Battery cell dynamic adjustment method, device, apparatus and storage medium

Abstract

A battery cell dynamic adjustment method, device, equipment and storage medium, by periodically collecting the corresponding target operating state parameters of a plurality of battery cells, and according to the target operating state parameter fusion history operation data, determine the abnormal level of a plurality of battery cells in the current monitoring period, and then according to the comprehensive operation index, dynamically adjust the charge and discharge rate, current or connection state of the target battery cell. By fusing the target operating state parameters and historical operation data of the plurality of battery cells, combining the pre-constructed battery cell comprehensive operation health state evaluation model to analyze the target battery cell that needs to be adjusted and the abnormal level of the target battery cell, and adjusting the state of the target battery cell combined with the abnormal level, to ensure that the battery cell works in an efficient and safe state, thereby improving the overall performance of the energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of wireless energy storage technology, and particularly relates to a method, device, equipment and storage medium for dynamic adjustment of a single battery cell. Background Technology

[0002] With the widespread application of renewable energy globally, energy storage systems have become an indispensable component of modern power grids and electric vehicles. Common energy storage systems typically consist of multiple battery cells, which require energy management and balancing to prevent overcharging, over-discharging, or uneven discharge due to performance differences between cells. Traditional energy storage systems are limited to simple state monitoring and data transmission, lacking real-time optimization and control of the overall state of individual battery cells. This results in inefficient control over the overall performance of the energy storage system and poses safety hazards. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, device, and storage medium for dynamic adjustment of battery cells. By integrating the target operating state parameters and historical operating data of multiple battery cells, and combining them with a pre-constructed comprehensive operating health status assessment model for battery cells, the target battery cells that need to be adjusted in state and the comprehensive operating indicators of the target battery cells are analyzed. The target battery cells are then adjusted in combination with the comprehensive operating indicators to ensure that the battery cells operate in a highly efficient and safe state, thereby improving the overall performance of the energy storage system.

[0004] The first aspect of this invention provides a method for dynamic adjustment of individual battery cells, applied to an energy storage system. The energy storage system includes multiple individual battery cells and a central processing system. The method for dynamic adjustment of individual battery cells is executed by the central processing system and includes:

[0005] For each of the multiple battery cells, their corresponding target operating state parameters are periodically collected;

[0006] The target operating status parameters are combined with the historical operating data of the battery cell and input into a pre-built comprehensive operating health status assessment model for analysis to determine the target battery cell and its abnormality level in the current monitoring cycle; the target battery cell is the battery cell that needs to be adjusted in its operating status.

[0007] The operating status of the target battery cell is dynamically adjusted based on the anomaly level.

[0008] In one embodiment, the target operating state parameters include battery temperature, state of charge, voltage, and current; the historical operating data includes historical temperature change rate and state of charge change rate.

[0009] In one embodiment, the step of fusing the target operating state parameters with the historical operating data of the battery cell includes:

[0010] Based on the historical temperature change rate and the state of charge change rate, missing values ​​and obviously unrealistic operating state parameters are removed from the target operating state parameters, and the remaining operating state parameters are normalized.

[0011] In one embodiment, the pre-built battery cell comprehensive operational health status assessment model includes a multi-dimensional threshold condition model, a logical judgment model, and a logical decision module;

[0012] The process involves integrating the target operating status parameters with the historical operating data of the battery cell, inputting them into a pre-built comprehensive operating health status assessment model for analysis, and determining the target battery cell and its anomaly level in the current monitoring period, including:

[0013] The normalized data is input into the multidimensional threshold condition model for analysis to obtain the first comprehensive operating index of each battery cell.

[0014] The normalized data is input into the logical judgment model to obtain the second comprehensive operating index of each battery cell.

[0015] The first comprehensive operating index and the second comprehensive operating index are input into the logical decision module for analysis to obtain the target battery cell and the abnormality level of the target battery cell in the current monitoring period.

[0016] In one embodiment, the step of inputting the normalized data into the multidimensional threshold condition model for analysis to obtain the first comprehensive operating index of each battery cell includes:

[0017] The normalized data is input into the multidimensional threshold condition model, which analyzes the normalized data based on a multi-level threshold system to obtain the first comprehensive operating index of each battery cell.

[0018] In one embodiment, the logical judgment model is a pairing space model constructed based on preset logical rules;

[0019] The normalized data is input into the logical judgment model to obtain the second comprehensive operating index of each battery cell, including:

[0020] The normalized data is input into the pair space model, which analyzes the data based on the preset logic rules and judgment combinations to obtain the second comprehensive operating index of each battery cell.

[0021] In one embodiment, the step of inputting the first comprehensive operating indicator and the second comprehensive operating indicator into the logical decision module for analysis to obtain the target battery cell and the anomaly level of the target battery cell in the current monitoring period includes:

[0022] The first and second comprehensive operating indicators are input into the logical decision module. The logical decision module analyzes the first and second comprehensive operating indicators using a predefined decision matrix to obtain a comprehensive operating score for each battery cell. Based on the comprehensive operating score, the target battery cell and its anomaly level are identified. Real-time operation and maintenance decisions are made for the target battery cell based on the anomaly level.

[0023] A second aspect of this application provides a battery cell dynamic adjustment device, comprising:

[0024] The acquisition module is used to periodically acquire the target operating status parameters corresponding to the multiple battery cells.

[0025] The determination module is used to input the target operating status parameters and the historical operating data of the battery cell into a pre-built comprehensive operating health status assessment model for analysis, and determine the target battery cell and the abnormality level of the target battery cell in the current monitoring cycle; the target battery cell is the battery cell that needs to be adjusted in operating status.

[0026] The adjustment module is used to dynamically adjust the operating status of the target battery cell according to the anomaly level.

[0027] In one embodiment, the target operating state parameters include battery temperature, state of charge, voltage, and current; the historical operating data includes historical temperature change rate and state of charge change rate.

[0028] In one embodiment, the determining module is specifically used to remove missing values ​​and obviously unrealistic operating state parameters from the target operating state parameters based on the historical temperature change rate and the state of charge change rate, and to normalize the remaining operating state parameters.

[0029] In one embodiment, the pre-constructed comprehensive operational health status assessment model for a single battery cell includes a multi-dimensional threshold condition model, a logical judgment model, and a logical decision-making module; the determining unit includes:

[0030] The first sub-unit is used to input the normalized data into the multidimensional threshold condition model for analysis, and to obtain the first comprehensive operating index of each battery cell.

[0031] The second sub-unit is used to input the normalized data into the logical judgment model to obtain the second comprehensive operating index of each battery cell.

[0032] The third sub-unit is used to analyze the first comprehensive operating index and the second comprehensive operating index input to the logical decision module to obtain the target battery cell and the abnormality level of the target battery cell in the current monitoring cycle.

[0033] In one embodiment, the first obtaining subunit is specifically used for:

[0034] The normalized data is input into the multidimensional threshold condition model, which analyzes the normalized data based on a multi-level threshold system to obtain the first comprehensive operating index of each battery cell.

[0035] In one embodiment, the logical judgment model is a pairing space model constructed based on preset logical rules; the second obtained sub-unit is specifically used for:

[0036] The normalized data is input into the pair space model, which analyzes the data based on the preset logic rules and judgment combinations to obtain the second comprehensive operating index of each battery cell.

[0037] In one embodiment, the third obtaining subunit is specifically used for:

[0038] The first and second comprehensive operating indicators are input into the logical decision module. The logical decision module analyzes the first and second comprehensive operating indicators using a predefined decision matrix to obtain a comprehensive operating score for each battery cell. Based on the comprehensive operating score, the target battery cell and its anomaly level are identified. Real-time operation and maintenance decisions are made for the target battery cell based on the anomaly level.

[0039] A third aspect of this application provides a battery cell dynamic adjustment device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; the processor executes the computer program to implement the steps of the battery cell dynamic adjustment method described in the first aspect above.

[0040] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the battery cell dynamic adjustment method described in the first aspect above.

[0041] The beneficial effects of this application's embodiments are as follows: A method, apparatus, device, and storage medium for dynamic adjustment of battery cells are provided. By periodically collecting target operating state parameters for multiple battery cells and fusing historical operating data with these parameters, the abnormality level of the multiple battery cells in the current monitoring cycle is determined. Then, based on the abnormality level, the charge / discharge rate, current, or connection status of the target battery cells is dynamically adjusted. The aim is to fuse the target operating state parameters and historical operating data of multiple battery cells, and combine this with a pre-built comprehensive battery cell operating health status assessment model to analyze the target battery cells requiring state adjustment and their abnormality levels. State adjustment is then performed on the target battery cells based on their abnormality levels to ensure that the battery cells operate in a highly efficient and safe state, thereby improving the overall performance of the energy storage system. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram illustrating the implementation process of a battery cell dynamic adjustment method provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of a battery cell dynamic adjustment device provided in an embodiment of this application;

[0045] Figure 3 This is a schematic diagram of a battery cell dynamic adjustment device provided in an embodiment of this application. Detailed Implementation

[0046] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0048] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0049] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0051] In the description of the embodiments of this application, the term "multiple frames" refers to two or more (including two).

[0052] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0053] This invention provides a method, apparatus, device, and storage medium for dynamic adjustment of battery cells. By integrating the target operating state parameters and historical operating data of multiple battery cells, and combining them with a pre-built comprehensive operating health status assessment model for battery cells, the method analyzes the target battery cells that need to be adjusted and their comprehensive operating indicators. The method then adjusts the target battery cells based on their comprehensive operating indicators to ensure that the battery cells operate in a highly efficient and safe state, thereby improving the overall performance of the energy storage system.

[0054] Please see Figure 1 , Figure 1This is a schematic diagram illustrating the implementation flow of a battery cell dynamic adjustment method according to an embodiment of this application. This battery cell dynamic adjustment method is applied to an energy storage system, which includes multiple battery cells and a central processing system. The battery cell dynamic adjustment method is executed by the central processing system, which can be integrated into a cloud or a local server. Figure 1 As can be seen, the battery cell dynamic adjustment method provided in this application includes:

[0055] S101: For multiple battery cells, periodically collect their corresponding target operating status parameters.

[0056] The target operating status parameters include battery temperature, state of charge, voltage, and current.

[0057] In this embodiment, each battery cell is equipped with a temperature sensor (such as an NTC), a voltage / current detection circuit (such as a Hall sensor), and a SOC estimation module (such as a coulomb counter). The central processing system periodically sends data synchronization acquisition tasks to the data acquisition unit (DAU). The DAU uses a multi-channel ADC to synchronously acquire the detection signals from each temperature sensor, voltage / current detection circuit, and SOC estimation module to obtain the target operating state parameters corresponding to multiple battery cells.

[0058] S102: Input the target operating status parameters and historical operating data of the battery cells into the pre-built comprehensive operating health status assessment model of the battery cells for analysis, and determine the target battery cell and the abnormality level of the target battery cell in the current monitoring period.

[0059] Among them, the target battery cell is the battery cell that needs to have its operating status adjusted.

[0060] Historical operational data includes historical temperature change rate and state of charge change rate within multiple different historical acquisition periods.

[0061] The target operating state parameters are integrated with the historical operating data of individual battery cells, including: based on the historical temperature change rate and state of charge change rate, missing values ​​and operating state parameters that are obviously inconsistent with reality are removed from the target operating state parameters, and the remaining operating state parameters are normalized.

[0062] Specifically, missing values ​​in target operating state parameters (such as voltage, current, temperature, and state of charge) are identified and deleted. A temperature change rate threshold is determined from historical operating data based on the temperature change rate, and temperature values ​​exceeding this threshold are discarded as obviously unrealistic operating state parameters. A reasonable range for the state of charge is determined based on the state of charge change rate, and states of charge outside this range are removed. The remaining data is then normalized. These technical solutions ensure data accuracy and completeness, reliably reflecting the performance of individual battery cells and providing high-quality data input for subsequent analysis.

[0063] The pre-built comprehensive operational health status assessment model for individual battery cells includes a multi-dimensional threshold condition model, a logical judgment model, and a logical decision-making module.

[0064] The normalized data is input into a pre-constructed comprehensive operational health status assessment model for battery cells for analysis to determine the target battery cell and its anomaly level in the current monitoring period. This includes: inputting the normalized data into a multi-dimensional threshold condition model for analysis to obtain the first comprehensive operational index for each battery cell; inputting the normalized data into a logical judgment model to obtain the second comprehensive operational index for each battery cell; and inputting the first and second comprehensive operational indices into a logical decision module for analysis to obtain the target battery cell and its anomaly level in the current monitoring period.

[0065] In this embodiment, the multidimensional threshold condition model is a condition model based on a multi-level threshold system. Normalized data is input into the multidimensional threshold condition model for analysis to obtain the first comprehensive operating index of each battery cell. This includes: inputting normalized data into the multidimensional threshold condition model; the multidimensional threshold condition model analyzing the normalized data based on a multi-level threshold system to obtain the first comprehensive operating index of each battery cell.

[0066] Specifically, the multi-dimensional threshold condition model provided in this application includes a three-level threshold system condition model. This three-level threshold system condition model presets three levels of thresholds for each target operating state parameter: safety, early warning, and alarm. When the value of a target operating state parameter falls within the corresponding threshold, the first comprehensive operating index for the corresponding state is output. For example, a temperature less than 1.2 corresponds to a safety threshold, a temperature greater than or equal to 1.2 and less than 1.8 corresponds to an early warning threshold, and a temperature greater than or equal to 1.8 corresponds to an alarm threshold. Similarly, a voltage less than 0.7 corresponds to a safety threshold, a voltage greater than or equal to 0.7 and less than 1.3 corresponds to an early warning threshold, and a voltage greater than or equal to 1.3 corresponds to an alarm threshold, and so on. The state corresponding to each operating state parameter (temperature, SOC, voltage, and current) is represented by binary code, for example, 00: safety, 01: early warning, and 11: alarm. The first comprehensive operating index is generated by an 8-bit binary word, for example: the first comprehensive operating index can be represented as: temperature 01 + SOC 00 + voltage 11 + current 01 → 01001101.

[0067] For example, the normalized data is input into a multi-dimensional threshold condition model. This model analyzes the normalized data based on a multi-level threshold system to obtain the first comprehensive operating index for each battery cell. This includes: inputting the normalized data into the multi-dimensional threshold condition model; analyzing the normalized data based on the multi-level threshold system; if a data value crosses from the safe zone to the warning or alarm zone, continuous observation is required, and the value remains consistent within a preset sampling period (e.g., three sampling periods); then, the first comprehensive operating index for the corresponding state is generated. Alternatively, if a data value falls back from the alarm or warning zone, the first comprehensive operating index for the corresponding state is immediately generated. This multi-dimensional threshold condition model not only avoids misjudgments due to instantaneous fluctuations but also ensures that the data remains in a safe state.

[0068] The logical judgment model is a pair space model constructed based on preset logical rules; the normalized data is input into the logical judgment model to obtain the second comprehensive operating index of each battery cell, including:

[0069] The normalized data is input into the pair space model, which analyzes the data based on preset logical rules and judgment combinations to obtain the second comprehensive operating index of each battery cell.

[0070] Specifically, the construction process of the coupled space model includes: mapping normalized data into Boolean propositions, such as: P001: temperature ∈ [0.2, 0.8] (normal range); P002: SOC change rate ≤ 0.1 / min; P003: voltage fluctuation standard deviation < 0.05; combining Boolean propositions through logical operations (such as AND / OR / NOT) to form health status judgment rules for individual battery cells, outputting binary judgment results, and mapping them into continuous probability values ​​to accommodate probabilistic distributions. For example: emergency fault = (P001 == 0) OR (P002 == 0) abnormal temperature or SOC; aging warning = (P003 == 0) AND (P004 == 1) abnormal voltage but normal temperature. Further, the output of the logical operations combined into Boolean propositions is mapped into continuous probability values ​​to facilitate correlation analysis between the logical decision module and the first comprehensive operating index.

[0071] For example, the normalized data is input into a logical judgment model to obtain the second comprehensive operating index of each battery cell. This includes: inputting the normalized data into a pair space model, where the pair space model maps the normalized data to Boolean propositions, calculates the truth value (0 or 1) of each Boolean proposition through preset logical operations, and obtains the second comprehensive operating index of each battery cell according to the judgment rules (e.g., 0.1 indicates normal operation, 0.9 indicates fault). Specifically, in this embodiment, the mapping is dynamically adjusted according to the importance of the operating state parameters. For example, the output corresponding to the key judgment rule, such as over-temperature, is mapped as follows: 0 is mapped to a low value close to 0 (e.g., 0.05), representing the probability of normal operation, which can be understood as "low risk but not absolutely certain"; 1 is mapped to a high value close to 1 (e.g., 0.95), representing the probability of abnormal operation, which can be understood as "high confidence abnormality". For example, secondary judgment rules (such as slight aging) correspond to the following mapping rules: mapping 0 to a high value close to 0 (such as 0.3), which can be understood as "low risk"; mapping 1 to a low value close to 1 (such as 0.7), which can be understood as "abnormal".

[0072] The logical judgment model in this embodiment is a pairwise space model of logical judgments, which is actually a combination of a series of logical rules and judgments used to determine the health of a battery cell based on the circular state parameters of multiple battery cells. These logical rules can be mathematically represented using logical expressions and Boolean algebra, making them easy to understand and calculate. In practical applications, the construction of logical expressions and the values ​​of parameters need to be determined based on the specific diagnostic requirements of the battery cell's operating parameters.

[0073] The first and second comprehensive operating indicators are input into the logic decision module for analysis to obtain the target battery cell and its anomaly level in the current monitoring period. This includes: inputting the first and second comprehensive operating indicators into the logic decision module, which analyzes the first and second comprehensive operating indicators through a predefined decision matrix, and identifying the target battery cell and its anomaly level in the current monitoring period from all battery cells based on the analysis results.

[0074] Specifically, the first comprehensive operational indicator (a threshold system code represented by an 8-bit binary word) is used to provide discrete state classification (such as safety / early warning / alarm); the second comprehensive operational indicator (the probability representing the state of a single battery cell output by the paired model) is used to supplement the confidence level of logical reasoning. The first and second comprehensive operational indicators are input into the logical decision module, which performs fusion analysis based on a predefined decision matrix to obtain a comprehensive operational score for each battery cell. Based on the comprehensive operational score, the target battery cell and its anomaly level are identified. Real-time operation and maintenance decisions are then made for the target battery cell based on this anomaly level.

[0075] For example, suppose an energy storage system contains four battery cells (Batt1-Batt4) with a monitoring cycle of 60ms. The first comprehensive operating indicator (threshold system encoding) is an 8-bit binary status word output in real time by a three-level threshold model (each 2 bits represent an indicator status), which are represented as follows: the first comprehensive operating indicator of battery cell Batt1 is 00 01 00 11, representing voltage warning and current alarm; the first comprehensive operating indicator of battery cell Batt2 is 00 00 01 00, representing SOC alarm; the first comprehensive operating indicator of battery cell Batt3 is 11 01 00 00, representing temperature alarm and voltage warning; and the first comprehensive operating indicator of battery cell Batt4 is 00 00 00 00, representing all normal operation.

[0076] The second comprehensive operating index is the fault probability (0.1-0.9) output by the pair space model, expressed as follows: the second comprehensive operating index of battery cell Batt1 is 0.8, representing an abnormal internal resistance trigger P003=1; the second comprehensive operating index of battery cell Batt2 is 0.3, representing SOC fluctuation but no combination rule trigger; the second comprehensive operating index of battery cell Batt3 is 0.95, representing temperature exceeding the limit (P001=1) AND voltage abnormality (P002=1); the second comprehensive operating index of battery cell Batt4 is 0.1, representing no abnormality.

[0077] The first and second comprehensive operating indicators of the four battery cells are input into the logic decision module. The logic decision module uses a predefined decision matrix for fusion analysis to obtain the comprehensive operating score of each battery cell. Based on the comprehensive operating score, the target battery cell and its anomaly level are identified. Real-time operation and maintenance decisions are made for the target battery cell based on the anomaly level.

[0078] For example, in the analysis process of battery cell Batt1: the inputs are 00 01 00 11 and 0.8; after fusion analysis based on the decision matrix, the overall operational score is 75, corresponding to a severe anomaly level. As another example, in the analysis process of battery cell Batt3: the inputs are 11 01 00 00 and 0.95; after fusion analysis based on the decision matrix, the overall operational score is 95, corresponding to an emergency fault level.

[0079] For example, a predefined decision matrix can be represented by the following Table 1:

[0080]

[0081] Table 1

[0082] S103: Dynamically adjust the operating status of the target battery cell according to the anomaly level.

[0083] For example, if the anomaly level of battery cell Batt1 is a severe anomaly, it will trigger the shutdown and maintenance procedure for battery cell Batt1. Similarly, if the anomaly level of battery cell Batt3 is an emergency fault, it will trigger the immediate circuit disconnection and alarm for battery cell Batt2.

[0084] As the above analysis shows, the battery cell dynamic adjustment method provided in this application periodically collects the corresponding target operating state parameters of multiple battery cells, and determines the anomaly level of multiple battery cells in the current monitoring cycle by integrating the target operating state parameters with historical operating data. Then, based on the anomaly level, the charging and discharging rate, current, or connection status of the target battery cell is dynamically adjusted. The aim is to integrate the target operating state parameters and historical operating data of multiple battery cells, combine them with a pre-built comprehensive battery cell operating health status assessment model to analyze the target battery cells requiring state adjustment and their anomaly levels, and adjust the state of the target battery cells according to the anomaly levels to ensure that the battery cells operate in a highly efficient and safe state, thereby improving the overall performance of the energy storage system.

[0085] Please see Figure 2 , Figure 2This is a schematic diagram of a battery cell dynamic adjustment device provided in an embodiment of this application. The battery cell dynamic adjustment device 20 provided in this embodiment includes modules or units used for performing... Figure 1 The steps in the corresponding embodiments. Please refer to the details. Figure 1 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 3 The battery cell dynamic adjustment device 20 includes:

[0086] The acquisition module 210 is used to periodically acquire the target operating state parameters corresponding to the plurality of battery cells;

[0087] The determination module 220 is used to input the target operating status parameters and the historical operating data of the battery cell into a pre-built comprehensive operating health status assessment model for analysis, and to determine the target battery cell and the abnormality level of the target battery cell in the current monitoring cycle; the target battery cell is the battery cell that needs to be adjusted in operating status.

[0088] The adjustment module 230 is used to dynamically adjust the operating status of the target battery cell according to the anomaly level.

[0089] In one embodiment, the target operating state parameters include battery temperature, state of charge, voltage, and current; the historical operating data includes historical temperature change rate and state of charge change rate.

[0090] In one embodiment, the determining module 220 is specifically used to remove missing values ​​and obviously unrealistic operating state parameters from the target operating state parameters based on the historical temperature change rate and the state of charge change rate, and to normalize the remaining operating state parameters.

[0091] In one embodiment, the pre-built battery cell comprehensive operational health status assessment model includes a multi-dimensional threshold condition model, a logical judgment model, and a logical decision module;

[0092] The determining unit includes:

[0093] The first sub-unit is used to input the normalized data into the multidimensional threshold condition model for analysis, and to obtain the first comprehensive operating index of each battery cell.

[0094] The second sub-unit is used to input the normalized data into the logical judgment model to obtain the second comprehensive operating index of each battery cell.

[0095] The third sub-unit is used to analyze the first comprehensive operating index and the second comprehensive operating index input to the logical decision module to obtain the target battery cell and the abnormality level of the target battery cell in the current monitoring cycle.

[0096] In one embodiment, the first obtaining subunit is specifically used for:

[0097] The normalized data is input into the multidimensional threshold condition model, which analyzes the normalized data based on a multi-level threshold system to obtain the first comprehensive operating index of each battery cell.

[0098] In one embodiment, the logical judgment model is a pairing space model constructed based on preset logical rules; the second obtained sub-unit is specifically used for:

[0099] The normalized data is input into the pair space model, which analyzes the data based on the preset logic rules and judgment combinations to obtain the second comprehensive operating index of each battery cell.

[0100] In one embodiment, the third obtaining subunit is specifically used for:

[0101] The first and second comprehensive operating indicators are input into the logical decision module. The logical decision module analyzes the first and second comprehensive operating indicators using a predefined decision matrix to obtain a comprehensive operating score for each battery cell. Based on the comprehensive operating score, the target battery cell and its anomaly level are identified. Real-time operation and maintenance decisions are made for the target battery cell based on the anomaly level.

[0102] Please see Figure 3 , Figure 3 This is a schematic diagram of a battery cell dynamic adjustment device provided in an embodiment of this application. Figure 3 It is understood that the battery cell dynamic adjustment device 300 includes: a processor 310, a memory 320, and a computer program 330 stored in the memory 320 and executable on the processor 310; when the processor 310 executes the computer program 330, it implements the steps in the above-described battery cell dynamic adjustment method embodiments, for example... Figure 1 The steps S101 to S103 are shown. Alternatively, when the processor 310 executes the computer program 330, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of modules 210 to 230 are shown.

[0103] For example, the computer program 330 may be divided into one or more modules / units, one or more of which are stored in the memory 320 and executed by the processor 310 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 330 in the battery cell dynamic adjustment device. For example, the computer program 330 may be divided into a data acquisition module, a determination module, and an adjustment module.

[0104] The battery cell dynamic adjustment device provided in this embodiment may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that... Figure 3 This is merely an example of a battery cell dynamic adjustment device and does not constitute a limitation on the battery cell dynamic adjustment device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the battery cell dynamic adjustment device may also include input / output devices, network access devices, buses, etc.

[0105] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0106] The memory 320 can be an internal storage unit of the battery cell dynamic regulation device, such as the hard drive or memory of the battery cell dynamic regulation device. The memory 320 can also be an external storage device of the battery cell dynamic regulation device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the battery cell dynamic regulation device. Furthermore, the battery cell dynamic regulation device can include both internal and external storage units. The memory 320 is used to store computer programs and other programs and data required by the battery cell dynamic regulation device. The memory 320 can also be used to temporarily store data that has been output or will be output.

[0107] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0108] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0110] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for dynamic adjustment of a single battery cell, characterized in that, The application is applied to an energy storage system, the energy storage system comprises a plurality of battery monomers and a central processing system, and the dynamic adjustment method of the battery monomers is executed by the central processing system, comprising: Periodically collecting target operating state parameters of the plurality of battery monomers; the target operating state parameters comprise battery temperature, state of charge, voltage and current; Based on historical temperature change rates and state of charge change rates in a plurality of different historical collection periods, removing operating state parameters with missing values and obviously inconsistent with actual operating state parameters in the target operating state parameters, and performing normalization processing on the remaining operating state parameters; inputting the normalized data into a multi-dimensional threshold condition model, the multi-dimensional threshold condition model analyzes the normalized data based on a multi-level threshold system to obtain first comprehensive operating indexes of each battery monomer; inputting the normalized data into a pair space model constructed based on a preset logical rule, the pair space model analyzes based on a preset logical rule and a judgment combination to obtain second comprehensive operating indexes of each battery monomer; inputting the first comprehensive operating indexes and the second comprehensive operating indexes into a logical decision module, the logical decision module analyzes the first comprehensive operating indexes and the second comprehensive operating indexes through a predefined decision matrix to obtain comprehensive operating scores of each battery monomer; identifying target battery monomers and abnormal levels of the target battery monomers based on the comprehensive operating scores; the target battery monomers are battery monomers that need to be adjusted in operating state; According to the abnormal level, dynamically adjusting the operating state of the target battery monomer.

2. A battery cell dynamic adjustment apparatus, characterized by, Comprise: The collection module is configured to periodically collect target operating state parameters of a plurality of battery monomers; the target operating state parameters comprise battery temperature, state of charge, voltage and current; The determination module is configured to remove operating state parameters with missing values and obviously inconsistent with actual operating state parameters in the target operating state parameters based on historical temperature change rates and state of charge change rates in a plurality of different historical collection periods, and perform normalization processing on the remaining operating state parameters; input the normalized data into a multi-dimensional threshold condition model, the multi-dimensional threshold condition model analyzes the normalized data based on a multi-level threshold system to obtain first comprehensive operating indexes of each battery monomer; input the normalized data into a pair space model constructed based on a preset logical rule, the pair space model analyzes based on a preset logical rule and a judgment combination to obtain second comprehensive operating indexes of each battery monomer; input the first comprehensive operating indexes and the second comprehensive operating indexes into a logical decision module, the logical decision module analyzes the first comprehensive operating indexes and the second comprehensive operating indexes through a predefined decision matrix to obtain comprehensive operating scores of each battery monomer; Identify target battery monomers and abnormal levels of the target battery monomers based on the comprehensive operating scores; The target battery monomers are battery monomers that need to be adjusted in operating state; An adjusting module is configured to dynamically adjust the operating state of the target battery cell according to the abnormality level.

3. A battery cell dynamic adjustment apparatus, characterized by, The method comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor; the processor implements the steps of the method of claim 1 when executing the computer program.

4. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 3. the computer program implements the steps of the method of claim 1 when executed by the processor.

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