Battery management system

Abstract

The invention relates to the technical field of battery management, and discloses a battery management system, and the system is characterized in that an extraction module is used for obtaining a preset battery charging and discharging management strategy of a to-be-managed battery, and extracting battery charging and discharging management parameters from the battery charging and discharging management strategy; the processing module is used for preprocessing the battery charging and discharging management parameters and generating target battery charging and discharging management parameters of the to-be-managed battery based on a preprocessing result; the calculation module is used for evaluating all the target battery charging and discharging management parameters to obtain an evaluation value of each target battery charging and discharging management parameter, and calculating a charging and discharging influence coefficient of the to-be-managed battery based on all the evaluation values; the optimization module is used for optimizing a preset battery charging and discharging management strategy based on the charging and discharging influence coefficient, intelligent charging and discharging are achieved by optimizing and adjusting the battery charging and discharging management strategy, the battery performance is effectively improved, the service life is prolonged, the maintenance cost is reduced, and the overall safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a battery management system. Background Art

[0002] In the field of modern energy storage, batteries, as an important device for storing and providing electrical energy, have penetrated into every aspect of our lives, from portable electronic devices to electric vehicles to large-scale electrical energy storage systems. With the increasing global demand for renewable and clean energy, improving battery efficiency and extending its service life have become a hot topic of research. The performance and life of batteries depend largely on how their charge and discharge are managed. Improper charging methods may cause the battery to be overcharged or undercharged, while improper discharge management may cause over-discharge, all of which will damage the internal structure of the battery, reduce its capacity, shorten its service life, and may cause safety risks such as battery overheating, leakage, and even explosion.

[0003] Traditional battery charge and discharge management usually adopts simple time-limited voltage control, that is, charging or discharging is stopped when the battery voltage reaches the set threshold. However, this method ignores the impact of different working conditions of the battery on its performance, such as temperature, aging degree, usage mode and other factors. In addition, traditional methods often fail to achieve real-time monitoring and dynamic adjustment of battery status, and cannot perform personalized management based on the actual usage of the battery, resulting in the failure to fully realize the potential of the battery and accelerating battery aging. Summary of the invention

[0004] The embodiment of the present invention provides a battery management system to solve the technical problem in the prior art that the battery cannot be intelligently managed for charging and discharging, and the battery safety and performance cannot be guaranteed.

[0005] In order to achieve the above object, the present invention provides a battery management system, comprising:

[0006] An extraction module, used to determine a battery to be managed, obtain a battery charge and discharge management strategy preset for the battery to be managed, and extract battery charge and discharge management parameters from the battery charge and discharge management strategy;

[0007] A processing module, used for preprocessing the battery charge and discharge management parameters, and generating target battery charge and discharge management parameters of the battery to be managed based on the preprocessing result;

[0008] A calculation module, used to evaluate all target battery charge and discharge management parameters, obtain an evaluation value of each target battery charge and discharge management parameter, and calculate the charge and discharge influence coefficient of the battery to be managed based on all the evaluation values;

[0009] The optimization module is used to optimize the preset battery charge and discharge management strategy based on the charge and discharge influence coefficient.

[0010] Furthermore, the processing module is used for:

[0011] The processing module is used to perform repeated parameter analysis on the battery charge and discharge management parameters and generate a code identification for each battery charge and discharge management parameter;

[0012] The processing module is used to generate a battery charge and discharge management parameter for a repeated code identifier, retain one code identifier in each group of repeated code identifiers, and delete the remaining code identifiers;

[0013] The processing module is used to output the importance value of the battery charge and discharge management parameter corresponding to each code mark based on the pre-trained importance analysis model;

[0014] The processing module is used to sort the numerical sizes of all importance values, and obtain a pre-set preset importance value, and divide the importance values ​​after sorting the numerical sizes based on the importance value to obtain a first parameter set and a second parameter set, wherein the importance values ​​between the maximum importance value and the preset importance value are divided into the first parameter set, and the importance values ​​between the minimum importance value and the preset importance value are divided into the second parameter set.

[0015] Furthermore, the processing module is used for:

[0016] The processing module is used to compensate the second parameter set based on the first parameter set to obtain a first prediction compensation result;

[0017] The processing module is used to obtain the data characteristics of the battery to be managed, and retrieve the historical charge and discharge conditions consistent with the data characteristics from the historical database;

[0018] The processing module is used to construct a data correction model based on the acquired historical charging and discharging conditions;

[0019] The processing module is used to upload the first compensation result to the data correction model for optimization to obtain an optimized compensation result.

[0020] Furthermore, the processing module is used for:

[0021] The processing module is used to determine the code identifier of the battery charge and discharge management parameter in the second parameter set;

[0022] The processing module is used to determine the two battery charge and discharge management parameters before and after each code mark, namely the front data parameter and the rear data parameter;

[0023] The processing module is used to obtain the tail data of the front data parameter and the head data of the back data parameter, and compare the difference between the tail data and the head data;

[0024] The processing module is used to compare the first difference between the tail data and the head data with a preset change list, so as to obtain the overlap between the first difference and the second difference corresponding to each group of change data in the preset change list;

[0025] The processing module is used to compare all overlaps of the same first difference situation and sort them according to the overlap size to obtain a corresponding overlap table, wherein the overlap table includes n rows and m columns, and each row corresponds to the first difference situation, and each column corresponds to each group of change arrays in the preset change list;

[0026] The processing module is used to obtain a standard change trajectory corresponding to each group of change data in the preset change list, and perform trajectory adjustment on each trajectory segment of the standard change trajectory according to the matching overlap as a first compensation result.

[0027] Furthermore, the calculation module is used for:

[0028] The calculation module is used to evaluate each target battery charge and discharge management parameter based on a pre-trained charge and discharge influence value model, and determine a maximum evaluation value and a minimum evaluation value from all evaluation values;

[0029] The calculation module is used to calculate the average evaluation value of all evaluation values, and calculate the charge and discharge influence coefficient of the battery to be managed according to the maximum evaluation value, the minimum evaluation value and the average evaluation value.

[0030] Furthermore, the calculation module is used for:

[0031] The calculation module is used to calculate the charge and discharge influence coefficient of the battery to be managed according to the following formula:

[0032]

[0033] Among them, g is the charge and discharge influence coefficient of the battery to be managed, n1 is the number of evaluation values, wi is the i-th evaluation value, rmin is the minimum evaluation value, rmax is the maximum evaluation value, and t is the average evaluation value.

[0034] Furthermore, the calculation module is used for:

[0035] The calculation module is used to collect historical charge and discharge cycle data of the battery to be managed, wherein each charge and discharge cycle data includes charging stage data, discharging stage data and corresponding battery performance indicators;

[0036] The calculation module is used to pre-process the collected historical charge and discharge cycle data, including cleaning, standardization and normalization, to eliminate data differences between different battery cells;

[0037] The calculation module is used to construct an initial charge and discharge impact value model using the preprocessed data set, and the model adopts a machine learning algorithm and predicts the charge and discharge impact value according to the changes in the battery performance indicators;

[0038] The computing module is used to train and verify the initial model under a plurality of different operating conditions to optimize the model parameters and improve the prediction accuracy;

[0039] The computing module is used to implement cross-validation techniques to ensure the generalization ability of the model on different battery cells and charge and discharge cycles;

[0040] The calculation module is used to adjust the model structure or parameters according to the results of training and verification until a predetermined accuracy standard is reached;

[0041] The calculation module is used to output the final charge and discharge impact value model.

[0042] Furthermore, the optimization module is used to:

[0043] The optimization module is used to obtain the charge and discharge management strategy factor corresponding to the battery charge and discharge management strategy;

[0044] The optimization module is used to set a plurality of charge and discharge influence coefficient intervals, wherein each charge and discharge influence coefficient interval includes a first preset charge and discharge influence coefficient and a second preset charge and discharge influence coefficient;

[0045] The optimization module is used to set a plurality of adjustment coefficients, wherein the adjustment coefficients correspond to the charge and discharge influence coefficients one by one;

[0046] The optimization module is used to traverse the charge and discharge influence coefficient in all charge and discharge influence coefficient intervals, determine the charge and discharge influence coefficient interval corresponding to the charge and discharge influence coefficient, and determine the corresponding adjustment coefficient;

[0047] The optimization module is used to adjust the charge and discharge management strategy factor based on the adjustment coefficient to obtain the adjusted charge and discharge management strategy factor, and control the battery to be managed by using the battery charge and discharge management strategy corresponding to the adjusted charge and discharge management strategy factor.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention discloses a battery management system, wherein an extraction module is used to obtain a battery charge and discharge management strategy preset for a battery to be managed, and extract battery charge and discharge management parameters from the battery charge and discharge management strategy; a processing module is used to preprocess the battery charge and discharge management parameters, and generate target battery charge and discharge management parameters for the battery to be managed based on the preprocessing result; a calculation module is used to evaluate all target battery charge and discharge management parameters, obtain an evaluation value of each target battery charge and discharge management parameter, and calculate the charge and discharge influence coefficient of the battery to be managed based on all the evaluation values; an optimization module is used to optimize the preset battery charge and discharge management strategy based on the charge and discharge influence coefficient, and realize intelligent charge and discharge by optimizing and adjusting the battery charge and discharge management strategy, thereby effectively improving battery performance, extending service life, reducing maintenance costs, and improving overall safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0051] Figure 1 A schematic structural diagram of a battery management system in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0052] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0053] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0054] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0056] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0057] like Figure 1 As shown, an embodiment of the present invention discloses a battery management system, including:

[0058] An extraction module, used to determine a battery to be managed, obtain a battery charge and discharge management strategy preset for the battery to be managed, and extract battery charge and discharge management parameters from the battery charge and discharge management strategy;

[0059] A processing module, used for preprocessing the battery charge and discharge management parameters, and generating target battery charge and discharge management parameters of the battery to be managed based on the preprocessing result;

[0060] A calculation module, used to evaluate all target battery charge and discharge management parameters, obtain an evaluation value of each target battery charge and discharge management parameter, and calculate the charge and discharge influence coefficient of the battery to be managed based on all the evaluation values;

[0061] The optimization module is used to optimize the preset battery charge and discharge management strategy based on the charge and discharge influence coefficient.

[0062] The beneficial effect of the above technical solution is: the present invention optimizes and adjusts the battery charging and discharging management strategy to achieve intelligent charging and discharging, effectively improve battery performance, extend service life, reduce maintenance costs and enhance overall safety.

[0063] In some embodiments of the present application, the processing module is used to:

[0064] The processing module is used to perform repeated parameter analysis on the battery charge and discharge management parameters and generate a code identification for each battery charge and discharge management parameter;

[0065] The processing module is used to generate a battery charge and discharge management parameter for a repeated code identifier, retain one code identifier in each group of repeated code identifiers, and delete the remaining code identifiers;

[0066] The processing module is used to output the importance value of the battery charge and discharge management parameter corresponding to each code mark based on the pre-trained importance analysis model;

[0067] The processing module is used to sort the numerical sizes of all importance values, and obtain a pre-set preset importance value, and divide the importance values ​​after sorting the numerical sizes based on the importance value to obtain a first parameter set and a second parameter set, wherein the importance values ​​between the maximum importance value and the preset importance value are divided into the first parameter set, and the importance values ​​between the minimum importance value and the preset importance value are divided into the second parameter set.

[0068] In this embodiment, the importance analysis model is pre-trained and can output the importance of each battery charge and discharge management parameter.

[0069] In this embodiment, the preset importance value is set according to actual conditions and is not specifically limited here.

[0070] In this embodiment, if there is an importance value that is the same as the preset importance value among all the importance values, the same importance value is classified into the first parameter set.

[0071] The beneficial effect of the above technical solution is that the present invention can analyze the battery charge and discharge management parameters, laying a foundation for the optimized management of the battery to be managed.

[0072] In some embodiments of the present application, the processing module is used to:

[0073] The processing module is used to compensate the second parameter set based on the first parameter set to obtain a first prediction compensation result;

[0074] The processing module is used to obtain the data characteristics of the battery to be managed, and retrieve the historical charge and discharge conditions consistent with the data characteristics from the historical database;

[0075] The processing module is used to construct a data correction model based on the acquired historical charging and discharging conditions;

[0076] The processing module is used to upload the first compensation result to the data correction model for optimization to obtain an optimized compensation result.

[0077] The beneficial effect of the above technical solution is: the present invention uploads the first compensation result to the data correction model for optimization, obtains the optimized compensation result, realizes the optimized compensation of the second parameter set, and can avoid the problem of fewer battery charge and discharge management parameters affecting the charge and discharge management effect.

[0078] In some embodiments of the present application, the processing module is used to:

[0079] The processing module is used to determine the code identifier of the battery charge and discharge management parameter in the second parameter set;

[0080] The processing module is used to determine the two battery charge and discharge management parameters before and after each code mark, namely the front data parameter and the rear data parameter;

[0081] The processing module is used to obtain the tail data of the front data parameter and the head data of the back data parameter, and compare the difference between the tail data and the head data;

[0082] The processing module is used to compare the first difference between the tail data and the head data with a preset change list, so as to obtain the overlap between the first difference and the second difference corresponding to each group of change data in the preset change list;

[0083] The processing module is used to compare all overlaps of the same first difference situation and sort them according to the overlap size to obtain a corresponding overlap table, wherein the overlap table includes n rows and m columns, and each row corresponds to the first difference situation, and each column corresponds to each group of change arrays in the preset change list;

[0084] The processing module is used to obtain a standard change trajectory corresponding to each group of change data in the preset change list, and perform trajectory adjustment on each trajectory segment of the standard change trajectory according to the matching overlap as a first compensation result.

[0085] In this embodiment, each battery charge and discharge management parameter corresponds to a code identifier, so the two previous and next battery charge and discharge management parameters can be directly determined.

[0086] In this embodiment, the preset change list is set in advance in combination with historical data, and can reflect the changes between two data.

[0087] The beneficial effect of the above technical solution is: the present invention is based on the standard change trajectory corresponding to each group of change data in the preset change list, and according to the matching overlap, each trajectory segment of the standard change trajectory is adjusted as the first compensation result, thereby achieving precise compensation and avoiding large compensation errors.

[0088] In some embodiments of the present application, the computing module is used to:

[0089] The calculation module is used to evaluate each target battery charge and discharge management parameter based on a pre-trained charge and discharge influence value model, and determine a maximum evaluation value and a minimum evaluation value from all evaluation values;

[0090] The calculation module is used to calculate the average evaluation value of all evaluation values, and calculate the charge and discharge influence coefficient of the battery to be managed according to the maximum evaluation value, the minimum evaluation value and the average evaluation value.

[0091] The calculation module is used to calculate the charge and discharge influence coefficient of the battery to be managed according to the following formula:

[0092]

[0093] Among them, g is the charge and discharge influence coefficient of the battery to be managed, n1 is the number of evaluation values, wi is the i-th evaluation value, rmin is the minimum evaluation value, rmax is the maximum evaluation value, and t is the average evaluation value.

[0094] The beneficial effect of the above technical solution is: the present invention calculates the average evaluation value of all evaluation values, and calculates the charge and discharge influence coefficient of the battery to be managed according to the maximum evaluation value, the minimum evaluation value and the average evaluation value, which can lay a foundation for the optimization of the battery charge and discharge management strategy and provide reliable data support for the optimization and adjustment of the battery charge and discharge management strategy.

[0095] In some embodiments of the present application, the computing module is used to:

[0096] The calculation module is used to collect historical charge and discharge cycle data of the battery to be managed, wherein each charge and discharge cycle data includes charging stage data, discharging stage data and corresponding battery performance indicators;

[0097] The calculation module is used to pre-process the collected historical charge and discharge cycle data, including cleaning, standardization and normalization, to eliminate data differences between different battery cells;

[0098] The calculation module is used to construct an initial charge and discharge impact value model using the preprocessed data set, and the model adopts a machine learning algorithm and predicts the charge and discharge impact value according to the changes in the battery performance indicators;

[0099] The computing module is used to train and verify the initial model under a plurality of different operating conditions to optimize the model parameters and improve the prediction accuracy;

[0100] The computing module is used to implement cross-validation techniques to ensure the generalization ability of the model on different battery cells and charge and discharge cycles;

[0101] The calculation module is used to adjust the model structure or parameters according to the results of training and verification until a predetermined accuracy standard is reached;

[0102] The calculation module is used to output the final charge and discharge impact value model.

[0103] In this embodiment, data cleaning, data standardization and data normalization processing are complicated and mature, and will not be introduced in detail here.

[0104] The beneficial effect of the above technical solution is that the present invention obtains a charge and discharge influence value model by processing historical charge and discharge cycle data, and can output the evaluation value of each target battery charge and discharge management parameter, further ensure the calculation accuracy of the charge and discharge influence coefficient, and simplify the calculation process.

[0105] In some embodiments of the present application, the optimization module is used to:

[0106] The optimization module is used to obtain the charge and discharge management strategy factor corresponding to the battery charge and discharge management strategy;

[0107] The optimization module is used to set a plurality of charge and discharge influence coefficient intervals, wherein each charge and discharge influence coefficient interval includes a first preset charge and discharge influence coefficient and a second preset charge and discharge influence coefficient;

[0108] The optimization module is used to set a plurality of adjustment coefficients, wherein the adjustment coefficients correspond to the charge and discharge influence coefficients one by one;

[0109] The optimization module is used to traverse the charge and discharge influence coefficient in all charge and discharge influence coefficient intervals, determine the charge and discharge influence coefficient interval corresponding to the charge and discharge influence coefficient, and determine the corresponding adjustment coefficient;

[0110] The optimization module is used to adjust the charge and discharge management strategy factor based on the adjustment coefficient to obtain the adjusted charge and discharge management strategy factor, and control the battery to be managed by using the battery charge and discharge management strategy corresponding to the adjusted charge and discharge management strategy factor.

[0111] In this embodiment, each battery charge and discharge management strategy corresponds to a charge and discharge management strategy factor.

[0112] In this embodiment, the first preset charge and discharge influence coefficient is smaller than the second preset charge and discharge influence coefficient.

[0113] In this embodiment, the adjustment coefficient corresponds to the charge and discharge influence coefficient interval one by one.

[0114] In this embodiment, the product value of the determined adjustment coefficient and the charge and discharge management strategy factor is used as the adjusted charge and discharge management strategy factor, and then the corresponding battery charge and discharge management strategy can be determined.

[0115] The beneficial effect of the above technical solution is: the present invention optimizes and adjusts the battery charging and discharging management strategy to achieve intelligent charging and discharging, effectively improve battery performance, extend service life, reduce maintenance costs and enhance overall safety.

[0116] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0117] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0118] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A battery management system, characterized in that: include: An extraction module, used to determine a battery to be managed, obtain a battery charge and discharge management strategy preset for the battery to be managed, and extract battery charge and discharge management parameters from the battery charge and discharge management strategy; A processing module, used for preprocessing the battery charge and discharge management parameters, and generating target battery charge and discharge management parameters of the battery to be managed based on the preprocessing result; A calculation module, used to evaluate all target battery charge and discharge management parameters, obtain an evaluation value of each target battery charge and discharge management parameter, and calculate the charge and discharge influence coefficient of the battery to be managed based on all the evaluation values; The optimization module is used to optimize the preset battery charge and discharge management strategy based on the charge and discharge influence coefficient.

2. The battery management system according to claim 1, characterized in that: The processing module is used for: The processing module is used to perform repeated parameter analysis on the battery charge and discharge management parameters and generate a code identification for each battery charge and discharge management parameter; The processing module is used to generate a battery charge and discharge management parameter for a repeated code identifier, retain one code identifier in each group of repeated code identifiers, and delete the remaining code identifiers; The processing module is used to output the importance value of the battery charge and discharge management parameter corresponding to each code mark based on the pre-trained importance analysis model; The processing module is used to sort the numerical sizes of all importance values, and obtain a pre-set preset importance value, and divide the importance values ​​after sorting the numerical sizes based on the importance value to obtain a first parameter set and a second parameter set, wherein the importance values ​​between the maximum importance value and the preset importance value are divided into the first parameter set, and the importance values ​​between the minimum importance value and the preset importance value are divided into the second parameter set.

3. The battery management system according to claim 2, characterized in that: The processing module is used for: The processing module is used to compensate the second parameter set based on the first parameter set to obtain a first prediction compensation result; The processing module is used to obtain the data characteristics of the battery to be managed, and retrieve the historical charge and discharge conditions consistent with the data characteristics from the historical database; The processing module is used to construct a data correction model based on the acquired historical charging and discharging conditions; The processing module is used to upload the first compensation result to the data correction model for optimization to obtain an optimized compensation result.

4. The battery management system according to claim 3, characterized in that: The processing module is used for: The processing module is used to determine the code identifier of the battery charge and discharge management parameter in the second parameter set; The processing module is used to determine the two battery charge and discharge management parameters before and after each code mark, namely the front data parameter and the rear data parameter; The processing module is used to obtain the tail data of the front data parameter and the head data of the back data parameter, and compare the difference between the tail data and the head data; The processing module is used to compare the first difference between the tail data and the head data with a preset change list, so as to obtain the overlap between the first difference and the second difference corresponding to each group of change data in the preset change list; The processing module is used to compare all overlaps of the same first difference situation and sort them according to the overlap size to obtain a corresponding overlap table, wherein the overlap table includes n rows and m columns, and each row corresponds to the first difference situation, and each column corresponds to each group of change arrays in the preset change list; The processing module is used to obtain a standard change trajectory corresponding to each group of change data in the preset change list, and perform trajectory adjustment on each trajectory segment of the standard change trajectory according to the matching overlap as a first compensation result.

5. The battery management system according to claim 1, characterized in that: The calculation module is used for: The calculation module is used to evaluate each target battery charge and discharge management parameter based on a pre-trained charge and discharge influence value model, and determine a maximum evaluation value and a minimum evaluation value from all evaluation values; The calculation module is used to calculate the average evaluation value of all evaluation values, and calculate the charge and discharge influence coefficient of the battery to be managed according to the maximum evaluation value, the minimum evaluation value and the average evaluation value.

6. The battery management system according to claim 5, characterized in that: The calculation module is used for: The calculation module is used to calculate the charge and discharge influence coefficient of the battery to be managed according to the following formula: Among them, g is the charge and discharge influence coefficient of the battery to be managed, n1 is the number of evaluation values, wi is the i-th evaluation value, rmin is the minimum evaluation value, rmax is the maximum evaluation value, and t is the average evaluation value.

7. The battery management system according to claim 5, characterized in that: The calculation module is used for: The calculation module is used to collect historical charge and discharge cycle data of the battery to be managed, wherein each charge and discharge cycle data includes charging stage data, discharging stage data and corresponding battery performance indicators; The calculation module is used to pre-process the collected historical charge and discharge cycle data, including cleaning, standardization and normalization, to eliminate data differences between different battery cells; The calculation module is used to construct an initial charge and discharge impact value model using the preprocessed data set, and the model adopts a machine learning algorithm and predicts the charge and discharge impact value according to the changes in the battery performance indicators; The computing module is used to train and verify the initial model under a plurality of different operating conditions to optimize the model parameters and improve the prediction accuracy; The computing module is used to implement cross-validation techniques to ensure the generalization ability of the model on different battery cells and charge and discharge cycles; The calculation module is used to adjust the model structure or parameters according to the results of training and verification until a predetermined accuracy standard is reached; The calculation module is used to output the final charge and discharge impact value model.

8. The battery management system according to claim 1, characterized in that: The optimization module is used to: The optimization module is used to obtain the charge and discharge management strategy factor corresponding to the battery charge and discharge management strategy; The optimization module is used to set a plurality of charge and discharge influence coefficient intervals, wherein each charge and discharge influence coefficient interval includes a first preset charge and discharge influence coefficient and a second preset charge and discharge influence coefficient; The optimization module is used to set a plurality of adjustment coefficients, wherein the adjustment coefficients correspond to the charge and discharge influence coefficients one by one; The optimization module is used to traverse the charge and discharge influence coefficient in all charge and discharge influence coefficient intervals, determine the charge and discharge influence coefficient interval corresponding to the charge and discharge influence coefficient, and determine the corresponding adjustment coefficient; The optimization module is used to adjust the charge and discharge management strategy factor based on the adjustment coefficient to obtain the adjusted charge and discharge management strategy factor, and control the battery to be managed by using the battery charge and discharge management strategy corresponding to the adjusted charge and discharge management strategy factor.

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