Battery pack status monitoring and balancing control method and system

Abstract

The present invention discloses a battery pack status monitoring and balancing control method and system, which relates to the field of battery monitoring and control technology. The method includes: splitting a plurality of detection battery cells according to the battery pack structure; monitoring the status parameters of the plurality of detection battery cells to obtain status data of the plurality of detection battery cells; obtaining a balancing control strategy based on the comparison and distribution relationship of the status data; inputting the balancing control strategy and status data into a balancing control module, performing balancing strategy execution configuration, and obtaining balancing control execution information. The present invention solves the technical problems in the prior art of being unable to comprehensively and accurately monitor the battery pack status, and of being difficult to precisely control the imbalance of the battery pack, resulting in the battery pack being unable to operate stably and efficiently. It achieves the technical effect of realizing comprehensive monitoring of the battery pack status and precise balancing control, ensuring the stable and efficient operation of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring and control, and in particular to a battery pack status monitoring and balancing control method and system. Background Art

[0002] With the widespread application of battery technology in many fields, such as electric vehicles and energy storage systems, the performance and stability of battery packs have become key factors. In actual use, a battery pack is composed of multiple battery cells. Due to differences in manufacturing processes, different usage environments and other factors, the performance of each battery cell will gradually become inconsistent, which will lead to a decline in the overall performance of the battery pack and even affect its safety and service life. Existing battery pack monitoring and control technologies have certain limitations and are unable to comprehensively and accurately monitor the status parameters of battery cells. It is difficult to achieve targeted and effective monitoring of different cells in series, parallel or mixed structures in the battery pack. In terms of balancing control, traditional methods often simply treat the batteries in a unified manner and cannot formulate and implement accurate balancing strategies based on the actual status of each battery cell, such as current, voltage, temperature, internal resistance, self-discharge rate and other differences.

[0003] The existing technology has technical problems such as being unable to fully and accurately monitor the status of the battery pack and being difficult to precisely control the imbalance problem of the battery pack, resulting in the battery pack being unable to operate stably and efficiently. Summary of the Invention

[0004] The present application provides a battery pack status monitoring and balancing control method and system, which is used to solve the technical problems in the existing technology that the battery pack status cannot be fully and accurately monitored, and the battery pack imbalance problem is difficult to accurately control, resulting in the battery pack being unable to operate stably and efficiently.

[0005] In view of the above problems, the present application provides a battery pack status monitoring and balancing control method and system.

[0006] In a first aspect of the present application, a method for monitoring and balancing a battery pack state is provided, the method comprising:

[0007] According to the battery pack structure, multiple detection battery cells are split; state parameters of the multiple detection battery cells are monitored, and the cell states are analyzed based on the state parameter monitoring data to obtain state data of the multiple detection battery cells; based on the comparison and distribution relationship of the state data, the balancing strategy of the battery pack state is analyzed in combination with the battery pack structure to obtain a balancing control strategy; the balancing control strategy and state data are input into the balancing control module, the balancing strategy execution configuration is performed, and the balancing control execution information is obtained, and the balancing control execution information includes the balancing executor and its execution control parameters.

[0008] A second aspect of the present application provides a battery pack status monitoring and balancing control system, the system comprising:

[0009] A detection battery cell splitting module is used to split multiple detection battery cells according to the battery pack structure; a state parameter monitoring module is used to monitor the state parameters of the multiple detection battery cells, perform cell state analysis based on the state parameter monitoring data, and obtain state data of the multiple detection battery cells; a balancing control strategy acquisition module is used to analyze the balancing strategy of the battery pack state based on the comparison distribution relationship of the state data and in combination with the battery pack structure to obtain the balancing control strategy; a balancing control execution information acquisition module is used to input the balancing control strategy and state data into the balancing control module, perform balancing strategy execution configuration, and obtain balancing control execution information, wherein the balancing control execution information includes the balancing executor and its execution control parameters.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] According to the battery pack structure, multiple test battery cells are separated; state parameters of the multiple test battery cells are monitored to obtain state data of the multiple test battery cells; based on the comparison and distribution relationship of the state data and in combination with the battery pack structure, the balancing strategy of the battery pack state is analyzed to obtain the balancing control strategy; the balancing control strategy and state data are input into the balancing control module, the balancing strategy execution configuration is performed, and the balancing control execution information is obtained. This achieves the technical effect of achieving comprehensive monitoring of the battery pack state and precise balancing control, ensuring the stable and efficient operation of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A flowchart of a battery pack status monitoring and balancing control method provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a battery pack status monitoring and balancing control system provided in an embodiment of the present application.

[0015] Description of reference numerals: battery cell splitting detection module 10 , state parameter monitoring module 20 , balancing control strategy acquisition module 30 , balancing control execution information acquisition module 40 . DETAILED DESCRIPTION

[0016] The present application provides a battery pack status monitoring and balancing control method and system to solve the technical problems in the existing technology that the battery pack status cannot be fully and accurately monitored, and the battery pack imbalance problem is difficult to accurately control, resulting in the battery pack being unable to operate stably and efficiently.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, the present application provides a battery pack status monitoring and balancing control method, the method comprising:

[0019] Step S100: splitting a plurality of test battery cells according to the battery pack structure.

[0020] Specifically, the battery pack's structure and connection relationships are first disassembled in detail to fully understand the battery pack's connection relationships, including series, parallel, and hybrid structures, and to clearly define the number of connection groups, that is, the number of battery cells in each connection relationship. Next, based on these disassembled battery pack connection relationships and number of connection groups, the battery pack structure is disassembled, ultimately obtaining multiple test battery cells. These test battery cells each have important attributes such as connection structure type, relationship between connection groups, and battery performance parameters, laying the foundation for subsequent battery pack status monitoring and balancing control.

[0021] Step S200: monitoring the state parameters of the plurality of detected battery cells, and performing cell state analysis based on the state parameter monitoring data to obtain state data of the plurality of detected battery cells.

[0022] Specifically, for the multiple test cells obtained by separation, collaborative monitoring groups are first established based on their connection structure types and the relationships between the connection groups: parallel groups and series groups. Because different groups respond differently to state parameters, corresponding state parameter monitoring equipment is configured accordingly to monitor state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate. For example, assume a battery pack consists of 10 cells. Cells 1-4 are connected in series as a group (denoted as series group 1), cells 5-8 are connected in series as a group (denoted as series group 2), and these two groups are connected in parallel. Cells 9 and 10 are connected in parallel separately (denoted as parallel group 1). When monitoring the current of series group 1, a high-precision current sensor placed in the series circuit measured a total current of 2A. Auxiliary sensors placed near each cell verified the current consistency. The auxiliary sensor measured a current of 2.01A at cell 1, 1.99A at cell 2, 2A at cell 3, and 2.02A at cell 4. These data are within the allowable error range and can be considered consistent. In terms of voltage monitoring, each battery cell is equipped with an independent high-precision, high-resolution voltage sensor. The voltage of cell No. 1 is 3.8V, the voltage of cell No. 2 is 3.75V, the voltage of cell No. 3 is 3.82V, and the voltage of cell No. 4 is 3.78V. For parallel groups, each parallel branch is equipped with a separate current sensor with high precision and wide range. The current of cell No. 9 is 1.2A, and the current of cell No. 10 is 1.15A. A total current sensor is set at the input and output ends of the group, and the measured total current is 2.35A, which is used for comparative analysis of current distribution. When monitoring voltage, a voltage sensor is configured for each branch. The voltage of cell No. 9 is 3.85V, and the voltage of cell No. 10 is 3.83V. Sensors are also set at the input and output ends to compare and analyze voltage anomalies. After obtaining the monitoring data, a battery status assessment model is constructed, which includes a battery state of charge assessment submodel and a health status assessment submodel. This model uses historical data for machine learning training. The state parameter monitoring data is input into the model. The state of charge (SOC) of each individual battery is evaluated by the SOC assessment sub-model, and the health status of each individual battery is evaluated by the health status assessment sub-model. Ultimately, the state data of multiple monitored battery cells is obtained, providing data support for the subsequent formulation of balancing strategies. For example, in the collected historical data, at a certain moment, the current of battery cell 1 was 1.5A, the voltage was 3.7V, the temperature was 25°C, the internal resistance was 0.05Ω, and the self-discharge rate was 0.01% / day. The battery SOC assessment sub-model (using a support vector machine algorithm) calculated that the SOC of this individual battery was 60%. Simultaneously, the health status assessment sub-model (using a neural network algorithm) assessed the battery's health status based on these parameters as 0.85 (where 0 represents severe battery damage and 1 represents a brand new, healthy battery), indicating that the battery is in good health.Similarly, other single cells were evaluated, such as the No. 2 single cell. At the same time point, the current was 1.4A, the voltage was 3.65V, the temperature was 24°C, the internal resistance was 0.055Ω, the self-discharge rate was 0.012% / day, the state of charge was 55%, and the health status was 0.82.

[0023] Step S300: analyzing a balancing strategy of the battery pack state based on the comparison distribution relationship of the state data and in combination with the battery pack structure to obtain a balancing control strategy.

[0024] Specifically, a cell connection diagram is first constructed based on the battery pack structure. The status data of each monitored cell is projected onto this diagram to visually display the status distribution of each cell. Subsequently, the status data of all cells and previously monitored status parameter data are combined to calculate the mean of the status data and the mean of the status parameter monitoring data. Using these means as a benchmark, the deviation of each cell is calculated, thereby obtaining a deviation comparison distribution relationship. For example, the SOC data of 10 cells (assuming the SOC values are 60%, 55%, 62%, 58%, 70%, 68%, 72%, 65%, 45%, and 48%, respectively) and the status parameter monitoring data are aggregated and averaged. The mean SOC is (60+55+62+58+70+68+72+65+45+48) / 10 = 60.3%. Calculate the deviation of the state of charge of each cell, such as the deviation of cell No. 1 is 60-60.3=-0.3%, the deviation of cell No. 2 is 55-60.3=-5.3%, and so on, until the deviation of cell No. 10 is calculated. The deviation comparison distribution relationship is obtained through these data, and the difference between each cell and the mean can be clearly seen. Then, based on the structure of the cell connection graph, the deviation comparison distribution relationship is partitioned and analyzed from the two dimensions of connection structure type and relationship between connection groups, and the cell with the largest group difference and the cell with the largest global difference are accurately found. Finally, with these two types of cells as the core targets, respectively, for the internal and global situations of the group, combined with battery performance parameters, analyze and formulate corresponding balancing strategies, thereby obtaining a balancing control strategy that includes one or more methods of intelligent balancing, passive balancing and active balancing, so as to achieve effective regulation of the battery pack state.

[0025] Step S400: inputting the balancing control strategy and state data into the balancing control module, performing balancing strategy execution configuration, and obtaining balancing control execution information, wherein the balancing control execution information includes a balancing executor and its execution control parameters.

[0026] Specifically, control lists are first established for intelligent balancing, passive balancing, and active balancing, detailing the control actuators, control relationships, and control parameters for each balancing method. The resulting balancing control strategy and the acquired battery cell status data are then input into the balancing control module. Based on this input, the module searches the control list for a control strategy path that both aligns with the balancing control strategy and maximizes the balance of the status data differences. Finally, based on the determined control strategy path, the corresponding balancing actuator is identified and its execution control parameters are determined, thereby obtaining complete balancing control execution information, providing a key basis for the subsequent actual execution of the battery pack balancing control operation.

[0027] In one possible implementation, step S100 further includes:

[0028] Step S110: disassemble the composition structure and connection relationship of the battery pack to obtain the battery pack connection relationship and the number of connection groups, wherein the battery pack connection relationship includes series structure, parallel structure, and hybrid structure, and the number of connection groups is the number of battery cells in the connection relationship.

[0029] Step S120: splitting the battery pack structure according to the battery pack connection relationship and the number of connection groups to obtain a plurality of detection battery cells, wherein the detection battery cells have connection structure types, relationship between connection groups, and battery performance parameters.

[0030] Specifically, in the initial stages of battery pack status monitoring and balancing control, the battery pack structure must be carefully sorted out, clearly presenting the layout and connection methods of its various components. On this basis, the battery pack connection relationships must be accurately judged and identified to determine whether they are series, parallel, or hybrid structures. At the same time, through individual counting and statistics, the number of battery cells contained in each connection relationship, i.e., the number of connection groups, is clearly defined, providing reliable data for subsequent battery pack status monitoring and balancing control work.

[0031] Based on the defined battery pack connection relationship (series, parallel, or hybrid structure) and the number of connection groups, the entire battery pack is disassembled using disassembly technology. During the disassembly process, each test battery cell separated from the battery pack is recorded and analyzed in detail to determine its specific properties. The connection structure type of each test battery cell is determined to clarify its connection method in the original battery pack; the relationship between the connection groups is sorted out to understand the mutual relationship between the cell and other battery cells; and the battery performance parameters of each test battery cell, such as voltage, internal resistance, and capacity, are measured. This information provides a critical data foundation for subsequent accurate monitoring of battery cell status, analysis of overall battery pack performance, and the development of effective balancing control strategies.

[0032] In one possible implementation, step S200 further includes:

[0033] Step S210: establishing a collaborative monitoring group according to the connection structure types and the relationship between the connection groups of the plurality of detection battery cells, wherein the collaborative monitoring group includes a parallel group and a series group.

[0034] Step S220: configuring the state parameter monitoring equipment of the collaborative monitoring group according to the response difference relationship between the parallel group and the series group to the state parameters, and performing state parameter monitoring on the plurality of detection battery cells.

[0035] Specifically, after the battery pack is split and multiple detection battery cells with properties such as connection structure type and relationship between connection groups are obtained, in order to monitor the status of battery cells more accurately and efficiently, it is necessary to classify and integrate them according to the connection characteristics of these cells. Analyze the connection structure type of each detection battery cell to determine whether it is connected in series or in parallel, while considering the relationship between the connection groups between them. Battery cells with the same connection method and similar relationship between groups are grouped together to establish a collaborative monitoring group. These collaborative monitoring groups are mainly divided into parallel groups and series groups. Through such classification, more targeted monitoring plans can be formulated for battery cell groups with different connection characteristics, thereby improving monitoring efficiency and accuracy, and laying the foundation for subsequent acquisition of accurate battery cell status data and ensuring stable operation of the battery pack.

[0036] Since parallel groups and series groups have different responses to state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate, it is necessary to reasonably configure the state parameter monitoring equipment based on these differences. The current, voltage, temperature, internal resistance, and self-discharge rate of each battery cell in the parallel group and the series group are tested separately to obtain the response data of each cell on these parameters, and then analyze the response differences between each cell. If there is no difference in the response of battery cells in the same group to the state parameters, then in order to effectively monitor the entire group, you can choose to configure a bus state parameter detection device, or to further ensure the accuracy of the monitoring data, configure a bus state parameter detection device and a verification state parameter detection device, and at the same time establish a collection mapping relationship between the state parameter detection device and each cell in the corresponding group to accurately obtain the data of each cell. When there are differences in the state parameter responses, in order to ensure that the state of each battery cell can be accurately monitored, it is necessary to configure a state parameter detection device for each battery cell. By configuring equipment in a targeted manner based on response differences, comprehensive and accurate status parameter monitoring of multiple battery cells can be achieved, providing reliable data support for subsequent analysis of the battery cell status and balanced control of the entire battery pack.

[0037] In one possible implementation, step S220 further includes:

[0038] Step S221: The state parameters include: current, voltage, temperature, internal resistance, and self-discharge rate.

[0039] Specifically, when monitoring the status parameters of multiple test battery cells, the key parameters involved include current, voltage, temperature, internal resistance, and self-discharge rate. Current, as a physical quantity that measures the flow of charge during battery charging and discharging, reflects the battery's power transfer during operation. It provides a visual representation of the battery's charge and discharge rate and is crucial for assessing the efficiency and safety of the battery pack. Voltage reflects the battery's energy storage and output capacity. Different voltage values represent different states of charge and are a key indicator for evaluating battery performance and remaining charge. Temperature is closely related to battery stability and lifespan. Excessively high or low temperatures can affect the battery's chemical reaction rate and performance, potentially even causing safety issues. Internal resistance affects the battery's charge and discharge efficiency and energy loss, and its magnitude reflects changes in the battery's internal physical and chemical properties. The self-discharge rate measures the rate at which a battery loses charge when not in operation. It reflects the degree of self-consumption and is a key parameter for evaluating the battery's long-term storage performance. By monitoring this suite of status parameters, the operating status of each battery cell can be comprehensively and accurately understood, providing a solid data foundation for subsequent battery pack status analysis and balancing control.

[0040] In one possible implementation, step S220 further includes:

[0041] Step S222: performing current, voltage, temperature, internal resistance, and self-discharge rate detection on each battery cell in the parallel group and the series group to obtain the state parameter response differences of each battery cell in the parallel group and the series group.

[0042] Step S223: When there is no difference in status parameter response, configure a bus status parameter detection device, or configure a bus status parameter detection device and a verification status parameter detection device, and establish a collection mapping relationship between the status parameter detection device and each single battery in the corresponding group.

[0043] Step S224: When there is a difference in the state parameter response, a state parameter detection device is configured for each battery cell.

[0044] Specifically, the system comprehensively captures the differences in the state parameter responses of each battery cell in parallel and series groups. For current monitoring, the series group uses high-precision current sensors placed in the series circuit to monitor the total current, while auxiliary sensors are placed near each cell to verify current consistency. The parallel group uses a separate high-precision, wide-range current sensor for each parallel branch, and a total current sensor is placed at the group's input and output terminals for comparative analysis of current distribution. For voltage monitoring, the series group equips each battery cell with an independent high-precision, high-resolution voltage sensor and employs a redundant design to enhance monitoring accuracy. The parallel group also uses a voltage sensor for each branch, and sensors are placed at the input and output terminals to compare and analyze voltage anomalies. For temperature monitoring, both the series and parallel groups install high-precision, fast-response temperature sensors at key locations in the battery pack, and a distributed system is used for comprehensive monitoring. Internal resistance monitoring requires high-precision and high-stability monitoring of each battery cell to detect issues such as battery aging. Self-discharge rate monitoring uses regular open-circuit voltage measurements combined with model estimation to achieve more accurate measurements and detect anomalies promptly.

[0045] In the battery pack status monitoring process, the monitoring equipment is configured based on the detection results of the differences in the responses of battery cell status parameters in parallel groups and series groups. When, after detailed testing, it is determined that there are no differences in the responses of individual battery cells in the same group (whether it is a parallel group or a series group) to status parameters such as current, voltage, temperature, internal resistance, and self-discharge rate, two configuration schemes will be adopted to achieve effective monitoring of the group status parameters. One scheme is to configure a bus status parameter detection device, which can uniformly collect the status parameters of the entire group. Through reasonable wiring and data transmission settings, the status parameters of all battery cells in the group can be obtained. The other scheme is to configure a bus status parameter detection device and a verification status parameter detection device. The bus detection device is responsible for the main parameter collection work, and the verification detection device is used to cross-check the collected data to ensure the accuracy of the data. In both configurations, it is necessary to establish a collection mapping relationship between the status parameter detection device and each single cell in the corresponding group. This means that it is necessary to clarify which single cell status parameters are collected by each detection device. Through precise circuit connection, address coding or software settings, it is ensured that the status parameters of each single cell can be accurately collected and matched, thereby providing a reliable data basis for subsequent battery pack status analysis and balancing control.

[0046] When, in a parallel group or series group, after testing state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate, differences in state parameter responses are found, in order to accurately grasp the actual state of each battery cell, it is necessary to configure a state parameter detection device for each battery cell. This is because differences in state parameter responses mean that there are inconsistencies in the states of each battery cell. If a general monitoring device configuration method is continued, subtle differences between cells will be missed, and the real-time state of each cell cannot be accurately obtained. Configuring a separate detection device for each cell allows for precise measurements of the unique conditions of each cell, ensuring that the current, voltage, temperature, internal resistance, self-discharge rate and other parameters of each battery cell can be independently and accurately monitored, thereby providing the most authentic and reliable data basis for subsequent accurate battery cell status analysis and the formulation of battery pack balancing strategies, ensuring stable and efficient operation of the battery pack.

[0047] In one possible implementation, step S200 further includes:

[0048] Step S230: constructing a battery state assessment model, including a battery state of charge assessment sub-model and a health state assessment sub-model, wherein the battery state of charge assessment sub-model and the health state assessment sub-model are both obtained by machine learning using historical data.

[0049] Step S240: inputting the state parameter monitoring data into the battery state evaluation model, performing state of charge evaluation of the single cell through the battery state of charge evaluation sub-model, performing state of health evaluation of the single cell through the health state evaluation sub-model, and obtaining state data of the multiple detected battery cells.

[0050] Specifically, when building the battery state assessment model, different machine learning algorithms are used to train the battery state of charge assessment sub-model and the state of health assessment sub-model to achieve accurate battery state assessment. The support vector machine (SVM) algorithm is used for the battery state of charge assessment sub-model. First, a large amount of historical data related to the battery state of charge is collected. This data includes state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate at different charge and discharge stages, and the corresponding true battery state of charge values are annotated. This data is preprocessed, such as normalization, to eliminate dimensional differences between different parameters and improve model training results. During training, the SVM algorithm searches for an optimal classification hyperplane in high-dimensional space that accurately separates data points with different states of charge. By adjusting the kernel function (such as the radial basis kernel function) and its parameters, the model is better adapted to the nonlinear relationships in the data. For example, in the training set, the algorithm continuously learns how to determine the battery's state of charge range based on the current combination of state parameters, thereby establishing a mapping between state parameters and state of charge. For the health status assessment sub-model, a neural network algorithm, a multi-layer perceptron (MLP), is used. Similarly, the collected historical data related to the battery health status is cleaned and preprocessed to remove outliers and noise. The MLP consists of an input layer, multiple hidden layers, and an output layer. The battery status parameters are fed into the neurons in the input layer. Nonlinear transformations (such as the Reluctant Unit (ReLU) activation function) are applied to the neurons in the hidden layers to automatically extract complex features from the data. The number of neurons and layers in the hidden layers can be adjusted and optimized based on the complexity of the actual data. The output layer outputs the battery health status assessment result, for example, a value between 0 and 1 representing the battery's health, with 0 representing severe damage and 1 representing a pristine state of health. During training, the backpropagation algorithm continuously adjusts the connection weights between neurons to minimize the error between the model's predicted health status values and the actual labeled health status values, thereby training a sub-model capable of accurately assessing the battery health status. Using these two machine learning algorithms, the battery state-of-charge assessment sub-model and the health status assessment sub-model are trained separately, ultimately constructing a complete and accurate battery health assessment model.

[0051] The model is fed with state parameter monitoring data, including current, voltage, temperature, internal resistance, and self-discharge rate, collected through carefully configured equipment. The battery state-of-charge assessment sub-model accurately evaluates the state of charge of each battery cell based on the correlation between state parameters and state of charge learned from extensive historical data during the training phase. It calculates the ratio of the current remaining charge in the cell to the total capacity, quantifying the battery's state of charge. Simultaneously, the health assessment sub-model, leveraging its learned relationships between internal battery conditions and state parameters, assesses the health of each cell, identifying potential issues such as capacity fade and abnormal internal resistance, and providing a comprehensive assessment of the battery's health. These two sub-models work in parallel and collaboratively to comprehensively and meticulously evaluate each tested cell, ultimately generating rich and in-depth state data for multiple cells. This data not only provides a visual representation of the battery's current charge level but also reveals its health. This data provides an essential foundation for developing battery pack balancing control strategies, effectively ensuring stable operation and efficient management of the battery pack.

[0052] In one possible implementation, step S300 further includes:

[0053] Step S310: constructing a cell connection diagram structure according to the battery pack structure, and projecting the status data of the battery cells into the cell connection diagram structure.

[0054] Step S320: Calculating the status data mean and the status parameter monitoring data mean based on the status data and status parameter monitoring data of all battery cells.

[0055] Step S330: Calculate the deviation of each battery cell based on the mean value of the state data and the mean value of the state parameter monitoring data to obtain a deviation comparison distribution relationship.

[0056] Step S340: performing a partition analysis of the connection structure type and the relationship between connection groups on the deviation comparison distribution relationship according to the monomer connection graph structure to obtain the monomer with the largest group difference and the monomer with the largest global difference.

[0057] Step S350: Analyzing the group and global balancing strategies with the single unit having the largest group difference and the single unit having the largest global difference as targets, and obtaining the balancing control strategy.

[0058] Specifically, a cell connection diagram is constructed based on the specific structure of the battery pack. This diagram intuitively displays the connection relationships between battery cells, such as series, parallel, or mixed connections. Subsequently, the battery cell status data, including state of charge and health, previously obtained through the battery status assessment model, is projected onto this cell connection diagram. The status of each cell in the battery pack is clearly visible in the context of the connection relationships, providing an intuitive perspective for subsequent analysis.

[0059] The previously acquired state data for all battery cells, such as the state of charge data obtained by the battery state-of-charge assessment submodel and the state of health data obtained by the state-of-health assessment submodel, as well as the state parameter monitoring data obtained from monitoring each battery cell, such as current, voltage, temperature, internal resistance, and self-discharge rate, are aggregated. These data are then processed using statistical methods. For the state data, the sum of all cell state data is calculated and then divided by the number of cells to obtain the mean state data value. This mean value reflects the average state of the battery cells in the battery pack. Similarly, the same calculation method is used for the state parameter monitoring data: the sum of each cell's current, voltage, temperature, internal resistance, self-discharge rate, and other parameters is calculated and then divided by the number of cells to obtain the corresponding mean state parameter monitoring data value. These means serve as an important reference for subsequent analysis, providing a basis for accurately determining the deviation of each battery cell from the overall average state, thereby laying the foundation for developing a reasonable battery pack balancing control strategy.

[0060] Compare each battery cell's status data with the corresponding mean status data, and calculate the difference between the two. This difference is the deviation of the battery cell's status data. Similarly, compare each battery cell's status parameter monitoring data with the mean status parameter monitoring data to calculate the individual deviations. By performing this calculation for all battery cells, a series of deviation data is obtained. These deviation data are sorted and compared according to the battery cell connection structure type and the relationship between connection groups to obtain a deviation comparison distribution relationship. This relationship clearly shows the degree to which different battery cells' status data and status parameter monitoring data deviate from the overall average level.

[0061] Using the cell connection diagram as a framework, the battery cells are divided into different areas or groups based on the connection structure type (series, parallel, hybrid) and the relationship between the connection groups. Within each group, the deviation data of each cell is compared to identify the cell with the largest state difference from other cells in the group, that is, the cell with the largest group difference. This cell has the most significant deviation from the average state in its group. At the same time, from the global perspective of the entire battery pack, the deviation data of all groups are combined to identify the cell with the largest difference from the overall average state, that is, the cell with the largest global difference. The precise identification of these two types of cells through this partition analysis method provides a clear goal for the subsequent formulation of targeted balancing strategies, which helps to more efficiently achieve balanced control of the battery pack state and ensure stable operation and performance optimization of the battery pack.

[0062] Based on the identified cells with the largest group and global differences, targeted balancing control strategies are developed. For the cell with the largest group difference, the differences between this cell and other cells in the group are analyzed, taking into account the internal connection structure of the group (e.g., series, parallel, or hybrid), as well as the battery performance parameters. This allows for a local balancing strategy tailored to the group. For example, if the group is in a series configuration, adjustments to the charging or discharging sequence may be considered to balance inter-cell differences. Based on this, the local balancing strategy is expanded and optimized from a global perspective, taking into account the relationship between global battery performance parameters. Further analysis of the balancing strategy for the cell with the largest global difference is conducted. Combining the local and global analysis results, the final balancing control strategy is determined, which may include one or more of intelligent balancing, passive balancing, and active balancing. This comprehensive analysis process effectively adjusts the overall state of the battery pack, ensuring a more balanced state among the cells in the pack and improving the pack's performance and stability.

[0063] In one possible implementation, step S350 further includes:

[0064] Step S351: taking the cell with the largest difference in the group as the target, performing local balancing strategy analysis according to the connection structure type and battery performance parameters within the group to obtain a local balancing strategy.

[0065] Step S352: Based on the local balancing strategy and the relationship between global battery performance parameters, a global balancing strategy is analyzed for the cell with the largest global difference to obtain the balancing control strategy, which is one or more of intelligent balancing, passive balancing, and active balancing.

[0066] Specifically, a detailed analysis of the state parameters of the cells with the greatest group variation, such as voltage, current, temperature, internal resistance, and self-discharge rate, is first performed to identify the parameters with the largest deviations and thus determine the primary factors causing their inconsistency with other cells in the group. Next, different priorities are applied depending on the group's connection structure. In a series group, since current is uniform throughout a series circuit, voltage variation becomes a key factor affecting battery pack performance, so the focus is on voltage variation. In a parallel group, even current distribution is crucial for stable battery pack operation, so current inequality is considered. Subsequently, an appropriate local balancing strategy is selected based on the analysis results. When cell voltages vary significantly within a group and the battery capacity is small, a passive balancing strategy is selected. This strategy uses resistors to dissipate energy from high-voltage cells, gradually bringing the voltages of all cells closer to uniformity. If current distribution is uneven within a group and the battery capacity is large, an active balancing strategy is selected. This strategy transfers energy from cells with higher current to cells with lower current through energy transfer, achieving balanced current distribution. When multiple state parameter inconsistencies exist among battery cells within a group, and high balancing accuracy and efficiency are required, intelligent balancing strategies are more suitable. These strategies utilize machine learning algorithms to learn from historical data and identify patterns in battery performance under different combinations of state parameters. When multiple state parameter inconsistencies are detected within a group, the algorithm uses this learned knowledge to calculate the optimal balancing target value for each cell. During the balancing process, the balancing control strategy is dynamically adjusted based on real-time conditions and the optimal target value. For example, if a battery cell is found to have excessively high voltage and abnormal temperature, the algorithm will comprehensively consider cooling measures and adjust energy transfer methods to avoid the potential problems caused by simply reducing the voltage. It also flexibly selects different balancing actuators (such as switches and converters) and control parameters (such as current level and time interval) based on factors such as the overall charge and discharge status of the battery pack and ambient temperature to achieve optimal balancing results while ensuring high precision and efficiency during the balancing process.

[0067] A thorough analysis of the various state parameters of the cell with the largest global variance, including voltage, current, temperature, internal resistance, and self-discharge rate, is conducted. This analysis, combined with its position within the entire battery stack and its connectivity with other cells, assesses its impact on the overall battery stack performance. If the voltage of the cell with the largest global variance is significantly higher than that of other cells and the overall battery stack capacity is relatively small, passive balancing is selected as the global balancing strategy. By inserting a resistor into the circuit, the high-voltage cell with the largest global variance discharges through the resistor, gradually dissipating its excess energy and reducing its voltage, bringing the voltage of the entire battery stack toward a consistent state and ensuring stable operation. When the current of the cell with the largest global variance exhibits significant anomalies and the overall battery stack capacity is large, active balancing becomes the preferred strategy. Leveraging circuit design and control technology, active balancing transfers energy from the cell with the highest current (the cell with the largest global variance) to the cell with the lowest current, achieving even current distribution across the entire battery stack and effectively preventing overcharging and discharging of some cells due to current imbalance. Intelligent balancing strategies play a key role when the cell with the largest global variance exhibits multiple inconsistencies in state parameters and high battery stack performance and life requirements are high. The intelligent balancing strategy combines the advantages of passive balancing and active balancing, using intelligent algorithms to monitor the overall state and real-time operating conditions of the battery pack in real time. Based on different operating scenarios and changes in battery status, the balancing control strategy is dynamically adjusted to accurately balance the cells with the largest global differences to achieve the best balancing effect and ensure that the battery pack is always in good operating condition. In addition, different balancing strategies can be flexibly combined based on the specific characteristics of the cells with the largest global differences and local balancing strategies. For example, after using active balancing to solve some problems locally, the global level can be further optimized by combining intelligent balancing and passive balancing, giving full play to the advantages of each strategy, improving the overall balancing efficiency and effect, and ultimately obtaining a balancing control strategy that includes one or more of intelligent balancing, passive balancing, and active balancing.

[0068] In one possible implementation, step S400 further includes:

[0069] Step S410: establishing control lists for intelligent balancing, passive balancing, and active balancing, respectively, including control actuators, control relationships, and control parameters.

[0070] Step S420: performing a path search in the control list according to the balance control strategy and the status data, and obtaining a control strategy path that matches the balance control strategy and maximizes the difference balance with the status data.

[0071] Step S430: Determine the corresponding control executor and its control parameters according to the control strategy path, and obtain the balanced control execution information.

[0072] Specifically, corresponding control lists are established for three different balancing methods: intelligent balancing, passive balancing, and active balancing. For intelligent balancing, the control actuators in the control list include intelligent power switches and high-precision sensor devices. These actuators are used to precisely control the energy flow and condition monitoring of the battery pack. The control relationship involves the coordinated operation logic between the actuators, such as how the sensors feed monitoring data to the controller, and how the controller controls the operation of the power switches based on this data. Control parameters include voltage regulation accuracy, current limit, balancing interval, etc. These parameters are dynamically adjusted to adapt to the real-time status of the battery pack. In the control list for passive balancing, the control actuators are primarily energy-consuming components such as resistors. The control relationship is relatively simple: when a voltage difference between battery cells is detected, the resistor is controlled to connect to the circuit, causing the energy of the high-voltage battery to dissipate as heat. Control parameters include the resistor value and the maximum current allowed. These parameters are determined based on the battery pack's voltage range, capacity, and expected balancing speed. In the control list for active balancing, the control actuators include inductors, capacitors, DC-DC converters, etc., which are used to transfer energy between batteries. The control relationship is reflected in how these components work together to achieve efficient energy transfer, such as how the DC-DC converter adjusts its output voltage and current based on the battery status. Control parameters include the power of energy transfer and the charge and discharge time constants of the inductor and capacitor. These parameters are set based on the characteristics of the battery pack and the balancing requirements to ensure a stable and efficient active balancing process. This lays a solid foundation for subsequent path search based on the balancing control strategy and battery status data, and for determining the final balancing control execution information.

[0073] Based on the existing balancing control strategy, a matching operation path is searched from the control lists of the three balancing methods. For example, if the balancing control strategy is determined to be intelligent balancing, the control list is screened. Furthermore, the goal of maximizing the balance of state data variance is considered. The system analyzes the current battery cell state data, such as the deviation from the mean of each cell's voltage, current, temperature, internal resistance, and self-discharge rate. Based on these deviations, the system searches the control list for a path that minimizes these differences. This involves comprehensively weighing the control actuators, control relationships, and control parameters in the control list to find the optimal combination. For example, for battery cells with significant voltage differences, the system selects appropriate control actuators (such as a specific switch combination or converter), determines their coordinated operation (when to turn them on and off, and in what order), and sets appropriate control parameters (such as the voltage adjustment amplitude and the control current magnitude). Ultimately, a control strategy path is developed that both meets the balancing control strategy requirements and maximizes the balance of the battery cell states, laying the foundation for the subsequent determination of specific actuators and control parameters.

[0074] Based on the defined control strategy path, the corresponding control actuators and their control parameters are precisely determined. Once determined, balancing control execution information is obtained. This information includes specific actuator operation instructions and detailed parameters, such as when to open and close a switch, the converter's operating mode, and parameter adjustments. This information is transmitted to the balancing control module, which drives the corresponding actuators to operate according to the set parameters, achieving balanced control of the battery pack and ensuring stable and efficient operation of the battery pack.

[0075] Embodiment 2 is based on the same inventive concept as a battery pack status monitoring and balancing control method in the above embodiment. Figure 2 As shown, the present application provides a battery pack status monitoring and balancing control system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0076] The detection battery cell splitting module 10 is used to split a plurality of detection battery cells according to the battery pack structure.

[0077] The state parameter monitoring module 20 is used to monitor the state parameters of the plurality of detected battery cells, perform cell state analysis based on the state parameter monitoring data, and obtain state data of the plurality of detected battery cells.

[0078] The balancing control strategy acquisition module 30 is configured to analyze the balancing strategy of the battery pack state according to the comparison distribution relationship of the state data and in combination with the battery pack structure, so as to obtain the balancing control strategy.

[0079] The balancing control execution information acquisition module 40 is used to input the balancing control strategy and status data into the balancing control module, perform balancing strategy execution configuration, and obtain balancing control execution information, which includes the balancing executor and its execution control parameters.

[0080] Furthermore, the system is also used to implement the following functions:

[0081] The composition structure and connection relationship of the battery pack are disassembled to obtain the battery pack connection relationship and the number of connection groups, wherein the battery pack connection relationship includes a series structure, a parallel structure, and a hybrid structure, and the number of connection groups is the number of battery cells in the connection relationship; the battery pack structure is disassembled according to the battery pack connection relationship and the number of connection groups to obtain multiple detection battery cells, and the detection battery cells have a connection structure type, a relationship between connection groups, and battery performance parameters.

[0082] Furthermore, the system is also used to implement the following functions:

[0083] A collaborative monitoring group is established based on the connection structure types and the relationship between the connection groups of the multiple detection battery cells, wherein the collaborative monitoring group includes a parallel group and a series group; based on the response difference relationship between the parallel group and the series group to the state parameters, the state parameter monitoring equipment of the collaborative monitoring group is configured to monitor the state parameters of the multiple detection battery cells.

[0084] Furthermore, the system is also used to implement the following functions:

[0085] The state parameters include: current, voltage, temperature, internal resistance, and self-discharge rate.

[0086] Furthermore, the system is also used to implement the following functions:

[0087] The current, voltage, temperature, internal resistance, and self-discharge rate of each battery cell in the parallel group and the series group are tested respectively to obtain the difference in the state parameter response of each battery cell in the parallel group and the series group; when there is no difference in the state parameter response, a bus state parameter detection device is configured for the same group, or a bus state parameter detection device and a verification state parameter detection device are configured, and a collection mapping relationship between the state parameter detection device and each single battery in the corresponding group is established; when the said state parameter response difference exists, a state parameter detection device is configured for each battery cell.

[0088] Furthermore, the system is also used to implement the following functions:

[0089] A battery state assessment model is constructed, including a battery state of charge assessment sub-model and a health state assessment sub-model, wherein the battery state of charge assessment sub-model and the health state assessment sub-model are both obtained by machine learning using historical data; the state parameter monitoring data is input into the battery state assessment model, the battery state of charge assessment sub-model is used to evaluate the state of charge of a single cell, and the health state assessment sub-model is used to evaluate the health state of a single cell, to obtain the state data of the multiple detected battery cells.

[0090] Furthermore, the system is also used to implement the following functions:

[0091] According to the battery pack structure, a cell connection diagram structure is constructed, and the status data of the battery cells are projected into the cell connection diagram structure; based on the status data and status parameter monitoring data of all battery cells, the status data mean and the status parameter monitoring data mean are calculated; based on the status data mean and the status parameter monitoring data mean, the deviation of each battery cell is calculated to obtain the deviation comparison distribution relationship; according to the cell connection diagram structure, the deviation comparison distribution relationship is partitioned into connection structure type and relationship between connection groups to obtain the cell with the largest group difference and the cell with the largest global difference; with the cell with the largest group difference and the cell with the largest global difference as the target, group and global balancing strategy analysis is performed to obtain the balancing control strategy.

[0092] Furthermore, the system is also used to implement the following functions:

[0093] Taking the cell with the largest difference in the group as the target, a local balancing strategy is analyzed according to the connection structure type and battery performance parameters within the group to obtain a local balancing strategy; based on the relationship between the local balancing strategy and the global battery performance parameters, a global balancing strategy is analyzed for the cell with the largest global difference to obtain the balancing control strategy, which is one or more of intelligent balancing, passive balancing and active balancing.

[0094] Furthermore, the system is also used to implement the following functions:

[0095] Establish control lists for intelligent balancing, passive balancing, and active balancing, respectively, including control actuators, control relationships, and control parameters; perform path search in the control lists according to the balancing control strategy and status data to obtain a control strategy path that matches the balancing control strategy and maximizes the balance of differences with the status data; determine the corresponding control actuator and its control parameters according to the control strategy path to obtain the balancing control execution information.

[0096] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0098] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, to the extent such modifications and variations fall within the scope of the present application and its equivalents, the present application is intended to include such modifications and variations.

Claims

1. A battery pack status monitoring and balancing control method, characterized in that: include: According to the battery pack structure, split multiple test battery cells; Monitor the state parameters of the plurality of detection battery cells, and perform cell state analysis based on the state parameter monitoring data to obtain state data of the plurality of detection battery cells; According to the comparison distribution relationship of the state data, combined with the battery pack structure, a balancing strategy analysis of the battery pack state is performed to obtain a balancing control strategy; Inputting the balancing control strategy and status data into the balancing control module, performing balancing strategy execution configuration, and obtaining balancing control execution information, wherein the balancing control execution information includes the balancing executor and its execution control parameters; The balancing strategy of the battery pack state is analyzed based on the comparison distribution relationship of the state data and combined with the battery pack structure to obtain the balancing control strategy, including: According to the battery pack structure, a cell connection diagram structure is constructed, and the status data of the battery cells are projected into the cell connection diagram structure; Calculate the mean of the status data and the mean of the status parameter monitoring data based on the status data and status parameter monitoring data of all battery cells; Calculating the deviation of each battery cell based on the mean of the state data and the mean of the state parameter monitoring data to obtain a deviation comparison distribution relationship; According to the structure of the monomer connection graph, the deviation comparison distribution relationship is analyzed by partitioning the connection structure type and the relationship between the connection groups to obtain the monomer with the largest group difference and the monomer with the largest global difference; Taking the monomer with the largest group difference and the monomer with the largest global difference as targets, performing group and global balancing strategy analysis to obtain the balancing control strategy; Monitoring the state parameters of the plurality of detection battery cells includes: Establishing a collaborative monitoring group according to the connection structure types and the relationship between the connection groups of the plurality of detection battery cells, wherein the collaborative monitoring group includes a parallel group and a series group; According to the response difference relationship between the parallel group and the series group to the state parameter, configuring the state parameter monitoring equipment of the collaborative monitoring group to monitor the state parameters of the multiple detection battery cells; Taking the monomer with the largest group difference and the monomer with the largest global difference as targets, group and global balancing strategies are analyzed to obtain the balancing control strategy, including: Taking the cell with the largest difference in the group as the target, performing a local balancing strategy analysis based on the connection structure type and battery performance parameters within the group to obtain a local balancing strategy; Based on the local balancing strategy and the relationship between the global battery performance parameters, a global balancing strategy is analyzed for the cell with the largest global difference to obtain the balancing control strategy, where the balancing control strategy is one or more of intelligent balancing, passive balancing, and active balancing; Inputting the balancing control strategy and status data into the balancing control module, performing balancing strategy execution configuration, and obtaining balancing control execution information includes: Establish control lists for intelligent balancing, passive balancing, and active balancing, including control actuators, control relationships, and control parameters; Performing a path search in the control list according to the balance control strategy and the status data to obtain a control strategy path that matches the balance control strategy and maximizes the difference balance with the status data; The corresponding control executor and its control parameters are determined according to the control strategy path to obtain the balanced control execution information.

2. The battery pack status monitoring and balancing control method according to claim 1, characterized in that: The method of splitting a plurality of testing battery cells according to the battery pack structure includes: Disassemble the battery pack's structure and connection relationship to obtain the battery pack connection relationship and the number of connection groups, where the battery pack connection relationship includes series structure, parallel structure, and hybrid structure, and the number of connection groups refers to the number of battery cells in the connection relationship; The battery pack structure is split according to the battery pack connection relationship and the number of connection groups to obtain a plurality of detection battery cells, each of which has a connection structure type, a relationship between connection groups, and battery performance parameters.

3. The battery pack status monitoring and balancing control method according to claim 1, characterized in that: The state parameters include: current, voltage, temperature, internal resistance, and self-discharge rate.

4. The battery pack status monitoring and balancing control method according to claim 3, characterized in that: According to the response difference relationship between the parallel group and the series group to the state parameter, the state parameter monitoring device of the collaborative monitoring group is configured, including: Conduct current, voltage, temperature, internal resistance, and self-discharge rate tests on each battery cell in the parallel and series groups to obtain the differences in the state parameter responses of each battery cell in the parallel and series groups. When there is no difference in status parameter response, configure a bus status parameter detection device for the same group, or configure a bus status parameter detection device and a verification status parameter detection device, and establish a collection mapping relationship between the status parameter detection device and each single battery in the corresponding group; When there are differences in the state parameter responses, each battery cell is equipped with a state parameter detection device.

5. The battery pack status monitoring and balancing control method according to claim 2, characterized in that: The single cell status is analyzed based on the status parameter monitoring data to obtain the status data of multiple detected battery cells, including: Constructing a battery state assessment model, including a battery state of charge assessment sub-model and a health state assessment sub-model, wherein the battery state of charge assessment sub-model and the health state assessment sub-model are both obtained by machine learning using historical data; The state parameter monitoring data is input into the battery state evaluation model, the state of charge of the single battery is evaluated through the battery state of charge evaluation sub-model, and the health state of the single battery is evaluated through the health state evaluation sub-model to obtain the state data of the multiple detected battery cells.

6. A battery pack status monitoring and balancing control system, characterized in that: The system is used to implement the battery pack status monitoring and balancing control method according to any one of claims 1 to 5, and the system includes: A detection battery cell splitting module is used to split multiple detection battery cells according to the battery pack structure; A state parameter monitoring module is used to monitor the state parameters of the plurality of detection battery cells, perform cell state analysis based on the state parameter monitoring data, and obtain state data of the plurality of detection battery cells; a balancing control strategy acquisition module, configured to analyze the balancing strategy of the battery pack state based on the comparison and distribution relationship of the state data and in combination with the battery pack structure, and obtain a balancing control strategy; The balancing control execution information acquisition module is used to input the balancing control strategy and status data into the balancing control module, perform balancing strategy execution configuration, and obtain balancing control execution information, wherein the balancing control execution information includes the balancing executor and its execution control parameters.

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