Battery pack state monitoring and equalization control method and system

Abstract

The invention discloses a battery pack state monitoring and equalization control method and system, and relates to the technical field of battery monitoring and control, and the method comprises the steps: splitting a plurality of detection battery monomers according to the structure of a battery pack; performing state parameter monitoring on the plurality of detection battery monomers to obtain state data of the plurality of detection battery monomers; obtaining a balance control strategy according to the comparison distribution relation of the state data; and inputting the equalization control strategy and the state data into an equalization control module, and performing equalization strategy execution configuration to obtain equalization control execution information. The technical problems that in the prior art, the state of the battery pack cannot be comprehensively and accurately monitored, accurate control is difficult to conduct aiming at the imbalance problem of the battery pack, and consequently the battery pack cannot stably and efficiently operate are solved, comprehensive monitoring and accurate balance control over the state of the battery pack are achieved, and the service life of the battery pack is prolonged. And stable and efficient operation of the battery pack is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring and control, and in particular to a battery pack state 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, the 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. The existing battery pack monitoring and control technology has certain limitations and cannot comprehensively and accurately monitor the state parameters of the battery cells. It is difficult to achieve targeted and effective monitoring of different cells in the series, parallel or mixed structures in the battery pack. In terms of balancing control, traditional methods often simply treat the battery in a unified manner and cannot formulate and implement accurate balancing strategies based on the actual state 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 are used to solve the technical problems in the prior art 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 battery pack status monitoring and balancing control method is provided, the method comprising: 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 according to the state parameter monitoring data to obtain state data of the multiple detection battery cells; according to the comparison 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 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, and the balancing control execution information includes the balancing executor and its execution control parameters.

[0007] A second aspect of the present application provides a battery pack state monitoring and balancing control system, the system comprising: 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, and perform cell state analysis based on the state parameter monitoring data to obtain the state data of the multiple detection battery cells; a balancing control strategy acquisition module is used to perform balancing strategy analysis 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 a balancing actuator and its execution control parameters.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: According to the battery pack structure, multiple detection battery cells are split; the state parameters of the multiple detection battery cells are monitored to obtain the state data of the multiple detection battery cells; according to the comparison distribution relationship of the state data, the balance strategy of the battery pack state is analyzed in combination with the battery pack structure to obtain the balance control strategy; the balance control strategy and state data are input into the balance control module to perform the balance strategy execution configuration and obtain the balance control execution information. The technical effect of realizing comprehensive monitoring and precise balance control of the battery pack state and ensuring stable and efficient operation of the battery pack is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 A schematic diagram of a battery pack status monitoring and balancing control method provided in an embodiment of the present application; 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.

[0011] 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

[0012] The present application provides a battery pack status monitoring and balancing control method and system to solve the technical problems in the prior art 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.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a battery pack state monitoring and balancing control method, the method comprising: Step S100: splitting a plurality of test battery cells according to the battery pack structure.

[0015] Specifically, firstly, the composition structure and connection relationship of the battery pack are disassembled in detail, and the connection relationship of the battery pack is fully understood, including the series structure, parallel structure, and mixed structure, and the number of connection groups, that is, the number of battery cells under each connection relationship, is clarified. Then, based on the battery pack connection relationship and the number of connection groups obtained from these disassembly, the battery pack structure is split, and finally multiple detection battery cells are obtained. These detection 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 work.

[0016] Step S200: monitoring the state parameters of the plurality of detected battery cells, and performing cell state analysis according to the state parameter monitoring data to obtain state data of the plurality of detected battery cells.

[0017] Specifically, for the multiple test battery cells obtained by splitting, first establish a collaborative monitoring group according to the type of connection structure and the relationship between the connection groups, namely, a parallel group and a series group. Since different groups have different responses to state parameters, it is necessary to configure corresponding state parameter monitoring equipment accordingly to monitor the state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate. For example, assuming that the battery pack consists of 10 battery cells, cells 1-4 are connected in series as a group (recorded as series group 1), cells 5-8 are connected in series as a group (recorded as series group 2), and these two groups are connected in parallel, and cells 9 and 10 are connected in parallel separately (recorded as parallel groups). When monitoring the current of series group 1, the high-precision current sensor set in the series circuit measures the total current as 2A. At the same time, the auxiliary sensor set near each cell verifies the current consistency. The auxiliary sensor at cell 1 measures the current as 2.01A, 1.99A at cell 2, 2A at cell 3, and 2.02A at cell 4. These data are within the allowable error range and the current can be considered consistent. In terms of voltage monitoring, each battery cell is equipped with an independent high-precision and 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 the parallel group, a current sensor with high precision and wide range is configured for each parallel branch. 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 total current is measured to be 2.35A, which is used to compare and analyze the 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 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. The model uses historical data for machine learning training. The state parameter monitoring data is input into the model, and the state of charge of the single battery is evaluated through the state of charge evaluation sub-model, and the health state of the single battery is evaluated with the help of the health state evaluation sub-model, and finally the state data of multiple detection battery cells are obtained to provide data support for the subsequent balancing strategy formulation. For example, in the collected historical data, at a certain moment, the current of battery No. 1 is 1.5A, the voltage is 3.7V, the temperature is 25℃, the internal resistance is 0.05Ω, and the self-discharge rate is 0.01% / day. After calculation by the battery state of charge evaluation sub-model (using the support vector machine algorithm), the state of charge of the single battery is 60%; at the same time, the health state evaluation sub-model (using the neural network algorithm) evaluates the health state of the battery based on these parameters. The value is 0.85 (0 represents serious damage to the battery, and 1 represents a brand new healthy state of the battery), that is, 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 charge state was assessed at 55%, and the health status was assessed at 0.82.

[0018] Step S300: analyzing 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 to obtain a balancing control strategy.

[0019] Specifically, a single-cell connection diagram structure is first constructed based on the battery pack structure, and the state data of each detected battery cell is projected into the diagram to intuitively present the state distribution of each cell. Subsequently, the state data of all battery cells and the previously monitored state parameter monitoring data are combined to calculate the mean of the state data and the mean of the state parameter monitoring data. Based on these means, the deviation of each battery cell is calculated to obtain the deviation comparison distribution relationship. For example, the state of charge data of 10 battery cells (assuming they are 60%, 55%, 62%, 58%, 70%, 68%, 72%, 65%, 45%, and 48% respectively) and the state parameter monitoring data are summarized to calculate the mean. The mean state of charge is (60+55+62+58+70+68+72+65+45+48) / 10=60.3%. Calculate the state of charge deviation 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. According to 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, so as to obtain 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.

[0020] Step S400: 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 a balancing executor and its execution control parameters.

[0021] Specifically, first, establish control lists for intelligent balancing, passive balancing, and active balancing, and record in detail the control actuators, control relationships, and control parameters under each balancing mode. Then, input the obtained balancing control strategy and the acquired battery cell status data into the balancing control module. Based on these input information, the module searches in the control list to find a control strategy path that is consistent with the balancing control strategy and can maximize the difference in balancing status data. Finally, based on the determined control strategy path, identify the corresponding balancing actuator and determine its execution control parameters, thereby obtaining complete balancing control execution information, which provides a key basis for the subsequent actual execution of the balancing control operation of the battery pack.

[0022] In a possible implementation, step S100 further includes: 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 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.

[0023] 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, connection group relationships, and battery performance parameters.

[0024] Specifically, in the initial stage of battery pack status monitoring and balancing control, first of all, it is necessary to carefully sort out the composition structure of the battery pack and clearly present the layout and connection method of its various components. On this basis, the connection relationship of the battery pack is accurately judged and identified to determine whether it belongs to a series structure, a parallel structure or a mixed structure. At the same time, by counting and counting one by one, the number of battery cells contained in each connection relationship, that is, the number of connection groups, is clarified to provide a reliable data basis for subsequent battery pack status monitoring and balancing control work.

[0025] Based on the clear battery pack connection relationship (series, parallel or mixed structure) and the number of connection groups, the entire battery pack is disassembled using splitting technology. During the splitting process, each test battery cell separated from the battery pack is recorded and analyzed in detail to give it specific properties. Determine the connection structure type of each test battery cell to clarify its connection method in the original battery pack; sort out the relationship between the connection groups to understand the mutual relationship between the cell and other battery cells; at the same time, measure the battery performance parameters of each test battery cell, such as voltage, internal resistance, capacity, etc. This information provides an important data basis for subsequent accurate monitoring of battery cell status, analysis of the overall performance of the battery pack, and formulation of effective balancing control strategies.

[0026] In a possible implementation, step S200 further includes: 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.

[0027] Step S220: configuring a state parameter monitoring device of a 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.

[0028] 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, improving monitoring efficiency and accuracy, and laying the foundation for subsequent acquisition of accurate battery cell status data and ensuring stable operation of battery packs.

[0029] Since the parallel group and the series group have different responses when facing 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 this difference. The current, voltage, temperature, internal resistance, and self-discharge rate of each battery cell in the parallel group and the series group are detected respectively, and the response data of each cell on these parameters are obtained, and then the response differences between the cells are analyzed. If there is no difference in the response of the battery cells in the same group to the state parameters, then in order to effectively monitor the entire group, a bus state parameter detection device can be configured, or in order to further ensure the accuracy of the monitoring data, a bus state parameter detection device and a verification state parameter detection device can be configured, and at the same time, a collection mapping relationship between the state parameter detection device and each single cell in the corresponding group is established to accurately obtain the data of each cell. When there is a difference in the state parameter response, 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.

[0030] In a possible implementation, step S220 further includes: Step S221: The state parameters include: current, voltage, temperature, internal resistance, and self-discharge rate.

[0031] Specifically, when monitoring the status parameters of multiple test battery cells, the status parameters involved include current, voltage, temperature, internal resistance, and self-discharge rate. Current is a physical quantity that measures the flow of charge during the battery charging and discharging process. It reflects the power transmission of the battery during operation and can intuitively display the battery charging and discharging rate. It is of great significance for judging the working 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 of the battery and are the key basis for evaluating battery performance and remaining power. Temperature is closely related to the stability and life of the battery. Too high or too low temperature will affect the chemical reaction rate and performance of the battery and may even cause safety problems. Internal resistance affects the battery's charging and discharging efficiency and energy loss. Its size reflects the changes in the physical and chemical properties of the battery. The self-discharge rate is used to measure the rate at which the battery loses power when it is not working. It reflects the degree of self-consumption of the battery and is an important parameter for evaluating the long-term storage performance of the battery. By monitoring this series of status parameters, the operating status of the battery cells can be fully and accurately grasped, providing a solid data foundation for subsequent battery pack status analysis and balancing control.

[0032] In a possible implementation, step S220 further includes: 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 difference of each battery cell in the parallel group and the series group.

[0033] 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 cell in the corresponding group.

[0034] Step S224: When there is a state parameter response difference, a state parameter detection device is configured for each battery cell.

[0035] Specifically, the state parameter response differences of each battery cell in the parallel group and the series group are fully obtained. In terms of current monitoring, the series group uses a high-precision current sensor set in the series loop to monitor the total current, and sets an auxiliary sensor near each cell to verify the current consistency; the parallel group configures a current sensor with high precision and wide range for each parallel branch, and sets a total current sensor at the input and output ends of the group for comparative analysis of current distribution. In voltage monitoring, the series group is equipped with an independent high-precision, high-resolution voltage sensor for each battery cell, and a redundant design is used to improve monitoring accuracy; the parallel group is also equipped with a voltage sensor for each branch, and sensors are set at the input and output ends to compare and analyze voltage anomalies. In temperature monitoring, both the series and parallel groups set high-precision, fast-response temperature sensors at key positions of the battery pack, and also use a distributed system to achieve all-round monitoring. Internal resistance monitoring requires high-precision and high-stability monitoring of each battery cell to detect problems such as battery aging. Self-discharge rate monitoring is more accurate through regular open circuit voltage measurement combined with model estimation to detect anomalies in a timely manner.

[0036] In the battery pack status monitoring process, the monitoring equipment is configured based on the detection results of the difference in the response of the battery cell status parameters in the parallel group and the series group. When, after detailed detection, it is determined that there is no difference in the response of each battery cell in the same group (whether it is a parallel group or a series group) to the status parameters such as current, voltage, temperature, internal resistance, and self-discharge rate, in order to achieve effective monitoring of the group status parameters, two configuration schemes will be adopted. One scheme is to configure a bus status parameter detection device, which can uniformly collect the status parameters of the entire group, and through reasonable wiring and data transmission settings, it can realize the acquisition of the status parameters of all battery cells in the group. Another 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, and to ensure that the status parameters of each single cell can be accurately collected and matched through precise circuit connection, address coding or software settings, thereby providing a reliable data basis for subsequent battery pack status analysis and balancing control.

[0037] When in a parallel group or a series group, after testing the state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate, it is found that there are differences in the state parameter responses, 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 the difference in state parameter responses means that there is inconsistency in the states of each battery cell. If the general monitoring equipment configuration method continues to be used, the subtle differences between the cells will be missed, and the real-time state of each cell cannot be accurately obtained. Configuring a separate detection device for each cell can accurately measure the unique situation 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 the subsequent accurate battery cell state analysis and the formulation of battery pack balancing strategies, and ensuring stable and efficient operation of the battery pack.

[0038] In a possible implementation, step S200 further includes: Step S230: construct 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.

[0039] Step S240: input the state parameter monitoring data into the battery state evaluation model, evaluate the state of charge of the single cell through the battery state of charge evaluation sub-model, and evaluate the state of health of the single cell through the health state evaluation sub-model, to obtain the state data of the multiple detected battery cells.

[0040] Specifically, when constructing a battery state assessment model, different machine learning algorithms are used to train the battery state of charge assessment submodel and the health state assessment submodel to achieve accurate assessment of the battery state. For the battery state of charge assessment submodel, the support vector machine (SVM) algorithm is used. First, a large amount of historical data related to the battery state of charge is collected. These data contain state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate of the battery at different charging and discharging stages, and the corresponding battery state of charge true value is marked. These data are preprocessed, such as normalization, to eliminate the dimensional differences between different parameters and improve the model training effect. During the training process, the SVM algorithm will find an optimal classification hyperplane in the high-dimensional space so that data points of different states of charge can be separated as accurately as possible. By adjusting the kernel function (such as the radial basis kernel function) and its parameters, the model can better fit the nonlinear relationship of the data. For example, in the training set, the algorithm will continuously learn how to determine the state of charge range of the battery based on the current state parameter combination, thereby establishing a mapping relationship between the state parameters and the state of charge. For the health status assessment sub-model, a neural network algorithm, multi-layer perceptron (MLP), is used to clean and pre-process the collected historical data related to the battery health status to remove outliers and noise data. MLP consists of an input layer, multiple hidden layers, and an output layer. The battery state parameters are used as neuron inputs in the input layer, and complex features in the data are automatically extracted through nonlinear transformations of neurons in the hidden layer (such as using the ReLU activation function). The number of neurons and the number of layers in the hidden layer can be adjusted and optimized according to the complexity of the actual data. The output layer outputs the evaluation results of the battery health status, such as a value between 0 and 1 to represent the health of the battery, 0 represents serious damage to the battery, and 1 represents the battery in a new health state. During the training process, the back propagation algorithm is used to continuously adjust the connection weights between neurons to minimize the error between the health status value predicted by the model and the actual labeled health status value, so as to train a sub-model that can accurately assess the battery health status. The battery state of charge assessment sub-model and health status assessment sub-model are trained respectively by these two machine learning algorithms, and finally a complete and accurate battery status assessment model is constructed.

[0041] The monitoring data of state parameters such as current, voltage, temperature, internal resistance, and self-discharge rate monitored by carefully configured equipment in the early stage are input into the model in an orderly manner. The battery state of charge evaluation submodel will accurately evaluate the state of charge of each single battery based on the association between the state parameters and the state of charge learned from a large amount of historical data during the training phase, and calculate the proportion of the current remaining power of the single battery to the total capacity, so as to quantify the state of charge of the battery. At the same time, the health status evaluation submodel evaluates the health status of the single battery based on the relationship between the internal condition of the battery and the state parameters learned by itself, determines whether the battery has potential problems such as capacity decay and abnormal internal resistance, and gives an evaluation result reflecting the health of the battery. Through the parallel and collaborative work of these two submodels, each detected battery cell is comprehensively and meticulously evaluated, and finally a rich and in-depth state data of multiple detected battery cells is obtained. These data not only intuitively present the current power status of the battery, but also reveal the health status of the battery, providing an indispensable and important basis for the subsequent formulation of the battery pack balance control strategy, and effectively ensuring the stable operation and efficient management of the battery pack.

[0042] In a possible implementation, step S300 further includes: 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.

[0043] Step S320: Calculate the mean value of the state data and the mean value of the state parameter monitoring data according to the state data and the state parameter monitoring data of all battery cells.

[0044] Step S330: Based on the state data mean and the state parameter monitoring data mean, the deviation of each battery cell is calculated to obtain a deviation comparison distribution relationship.

[0045] Step S340: performing connection structure type and connection group relationship partition analysis 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.

[0046] Step S350: 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.

[0047] Specifically, based on the specific structure of the battery pack, a single-cell connection graph structure is constructed. This graph structure can intuitively display the connection relationship between battery cells, such as series, parallel or mixed connection. Subsequently, the battery cell status data previously obtained through the battery status assessment model, including state of charge, health status, etc., are projected into the single-cell connection graph structure. The status of each cell in the battery pack is clear at a glance in the context of the connection relationship, providing an intuitive perspective for subsequent analysis.

[0048] The previously acquired state data of all battery cells, such as the state of charge data obtained by the battery state of charge assessment submodel, the health state data obtained by the health state assessment submodel, etc., as well as the state parameter monitoring data obtained by monitoring each battery cell, such as current, voltage, temperature, internal resistance and self-discharge rate, are summarized. These data are processed separately using statistical methods. For the state data, the mean of the state data is obtained by calculating the sum of all the single-cell state data and then dividing it by the number of cells. The mean reflects the average level of the state of the battery cells in the battery pack. Similarly, the same calculation method is used for the state parameter monitoring data, that is, the current, voltage, temperature, internal resistance, self-discharge rate and other parameters of all cells are summed up respectively, and then divided by the number of cells to obtain the corresponding state parameter monitoring data mean. These means serve as an important reference benchmark for subsequent analysis, providing a basis for accurately judging the deviation of each battery cell from the overall average state, and thus laying the foundation for formulating a reasonable battery pack balancing control strategy.

[0049] Compare the status data of each battery cell with the corresponding status data mean, and calculate the difference between the two. This difference is the deviation of the battery cell status data; similarly, compare the status parameter monitoring data of each battery cell with the status parameter monitoring data mean, and calculate the respective deviations. By performing such calculations on all battery cells, a series of deviation data are obtained. By sorting and comparing these deviation data according to the connection structure type of the battery cell and the relationship between the connection groups, the deviation comparison distribution relationship can be obtained. This relationship clearly shows the degree of deviation of different battery cells from the overall average level in terms of status data and status parameter monitoring data.

[0050] Using the monomer connection diagram as the framework, the battery monomers are divided into different areas or groups according to the connection structure type (series, parallel, mixed) and the relationship between the connection groups. Within each group, the deviation data of each monomer is compared to find the monomer with the largest state difference from other monomers in the group, that is, the monomer with the largest group difference. This monomer has the most significant deviation from the average state in the 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 monomer with the largest difference from the overall average state, that is, the monomer with the largest global difference. The precise determination of these two types of monomers through such partition analysis 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.

[0051] Based on the determined monomers with the largest group differences and the largest global differences, targeted balancing control strategies are formulated. For the monomer with the largest group difference, the difference characteristics between the monomer and other monomers in the group are analyzed in combination with the internal connection structure type of the group to which it belongs, such as series, parallel or mixed connection, and the performance parameters of the battery, so as to formulate a local balancing strategy suitable for the group. For example, if the group is a series structure, consider balancing the differences between the monomers by adjusting the charging or discharging sequence. On this basis, from a global perspective, according to the relationship between the global battery performance parameters, the local balancing strategy is expanded and optimized, and the balancing strategy of the monomer with the largest global difference is further analyzed. Combining the local and global analysis results, the final balancing control strategy is determined, which covers one or more methods of intelligent balancing, passive balancing and active balancing. Through this comprehensive analysis process, the overall state of the battery pack can be effectively adjusted to ensure that the state of each monomer in the battery pack is more balanced, and the performance and stability of the battery pack are improved.

[0052] In a possible implementation, step S350 further includes: 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.

[0053] Step S352: Based on the local balancing strategy and the relationship between the global battery performance parameters, the global balancing strategy is analyzed for the single 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.

[0054] Specifically, first, the state parameters such as voltage, current, temperature, internal resistance, and self-discharge rate of the monomer with the largest difference in the group are analyzed in detail to find the parameters with the largest deviation, so as to determine the main factors that cause it to be inconsistent with other monomers in the group. Then, different focuses are taken according to the connection structure type of the group. If it is a series group, since the current is equal everywhere in the series circuit, the voltage difference becomes a key factor affecting the performance of the battery pack, so the focus is on the voltage difference; if it is a parallel group, whether the current distribution is uniform is crucial to the stable operation of the battery pack, so the problem of uneven current distribution is considered. Then, the appropriate local balancing strategy is selected according to the analysis results. When the voltage difference of the battery cells in the group is large and the battery capacity is small, the passive balancing strategy is selected, which consumes the energy of the high-voltage battery with the help of resistance, so that the voltage of each battery gradually tends to be consistent. If there is uneven current distribution in the battery cells in the group and the battery capacity is large, the active balancing strategy is selected to transfer the energy of the battery with larger current to the battery with smaller current through energy transfer to achieve balanced current distribution. When there are multiple inconsistent state parameters of battery cells in the group and there are high requirements for the accuracy and efficiency of balancing, the intelligent balancing strategy is more appropriate. The machine learning algorithm is used to learn historical data and identify the performance change pattern of the battery under different state parameter combinations. When it is detected that there are multiple inconsistent state parameters of battery cells in the group, the algorithm will calculate the optimal balancing target value for each single battery based on the learned knowledge. During the balancing process, the balancing control strategy is dynamically adjusted according to the real-time status and the optimal target value. For example, when it is found that the voltage of a battery cell is too high and the temperature is abnormal, the algorithm will comprehensively consider the heat dissipation measures and adjust the energy transfer method to avoid other problems caused by simply reducing the voltage. It can also flexibly select different balancing actuators (such as switches, converters, etc.) and control parameters (such as current size, time interval, etc.) according to the overall charge and discharge state of the battery pack, ambient temperature and other factors to achieve the best balancing effect while ensuring high accuracy and efficiency of the balancing process.

[0055] The various state parameters of the single cell with the largest global difference, including voltage, current, temperature, internal resistance and self-discharge rate, are analyzed in depth. Combined with its position in the entire battery pack and its connection relationship with other single cells, its influence on the overall performance of the battery pack is evaluated. If the voltage of the single cell with the largest global difference is significantly higher than that of other batteries and the capacity of the entire battery pack is small, passive balancing is selected as the global balancing strategy. By connecting a resistor to the circuit, the high-voltage single cell with the largest global difference is discharged through the resistor, gradually consuming its excess energy, thereby reducing the voltage, making the voltage of the global battery pack tend to be consistent, and ensuring the stable operation of the battery pack. When the current of the single cell with the largest global difference is obviously abnormal and the capacity of the entire battery pack is large, the active balancing strategy becomes the first choice. With the help of circuit design and control technology, the active balancing strategy can transfer the energy of the battery with larger current (the single cell with the largest global difference) to the battery with smaller current, realize the uniform distribution of current in the global battery pack, and effectively avoid the problem of overcharging and discharging of some batteries due to uneven current. When there are multiple inconsistencies in the state parameters of the single cell with the largest global difference and high requirements for the performance and life of the battery pack, the intelligent balancing strategy plays a key role. The intelligent balancing strategy combines the advantages of passive balancing and active balancing, and uses intelligent algorithms to monitor the overall status and real-time working conditions of the battery pack in real time. According to different working scenarios and changes in battery status, the balancing control strategy is dynamically adjusted to accurately balance the single cell with the largest global difference 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 according to the specific characteristics of the single cell with the largest global difference and the local balancing strategy. For example, after using active balancing to solve some problems locally, the global level is 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 finally obtaining a balancing control strategy that includes one or more of intelligent balancing, passive balancing and active balancing.

[0056] In a possible implementation, step S400 further includes: Step S410: establishing control lists for intelligent balancing, passive balancing and active balancing respectively, including control actuators, control relationships and control parameters.

[0057] Step S420: performing a path search in the control list according to the balanced control strategy and the status data, and obtaining a control strategy path that matches the balanced control strategy and maximizes the difference balance with the status data.

[0058] Step S430: Determine the corresponding control executor and its control parameters according to the control strategy path, and obtain the balanced control execution information.

[0059] 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, which are used to accurately control the energy flow and status monitoring of the battery. The control relationship involves the collaborative working logic between the actuators, such as how the sensor feeds back the monitoring data to the controller, and how the controller controls the action of the power switch based on this data. The control parameters include voltage regulation accuracy, current limit value, balancing time interval, etc. These parameters are dynamically adjusted to adapt to the real-time status of the battery pack. In the control list of passive balancing, the control actuators are mainly energy-consuming elements such as resistors, and their control relationship is relatively simple, that is, when the voltage difference of the battery cells is detected, the circuit is connected by controlling the resistor to dissipate the energy of the high-voltage battery in the form of heat. The control parameters include the resistance value of the resistor, the maximum current allowed to pass, etc. These parameters are determined according to the voltage range, capacity, and expected balancing speed of the battery pack. In the control list of active balancing, the control actuators include inductors, capacitors, DC-DC converters, etc., which are used to realize energy transfer between batteries. The control relationship is reflected in how these components work together to complete the efficient transfer of energy, such as how the DC-DC converter adjusts the output voltage and current according to the battery status. The control parameters include the power of energy transfer, the charge and discharge time constants of the inductor and capacitor, etc. These parameters are set according to the characteristics of the battery pack and the balancing requirements to ensure that the active balancing process is stable and efficient, thus laying a solid foundation for subsequent path search based on the balancing control strategy and battery status data and determining the final balancing control execution information.

[0060] According to the existing balancing control strategy, the matching operation path is found from the control list of the three balancing methods. For example, if the balancing control strategy is determined to be intelligent balancing, it is screened in the control list of intelligent balancing. At the same time, the goal of maximizing the balancing of state data differences is considered. It will analyze the state data of the current battery cells, such as the deviation of the parameters such as voltage, current, temperature, internal resistance and self-discharge rate of each cell from the mean. According to these deviations, the path that can minimize these differences is searched in the control list. This means that the control actuators, control relationships and control parameters in the control list will be weighed comprehensively to find an optimal combination. For example, for several battery cells with large voltage differences, appropriate control actuators (such as specific switch combinations or converters) will be selected, the collaborative working relationship between them (when to turn on, turn off, in what order to work, etc.) will be determined, and appropriate control parameters (such as adjusting the voltage amplitude, controlling the current size, etc.) will be set, and finally a control strategy path that meets the requirements of the balancing control strategy and can maximize the balance of the battery cell state will be formed, laying the foundation for the subsequent determination of specific actuators and control parameters.

[0061] According to the determined control strategy path, the corresponding control actuator and its control parameters are accurately determined. Once determined, the balanced control execution information is obtained, which contains specific actuator operation instructions and detailed parameters, such as when a switch is turned on and off, the working mode and parameter adjustment of the converter, etc. This information will be transmitted to the balanced control module, thereby driving the corresponding actuator to operate according to the set parameters, realizing balanced control of the battery pack and ensuring stable and efficient operation of the battery pack.

[0062] Embodiment 2 is based on the same inventive concept as a battery pack state 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, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The detection battery cell splitting module 10 is used to split a plurality of detection battery cells according to the battery pack structure.

[0063] The state parameter monitoring module 20 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.

[0064] The balancing control strategy acquisition module 30 is used 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 to obtain the balancing control strategy.

[0065] The balancing control execution information acquisition module 40 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 a balancing executor and its execution control parameters.

[0066] Furthermore, the system is also used to implement the following functions: 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.

[0067] Furthermore, the system is also used to implement the following functions: 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; and a state parameter monitoring device of the collaborative monitoring group is configured based on the response difference relationship between the parallel group and the series group to the state parameters to monitor the state parameters of the multiple detection battery cells.

[0068] Furthermore, the system is also used to implement the following functions: The state parameters include: current, voltage, temperature, internal resistance, and self-discharge rate.

[0069] Furthermore, the system is also used to implement the following functions: The current, voltage, temperature, internal resistance and self-discharge rate of each battery cell in the parallel group and the series group are detected respectively to obtain the state parameter response difference of each battery cell in the parallel group and the series group; when there is no state parameter response difference, 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 state parameter response difference exists, a state parameter detection device is configured for each battery cell.

[0070] Furthermore, the system is also used to implement the following functions: 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 state of charge of a single cell is evaluated through the battery state of charge assessment sub-model, and the health state of a single cell is evaluated through the health state assessment sub-model, to obtain the state data of the multiple detected battery cells.

[0071] Furthermore, the system is also used to implement the following functions: 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; according to 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 connection group relationship 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.

[0072] Furthermore, the system is also used to implement the following functions: Taking the single 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 single 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.

[0073] Furthermore, the system is also used to implement the following functions: 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 list 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 the status data difference; determine the corresponding control actuator and its control parameters according to the control strategy path to obtain the balancing control execution information.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0076] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these 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; Performing state parameter monitoring on the multiple detection battery cells, performing cell state analysis based on the state parameter monitoring data, and obtaining state data of the multiple detection battery cells; According to the comparison 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 a balancing control module to perform balancing strategy execution configuration and obtain balancing control execution information, wherein the balancing control execution information includes a balancing executor and its execution control parameters.

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 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 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 split 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, connection group relationships, and battery performance parameters.

3. The battery pack status monitoring and balancing control method according to claim 2, characterized in that: 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 difference in responses of the parallel group and the series group to the state parameters, a state parameter monitoring device of the cooperative monitoring group is configured to monitor the state parameters of the multiple detection battery cells.

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

5. The battery pack status monitoring and balancing control method according to claim 4, 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 group and the series group to obtain the state parameter response differences of each battery cell in the parallel group and the series group; When there is no difference in status parameter response, a bus status parameter detection device is configured for the same group, or a bus status parameter detection device and a verification status parameter detection device are configured, and a collection mapping relationship between the status parameter detection device and each single cell in the corresponding group is established; When there is a difference in the state parameter response, a state parameter detection device is configured for each battery cell.

6. The battery pack status monitoring and balancing control method according to claim 2, characterized in that: According to the state parameter monitoring data, the single cell state analysis is performed to obtain the state 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 cell is evaluated through the battery state of charge evaluation sub-model, and the health state of the single cell is evaluated through the health state evaluation sub-model, so as to obtain the state data of the multiple detected battery cells.

7. The battery pack status monitoring and balancing control method according to claim 2, characterized in that: According to the comparison 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, including: According to the battery pack structure, a single cell connection diagram structure is constructed, and the state data of the battery single cells are projected into the single cell connection diagram structure; Calculate the mean of the state data and the mean of the state parameter monitoring data according to the state data and the state parameter monitoring data of all battery cells; Based on the state data mean and the state parameter monitoring data mean, calculating the deviation of each battery cell to obtain a deviation comparison distribution relationship; According to the monomer connection graph structure, the deviation comparison distribution relationship is analyzed by connection structure type and connection group relationship partition, so as 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, group and global balancing strategies are analyzed to obtain the balancing control strategy.

8. The battery pack status monitoring and balancing control method according to claim 7, characterized in that: 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 local balancing strategy analysis according to the connection structure type and battery performance parameters within the group, and obtaining a local balancing strategy; Based on the local balancing strategy and the relationship between the global battery performance parameters, the global balancing strategy is analyzed for the single 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.

9. The battery pack status monitoring and balancing control method according to claim 8, characterized in that: The balancing control strategy and status data are input into the balancing control module to perform balancing strategy execution configuration and obtain balancing control execution information, including: 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 balanced control strategy and the state data to obtain a control strategy path that matches the balanced control strategy and maximizes the balance of the difference with the state data; The corresponding control executor and its control parameters are determined according to the control strategy path to obtain the balanced control execution information.

10. A battery pack status monitoring and balancing control system, characterized in that: The system is used to implement a battery pack state monitoring and balancing control method according to any one of claims 1 to 9, and the system comprises: 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, 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, used 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, to 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 a balancing executor and its execution control parameters.

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