and the preset balanced power value, determine the basic weight value of each battery cell;

[0018] The equalization protection coefficient of each battery cell is determined based on its current health value, current internal resistance value, and current temperature value.

[0019] The basic weight value of each cell is multiplied by the equalization protection coefficient to obtain the equalization weight value of each cell.

[0020] The balanced weight values ​​of all cells in the same type of candidate cell set are added together to obtain the total weight value of the same type of candidate cell set; wherein, all cells with positive basic weight values ​​are regarded as the first type of candidate cell set, and all cells with negative basic weight values ​​are regarded as the second type of candidate cell set.

[0021] In one possible implementation of the first aspect, determining the equalization protection coefficient for each of the battery cells based on their current health value, current internal resistance value, and current temperature value includes:

[0022] Based on the current health value of each battery cell and the preset retirement threshold, the health coefficient of each battery cell is calculated.

[0023] The temperature coefficient of each battery cell is calculated based on the current temperature value of each battery cell and the preset temperature threshold.

[0024] The internal resistance coefficient of each cell is calculated based on its current internal resistance value and nominal internal resistance value.

[0025] The balance protection coefficient for each cell is obtained by multiplying the health coefficient, the temperature coefficient, and the internal resistance coefficient.

[0026] In one possible implementation of the first aspect, determining the capacitor equalization processing operation type based on the total weight value of the candidate cell set of the same type includes:

[0027] The total weight value of the first set of candidate cells of the same type and the total weight value of the second set of candidate cells of the same type are added together to obtain the total weight value of the candidates;

[0028] The total weight value of the first set of candidate cells of the same type is compared with the total weight value of the candidates to obtain a first probability value, and the total weight value of the second set of candidate cells of the same type is compared with the total weight value of the candidates to obtain a second probability value;

[0029] If the first probability value is greater than the second probability value, the capacitor equalization operation type is determined to be the discharge operation;

[0030] If the second probability value is greater than the first probability value, the capacitor equalization processing operation type is determined to be the charging operation.

[0031] In one possible implementation of the first aspect, determining the target cell among the plurality of cells based on the capacitor equalization processing operation type, the cumulative probability interval corresponding to each cell, and the true random number includes:

[0032] Based on the capacitor equalization processing operation type, a target set of cells that need to undergo capacitor equalization processing is determined from the first set of candidate cells of the same type or the second set of candidate cells of the same type.

[0033] The cells in the target cell set corresponding to the cumulative probability interval in which the true random number is located are determined as the target cells.

[0034] In one possible implementation of the first aspect, converting the equilibrium weight value of each of the battery cells into a probability distribution value to obtain the cumulative probability interval corresponding to each of the battery cells includes:

[0035] The ratio of the balanced weight value of each cell in each candidate cell set of the same type to the total weight value of each candidate cell set of the same type is used as the probability distribution value corresponding to each cell.

[0036] Based on the probability distribution value of each battery cell, the cumulative probability interval corresponding to each battery cell is determined.

[0037] Secondly, embodiments of this application provide a capacitor equalization device for a battery pack, comprising:

[0038] The acquisition module is used to acquire the current status data of multiple cells in the battery pack;

[0039] The generation module is used to generate truly random numbers by using quantum vacuum fluctuations as the source of randomness through a quantum random number generator;

[0040] The determination module is used to determine the target battery cell and the type of capacitor equalization operation that need to be performed on the multiple battery cells based on the current status data of the multiple battery cells and the true random number.

[0041] The processing module is used to perform capacitor equalization processing on the target cell according to the capacitor equalization processing operation type, and obtain the equalized state data of the target cell.

[0042] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the capacitor balancing method for the battery pack described in any of the above claims.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the capacitor balancing method for the battery pack described in any of the preceding claims.

[0044] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the battery pack capacitance balancing method described in any of the first aspects above.

[0045] The beneficial effects of the embodiments in this application compared with the prior art are:

[0046] This application provides a method for capacitor balancing in a battery pack, comprising: first, acquiring the current state data of multiple cells in the battery pack; then, generating a true random number using a quantum random number generator with quantum vacuum fluctuations as the source of randomness; and determining the target cell requiring capacitor balancing and the type of capacitor balancing operation based on the current state data of the multiple cells and the true random number; finally, performing capacitor balancing on the target cell according to the type of capacitor balancing operation to obtain the balanced state data of the target cell. By using a quantum random number generator to generate unpredictable true random numbers using quantum vacuum fluctuations, and combining this with real-time cell state data to dynamically calculate the balancing weight value and operation type, the method effectively avoids the problem of overuse of specific cells caused by fixed balancing strategies, improves the randomness and fairness of capacitor balancing, and thus extends the service life of the battery pack. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic flowchart of a battery pack capacitor balancing method provided in an embodiment of this application;

[0049] Figure 2 This is a schematic diagram of the structure of a capacitor equalization device for a battery pack according to an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0051] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0052] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0053] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0054] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0055] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0057] In the capacitor balancing process of the battery management system, the traditional fixed balancing strategy is implemented by polling or prioritization, which causes specific cells to be repeatedly selected for charging and discharging operations, thereby causing local accelerated aging. At the same time, the static capacity balancing mode relies on historical data to predict the battery state and cannot respond to the real-time dynamic changes in the battery pack operating environment, resulting in a disconnect between balancing decisions and actual needs.

[0058] Furthermore, the random sequences provided by pseudo-random number generators (PRNGs) have predictability limitations, restricting the stochastic optimization capability of balancing strategies and affecting the uniformity of load distribution. These issues directly lead to the overall degradation of battery pack lifespan, decreased system stability, and potential safety hazards.

[0059] For example, when an electric vehicle battery management system operates under frequent start-stop conditions in urban environments, the battery pack is exposed to a dynamic environment of temperature fluctuations and sudden load changes. In this situation, a fixed polling strategy causes cells located at the edge of the battery pack to continuously bear the balancing task, resulting in a significantly higher aging rate than cells in the central area. The static capacity balancing mode, based on historical charge-discharge curves, predicts state parameters that deviate from the actual operating conditions, leading to inaccurate timing of balancing operations. Furthermore, the balancing sequence generated by pseudo-random numbers is easily identified by external interference sources due to the algorithm's periodicity, causing specific cells to be repeatedly selected, further exacerbating aging inconsistencies. In this scenario, battery pack capacity degradation accelerates, requiring frequent system intervention for maintenance to maintain basic functionality.

[0060] If the above problems are not addressed, the aging differences among cells within the battery pack will continue to widen, leading to a non-linear decrease in overall usable capacity and an increase in system failure rate. Ineffective equalization operations will increase the thermal management burden and may trigger thermal runaway risks. Simultaneously, the battery pack's lifespan will be shortened, maintenance complexity will increase, and the long-term reliability of the system will be seriously threatened. To address this, this application provides a capacitor equalization method for battery packs.

[0061] Please see Figure 1 , Figure 1 This is a schematic flowchart of a battery pack capacitance balancing method according to an embodiment of this application. As an example and not a limitation, this method is applied to the process of capacitance balancing of multiple cells in a battery pack, and includes:

[0062] S11. Obtain the current status data of multiple cells in the battery pack.

[0063] S12. A true random number is generated by using quantum vacuum fluctuations as the source of randomness through a quantum random number generator.

[0064] S13. Based on the current status data and true random numbers of multiple battery cells, determine the target battery cells and the type of capacitor balancing operation that need to be performed on the multiple battery cells.

[0065] S14. Based on the capacitor equalization processing operation type, perform capacitor equalization processing on the target cell to obtain the equalized state data of the target cell.

[0066] A battery pack is an electrical energy storage device that can provide specific voltage and capacity output. It can be composed of multiple cells combined in series, parallel, or mixed connections. Currently, battery packs are widely used in electric vehicles, energy storage systems, and other fields. The battery cell is the basic building block of a battery pack and the smallest component that enables the conversion between electrical energy and chemical energy.

[0067] In this embodiment, the battery pack capacitor equalization method first acquires the current state data of multiple cells in the battery pack. The current state data of a cell refers to a multi-dimensional set of parameters reflecting the real-time operating state of the cell. This data may include the current State of Charge (SOC) value collected by the power monitoring module, the current State of Health (SOH) value recorded by the battery management system, the current internal resistance value acquired by the internal resistance measurement unit, and the current temperature value detected by the temperature sensor, etc. The purpose is to provide comprehensive state information for equalization decisions. This data can be used to evaluate the performance of the cells and determine whether capacitor equalization processing is necessary.

[0068] Then, a quantum random number generator is used to generate truly random numbers, employing quantum vacuum fluctuations as the source of randomness. A quantum random number generator is a device that uses the principles of quantum mechanics to generate random numbers. Unlike traditional algorithm-based pseudo-random number generators, quantum random number generators can generate truly random numbers; their randomness originates from the inherent uncertainty of the quantum world, preventing the algorithm from being reverse-engineered. In quantum field theory, even in a vacuum state of absolute zero where no matter exists, the quantum field still exhibits tiny energy fluctuations. These fluctuations are one of the fundamental characteristics of quantum mechanics, and the quantum random number generator can utilize these quantum vacuum fluctuations as the source of randomness to generate truly random numbers. Truly random numbers refer to truly random and unpredictable sequences of numbers. Unlike pseudo-random numbers, truly random numbers are non-repeatable and unpredictable, providing a more reliable and random basis for decision-making in capacitor equalization processing.

[0069] In this embodiment, the process of generating unpredictable random numbers using quantum physical phenomena is described. The quantum random number generator uses random signals generated by quantum vacuum fluctuations and converts them into truly random numbers in digital form through specific conversion and processing circuits. Its main purpose is to provide an absolutely uncopyable random source to eliminate the risk of predictability, ensure physical non-cloning, and improve the randomness and fairness of capacitor equalization processing.

[0070] Furthermore, based on the current state data of multiple cells and true random numbers, the target cell requiring capacitor balancing and the type of capacitor balancing operation can be determined. Capacitor balancing can be understood as addressing the issue of inconsistent capacity among cells in a battery pack. It involves adjusting the charge distribution among cells using specific control strategies to make their capacities more uniform, thereby improving the overall performance and lifespan of the battery pack. The target cell is the cell that requires capacitor balancing during the process, determined based on the current state data and true random numbers. This can be a cell with excessively high or low capacity. The type of capacitor balancing operation refers to the specific balancing method adopted for the target cell. Common examples include charging (charging cells with lower capacity) and discharging (discharging cells with higher capacity).

[0071] In this embodiment, the battery cells are initially screened based on current state data to identify cells with significant capacity differences as candidate target cells. Then, true random numbers are used to further screen and rank the candidate target cells to determine the final target cells. The type of capacitor balancing operation is determined based on the current state of the target cells. A state-based random sampling algorithm is used to dynamically select the cells to be processed and their corresponding operation methods. For example, the selection probability is set based on the deviation between the state data and a preset reference value, and random numbers are used for sampling. This is mainly to achieve a uniform distribution of the balanced load to avoid overuse of specific cells. It should be understood that this method frees the balancing decision-making process from the limitations of a static capacity allocation mode, enabling it to adapt to dynamic changes in the battery pack environment. The quantum-level random decision-making mechanism fundamentally eliminates the risk of pseudo-random numbers being predictable, providing true random optimization capabilities for the balancing strategy. The selection of target cells, combining real-time state and true random numbers, ensures a uniform distribution of the balanced load throughout the battery pack, preventing specific cells from aging prematurely due to frequent charging and discharging, thereby effectively extending the overall lifespan of the battery pack and maintaining performance stability.

[0072] Finally, the target cell undergoes capacitor balancing according to the operation type, and the post-balancing state data is obtained. The post-balancing state data refers to the new values ​​of the target cell's charge, voltage, and other state parameters after capacitor balancing. For example, if the operation type is charging, the target cell is charged through a charging circuit while monitoring its voltage, current, and other parameters; charging stops when the predetermined balancing target is reached. If the operation type is discharging, the target cell is discharged through a discharging circuit while monitoring its parameter changes; discharging stops when the predetermined balancing target is reached. During the capacitor balancing process, the post-balancing state data of the target cell is collected in real time for subsequent analysis and evaluation of the balancing effect.

[0073] It is understood that this application provides a battery pack capacitor balancing method, comprising: first, acquiring the current state data of multiple cells in the battery pack; then, generating a true random number using a quantum random number generator with quantum vacuum fluctuations as the source of randomness; and determining the target cell requiring capacitor balancing and the type of capacitor balancing operation based on the current state data of the multiple cells and the true random number; finally, performing capacitor balancing on the target cell according to the type of capacitor balancing operation to obtain the balanced state data of the target cell. By using a quantum random number generator to generate unpredictable true random numbers using quantum vacuum fluctuations, and combining this with real-time cell state data to dynamically calculate the balancing weight value and operation type, the overuse of specific cells caused by fixed balancing strategies is effectively avoided, improving the randomness and fairness of capacitor balancing, thereby extending the battery pack's lifespan.

[0074] In one possible implementation, based on the current state data of multiple battery cells and a true random number, the target battery cells requiring capacitor balancing and the type of capacitor balancing operation are determined, including:

[0075] Based on the current state data of each cell, the balanced weight value of each cell and the total weight value of the candidate cell set of the same type are calculated.

[0076] The type of capacitor balancing operation is determined based on the total weight value of the candidate cell set of the same type. This type of operation includes discharging and charging.

[0077] The balanced weight value of each cell is converted into a probability distribution value, thus obtaining the cumulative probability interval for each cell.

[0078] Based on the capacitor equalization processing operation type, the cumulative probability interval corresponding to each cell, and the true random number, the target cell among multiple cells is determined.

[0079] The equalization weight value is a data point used to measure the importance or urgency of each cell in the capacitor equalization process, i.e., a numerical value quantifying the equalization priority of the cells. It can be calculated based on the real-time state parameters of the cells. The larger the weight value, the more the cell needs capacitor equalization. The purpose is to give high-demand cells a higher selection probability and avoid ineffective charging and discharging. The candidate cell set of the same type can be understood as a set of cells divided according to the direction of the equalization operation. It can be achieved by using the sign of the basic weight value as the grouping basis, aiming to summarize the overall demand intensity of similar operations. The cumulative probability interval is the probability distribution interval formed after normalizing the equalization weight values. It can be calculated by accumulating probabilities, aiming to provide a state-based weight distribution for random selection. Target cell determination refers to the process of locating the cell within the cumulative probability interval based on the operation type and true random numbers. This can be achieved by matching true random numbers generated by a quantum random number generator with the probability interval, aiming to ensure that the selection process is both based on state weights and completely random.

[0080] Specifically, in this embodiment, firstly, the equalization weight value of each cell and the total weight value of the candidate cell set of the same type are calculated based on the current state data of the cells, quantifying the real-time state of the cells into comparable values. Next, the capacitor equalization operation type is determined based on the proportion of the total weight value, ensuring that the operation direction aligns with the imbalance trend of the battery pack. Subsequently, the equalization weight value is converted into a cumulative probability interval, establishing a mapping between state priority and random selection. Finally, the target cell is determined by combining the operation type, cumulative probability interval, and true random number, achieving state-driven random selection. This process ensures that the equalization strategy dynamically adapts to changes in the battery pack state, while avoiding the local aging and pseudo-random predictability problems caused by fixed strategies.

[0081] Through the above solution, this application effectively solves the problem of accelerated aging of some cells due to frequent charging and discharging caused by the fixed balancing strategy. At the same time, it overcomes the defect that the static capacity balancing mode is difficult to adapt to dynamic changes, ensuring that the capacitor balancing process is based on the actual state requirements of the cells and extending the overall service life of the battery pack.

[0082] In one possible implementation, the current status data includes: current battery level, current health status, current internal resistance, and current temperature.

[0083] Based on the current state data of each cell, the balanced weight value of each cell and the total weight value of the candidate cell set of the same type are calculated, including:

[0084] The basic weight value of each cell is determined based on its current power value and the preset balanced power value.

[0085] The equalization protection coefficient for each cell is determined based on its current health value, current internal resistance value, and current temperature value.

[0086] The basic weight value of each cell is multiplied by the equalization protection coefficient to obtain the equalization weight value of each cell.

[0087] The balanced weight values ​​of all cells in the same type of candidate cell set are added together to obtain the total weight value of the same type of candidate cell set; among them, all cells with positive basic weight values ​​are regarded as the first type of candidate cell set, and all cells with negative basic weight values ​​are regarded as the second type of candidate cell set.

[0088] The basic weight value is an indicator that quantifies the degree of deviation in the battery cell's charge level. It can be implemented using the absolute value or normalized difference between the current charge level and the preset equalization charge level, and its purpose is to characterize the urgency of the battery cell's equalization operation. In this embodiment, the formula for calculating the basic weight value can be:

[0089] ;

[0090] in, Basic weight value, This represents the current charge level of the battery cell. This is a preset balanced power value. A positive number indicates that the battery cell needs to be discharged; A negative number indicates that the battery cell needs to be charged.

[0091] Balanced protection coefficient The dynamic adjustment factor, reflecting the cell's safety status, can be determined using a lookup table or empirical formula based on health, internal resistance, and temperature parameters. For example, the coefficient value is automatically reduced when the health value falls below a preset threshold. This aims to avoid performing balancing operations when the cell's condition is abnormal. (Balancing weight value) The final balanced priority index, modulated by safety factors, can be obtained by multiplying the basic weight value and the balanced protection coefficient. Its purpose is to achieve a balance between power demand and safety protection. In this embodiment, .

[0092] The same type of candidate cell set refers to the grouping of cells based on the sign of the basic weight value. Cells with positive basic weight values ​​can be classified into the first same type of candidate cell set, and cells with negative basic weight values ​​can be classified into the second same type of candidate cell set. Discharging operation is performed on cells in the first same type of candidate cell set, and charging operation is performed on cells in the second same type of candidate cell set. The purpose is to ensure that the equalization operation type matches the cell state.

[0093] Specifically, the solution proposed in this application achieves refined control of cell balancing weights by integrating multi-dimensional state parameters and introducing a dynamic protection mechanism. First, a basic weight value is determined based on the deviation between the current cell charge value and the preset balancing charge value; this value directly reflects the urgency of cell balancing. Second, a balancing protection coefficient is dynamically generated by combining health, internal resistance, and temperature parameters. This coefficient maintains a high value when the battery is in good condition to support balancing operations and automatically decreases when the condition is abnormal to suppress balancing demands. Subsequently, the basic weight value is multiplied by the balancing protection coefficient to obtain the balancing weight value. This multiplication and fusion mechanism enables real-time modulation of balancing priority by safety factors. Finally, based on the sign of the basic weight value, the cells are divided into first and second candidate cell sets of the same type, and the total weight value is calculated separately for each set. This ensures that discharging and charging operations are performed in cell groups with higher and lower charge values, respectively, while the balancing strategy adapts to real-time changes in the battery pack state due to the influence of the protection coefficient.

[0094] Through the above scheme, this application can comprehensively consider the battery health status, internal resistance characteristics and temperature conditions when determining the cell balancing priority, effectively avoiding the balancing operation under abnormal conditions such as low cell health, excessive temperature or excessive internal resistance, thereby reducing the battery aging rate and eliminating potential safety hazards.

[0095] In practical applications, some of the embodiments described above in this application propose to determine the equalization protection coefficient of each cell to comprehensively evaluate the safety boundary of the battery state. However, in the implementation process, if the protection coefficient is calculated by relying only on a single state parameter or simple rules, it is impossible to fully integrate the multi-dimensional dynamic effects of health, internal resistance and temperature. This may result in the equalization operation being performed under conditions of insufficient battery health, abnormal temperature or deteriorated internal resistance, which may exacerbate the risk of battery aging or cause safety hazards.

[0096] In one possible implementation, a balancing protection coefficient for each cell is determined based on its current health value, current internal resistance value, and current temperature value, including:

[0097] The health coefficient of each cell is calculated based on its current health value and the preset retirement threshold.

[0098] The temperature coefficient of each cell is calculated based on its current temperature value and the preset temperature threshold.

[0099] The internal resistance coefficient of each cell is calculated based on its current internal resistance and nominal internal resistance.

[0100] Multiplying the health coefficient, temperature coefficient, and internal resistance coefficient together yields the balance protection coefficient for each cell.

[0101] The health coefficient is an assessment indicator that quantifies the risk of remaining battery life. It can be implemented using a linear decay function or an exponential decay function, such as the ratio or difference function between the health value and a preset retirement threshold. Its purpose is to significantly reduce the priority of equalization operations when the battery health is close to the retirement threshold, preventing additional stress on aging batteries. In practical applications, cells with poor health values ​​should avoid frequent, high-current equalization. In this embodiment, the formula for calculating the health coefficient can be:

[0102] ;

[0103] in, For health index, This represents the current health value of the battery cell. A preset retirement threshold is set. For example, in this embodiment, It can be 0.7.

[0104] The temperature coefficient is a dynamic parameter reflecting the thermal safety of a battery. It can be implemented using a piecewise constant function or a continuously decaying function. For example, it can maintain a constant value within a preset temperature threshold range and decrease proportionally when the temperature exceeds the range. The purpose is to avoid performing equalization operations under extreme high or low temperature environments, effectively mitigating the risk of thermal runaway. In this embodiment, an optimal temperature range is defined. (For example, 15℃-35℃), if the temperature exceeds this optimal range, a severe penalty will be imposed, namely:

[0105] like ,but ;

[0106] like ,but ;

[0107] like ,but .

[0108] in, For temperature coefficient, This is the current temperature value of the battery cell. To preset the minimum temperature threshold, This is the preset maximum temperature threshold.

[0109] The internal resistance coefficient is a implicit indicator of the degree of aging inside the battery. It can be implemented using the reciprocal of the internal resistance ratio or a nonlinear mapping function, such as a function of the ratio of the current internal resistance to the nominal internal resistance value. Its purpose is to limit the equalization current intensity and eliminate potential safety hazards when the internal resistance deviates from the nominal internal resistance value. In this embodiment, the formula for calculating the internal resistance coefficient can be:

[0110] ;

[0111] in, The internal resistance coefficient is... This represents the current internal resistance value of the battery cell. This is the nominal internal resistance value.

[0112] The equilibrium protection coefficient refers to a safety assessment value that integrates multi-dimensional state parameters through a nonlinear coupling mechanism. It is obtained by multiplying the health coefficient, temperature coefficient, and internal resistance coefficient. Its purpose is to ensure that the protection coefficient can be reduced dominantly when any state parameter deteriorates, thereby dynamically adapting to the complex operating conditions of the battery pack.

[0113] Specifically, this embodiment first calculates a health coefficient based on the current health value of each cell and a preset retirement threshold to accurately quantify the risk of remaining battery life; secondly, it calculates a temperature coefficient based on the current temperature value and a preset temperature threshold to form a real-time response mechanism for the battery's thermal state; simultaneously, it calculates an internal resistance coefficient based on the current internal resistance value and the nominal internal resistance value to capture implicit signals of internal aging and faults in the battery; finally, it multiplies the three values ​​to generate a balance protection coefficient. This coefficient, as a nonlinear coupling result of multi-dimensional parameters, is used to calculate the subsequent balance weight value, thereby adaptively adjusting the balance strategy during the dynamic changes in battery state and prioritizing the safety boundary of the worst-performing cell.

[0114] Through the above technical solution, this embodiment can comprehensively integrate multi-dimensional state parameters such as battery health, temperature and internal resistance, dynamically generate a balancing protection coefficient, and effectively avoid performing balancing operations when the battery health is insufficient, the temperature is abnormal or the internal resistance is deteriorated, thereby reducing the risk of battery aging and improving system safety.

[0115] In some of the embodiments described above in this application, a method for determining the capacitor balancing operation type based on the aggregate weight value of candidate cells of the same type is proposed. However, in its implementation, if the operation type selection mechanism lacks dynamic adaptability and makes decisions based solely on preset thresholds or fixed logic, it will be unable to adjust in a timely manner according to the actual changes in the battery pack's state. This can easily lead to the balancing operation being overly concentrated on a single type (such as continuous discharge or charging), causing some cells to be frequently charged and discharged, thus accelerating aging. At the same time, due to the overly deterministic nature of the decision-making process, it is difficult to achieve true stochastic optimization, thereby reducing balancing efficiency.

[0116] In one possible implementation, the capacitor equalization operation type is determined based on the total weight value of the candidate cell set of the same type, including:

[0117] The total weight value of the first set of candidate cells of the same type is added to the total weight value of the second set of candidate cells of the same type to obtain the total weight value of the candidates.

[0118] The total weight value of the first set of candidate cells of the same type is compared with the total weight value of the candidates to obtain a first probability value, and the total weight value of the second set of candidate cells of the same type is compared with the total weight value of the candidates to obtain a second probability value.

[0119] If the first probability value is greater than the second probability value, the capacitor equalization processing operation type is determined to be a discharge operation.

[0120] If the second probability value is greater than the first probability value, the capacitor equalization operation type is determined to be a charging operation.

[0121] Specifically, the total weight value of the first set of candidate cells of the same type is the sum of the balanced weight values ​​of all cells in the first set of candidate cells of the same type, and the total weight value of the second set of candidate cells of the same type is the sum of the balanced weight values ​​of all cells in the second set of candidate cells of the same type. The total candidate weight value is a comprehensive index obtained by adding the total weight values ​​of the first set of candidate cells of the same type and the second set of candidate cells of the same type. It can be implemented using arithmetic addition and aims to quantify the overall balanced demand intensity of the battery pack. The first probability value can be understood as the proportion of the total weight value of the first set of candidate cells of the same type to the total candidate weight value. It can be calculated using floating-point division and aims to reflect the relative demand for discharge operations. Similarly, the second probability value is the proportion of the total weight value of the second set of candidate cells of the same type to the total candidate weight value. It can be implemented using floating-point division and aims to reflect the relative demand for charging operations.

[0122] In this embodiment, the total weight value of the first set of candidate cells of the same type and the total weight value of the second set of candidate cells of the same type are first added together to obtain the total candidate weight value. This comprehensively integrates the balancing demand intensity of all candidate cells in the battery pack, providing a global benchmark for operation type decision-making. Subsequently, the total weight value of the first set of candidate cells of the same type is compared with the total candidate weight value to obtain a first probability value, and the total weight value of the second set of candidate cells of the same type is compared with the total candidate weight value to obtain a second probability value. This makes the operation type selection directly related to the relative demand intensity of the current battery state, avoiding decision bias caused by fixed threshold settings. Finally, a discharge operation is determined when the first probability value is greater than the second probability value, or a charging operation is determined when the second probability value is greater than the first probability value. This ensures that the balancing operation always prioritizes the most urgent demand direction, preventing the problem of over-processing of cells caused by improper operation type selection, thereby forming a complete decision-making mechanism that dynamically adapts to changes in the battery pack state.

[0123] As a specific implementation method, the solution of this application is implemented as follows: The central processing unit in the battery management system collects the total weight value of the first and second sets of candidate cells of the same type in real time, calculates the total weight value of the candidates through the arithmetic operation unit, and generates a first probability value and a second probability value using the proportional calculation module; when the first probability value is significantly higher than the second probability value, the system automatically triggers the discharge equalization circuit to release energy for high-capacity cells; conversely, when the second probability value is significantly higher than the first probability value, the system activates the charging equalization circuit to replenish energy for low-capacity cells. The entire process is dynamically switched by the control logic unit based on the probability comparison result.

[0124] Through the above technical solution, this embodiment can dynamically adjust the capacitor equalization processing operation type according to the actual state of the battery pack, avoid excessive concentration of equalization operation on a single type, effectively reduce the accelerated aging problem caused by frequent charging and discharging of some cells, enhance the random adaptability of the decision-making process, and improve the overall efficiency of capacitor equalization processing and the service life of the battery pack.

[0125] In one possible implementation, the target cell among multiple cells is determined based on the capacitor equalization processing operation type, the cumulative probability interval corresponding to each cell, and a true random number, including:

[0126] Based on the type of capacitor equalization operation, the target set of cells that need to be capacitor equalized is determined from either the first set of candidate cells of the same type or the second set of candidate cells of the same type.

[0127] The cells in the target cell set corresponding to the cumulative probability interval of the true random number are identified as target cells.

[0128] Specifically, the capacitor balancing operation type is either a discharging operation or a charging operation, which can be implemented using a dynamic decision-making mechanism based on cell status data. The purpose is to distinguish between different types of balancing needs. The cumulative probability interval can be understood as a probability distribution interval calculated based on the cell balancing weight values. It can be obtained by summing the ratio of the balancing weight value of each cell to the total weight value of the candidate cell set of the same type, reflecting the urgency of the cell status. The target cell set is the set of candidate cells selected according to the capacitor balancing operation type. This can be understood as selecting the cell set with positive base weight values ​​when the operation type is discharging, or selecting the cell set with negative base weight values ​​when the operation type is charging, thus limiting the range of cells that need to be processed under the current operation type.

[0129] Specifically, in this embodiment, the target cell set is first determined based on the type of capacitor balancing operation. When the operation type is a discharge operation, cells are selected from the first set of candidate cells of the same type; when the operation type is a charging operation, cells are selected from the second set of candidate cells of the same type. This strictly limits the selection range to the cell set related to the current operation type. Subsequently, a quantum-generated true random number is matched with the cumulative probability interval. Since the cumulative probability interval is calculated based on the cell balancing weight value, cells in worse condition occupy a larger probability interval, and the probability of the true random number falling within this interval is higher. However, the unpredictability of the true random number ensures that the selection process is not fixed, realizing weighted random selection. This prioritizes the processing of cells in worse condition while avoiding the repeated selection of specific cells, effectively distributing the balancing load.

[0130] As a specific implementation method, the solution of this application is implemented as follows: During battery pack management, when the capacitor equalization operation type is determined to be a discharge operation, the system determines the target cell set from the first set of candidate cells of the same type; subsequently, the system obtains a true random number generated by a quantum random number generator and compares it with the cumulative probability interval of each cell, and determines the cell corresponding to the cumulative probability interval into which the true random number falls as the target cell for discharge equalization processing. For example, the quantum random number generator can be a random number generation chip based on semiconductor quantum dots, and the calculation of the cumulative probability interval is based on state parameters such as the cell's charge deviation, health status, and temperature, thereby ensuring that the selection of the target cell reflects both the actual state requirements of the cell and has unpredictability.

[0131] It should be understood that the above scheme effectively avoids the problem of overuse of specific cells caused by the fixed polling strategy, distributes the load to balance the load, and prevents accelerated aging of local cells due to frequent charging and discharging, thereby extending the overall service life and reliability of the battery pack.

[0132] Traditional battery pack capacitor balancing methods typically employ fixed balancing strategies or pseudo-random number adjustment mechanisms when determining target cells. However, in their implementation, if the probability distribution value is not normalized based on the total weight value of the same type of candidate cell set, the probability distribution will be inaccurate and will not be able to truly reflect the balancing demand weight of each cell. This will result in deviations when randomly selecting target cells, which may cause some cells to frequently participate in balancing and accelerate aging.

[0133] In one possible implementation, the equilibrium weight value of each battery cell is converted into a probability distribution value, resulting in the cumulative probability interval for each battery cell, including:

[0134] The ratio of the balanced weight value of each cell in each candidate cell set of the same type to the total weight value of each candidate cell set of the same type is used as the probability distribution value corresponding to each cell.

[0135] Based on the probability distribution value of each battery cell, determine the cumulative probability interval corresponding to each battery cell.

[0136] In practical applications, the probability distribution value is a relative proportion obtained by normalizing the equilibrium weight values. This ratio can be calculated using arithmetic division or a hardware accelerator to ensure that the sum of the probability distribution values ​​of all cells is 1, thereby accurately quantifying the relative equilibrium demand weight of each cell in the candidate set. Without normalization, absolute differences in weight values ​​can lead to over-amplification of high-demand cells or neglect of low-demand cells, undermining the fairness of random selection. The cumulative probability interval can be understood as a continuous range of values ​​formed by accumulating the probability distribution values ​​sequentially by cell. This interval can be constructed using an accumulator circuit or a software iterative algorithm. Its purpose is to provide a clear matching basis for true random numbers, ensuring that random numbers are strictly mapped to corresponding cells according to their weight proportions. Without constructing a cumulative interval, the random number matching process will not reflect weight differences, leading to a disconnect between the selection results and the actual equilibrium demand of the cells.

[0137] Specifically, in this embodiment, normalization is achieved by comparing the equilibrium weight value with the total weight value of the candidate cell set of the same type, ensuring that the probability distribution value accurately reflects the relative equilibrium demand intensity of each cell. Based on this, a continuous cumulative probability interval sequence is constructed according to the normalized probability distribution value, enabling the true random numbers generated by the quantum random number generator to be strictly matched to the corresponding cells according to the weight ratio. Since the normalization process eliminates the interference of the absolute value of the weights, the sum of the probability distribution values ​​is always 1, thus ensuring that the random selection process is entirely based on the actual equilibrium demand of the cells. Simultaneously, the continuous design of the cumulative probability interval makes the true random number matching process deterministic, avoiding selection bias caused by interval breaks. Overall, this embodiment works organically with the target cell determination mechanism in the aforementioned capacitor equalization method, solving the target cell selection bias problem caused by probability distortion through the synergy of normalized probability distribution and cumulative interval construction.

[0138] As a specific implementation method, the solution of this application is implemented as follows: During the battery pack balancing process, when the first candidate cell set of the same type contains three cells, their balancing weight values ​​are 0.3, 0.5, and 0.2, respectively, and the total weight value of the candidate cell set is 1.0; the balancing weight value of each cell is divided by the total weight value to obtain probability distribution values ​​of 0.3, 0.5, and 0.2; then, the probability distribution values ​​are accumulated according to the cell order to form cumulative probability intervals [0, 0.3), [0.3, 0.8), and [0.8, 1.0]. When the quantum random number generator outputs a true random number of 0.75, this value falls into the interval [0.3, 0.8), thus determining the cell corresponding to the balancing weight value of 0.5 as the target cell. In this embodiment, the normalization of the probability distribution values ​​ensures that the selection probability strictly corresponds to the weight ratio, and the construction of the cumulative probability interval enables the random number matching process to be executed accurately.

[0139] Through the above technical solution, this embodiment effectively avoids the target cell selection deviation caused by probability distribution distortion, and enables the balancing process to dynamically adapt to changes in the battery pack state. Specifically, the normalization process ensures that the balancing demand weight of each cell is accurately quantified, preventing cells with low health or high internal resistance from being frequently selected, thereby reducing the overcharging and discharging of some cells and slowing down the overall aging speed of the battery pack.

[0140] In practical applications, assuming a battery pack consisting of four cells (Cell1-Cell4) connected in series, its current state monitoring data is shown in Table 1. Table 1 shows the current state monitoring data of each cell in the battery pack.

[0141] Table 1 Current status monitoring data of each cell in the battery pack

[0142]

[0143] As shown in Table 1, the problem with this battery pack is that Cell3 has the highest SOC (making it more prone to overcharging), but the worst SOH (lowest capacity, high internal resistance), and also the highest temperature. Therefore, Cell3 is clearly the "weakest link" cell. Traditional fixed-capacitor balancing strategies might involve continuously discharging and balancing Cell3, or prioritizing its processing. This would further accelerate the degradation of Cell3, creating a vicious cycle.

[0144] For the aforementioned battery pack, the capacitor balancing method provided in the embodiments of this application is used to perform capacitor balancing processing on the battery pack, specifically as follows:

[0145] Step 1: Monitor the battery pack status and calculate the basic weight value of each cell. ).

[0146] Assumption ,but:

[0147] For Cell1: ;

[0148] For Cell2: ;

[0149] For Cell3: ;

[0150] For Cell4: .

[0151] Step 2: Calculate the equalization protection coefficient for each cell ( ).

[0152] Assumption =0.7, =35℃, =30mΩ, then:

[0153] For Cell1, ; ; ;but ;

[0154] For Cell2 ; ; ;but ;

[0155] For Cell3 ; ; ;but ;

[0156] For Cell4 ; ; ;but .

[0157] Step 3: Calculate the balance weight value of each cell ( ).

[0158] ; ; ; .

[0159] Therefore, the cells Cell1, Cell2, and Cell4 that need charging form the second set of candidate cells of the same type, and the total weight of the second set of candidate cells of the same type is [value missing]. Cell3, the cell that needs to be discharged, is the first candidate cell set of the same type. The total weight of the first candidate cell set of the same type is [value missing]. The total weight of the candidates is .

[0160] Step 4: Calculate the cumulative probability interval for each cell.

[0161] The probability distribution values ​​for each battery cell are as follows:

[0162] For Cell1: For Cell2: ;

[0163] For Cell3: For Cell4: ;

[0164] The cumulative probability interval for each cell in the second set of candidate cells of the same type is as follows: for Cell1: [0, 0.33); for Cell2: [0.33, 0.66); for Cell4: [0.66, 1.0]. Since there is only one cell, Cell3, in the first set of candidate cells of the same type, the cumulative probability interval for Cell3 is [0, 1].

[0165] Step 5: Determine the capacitor equalization processing operation type and the target cell.

[0166] The first probability value is ;

[0167] The second probability value is ;

[0168] Since the second probability value is greater than the first probability value, the current capacitor equalization operation type is a charging operation, and the second set of candidate cells of the same type is the target set of cells.

[0169] Assuming the true random number is R=0.45, then R=0.45 falls within the cumulative probability interval [0.33, 0.66) corresponding to Cell2, thus determining the target cell as Cell2. Subsequently, the Cell2 cell is charged, and then the state data of the Cell2 cell after equalization is obtained.

[0170] It should be noted that in this embodiment, the "short-board" cell is implicitly protected and flexibly handled. Continuing the example above, Cell3 (the short-board cell) is "protected" by the system during the weight calculation stage due to its poor state, preventing it from being forced to undergo high-current balancing most of the time. However, the system does not completely forget about it. When a specific random number is generated by true random number generator (e.g., R=0.25), or when Cell3's state parameters change slightly within the protection threshold, it still has a certain probability of being included in the balancing queue. This low-probability, flexible intervention can prevent the inconsistency from expanding and avoid "over-penalizing" the short-board cell.

[0171] Understandably, traditional battery pack capacitor balancing methods employ fixed polling (e.g., Cell1-Cell2-Cell4-Cell1-Cell2...), resulting in predictable and regular degradation of each cell. Some cells may always be balanced at specific temperatures and SOC states, and this patterned stress accelerates their aging. However, in this embodiment, the introduction of true random numbers transforms the balancing sequence into a random sequence (e.g., Cell2-Cell4-Cell2-Cell1-Cell4...). This non-patterned, unpredictable balancing stress, similar to the randomization of "rest periods," ensures that stress is evenly distributed across the entire battery pack, thereby slowing down the overall aging rate. Furthermore, this embodiment enhances system security and robustness. Because the balancing strategy's decision factors include unpredictable quantum random numbers, external attackers cannot predict the next balancing action by analyzing historical data, thus preventing timed attacks targeting specific cells.

[0172] It should also be noted that this application can utilize quantum random numbers to generate dynamic keys to encrypt the communication data of the battery management system (BMS). It also includes a server for remotely uploading and storing monitoring data. The server uses this data for simulation and learning, and generates battery capacity allocation strategies. These strategies can be combined with those generated by random numbers to select the optimal strategy for execution. The server establishes a closed loop of "monitoring-learning-optimization." It collects massive amounts of battery pack operating data, compares the data before and after the implementation of the dynamic capacity allocation strategy to evaluate the actual effect of the strategy, and optimizes accordingly. This optimization can improve specific numerical parameters, making the agent's "values" more accurate; it can also optimize the underlying model (such as digital twins) to make simulations and predictions more precise; and it can optimize the decision-making framework itself (such as state definitions), making the agent "more capable of thinking."

[0173] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0174] A battery pack capacitor balancing method corresponding to the above embodiment, Figure 2 This diagram illustrates the structure of a capacitor equalization device for a battery pack according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown.

[0175] Reference Figure 2 The capacitor equalization device 2 of the battery pack in this embodiment includes:

[0176] The acquisition module 21 is used to acquire the current status data of multiple cells in the battery pack.

[0177] The generation module 22 is used to generate a true random number by using quantum vacuum fluctuations as the source of randomness through a quantum random number generator.

[0178] The determination module 23 is used to determine the target cells and the type of capacitor balancing operation that need to be performed on multiple cells based on the current status data and true random numbers of multiple cells.

[0179] The processing module 24 is used to perform capacitor equalization processing on the target cell according to the capacitor equalization processing operation type, and obtain the equalized state data of the target cell.

[0180] Furthermore, module 23 is defined as including:

[0181] The weight calculation submodule is used to calculate the balanced weight value of each battery cell and the total weight value of the candidate battery cell set of the same type based on the current state data of each battery cell.

[0182] The operation type determination submodule is used to determine the capacitor equalization processing operation type based on the total weight value of the candidate cell set of the same type; among which, the capacitor equalization processing operation type includes discharge operation and charging operation.

[0183] The probability interval determination submodule is used to convert the balanced weight value of each battery cell into a probability distribution value, thereby obtaining the cumulative probability interval corresponding to each battery cell.

[0184] The target cell determination submodule is used to determine the target cell among multiple cells based on the capacitor equalization processing operation type, the cumulative probability interval corresponding to each cell, and a true random number.

[0185] Furthermore, the current status data includes: current battery level, current health level, current internal resistance, and current temperature. The weight calculation submodule includes:

[0186] The basic weight calculation unit is used to determine the basic weight value of each cell based on the current power value of each cell and the preset balanced power value.

[0187] The equalization protection coefficient calculation unit is used to determine the equalization protection coefficient of each cell based on the current health value, current internal resistance value, and current temperature value of each cell.

[0188] The equalization weight value calculation unit is used to multiply the basic weight value of each cell by the equalization protection coefficient to obtain the equalization weight value of each cell.

[0189] The total weight value calculation unit is used to add up the balanced weight values ​​of all cells in the same type of candidate cell set to obtain the total weight value of the same type of candidate cell set; wherein, all cells with positive basic weight values ​​are regarded as the first type of candidate cell set, and all cells with negative basic weight values ​​are regarded as the second type of candidate cell set.

[0190] The equalization protection factor calculation unit includes:

[0191] The health coefficient calculation subunit is used to calculate the health coefficient of each cell based on the current health value of each cell and the preset retirement threshold.

[0192] The temperature coefficient calculation subunit is used to calculate the temperature coefficient of each cell based on the current temperature value of each cell and the preset temperature threshold.

[0193] The internal resistance coefficient calculation subunit is used to calculate the internal resistance coefficient of each cell based on the current internal resistance value and the nominal internal resistance value of each cell.

[0194] The total coefficient calculation subunit is used to multiply the health coefficient, temperature coefficient, and internal resistance coefficient to obtain the balanced protection coefficient of each cell.

[0195] The operation type determination submodule includes:

[0196] The candidate total weight value calculation unit is used to add the total weight value of the first candidate cell set of the same type and the total weight of the second candidate cell set of the same type to obtain the candidate total weight value.

[0197] The probability value calculation unit is used to compare the total weight value of the first set of candidate cells of the same type with the total weight value of the candidates to obtain a first probability value, and to compare the total weight value of the second set of candidate cells of the same type with the total weight value of the candidates to obtain a second probability value.

[0198] The discharge operation determination unit is used to determine the capacitor equalization processing operation type as a discharge operation when the first probability value is greater than the second probability value.

[0199] The charging operation determination unit is used to determine the capacitor equalization processing operation type as a charging operation when the second probability value is greater than the first probability value.

[0200] The target cell determination submodule includes:

[0201] The target cell set determination unit is used to determine the target cell set that needs to undergo capacitor equalization processing from a first candidate cell set of the same type or a second candidate cell set of the same type, based on the capacitor equalization processing operation type.

[0202] The target cell determination unit is used to determine the cells in the target cell set corresponding to the cumulative probability interval of the true random number as the target cells.

[0203] The probability interval determination submodule includes:

[0204] The probability distribution value determination unit is used to take the ratio of the balanced weight value of each cell in each candidate cell set of the same type to the total weight value of each candidate cell set of the same type as the probability distribution value corresponding to each cell.

[0205] The cumulative probability interval determination unit is used to determine the cumulative probability interval for each battery cell based on the probability distribution value of each battery cell.

[0206] It should be noted that the information interaction and execution process between the modules in the capacitor equalization device 2 of the battery pack mentioned above are based on the same concept as the method embodiment of this application. For details on their specific functions and technical effects, please refer to the method embodiment section, and they will not be repeated here.

[0207] This application also provides a terminal device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. (Refer to...) Figure 3 The terminal device 3 in this embodiment includes a memory 31, a processor 32, and a computer program stored in the memory 31 and executable on the processor 32. When the processor 32 executes the computer program, it implements the steps in the battery pack capacitance balancing method embodiment described above.

[0208] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0209] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0210] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0211] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0212] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0213] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0214] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0215] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method of capacitance equalization of a battery pack, characterized by, The method comprises: obtaining current state data of a plurality of battery cells in a battery pack; generating a true random number by a quantum random number generator using quantum vacuum fluctuations as a source of randomness; determining target battery cells and a type of capacitive equalization processing operation that the plurality of battery cells need to undergo capacitive equalization processing according to the current state data of the plurality of battery cells and the true random number; performing capacitive equalization processing on the target battery cells according to the type of capacitive equalization processing operation to obtain equalized state data of the target battery cells; wherein the determining of the target battery cells and the type of capacitive equalization processing operation that the plurality of battery cells need to undergo capacitive equalization processing according to the current state data of the plurality of battery cells and the true random number comprises: calculating an equalization weight value of each battery cell and a total weight value of a same-type candidate battery cell set according to the current state data of each battery cell; determining the type of capacitive equalization processing operation according to the total weight value of the same-type candidate battery cell set; wherein the type of capacitive equalization processing operation includes discharging operation and charging operation; converting the equalization weight value of each battery cell into a probability distribution value to obtain a corresponding cumulative probability interval of each battery cell; determining the target battery cells in the plurality of battery cells according to the type of capacitive equalization processing operation, the corresponding cumulative probability interval of each battery cell, and the true random number.

2. The method of claim 1, wherein the step of determining the capacity of each battery cell is performed by a battery management system. The current state data includes current capacity value, current health value, current internal resistance value, and current temperature value. The calculating of the equalization weight value of each battery cell and the total weight value of the same-type candidate battery cell set according to the current state data of each battery cell comprises: determining a basic weight value of each battery cell according to a current capacity value of each battery cell and a preset equalization capacity value; determining an equalization protection coefficient of each battery cell according to a current health value, a current internal resistance value, and a current temperature value of each battery cell; multiplying the basic weight value of each battery cell by the equalization protection coefficient to obtain the equalization weight value of each battery cell; adding the equalization weight values of all battery cells in the same-type candidate battery cell set to obtain the total weight value of the same-type candidate battery cell set; wherein all battery cells with positive basic weight values are taken as a first same-type candidate battery cell set, and all battery cells with negative basic weight values are taken as a second same-type candidate battery cell set.

3. The method of claim 2, wherein the step of determining the cell with the lowest state of charge is performed by a microcontroller. The determining of the equalization protection coefficient of each battery cell according to the current health value, the current internal resistance value, and the current temperature value of each battery cell comprises: calculating a health coefficient of each battery cell according to a current health value of each battery cell and a preset retirement threshold value; calculating a temperature coefficient of each battery cell according to a current temperature value of each battery cell and a preset temperature threshold value; calculating an internal resistance coefficient of each battery cell according to a current internal resistance value of each battery cell and a nominal internal resistance value; multiplying the health coefficient, the temperature coefficient, and the internal resistance coefficient to obtain the equalization protection coefficient of each battery cell.

4. The method of claim 3, wherein the step of determining the cell with the lowest state of charge is performed by a microcontroller. The method comprises the following steps: adding the total weight value of the first same-type candidate battery cell set and the total weight value of the second same-type candidate battery cell set to obtain a candidate total weight value; comparing the total weight value of the first same-type candidate battery cell set with the candidate total weight value to obtain a first probability value, and comparing the total weight value of the second same-type candidate battery cell set with the candidate total weight value to obtain a second probability value; in the case that the first probability value is greater than the second probability value, determining that the capacity equalization processing operation type is the discharging operation; in the case that the second probability value is greater than the first probability value, determining that the capacity equalization processing operation type is the charging operation.

5. The method of claim 4, wherein the step of determining the cell with the lowest state of charge is performed by a microcontroller. The method comprises the following steps: determining a target battery cell set that needs to be subjected to capacity equalization processing from the first same-type candidate battery cell set or the second same-type candidate battery cell set according to the capacity equalization processing operation type; determining a battery cell in the target battery cell set corresponding to the cumulative probability interval in which the true random number is located as the target battery cell.

6. The method of claim 4, wherein the step of determining the cell with the lowest state of charge is performed by a microcontroller. The method comprises the following steps: taking the ratio of the equalization weight value of each battery cell in each same-type candidate battery cell set to the total weight value of each same-type candidate battery cell set as the probability distribution value corresponding to each battery cell; determining the cumulative probability interval corresponding to each battery cell according to the probability distribution value of each battery cell.

7. A capacitor balancing device for a battery pack, characterized in that, The method comprises the following steps: an acquisition module is configured to acquire current state data of a plurality of battery cells in a battery pack; a generation module is configured to generate a true random number by taking quantum vacuum fluctuation as a randomness source by using a quantum random number generator; a determination module is configured to determine a target battery cell and a capacity equalization processing operation type of the plurality of battery cells that need to be subjected to capacity equalization processing according to the current state data of the plurality of battery cells and the true random number; a processing module is configured to perform capacity equalization processing on the target battery cell according to the capacity equalization processing operation type to obtain equalized state data of the target battery cell. The determination module comprises: a weight value calculation submodule is configured to calculate an equalization weight value of each battery cell and a total weight value of a same-type candidate battery cell set according to the current state data of each battery cell; an operation type determination submodule is configured to determine the capacity equalization processing operation type according to the total weight value of the same-type candidate battery cell set; wherein the capacity equalization processing operation type comprises a discharging operation and a charging operation; a probability interval determination submodule is configured to convert the equalization weight value of each battery cell into a probability distribution value to obtain a cumulative probability interval corresponding to each battery cell. The target battery cell determination sub-module is configured to determine the target battery cell from the capacitance equalization processing operation type, the cumulative probability interval corresponding to each battery cell, and the true random number.

8. A terminal device, comprising: A computer program product includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method according to any one of claims 1 to 6.

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