A method and system for equalization control of a lithium battery energy storage system

Abstract

The application provides a kind of equalization control method and system of lithium battery energy storage system, it is related to energy storage system equalization control technical field.The method comprises: obtaining the environment parameter of the current environment where lithium battery is located, respectively the battery model of lithium battery and the environment parameter of the environment where it is located are input into first charge-discharge analysis model, the predicted temperature of lithium battery under different charge-discharge mode is determined;The predicted temperature under different charge-discharge mode is input into second charge-discharge analysis model, and the charge-discharge strategy of lithium battery is obtained;According to the charge-discharge strategy of each lithium battery, the lithium battery energy storage system is controlled to carry out charge-discharge operation.The application determines the best charge-discharge strategy of each lithium battery in the lithium battery energy storage system under different environment temperatures by analyzing the use data of different models of lithium battery, improves the charge-discharge power of lithium battery energy storage system while ensuring the stability of lithium battery performance, prolongs the service life of lithium battery, and improves the utilization rate of lithium battery.

Description

Technical Field

[0001] This invention relates to the field of energy storage system equalization control technology, and particularly to an equalization control method and system for a lithium battery energy storage system. Background Technology

[0002] Energy storage systems are an important component of power systems. Currently, battery energy storage has advantages such as mature technology, safety and reliability, and ease of installation, making it one of the most commonly used types of energy storage systems. Lithium batteries, due to their high energy density, long lifespan, and environmental friendliness, are widely used in the energy storage field as a high-performance energy storage product.

[0003] While lithium batteries offer various advantages, they are significantly affected by temperature during actual use. Ambient temperature variations are one factor influencing lithium battery performance, and the charging and discharging process also generates heat; excessively high temperatures can severely impact performance. Unlike ordinary battery packs, energy storage systems integrate a large number of batteries, and it's difficult to completely standardize the specifications of each battery. While controlling each battery to operate using the same charging and discharging method can reduce the management complexity of the energy storage system, different charging and discharging methods are unlikely to be suitable for all batteries, resulting in insufficient system stability and low utilization of each battery. Summary of the Invention

[0004] This application provides a method and system for equalization control of a lithium battery energy storage system, aiming to solve at least one technical problem existing in the above-mentioned background art.

[0005] As one aspect of this application, a method for equalization control of a lithium battery energy storage system is provided, comprising:

[0006] To obtain usage sample data and specification parameter data of various models of lithium batteries in a lithium battery energy storage system, wherein the lithium battery energy storage system includes multiple lithium batteries for energy storage, and the multiple lithium batteries include at least one battery model, the usage sample data includes at least one of the following: charging and discharging method, battery temperature and environmental parameters, and the specification parameter data includes at least one of the following: maximum charge and discharge cycles, maximum charge and discharge voltage, maximum charge and discharge current, rated temperature, rated input power and rated output power;

[0007] A training dataset is constructed based on the usage sample data of various lithium battery models. A pre-constructed first charge-discharge analysis model is trained using the training dataset. The first charge-discharge analysis model is a neural network model. The first charge-discharge analysis model is used to analyze the predicted temperature of different lithium battery models under different charge-discharge methods.

[0008] The environmental parameters of the current environment of the lithium battery in the lithium battery energy storage system are obtained. The battery model and environmental parameters of the environment of each lithium battery in the lithium battery energy storage system are input into the first charge-discharge analysis model. The predicted temperature of each lithium battery under different charge-discharge methods is determined by the first charge-discharge analysis model.

[0009] The predicted temperature of each lithium battery under different charging and discharging methods is input into the second charging and discharging analysis model to obtain the charging and discharging strategy of each lithium battery.

[0010] The lithium battery energy storage system is controlled to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery.

[0011] Furthermore, the step of inputting the predicted temperature of each lithium battery under different charge and discharge modes into the second charge and discharge analysis model to obtain the charge and discharge strategy for each lithium battery includes:

[0012] The second charge-discharge analysis model stores the specification parameter data for each type of lithium battery;

[0013] After receiving the predicted temperature of each lithium battery under different charging and discharging methods, the second charge-discharge analysis model obtains the operating state of the lithium battery energy storage system; the operating state includes charging state and discharging state.

[0014] For any lithium battery, the second charge-discharge analysis model determines the corresponding specification parameter data based on the working state and the battery model of the lithium battery, and determines the first power of the lithium battery based on the rule parameter data. The first power is less than the rated input power or rated output power in the rule parameter data, and the predicted temperature under the charge-discharge mode corresponding to the first power is less than the rated temperature in the rule parameter data.

[0015] The first power of each lithium battery is determined, thereby obtaining the charge and discharge strategy for each lithium battery.

[0016] Furthermore, a training dataset is constructed based on usage sample data of various lithium battery models. The pre-constructed first charge-discharge analysis model is then trained using this training dataset, including:

[0017] At least one data set centered on each battery model is constructed based on the battery signal. For each data set, multiple sub-data sets are constructed using ambient temperature and charging / discharging method as single variables. Each sub-data set records usage sample data of different charging / discharging methods under the same ambient temperature, or usage sample data of different ambient temperatures under the same charging / discharging method. Multiple sub-data sets are combined to form a training dataset for training the first charging / discharging analysis model.

[0018] Furthermore, after obtaining the charge / discharge strategy for each lithium battery, the following is also included:

[0019] The operating status of the lithium battery energy storage system and the current energy storage capacity of each lithium battery are obtained. The operating status of the lithium battery energy storage system, the current energy storage capacity of each lithium battery, and the charging and discharging strategy are input into the third charging and discharging analysis model. The charging and discharging strategy of each lithium battery is adjusted through the third charging and discharging analysis model. The lithium battery energy storage system is controlled to perform charging and discharging operations according to the adjusted charging and discharging strategy of each lithium battery.

[0020] Furthermore, adjusting the charging and discharging strategy of each lithium battery through the third charging and discharging analysis model includes:

[0021] If the lithium battery energy storage system is in a charging state, lithium batteries with current energy storage capacity lower than the first preset energy threshold are selected, and the charging strategy of lithium batteries with energy storage capacity lower than the first preset energy threshold is adjusted according to the battery protection scheme stored in the third charge and discharge analysis model.

[0022] The battery protection scheme records the charging protection strategy for each model of lithium battery. The charging strategy includes the charging strategy to be adopted when the battery capacity is lower than a first preset capacity threshold.

[0023] Furthermore, in the process of controlling the lithium battery energy storage system to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery, the method further includes:

[0024] If the energy storage capacity of any lithium battery is lower than the first preset energy threshold, the discharge operation of the lithium battery will be stopped.

[0025] If the energy storage capacity of any lithium battery exceeds the second preset energy threshold, the lithium battery charging operation will be stopped.

[0026] As another aspect of this application, a balanced control system for a lithium battery energy storage system is provided, comprising:

[0027] The data acquisition module is used to acquire usage sample data and specification parameter data of various models of lithium batteries in the lithium battery energy storage system;

[0028] The sample preparation module is used to construct a training dataset based on the usage sample data of various lithium battery models.

[0029] The model training module is used to train the pre-built first charge-discharge analysis model using the training dataset;

[0030] The strategy generation module is used to determine the predicted temperature of each lithium battery under different charging and discharging modes through the first charging and discharging analysis model, input the predicted temperature of each lithium battery under different charging and discharging modes into the second charging and discharging analysis model, and generate the charging and discharging strategy for each lithium battery.

[0031] The equalization control module is used to control the lithium battery energy storage system to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery.

[0032] Furthermore, the system also includes:

[0033] The strategy adjustment module is used to adjust the charge and discharge strategy of each lithium battery through a third charge and discharge analysis model.

[0034] The present invention has the following advantages:

[0035] 1. This invention analyzes the usage data of different types of lithium batteries to determine the optimal charging and discharging strategy for each lithium battery in a lithium battery energy storage system under different ambient temperatures. While ensuring the performance stability of lithium batteries, it improves the charging and discharging power of the lithium battery energy storage system, extends the service life of lithium batteries, and enhances the utilization rate of lithium batteries.

[0036] 2. This invention combines the lithium battery's energy storage data to intelligently adjust the lithium battery's charging and discharging strategy when the energy storage is too high or too low, further reducing the impact of improper charging and discharging methods on the lithium battery's lifespan. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the structures shown in these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating a balanced control method for a lithium battery energy storage system provided in an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the structure of a balanced control system for a lithium battery energy storage system provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, some embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0041] Figure 1 This is a flowchart illustrating a method for equalization control of a lithium battery energy storage system provided in an embodiment of the present invention. As one aspect of this embodiment, see [link to relevant documentation]. Figure 1 A method for equalization control of a lithium battery energy storage system, comprising:

[0042] S10. Obtain usage sample data and specification parameter data of various types of lithium batteries in the lithium battery energy storage system;

[0043] It is worth noting that a lithium battery energy storage system generally includes multiple lithium batteries for energy storage. These multiple batteries form a single battery pack, and multiple battery packs constitute the energy storage module within the system. To ensure the stability of the energy storage system, the lithium batteries used in a single battery pack are mostly of the same model. However, considering the assembly cost of the energy storage system, the lithium battery models between each battery pack may differ. For example, a manufacturer may produce multiple series of lithium batteries, with minimal differences between each series. Therefore, these series can be used to assemble a lithium battery energy storage system. This application applies to lithium battery energy storage systems composed of single and multiple battery models. This embodiment uses a lithium battery energy storage system containing multiple battery models as an example. The information carried by the collected sample data includes, but is not limited to, charging and discharging methods, battery temperature, and environmental parameters. Specification data includes, but is not limited to, maximum charge and discharge cycles, maximum charge and discharge voltage, maximum charge and discharge current, rated temperature, rated input power, and rated output power.

[0044] S20. A training dataset is constructed based on the usage sample data of various lithium batteries, and the pre-constructed first charge-discharge analysis model is trained using the training dataset.

[0045] It is worth noting that the first charge-discharge analysis model is a pre-built neural network model. The purpose of the trained first charge-discharge analysis model is to analyze the predicted temperature of different types of lithium batteries under different charge-discharge methods.

[0046] S30. Obtain the environmental parameters of the current environment of the lithium battery in the lithium battery energy storage system, input the battery model of the lithium battery and the environmental parameters of the environment into the first charge and discharge analysis model, and output the predicted temperature of each lithium battery under different charge and discharge methods.

[0047] It is worth noting that after training the first charge-discharge analysis model, for the lithium battery energy storage system to be analyzed, the battery model information of each lithium battery in the system is obtained, and the environmental parameters of the current environment of each lithium battery are collected. Specifically, the environmental parameters can be collected by various sensors deployed in the lithium battery energy storage system. The collected parameters include, but are not limited to, ambient temperature, humidity, and atmospheric pressure. After inputting the obtained information into the first charge-discharge analysis model, the data is analyzed by the first charge-discharge analysis model, and finally the predicted temperature of each lithium battery in the lithium battery energy storage system under different charge-discharge methods under the current environmental parameters is obtained. The charge-discharge method is specifically a combination of charge-discharge voltage, current, etc. Generally, each lithium battery can be charged and discharged under different charging methods. The higher the power of the charging method, the faster the charge-discharge speed, but the higher the temperature of the lithium battery will be.

[0048] S40. Input the predicted temperature of each lithium battery under different charging and discharging methods into the second charging and discharging analysis model to obtain the charging and discharging strategy of each lithium battery.

[0049] It's worth noting that, even with a constant charging method, the temperature of a lithium battery is affected by changes in ambient temperature. For example, in winter, at sub-zero temperatures, a lithium battery operating under a certain charging / discharging method may not reach its rated temperature. Conversely, in summer, operating under the same method might result in excessively high temperatures. Continuously controlling the lithium battery to operate under this charging / discharging method will negatively impact its performance and reduce its lifespan. To improve ease of control, a uniform control approach can be adopted, where each lithium battery operates under the same charging / discharging method. However, due to differences in lithium battery models, excessively high power for a charging / discharging method may cause some batteries to overheat. Conversely, while choosing a lower power charging / discharging method results in less impact on battery performance, the overall power of the energy storage system is not maximized, leading to low efficiency.

[0050] In this case, after determining the predicted temperature of each lithium battery under different charging and discharging methods, each lithium battery is analyzed individually using a second charging and discharging analysis model to obtain the charging and discharging strategy for each lithium battery. The charging and discharging strategy for each lithium battery records the optimal charging and discharging method suitable for the lithium battery, which maximizes power while ensuring that the temperature of the lithium battery does not get too high.

[0051] S50: Control the lithium battery energy storage system to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery.

[0052] As an exemplary implementation, for step S20, a training dataset is constructed based on usage sample data of various lithium battery models. The pre-constructed first charge-discharge analysis model is then trained using this training dataset, specifically including:

[0053] At least one data set centered on each battery model is constructed based on the battery signals, wherein the number of data sets depends on the number of lithium battery models collected;

[0054] For each dataset, multiple sub-datasets are constructed using ambient temperature and charging / discharging method as single variables. Each sub-dataset records usage sample data for different charging / discharging methods under the same ambient temperature, or usage sample data for different ambient temperatures under the same charging / discharging method.

[0055] For example, taking ambient temperature as a single variable, the ambient temperature can be -1℃, 15℃, 23℃, 38℃, etc. The usage sample data recorded in each data set corresponds to the same battery model. The usage sample data recorded in the sub-data set with ambient temperature as a single variable also corresponds to the same ambient temperature and the same charging or discharging method, that is, multiple usage sample data under different charging or discharging methods under the same ambient temperature. Taking charging and discharging method as an example, the usage sample data recorded in each sub-data set with charging and discharging method as a single variable corresponds to the same charging or discharging method, that is, multiple usage sample data under different ambient temperatures under the same charging or discharging method.

[0056] A training dataset is composed of multiple sub-data sets, which is used to train the first charge-discharge analysis model.

[0057] As an exemplary implementation, for step S40, the predicted temperature of each lithium battery under different charge and discharge modes is input into the second charge and discharge analysis model to obtain the charge and discharge strategy for each lithium battery, specifically including:

[0058] The second charge-discharge analysis model stores the specification parameters of each type of lithium battery, which can be extracted from the lithium battery instruction manual provided by the manufacturer.

[0059] After receiving the predicted temperature of each lithium battery under different charging and discharging methods, the second charge and discharge analysis model obtains the working state of the lithium battery energy storage system. The working state includes charging state and discharging state. The working state of the lithium battery energy storage system is not limited to the current working state, but can also be the working state that is about to change.

[0060] For any lithium battery, the second charge-discharge analysis model determines the corresponding specification parameter data based on the working state and the battery model of the lithium battery, and determines the first power of the lithium battery based on the rule parameter data;

[0061] It is worth noting that, taking the charging state as an example, the principle for determining the first power is that the predicted temperature under the charging mode corresponding to the first power should not be too high, and the higher the first power, the better, so as to improve the overall working efficiency of the energy storage system. That is, the first power is less than the rated input power in the rule parameter data, the predicted temperature under the charging and discharging mode corresponding to the first power is less than the rated temperature in the rule parameter data, and under the aforementioned two conditions, the higher the first power, the better.

[0062] The first power of each lithium battery is determined by the above method, thereby obtaining the charging and discharging strategy for each lithium battery.

[0063] As an exemplary implementation, for step S40, after obtaining the charge / discharge strategy for each lithium battery, the following is also included:

[0064] The operating status of the lithium battery energy storage system and the current storage capacity of each lithium battery are obtained, and the operating status of the lithium battery energy storage system, the current storage capacity of each lithium battery, and the charging and discharging strategy are input into the third charging and discharging analysis model.

[0065] It is worth noting that the third charge-discharge analysis model is used to adjust the charge-discharge strategy and reduce the impact on the lithium battery during the charge-discharge process. Specifically, when the lithium battery is too low, if it is charged directly with a high-power charging method, the battery's lifespan will be affected. Therefore, it can be charged with a lower-power charging method to improve the lifespan of the lithium battery.

[0066] Specifically, the charging and discharging strategy for each lithium battery is adjusted using a third charging and discharging analysis model, including:

[0067] If the lithium battery energy storage system is in the charging state, lithium batteries with current energy storage capacity lower than the first preset energy threshold are selected. For the selected lithium batteries, the charging strategy of the lithium batteries with energy storage capacity lower than the first preset energy threshold is adjusted according to the battery protection scheme stored in the third charge and discharge analysis model.

[0068] It is worth noting that the battery protection scheme records the charging protection strategy for each model of lithium battery. The charging strategy specifically includes the charging strategy to be adopted when the battery capacity is lower than the first preset capacity threshold, i.e. the specific charging method. After adjusting the charging and discharging strategy of each lithium battery through the third charging and discharging analysis model, the lithium battery energy storage system is controlled to perform charging and discharging operations according to the adjusted charging and discharging strategy of each lithium battery.

[0069] When the charge level is below the first preset power threshold, the charging strategy required to charge the lithium battery to a level not lower than the first preset power threshold can be used. The charging strategy output by the second charge-discharge analysis model can be used for charging.

[0070] As an exemplary implementation, the process of controlling the charging and discharging operation of the lithium battery energy storage system according to the charging and discharging strategy of each lithium battery also includes:

[0071] If the energy storage capacity of any lithium battery is lower than the first preset energy threshold, the discharge operation of the lithium battery will be stopped.

[0072] If the energy storage capacity of any lithium battery exceeds the second preset energy threshold, the lithium battery charging operation will be stopped.

[0073] It is worth noting that during charging and discharging, excessively high or low energy storage capacity of lithium batteries will affect their lifespan. Therefore, in actual use, such as during energy storage, when a lithium battery is charged to a level higher than the second preset energy threshold, charging of that lithium battery is stopped, and the remaining lithium batteries with energy storage capacity lower than the second preset energy threshold store the energy. During discharging, when a lithium battery is discharged to a level lower than the first preset energy threshold, discharging of that lithium battery is stopped, and the remaining lithium batteries with energy storage capacity higher than the first preset energy threshold release the energy.

[0074] This application provides a balanced control method for a lithium battery energy storage system. By analyzing the usage data of different types of lithium batteries, the optimal charging and discharging strategy for each lithium battery in the lithium battery energy storage system under different ambient temperatures is determined. This method improves the charging and discharging power of the lithium battery energy storage system while ensuring the performance stability of the lithium batteries, extending the service life of the lithium batteries, and improving the utilization rate of the lithium batteries.

[0075] This application provides a balanced control method for a lithium battery energy storage system, which, in conjunction with the lithium battery's energy storage data, intelligently adjusts the lithium battery's charging and discharging strategy when the energy storage is too high or too low, further reducing the impact of improper charging and discharging methods on the lithium battery's lifespan.

[0076] As another aspect of the present invention, based on the equalization control method for a lithium battery energy storage system provided in the present invention, see [link to relevant documentation]. Figure 2 A balanced control system for a lithium battery energy storage system, comprising:

[0077] The data acquisition module is used to acquire usage sample data and specification parameter data of various models of lithium batteries in the lithium battery energy storage system;

[0078] The sample data should include at least one of the following: charging / discharging method, battery temperature, and environmental parameters; the specification data should include at least one of the following: maximum number of charge / discharge cycles, maximum charge / discharge voltage, maximum charge / discharge current, rated temperature, rated input power, and rated output power.

[0079] The sample preparation module is used to construct a training dataset based on the usage sample data of various lithium battery models.

[0080] The model training module is used to train the pre-built first charge-discharge analysis model using the training dataset. The first charge-discharge analysis model is a neural network model and is used to analyze the predicted temperature of different types of lithium batteries under different charge-discharge methods.

[0081] The strategy generation module is used to determine the predicted temperature of each lithium battery under different charging and discharging modes through the first charging and discharging analysis model, and input the predicted temperature of each lithium battery under different charging and discharging modes into the second charging and discharging analysis model to generate the charging and discharging strategy for each lithium battery.

[0082] The equalization control module is used to control the charging and discharging operation of the lithium battery energy storage system according to the charging and discharging strategy of each lithium battery.

[0083] As an example implementation, the system also includes:

[0084] The strategy adjustment module is used to adjust the charge and discharge strategy of each lithium battery through a third charge and discharge analysis model.

[0085] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for equalization control of a lithium battery energy storage system, characterized in that, include: To obtain usage sample data and specification parameter data of various models of lithium batteries in a lithium battery energy storage system, wherein the lithium battery energy storage system includes multiple lithium batteries for energy storage, and the multiple lithium batteries include multiple battery models, the usage sample data includes at least one of the following: charging and discharging method, battery temperature and environmental parameters, and the specification parameter data includes at least one of the following: maximum charge and discharge cycles, maximum charge and discharge voltage, maximum charge and discharge current, rated temperature, rated input power and rated output power; A training dataset is constructed based on the usage sample data of various lithium battery models. A pre-constructed first charge-discharge analysis model is trained using the training dataset. The first charge-discharge analysis model is a neural network model. The first charge-discharge analysis model is used to analyze the predicted temperature of different lithium battery models under different charge-discharge methods. The environmental parameters of the current environment of the lithium battery in the lithium battery energy storage system are obtained. The battery model and environmental parameters of the environment of each lithium battery in the lithium battery energy storage system are input into the first charge-discharge analysis model. The predicted temperature of each lithium battery under different charge-discharge methods is determined by the first charge-discharge analysis model. The predicted temperature of each lithium battery under different charging and discharging methods is input into the second charging and discharging analysis model to obtain the charging and discharging strategy of each lithium battery. The lithium battery energy storage system is controlled to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery; The step of inputting the predicted temperature of each lithium battery under different charging and discharging modes into the second charging and discharging analysis model to obtain the charging and discharging strategy for each lithium battery includes: The second charge-discharge analysis model stores the specification parameter data for each type of lithium battery; After receiving the predicted temperature of each lithium battery under different charging and discharging methods, the second charge-discharge analysis model obtains the operating state of the lithium battery energy storage system; the operating state includes charging state and discharging state. For any lithium battery, the second charge-discharge analysis model determines the corresponding specification parameter data based on the working state and the battery model of the lithium battery, and determines the first power of the lithium battery based on the specification parameter data. The first power is less than the rated input power or rated output power in the specification parameter data, and the predicted temperature under the charge-discharge mode corresponding to the first power is less than the rated temperature in the specification parameter data. The first power of each lithium battery is determined, thereby obtaining the charge and discharge strategy for each lithium battery.

2. The equalization control method for a lithium battery energy storage system as described in claim 1, characterized in that, A training dataset is constructed based on usage sample data of various lithium battery models. A pre-constructed first charge-discharge analysis model is then trained using this training dataset, including: At least one dataset centered on each battery model is constructed according to the battery model. For each dataset, multiple sub-datasets are constructed using ambient temperature and charging / discharging method as single variables. Each sub-dataset records usage sample data of different charging / discharging methods under the same ambient temperature, or usage sample data of different ambient temperatures under the same charging / discharging method. Multiple sub-datasets are combined to form a training dataset for training the first charging / discharging analysis model.

3. The equalization control method for a lithium battery energy storage system as described in claim 1, characterized in that, After obtaining the charge / discharge strategy for each lithium battery, the following is also included: The operating status of the lithium battery energy storage system and the current energy storage capacity of each lithium battery are obtained. The operating status of the lithium battery energy storage system, the current energy storage capacity of each lithium battery, and the charging and discharging strategy are input into the third charging and discharging analysis model. The charging and discharging strategy of each lithium battery is adjusted through the third charging and discharging analysis model. The lithium battery energy storage system is controlled to perform charging and discharging operations according to the adjusted charging and discharging strategy of each lithium battery.

4. The equalization control method for a lithium battery energy storage system as described in claim 3, characterized in that, The adjustment of the charging and discharging strategy for each lithium battery using the third charging and discharging analysis model includes: If the lithium battery energy storage system is in a charging state, lithium batteries with current energy storage capacity lower than the first preset energy threshold are selected, and the charging strategy of lithium batteries with energy storage capacity lower than the first preset energy threshold is adjusted according to the battery protection scheme stored in the third charge and discharge analysis model. The battery protection scheme records the charging protection strategy for each model of lithium battery. The charging strategy includes the charging strategy to be adopted when the battery capacity is lower than a first preset capacity threshold.

5. The equalization control method for a lithium battery energy storage system as described in claim 4, characterized in that, The process of controlling the lithium battery energy storage system to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery also includes: If the energy storage capacity of any lithium battery is lower than the first preset energy threshold, the discharge operation of the lithium battery will be stopped. If the energy storage capacity of any lithium battery exceeds the second preset energy threshold, the lithium battery charging operation will be stopped.

6. A balance control system for a lithium battery energy storage system, characterized in that, The system is used to implement the equalization control method of a lithium battery energy storage system according to any one of claims 3-5, including: The data acquisition module is used to acquire usage sample data and specification parameter data of various models of lithium batteries in the lithium battery energy storage system; The sample preparation module is used to construct a training dataset based on the usage sample data of various lithium battery models. The model training module is used to train the pre-built first charge-discharge analysis model using the training dataset; The strategy generation module is used to determine the predicted temperature of each lithium battery under different charging and discharging modes through the first charge and discharge analysis model, input the predicted temperature of each lithium battery under different charging and discharging modes into the second charge and discharge analysis model, and generate a charge and discharge strategy for each lithium battery. The equalization control module is used to control the lithium battery energy storage system to perform charging and discharging operations according to the charging and discharging strategy of each lithium battery.

7. The system as described in claim 6, characterized in that, The system also includes: The strategy adjustment module is used to adjust the charge and discharge strategy of each lithium battery through a third charge and discharge analysis model.

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