Integration method of solid-state battery module in energy storage system

Abstract

The embodiment of the invention relates to a method for integrating solid-state battery modules in an energy storage system, and the method comprises the steps: constructing a solid-state battery network which comprises a plurality of groups of solid-state battery modules; a controller network is constructed, the controller network comprises an energy storage system controller, and the energy storage controller is used for monitoring the operation state of the solid-state battery network and predicting the SOC of each solid-state battery module based on the monitored operation state; and performing equalization control on the solid-state battery network based on the predicted SOC and the monitored operation state. According to the technical scheme provided by the embodiment of the invention, the optimal estimation result is determined by fusing the prediction state and the measurement state of the system and utilizing the covariance of the prediction state and the measurement state, the characteristics of the solid-state battery are considered, so that the SOC is reflected by complementary information provided by the voltage and the pressure, and the accuracy of SOC prediction is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of power system energy storage, and in particular to a method for integrating a solid-state battery module into an energy storage system. Background Art

[0002] Solid-state batteries are batteries that use solid electrodes and solid electrolytes. The internal state monitoring of solid-state batteries is more challenging than that of traditional liquid batteries. For example, solid-state electrolytes make the measurement of the internal resistance of the battery not as intuitive as liquid batteries because the ion transport mechanism of solid-state electrolytes is more complicated. How to accurately monitor the state of charge (hereinafter referred to as "SOC") of solid-state batteries is crucial to the precise control and safe operation of energy storage systems. In addition, the consistency of solid-state batteries during the manufacturing process is more difficult to control than traditional batteries, which will lead to performance differences between individual batteries in the solid-state battery pack. Therefore, how to achieve balanced control of solid-state battery modules in energy storage systems is also an important issue faced during system integration. Summary of the invention

[0003] Based on the above situation of the prior art, the purpose of an embodiment of the present invention is to provide a method for integrating a solid-state battery module in an energy storage system, and to achieve accurate state monitoring and balanced control of the solid-state battery module by building a controller network during the integration process.

[0004] To achieve the above object, according to one aspect of the present invention, a method for integrating a solid-state battery module in an energy storage system is provided, wherein the energy storage system comprises a solid-state battery network and a controller network, and the integration method comprises the steps of:

[0005] Constructing a solid-state battery network, wherein the solid-state battery network includes a plurality of groups of solid-state battery modules, each group of solid-state battery modules includes one or more solid-state battery cells, and each group of solid-state battery modules is connected to a DC bus;

[0006] Construct a controller network, the controller network includes an energy storage system controller, the energy storage controller is used to monitor the operating status of the solid-state battery network, and based on the monitored operating status, predict the SOC of each solid-state battery module; based on the predicted SOC and the monitored operating status, balance control of the solid-state battery network.

[0007] Furthermore, the operating status includes real-time charge and discharge current, real-time voltage, real-time temperature and real-time pressure.

[0008] Furthermore, based on the monitored operating status, the SOC of each solid-state battery module is predicted, including the following steps:

[0009] A state space model of a solid-state battery module is established, and the state equation of the state space model is expressed as:

[0010]

[0011] An observation equation is established, which is expressed as:

[0012]

[0013] Based on the state equation, according to the SOC and the charge and discharge current of the solid-state battery module at time k, the SOC of the solid-state battery module at time k+1 is predicted to obtain a first predicted value of the SOC;

[0014] According to the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1 and the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 obtained according to the observation equation, the first predicted value of the SOC is corrected to obtain a second predicted value of the SOC;

[0015] Iterate the above prediction steps and correction steps, and when the preset convergence conditions are met, obtain the final prediction value of SOC;

[0016] Among them, x k represents the SOC of the solid-state battery module at time k, x k+1 represents the SOC of the solid-state battery module at time k+1, represents the Coulomb efficiency, I k represents the charge and discharge current of the solid-state battery module at time k, Δt represents the sampling time interval, Q represents the rated capacity of the solid-state battery module; V k represents the voltage of the solid-state battery module at time k, f(x k ) represents the function of voltage and SOC; P k represents the pressure of the solid-state battery module at time k, g(x k ) represents the function of pressure and SOC, ∈ V,k and ∈ P,k represent the measurement noise respectively.

[0017] Further, the first predicted value of the SOC is corrected to obtain a second predicted value of the SOC, including:

[0018]

[0019] in, represents the second predicted value of SOC, x k+1 represents the first predicted value of SOC, z k+1 represents the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1, h(x k+1 ) According to the observation equation, the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 are obtained, K k+1 represents the Kalman gain.

[0020] Further, based on the predicted SOC and the monitored operating state, the solid-state battery network is balanced and controlled, including:

[0021] When the predicted SOC meets the active balancing condition, the active balancing control is started;

[0022] The active balancing current is adjusted according to the real-time temperature of the solid-state battery module.

[0023] Furthermore, the active balancing condition includes:

[0024]

[0025] Among them, SOC i Represents the SOC of solid-state battery module i, Represents the average SOC value of N groups of solid-state battery modules, ΔSOC th Indicates the preset SOC difference threshold.

[0026] Furthermore, the active balancing current is adjusted according to the real-time temperature of the solid-state battery module, including:

[0027] Establishing temperature-balanced current regulation function;

[0028] Based on the temperature-balancing current adjustment function, when the real-time temperature of the solid-state battery module is within a first threshold range, the initial balancing current is used as the active balancing current; when the real-time temperature of the solid-state battery module is within a second threshold range, the active balancing current is determined according to the real-time temperature; when the real-time temperature of the solid-state battery module is within a third threshold range, the upper limit balancing current is used as the active balancing current.

[0029] Furthermore, the temperature-balanced current adjustment function is expressed as:

[0030]

[0031] Among them, I eq (T) represents the active balancing current when the real-time temperature is T, I eq0 Indicates the preset initial balancing current, I eq1 Indicates the preset upper limit balancing current, T 1 represents the first temperature threshold, T 2 represents the second temperature threshold, T 1 <T 2 .

[0032] Furthermore, the solid-state battery network also includes a plurality of DC-DC bidirectional converters, a plurality of groups of first controllable switches and a plurality of groups of second controllable switches; each group of solid-state battery modules is connected to the DC bus in sequence through the first controllable switch, the DC-DC bidirectional converter and the second controllable switch.

[0033] Furthermore, the method also includes: using an energy storage system controller to control the first controllable switch and the second controllable switch to perform balanced control on the solid-state battery network.

[0034] In summary, the embodiment of the present invention provides a method for integrating a solid-state battery module in an energy storage system, wherein the energy storage system includes a solid-state battery network and a controller network, and the integration method includes the steps of: constructing a solid-state battery network, wherein the solid-state battery network includes several groups of solid-state battery modules, each group of solid-state battery modules includes one or more solid-state battery cells, and each group of solid-state battery modules is connected to a DC bus; constructing a controller network, wherein the controller network includes an energy storage system controller, wherein the energy storage controller is used to monitor the operating state of the solid-state battery network, and predict the SOC of each solid-state battery module based on the monitored operating state; and balance the solid-state battery network based on the predicted SOC and the monitored operating state. The technical solution provided by the embodiment of the present invention determines the optimal estimation result by integrating the predicted state and the measured state of the system, and uses the covariance of the two to consider the complex relationship between the SOC and the voltage and pressure due to the characteristics of the solid-state battery, and uses the voltage and pressure to provide complementary information to reflect the SOC, thereby improving the accuracy of the SOC prediction. At the same time, the prediction method of the embodiment of the present invention based on the dynamic system can be adjusted according to the real-time state of the system, thereby improving the dynamic adaptability of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a method for integrating a solid-state battery module into an energy storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0037] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0038] The technical solution of the present invention is described in detail below with reference to the accompanying drawings. An embodiment of the present invention provides a method for integrating a solid-state battery module in an energy storage system, wherein the energy storage system includes a solid-state battery network and a controller network. Figure 1 A flow chart of a method for integrating a solid-state battery module in an energy storage system according to an embodiment of the present invention is shown in FIG. Figure 1 As shown, the integration method includes the following steps:

[0039] S202, construct a solid-state battery network, the solid-state battery network includes several groups of solid-state battery modules, several DC-DC bidirectional converters, several groups of first controllable switches and several groups of second controllable switches, the solid-state battery modules, the first controllable switches, the DC-DC bidirectional converters and the second controllable switches are all connected one by one, and each group of solid-state battery modules is connected to the DC bus in turn through the first controllable switch, the DC-DC bidirectional converter and the second controllable switch. The DC bus can, for example, connect the output to a power grid, a load or other electrical equipment to provide power supply. Each group of solid-state battery modules may include one or more solid-state battery cells. In the case of including multiple solid-state battery cells, multiple solid-state battery cells may be connected in series, and the specific selection may be based on the voltage level or energy demand required in the application scenario.

[0040] S204. Construct a controller network, wherein the controller network includes an energy storage system controller. The energy storage controller is used to monitor the operating status of the solid-state battery network, and predict the SOC of each solid-state battery module based on the monitored operating status; and perform balanced control on the solid-state battery network based on the predicted SOC and the monitored operating status. In an embodiment of the present invention, the operating status includes real-time charging and discharging current, real-time voltage, real-time temperature and real-time pressure. The above-mentioned operating status data can be obtained through sensors arranged on the solid-state battery module. When performing balanced control, the energy storage system controller can be used to control the first controllable switch and the second controllable switch, and the DC-DC bidirectional converter can be used to achieve balanced control of the solid-state battery network.

[0041] Based on the characteristics of solid-state batteries mentioned in the background technology, an embodiment of the present invention provides a method for predicting the SOC of a solid-state battery module based on the operating status data monitored by the energy storage system controller. The method is based on Kalman filtering, and the predicted state and measured state of the system are integrated, and the covariance of the two is used to determine the optimal estimation result. Based on the monitored operating state, the SOC of each solid-state battery module is predicted, including the following steps:

[0042] S20411. Establish a state space model of the solid-state battery module, and establish a state equation according to the charge and discharge characteristics of the solid-state battery. The state equation of the state space model is expressed as:

[0043]

[0044] Among them, x k represents the SOC of the solid-state battery module at time k, x k+1 represents the SOC of the solid-state battery module at time k+1, represents the Coulomb efficiency, I k represents the charge and discharge current of the solid-state battery module at time k, Δt represents the sampling time interval, and Q represents the rated capacity of the solid-state battery module.

[0045] S20412. Establish an observation equation. In an embodiment of the present invention, the voltage and pressure of the solid-state battery module are linked to the SOC to establish an observation equation. For solid-state batteries, in addition to the conventional state data such as current and voltage that will affect the SOC, the volume of the solid-state battery may change slightly during the charging and discharging process due to processes such as ion embedding and extraction. By monitoring the pressure data and considering the impact of the pressure data on the SOC, the SOC prediction of the solid-state battery can be made more accurate. For example, a pressure sensor can be set at the contact point between the support structure of the solid-state battery module and the battery module. When the ion embedding and extraction of the solid-state battery causes a change in the battery volume, the pressure sensor can monitor the pressure value. The observation equation established above can be expressed as:

[0046]

[0047] Among them, V k represents the voltage of the solid-state battery module at time k, f(x k ) represents the function of voltage and SOC; P k represents the pressure of the solid-state battery module at time k, g(x k ) represents the function of pressure and SOC, ∈ V,k and ∈ P,kThey represent measurement noise respectively, which can be determined according to the accuracy of the voltage sensor and the pressure sensor. The functional relationship between voltage and SOC and the functional relationship between pressure and SOC in the above observation equation can be obtained through experiments.

[0048] S20413. Based on the state equation, according to the SOC and charge / discharge current of the solid-state battery module at time k, the SOC of the solid-state battery module at time k+1 is predicted to obtain a first predicted value of the SOC. For example, the SOC initial estimated value x 0 , can be determined based on the initial voltage and pressure of the battery module and empirical values; at the same time, the initial prediction error covariance P is set 0 , can be set according to the confidence level of the initial estimate. Based on the above state equation, the SOC of the solid-state battery module at the next moment is predicted. At the same time, the error covariance of the prediction stage is updated according to the prediction results:

[0049]

[0050] Among them, P k+1 represents the error covariance at time k+1, P k represents the error covariance at time k, A k Represents the state transfer matrix, in the embodiment of the present invention, A k =1,Q k Represents the process noise covariance, which can be determined based on experiments or empirical values. The error covariance of the prediction stage is updated according to the prediction results. After predicting the SOC at the next moment based on the state equation, the uncertainty assessment of the prediction results is updated accordingly, taking into account the dynamic characteristics of the system itself and the uncertainty caused by process noise.

[0051] S20414. According to the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1 and the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 obtained according to the observation equation, the first predicted value of SOC is corrected to obtain a second predicted value of SOC.

[0052] In the embodiment of the present invention, the second predicted value of SOC is obtained based on the following formula:

[0053]

[0054] in, represents the second predicted value of SOC, x k+1 represents the first predicted value of SOC, z k+1 represents the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1, h(x k+1 ) According to the observation equation, the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 are obtained, K k+1represents the Kalman gain, which can be calculated according to the following formula:

[0055]

[0056] Among them, H k+1 represents the observation matrix, which is composed of the partial derivatives of the above functions of voltage and SOC and pressure and SOC, R k+1 Represents the measurement noise covariance matrix, which can be determined based on the accuracy of the voltage sensor and the pressure sensor. The Kalman gain can give the measured value a corresponding weight when updating the predicted value. For example, if the measurement noise covariance is small, it means that the measured value is more reliable, the Kalman gain will be relatively large, and the measured value will be more likely to be trusted when updating the SOC predicted value.

[0057] S20415, error covariance in the update correction phase

[0058]

[0059] Where I represents the identity matrix, P k+1 Represents the error covariance of the above prediction stage. The error covariance in the update correction stage is to adjust the prediction error covariance according to the Kalman gain and the observation matrix after the measurement values ​​are integrated. It can reflect the reassessment of the uncertainty of the SOC prediction after combining the measurement values. Unlike the error covariance update in the prediction stage, this update process takes into account the credibility of the measurement value (reflected by the Kalman gain) and the difference between the measurement value and the prediction value, so that the uncertainty of the SOC prediction can be updated more accurately.

[0060] S20416, iterate the above steps, and when the preset convergence conditions are met, obtain the final predicted value of SOC. When the error covariance of the correction stage converges to within the preset threshold range, the second predicted value of SOC obtained in this iteration is used as the final predicted value. There are many uncertain factors in the solid-state battery module, such as measurement noise (limited accuracy of voltage and pressure sensors) and process noise (complex chemical reactions and physical changes inside the battery). In the embodiment of the present invention, Kalman filtering is used to quantify these uncertainties by updating the error covariance. In the prediction step, considering the process noise covariance, the uncertainty of the state space model can be transferred to the prediction of the next moment; in the update step, combined with the measurement noise covariance, the SOC prediction value is adjusted according to the credibility of the measurement. Thereby, uncertainty can be effectively handled, and a reliable SOC prediction value can still be provided in the presence of noise interference. After obtaining the final predicted value of SOC, the solid-state battery network can be balanced and controlled based on the predicted SOC of the solid-state battery module and the monitored operating status, including the following steps:

[0061] S20421. When the predicted SOC meets the active balancing condition, start active balancing control. The active balancing condition can be set as:

[0062]

[0063] Among them, SOC i Represents the SOC of solid-state battery module i, Represents the average SOC of several groups of solid-state battery modules, ΔSOC th Indicates the preset SOC difference threshold.

[0064] S20422. According to the real-time temperature of the solid-state battery module, the active balancing current is adjusted. First, a temperature-balancing current adjustment function is established. The temperature-balancing current adjustment function can be expressed as:

[0065]

[0066] Among them, I eq (T) represents the active balancing current when the real-time temperature is T, I eq0 Indicates the preset initial balancing current, I eq1 Indicates the preset upper limit balancing current, T 1 represents the first temperature threshold, T 2 represents the second temperature threshold, T 1 <T 2 .

[0067] Based on the above temperature-balanced current regulation function, when the real-time temperature of the solid-state battery module is within the first threshold range (for example, T <T 1 ), the initial balancing current I eq0 As an active balancing current; when the real-time temperature of the solid-state battery module is within the second threshold range (e.g., T 1 ≤T <T 2 ), the active balancing current is determined according to the real-time temperature; when the real-time temperature of the solid-state battery module is within a third threshold range (for example, T ≥ T 2 ), the upper limit balancing current is used as the active balancing current. In the initial stage of active balancing, the charge and discharge operations are performed according to the preset initial balancing current to balance the SOC differences between the solid-state battery modules. As the balancing process proceeds, the temperature of the solid-state battery module will change. At this time, it is necessary to dynamically adjust the balancing current according to the temperature change to ensure that the solid-state battery module is efficiently actively balanced within a safe temperature range. The above adjustment method can adapt to the characteristics of the solid-state battery module at different temperatures, and effectively avoid the performance degradation or safety hazards of the solid-state battery module caused by excessive temperature.

[0068] In summary, the embodiment of the present invention relates to a method for integrating a solid-state battery module in an energy storage system, wherein the energy storage system includes a solid-state battery network and a controller network, and the integration method includes the steps of: constructing a solid-state battery network, wherein the solid-state battery network includes several groups of solid-state battery modules, each group of solid-state battery modules includes one or more solid-state battery cells, and each group of solid-state battery modules is connected to a DC bus; constructing a controller network, wherein the controller network includes an energy storage system controller, wherein the energy storage controller is used to monitor the operating state of the solid-state battery network, and predict the SOC of each solid-state battery module based on the monitored operating state; and balance the solid-state battery network based on the predicted SOC and the monitored operating state. The technical solution provided by the embodiment of the present invention determines the optimal estimation result by integrating the predicted state and the measured state of the system, and uses the covariance of the two to consider the complex relationship between the SOC and the voltage and pressure due to the characteristics of the solid-state battery, and uses the voltage and pressure to provide complementary information to reflect the SOC, thereby improving the accuracy of the SOC prediction. At the same time, the prediction method of the embodiment of the present invention based on the dynamic system can be adjusted according to the real-time state of the system, thereby improving the dynamic adaptability of the prediction.

[0069] It should be understood that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention (including claims) is limited to these examples; under the idea of ​​the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of one or more embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity. The above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the scope of protection of the present invention. In addition, the claims attached to the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the attached claims, or the equivalent forms of such scope and boundaries.

Claims

1. A method for integrating a solid-state battery module in an energy storage system, characterized in that: The energy storage system includes a solid-state battery network and a controller network, and the integration method includes the steps of: Constructing a solid-state battery network, wherein the solid-state battery network includes a plurality of groups of solid-state battery modules, each group of solid-state battery modules includes one or more solid-state battery cells, and each group of solid-state battery modules is connected to a DC bus; Construct a controller network, the controller network includes an energy storage system controller, the energy storage controller is used to monitor the operating status of the solid-state battery network, and based on the monitored operating status, predict the SOC of each solid-state battery module; based on the predicted SOC and the monitored operating status, balance control of the solid-state battery network.

2. The method according to claim 1, characterized in that The operating status includes real-time charge and discharge current, real-time voltage, real-time temperature and real-time pressure.

3. The method according to claim 2, characterized in that Based on the monitored operating status, the SOC of each solid-state battery module is predicted, including the following steps: A state space model of a solid-state battery module is established, and the state equation of the state space model is expressed as: An observation equation is established, which is expressed as: Based on the state equation, according to the SOC and the charge and discharge current of the solid-state battery module at time k, the SOC of the solid-state battery module at time k+1 is predicted to obtain a first predicted value of the SOC; According to the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1 and the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 obtained according to the observation equation, the first predicted value of the SOC is corrected to obtain a second predicted value of the SOC; Iterate the above prediction steps and correction steps, and when the preset convergence conditions are met, obtain the final prediction value of SOC; Among them, x k represents the SOC of the solid-state battery module at time k, x k+1 represents the SOC of the solid-state battery module at time k+1, represents the Coulomb efficiency, I k represents the charge and discharge current of the solid-state battery module at time k, Δt represents the sampling time interval, Q represents the rated capacity of the solid-state battery module; V k represents the voltage of the solid-state battery module at time k, f(x k ) represents the function of voltage and SOC; P k represents the pressure of the solid-state battery module at time k, g(x k ) represents the function of pressure and SOC, ∈ V,k and ∈ P,k represent the measurement noise respectively.

4. The method according to claim 3, characterized in that Correcting the first predicted value of the SOC to obtain a second predicted value of the SOC includes: in, represents the second predicted value of SOC, x k+1 represents the first predicted value of SOC, z k+1 represents the measured values ​​of the voltage and pressure of the solid-state battery module at time k+1, h(x k+1 ) According to the observation equation, the predicted values ​​of the voltage and pressure of the solid-state battery module at time k+1 are obtained, K k+1 represents the Kalman gain.

5. The method according to claim 2, characterized in that: Based on the predicted SOC and the monitored operating state, the solid-state battery network is balanced and controlled, including: When the predicted SOC meets the active balancing condition, the active balancing control is started; The active balancing current is adjusted according to the real-time temperature of the solid-state battery module.

6. The method according to claim 5, characterized in that The active balancing conditions include: Among them, SOC i Represents the SOC of solid-state battery module i, Represents the average SOC value of N groups of solid-state battery modules, ΔSOC th Indicates the preset SOC difference threshold.

7. The method according to claim 6, characterized in that According to the real-time temperature of the solid-state battery module, the active balancing current is adjusted, including: Establishing temperature-balanced current regulation function; Based on the temperature-balancing current adjustment function, when the real-time temperature of the solid-state battery module is within a first threshold range, the initial balancing current is used as the active balancing current; when the real-time temperature of the solid-state battery module is within a second threshold range, the active balancing current is determined according to the real-time temperature; when the real-time temperature of the solid-state battery module is within a third threshold range, the upper limit balancing current is used as the active balancing current.

8. The method according to claim 7, characterized in that The temperature-balanced current regulation function is expressed as: Among them, I eq (T) represents the active balancing current when the real-time temperature is T, I eq0 Indicates the preset initial balancing current, I eq1 represents the preset upper limit balancing current, T1 represents the first temperature threshold, T2 represents the second temperature threshold, T1 represents <T2。 9. The method according to any one of claims 1 to 8, characterized in that: The solid-state battery network also includes a plurality of DC-DC bidirectional converters, a plurality of groups of first controllable switches and a plurality of groups of second controllable switches; each group of solid-state battery modules is connected to the DC bus in sequence through the first controllable switch, the DC-DC bidirectional converter and the second controllable switch.

10. The method according to claim 9, characterized in that The method further includes: using an energy storage system controller to control the first controllable switch and the second controllable switch to perform balanced control on the solid-state battery network.

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