Electric signal control method, device and equipment of energy storage system, medium and product

Abstract

The invention discloses an electric signal control method, device and equipment of an energy storage system, a medium and a product. The method comprises the steps of determining an error and a sliding mode surface of a direct-current electric signal based on an expected value and an actual value of the direct-current electric signal output by an energy storage system; performing proportional-integral adjustment on the error through a target controller, performing sliding mode adjustment on the sliding mode surface, and determining an adjustment value of the direct-current electric signal; determining the number N of input modules at least based on the reference direct-current electric signal and the average electric signal of one sub-module in the energy storage system; the reference direct-current signal is the sum of the adjustment value of the direct-current signal and the expected value of the direct-current signal; n is an integer greater than zero; n sub-modules in the energy storage system are controlled to be conducted through an energy storage valve control module of the energy storage system, so that the energy storage system outputs direct current signals according to the output capacity of the N sub-modules. The scheme can be suitable for the control of linear and nonlinear systems, and has the characteristics of wide application scene, quick response, good stability and high robustness.

Description

Technical Field

[0001] This application relates to the field of power, and includes, but is not limited to, an electrical signal control method, device, equipment, medium, and product for an energy storage system. Background Technology

[0002] Energy storage systems are playing an increasingly important role in renewable energy generation, grid peak shaving, and electric vehicle charging. Energy storage technology enables smooth power system operation, improves energy efficiency, and enhances grid flexibility. Among these technologies, DC voltage control is a crucial component of energy storage systems, directly affecting the safety, reliability, and lifespan of the storage units.

[0003] For DC voltage control, proportional-integral-derivative (PID) feedback control is commonly used in related technologies. However, due to the nonlinear, time-varying, and uncertain characteristics of DC voltage control, PID controllers struggle to meet the requirements for control accuracy, fast response, and anti-interference capabilities. Summary of the Invention

[0004] To address the aforementioned issues, this application provides an electrical signal control method, apparatus, device, medium, and product for an energy storage system. This solution is applicable to the control of both linear and nonlinear systems, and features wide applicability, rapid response, good stability, and high robustness.

[0005] The technical solution of this application is implemented as follows:

[0006] In a first aspect, this application provides an electrical signal control method for an energy storage system. The method includes: determining the error and sliding surface of the DC signal based on the expected and actual values ​​of the DC signal output by the energy storage system; adjusting the error proportionally and integrally by a target controller and adjusting the sliding surface by sliding mode to determine the adjustment value of the DC signal; determining the number of modules N to be engaged based on at least a reference DC signal and the average electrical signal of one sub-module in the energy storage system; the reference DC signal is the sum of the adjustment value and the expected value of the DC signal; N is an integer greater than zero; and controlling the N sub-modules in the energy storage system to conduct through the energy storage valve control module of the energy storage system, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0007] As can be seen, in the process of controlling the electrical signal, since the actual electrical signal value is the DC signal output by the output capability of N sub-modules, it may not be completely consistent with the expected value of the DC signal. Traditional PID control may suffer from instability. The target controller of this application integrates PI control and sliding mode control, determining the adjustment value of the electrical signal through PI adjustment and sliding mode adjustment. This control scheme can be applied to linear, nonlinear, and unlocking scenarios. PI adjustment achieves fast response; sliding mode control meets the requirements of stability and robustness. This scheme combines the advantages of PI control and sliding mode control, and features wide application scenarios, fast response, good stability, and high robustness.

[0008] In one possible implementation, the target controller performs proportional-integral adjustment on the error and sliding mode adjustment on the sliding surface to determine the adjustment value of the DC signal. This includes: determining the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller; determining the proportional adjustment value by multiplying the proportional gain by the error; determining the integral adjustment value by multiplying the integral of the error by the integral of the error; determining the sliding mode adjustment value based on the sliding surface, saturation function, and sliding mode gain; and determining the adjustment value of the DC signal by summing the proportional adjustment value, integral adjustment value, and sliding mode adjustment value.

[0009] As can be seen, in this possible real-time method, the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller are first determined. Then, based on these gains and saturation functions, the proportional adjustment value, integral adjustment value, and sliding mode adjustment value are determined respectively. Finally, the adjustment value of the DC signal is obtained from the proportional adjustment value, integral adjustment value, and sliding mode adjustment value. The adjustment value can be obtained by direct summation, or weighted summation can be performed by adding weight coefficients to various adjustment values ​​according to actual needs. It has the characteristics of flexible implementation and reliability.

[0010] In one possible implementation, determining the integral gain in the target controller includes: determining whether the error of the DC signal is greater than a first error threshold; if the error of the DC signal is greater than the first error threshold, determining the integral gain based on the error of the DC signal; wherein, the larger the absolute value of the error of the DC signal, the closer the integral gain is to zero; the smaller the absolute value of the error of the DC signal, the closer the integral gain is to the first integral gain; and if the error of the DC signal is less than or equal to the first error threshold, determining that the integral gain is zero.

[0011] As can be seen, by setting a first error threshold, when the error of the DC signal is less than or equal to the first error threshold, the integral gain is set to zero to achieve integral separation, thereby avoiding the generation of a large integral value of the system voltage near the equilibrium point, which would lead to poor system stability; thus further improving stability.

[0012] In one possible implementation, determining the integral gain based on the error of the DC signal includes: determining an inverse proportional function and a first exponential function for integral adjustment; determining an inverse proportional adjustment value based on the absolute value of the DC signal error and the inverse proportional function; wherein, the larger the absolute value of the DC signal error, the closer the inverse proportional adjustment value is to zero; the smaller the absolute value of the DC signal error, the closer the inverse proportional adjustment value is to a first value; determining a first exponential adjustment value based on the absolute value of the DC signal error and the first exponential function; wherein, the larger the absolute value of the DC signal error, the closer the first exponential adjustment value is to zero; the smaller the absolute value of the DC signal error, the closer the first exponential adjustment value is to a second value; summing the inverse proportional adjustment value and the first exponential adjustment value, and multiplying by a reference integral coefficient to obtain the integral gain; wherein, the first integral gain is the result of multiplying the sum of the first value and the second value by the reference integral coefficient.

[0013] It can be seen that when the absolute value of the error is large, the function value will approach 0, thereby reducing the integral gain and preventing integral saturation. When the absolute value of the error is small, the coefficient value will approach the first integral gain, maintaining a high integral gain and achieving fast response. Furthermore, determining the integral gain using inverse proportional and exponential functions is simple and reliable to implement.

[0014] In one possible implementation, determining the saturation function in the target controller includes: determining a second exponential function, a proportional function, and a hyperbolic tangent function for sliding mode adjustment; determining a first coefficient based on a first reference parameter of the saturation function, the sliding surface, and the exponential function; determining a second coefficient based on the second reference parameter of the saturation function, the sliding surface, and the proportional function; and determining the saturation function based on the first coefficient, the second coefficient, the sliding surface, and the hyperbolic tangent function; wherein, the larger the absolute value of the sliding surface, the smaller the value of the first coefficient and the larger the value of the second coefficient; the smaller the absolute value of the sliding surface, the larger the value of the first coefficient and the smaller the value of the second coefficient; and the saturation function ranges between a third value and a fourth value.

[0015] It can be seen that when the sliding surface s is large, the first coefficient decreases and the second coefficient increases, improving stability; when the sliding surface s is small, the first coefficient increases and the second coefficient decreases, improving response speed. This saturation function, implemented using exponential, proportional, and hyperbolic tangent functions, can achieve smooth transitions, avoid abrupt changes, and enhance overall robustness. The adaptive saturation function can dynamically adjust the control characteristics according to the actual operating state of the sliding mode controller, improving the performance of sliding mode control.

[0016] In one possible implementation, determining the sliding mode gain in the target controller includes: determining the error derivative of the DC signal based on the error of the DC signal; fuzzifying the error and the error derivative to obtain a fuzzy input of the error and a fuzzy input of the error derivative; searching in a fuzzy rule base for a fuzzy output of the sliding mode gain corresponding to the fuzzy input of the error and the fuzzy input of the error derivative; and defuzzifying the fuzzy output of the sliding mode gain to obtain the sliding mode gain.

[0017] In sliding mode control, larger disturbances require a larger switching gain to ensure the stability of the closed-loop system, which leads to chattering. Fuzzy controllers offer better robustness and adaptability compared to traditional controllers. In practical applications, fuzzy controllers can be combined with sliding mode control, and the sliding gain can be optimized by adjusting the parameters of the fuzzy set and the rule base, thereby eliminating oscillations.

[0018] In one possible implementation, the number of modules N to be put into operation is determined based at least on a reference DC signal and the average electrical signal of a submodule in the energy storage system, including: dividing the reference DC signal by the average electrical signal to obtain an integer part and a fractional part; obtaining the current amplitude of a reference triangular wave; the amplitude range of the reference triangular wave is zero to one; if the fractional part is greater than or equal to the amplitude, N is determined to be the integer part plus 1; if the fractional part is less than the amplitude, N is determined to be the integer part.

[0019] In practice, dividing the reference DC signal by the average signal does not necessarily yield an integer value. When the result includes a decimal part, it is usually rounded down directly. This can lead to stability issues. In this implementation, the number of modules N is determined based on a triangular wave method. The system can be controlled to change back and forth between N and N+1 according to the carrier period, thus improving stability.

[0020] In one possible implementation, determining the error of the DC signal and the sliding surface based on the expected and actual values ​​of the DC signal output by the energy storage system includes: determining the error of the DC signal based on the expected and actual values ​​of the DC signal; determining the error derivative of the DC signal based on the error of the DC signal; and determining the sliding surface based on the error of the DC signal and the error derivative of the DC signal.

[0021] As can be seen, in this embodiment, the sliding surface is determined based on the error of the current signal and the error derivative of the DC signal. On the basis of realizing sliding mode control, the weight can be increased according to actual needs, which has the characteristics of flexible implementation.

[0022] In one possible implementation, when the DC signal is a DC voltage, the method further includes: determining that the energy storage system is in a constant voltage control mode; and, when it is determined that the energy storage valve in the energy storage system is unlocked, performing an error of the DC signal and a sliding surface based on the expected value and actual value of the DC signal output by the energy storage system.

[0023] It can be seen that the electrical signal control method of this energy storage system can be applied to the energy storage valve unlocking scenario in constant voltage mode. In energy storage systems, the system voltage changes at the moment of unlocking due to variations in the number of engaged sub-modules (changes in average capacitor voltage), resulting in disturbances. This target controller can improve the stability of the voltage at the moment of unlocking.

[0024] Secondly, this application provides an electrical signal control device for an energy storage system, the device comprising:

[0025] The first determining unit is used to determine the error of the DC signal and the sliding surface based on the expected value and the actual value of the DC signal output by the energy storage system.

[0026] The second determining unit is used to perform proportional-integral adjustment of the error through the target controller, perform sliding mode adjustment of the sliding surface, and determine the adjustment value of the DC signal;

[0027] The third determining unit is used to determine the number of modules N to be put into operation, based at least on a reference DC signal and the average electrical signal of one sub-module in the energy storage system; the reference DC signal is the sum of the adjusted value of the DC signal and the expected value of the DC signal; N is an integer greater than zero.

[0028] The control unit is used to control the conduction of N sub-modules in the energy storage system through the energy storage valve control module, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0029] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program or instructions, which, when executed by the processor, implement any of the methods provided in the first aspect.

[0030] Fourthly, this application also provides a computer-readable storage medium storing a computer program or instructions that, when executed by a processor, implement any of the methods provided in the first aspect above.

[0031] Fifthly, this application also provides a computer program product comprising a computer program or instructions that, when executed by a processor, implement any of the methods provided in the first aspect above.

[0032] It should be noted that the electrical signal control device, electronic equipment, storage medium, and computer program product of the energy storage system have the same technical effect as the electrical signal control method of the energy storage system. For the technical effect of the electrical signal control device, electronic equipment, storage medium, and computer program product of the energy storage system, please refer to the detailed description in the first aspect above, which will not be repeated here. Attached Figure Description

[0033] Figure 1 A schematic diagram of an optional structure of the control system for electrical signals provided in an embodiment of this application;

[0034] Figure 2 A schematic flowchart of a first optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0035] Figure 3 A schematic diagram of a second optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0036] Figure 4 A schematic diagram of a third optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0037] Figure 5 A schematic flowchart illustrating a fourth optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0038] Figure 6 A schematic diagram of a fifth optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0039] Figure 7 A schematic flowchart illustrating a sixth optional method for controlling the electrical signal of an energy storage system provided in an embodiment of this application;

[0040] Figure 8 A schematic diagram of a seventh optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0041] Figure 9 A schematic diagram of an eighth optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0042] Figure 10 A schematic diagram of a ninth optional electrical signal control method for an energy storage system provided in an embodiment of this application;

[0043] Figure 11 A schematic diagram of another optional structure of the control system for electrical signals provided in an embodiment of this application;

[0044] Figure 12This is a schematic diagram of an optional structure for fuzzy control provided in an embodiment of this application;

[0045] Figure 13 This is a schematic diagram of an optional structure of the controller provided in an embodiment of this application;

[0046] Figure 14 The integral gain K provided in the embodiments of this application I An optional flowchart illustrating the optimization process;

[0047] Figure 15 A schematic diagram of an optional voltage control process provided in an embodiment of this application;

[0048] Figure 16 A schematic diagram of an optional structure for the comparison process of the carrier comparison module provided in an embodiment of this application;

[0049] Figure 17 A schematic diagram of an optional carrier comparison function provided in an embodiment of this application;

[0050] Figure 18 A schematic diagram of an optional structure of the electrical signal control device for an energy storage system provided in an embodiment of this application;

[0051] Figure 19 This is a schematic diagram of an optional structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of the application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0053] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0054] In the following description, the terms "first," "second," and "third" are used only to distinguish different objects and do not represent a specific order of objects, nor are they constituting a chronological order. It is understood that "first," "second," and "third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] This application provides an electrical signal control method, apparatus, device, medium, and product for an energy storage system. In practical applications, the electrical signal control method for the energy storage system is implemented by the electrical signal control device of the energy storage system. The functional entities of the electrical signal control device of the energy storage system can be implemented collaboratively by the hardware resources of electronic devices, such as computing resources and communication resources like processors.

[0057] The following describes various embodiments of the electrical signal control method, apparatus, equipment, medium, and product for the energy storage system provided in this application.

[0058] To facilitate understanding, we will first explain the control system for electrical signals.

[0059] For example, refer to Figure 1 As shown, the electrical signal control system 10 may include: a converter valve control module 10, an energy storage valve control module 20, and multiple sub-modules 30.

[0060] The converter valve control module 10 is used to convert AC signals into DC signals.

[0061] The energy storage valve control module 20 is used to: determine the error and sliding surface of the DC signal based on the expected and actual values ​​of the DC signal output by the energy storage system; adjust the error proportionally and integrally through the target controller, adjust the sliding surface, and determine the adjustment value of the DC signal; determine the number of modules N to be put into operation based on at least a reference DC signal and the average electrical signal of one sub-module in the energy storage system; the reference DC signal is the sum of the adjustment value of the DC signal and the expected value of the DC signal; N is an integer greater than zero; and control the N sub-modules in the energy storage system to conduct through the energy storage valve control module, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0062] The energy storage valve control module 20 may include, but is not limited to, an energy storage valve and a controller.

[0063] Submodule 30 is used to output electrical signals. Submodule 30 includes some Insulated-Gate Bipolar Transistor (IGBT) circuits.

[0064] In a first aspect, embodiments of this application provide an electrical signal control method for an energy storage system, used to control the electrical signal so that the output electrical signal is stabilized near the desired value of the electrical signal.

[0065] Below, let's take electronic devices (e.g., can be...) Figure 1 Taking the controller in the system as the execution subject, this paper explains the electrical signal control method of the energy storage system.

[0066] refer to Figure 2 The process may include, but is not limited to, S201 to S204 described below.

[0067] S201. Based on the expected and actual values ​​of the DC signal output by the energy storage system, determine the error of the DC signal and the sliding surface.

[0068] DC signals can include, but are not limited to, DC voltage signals or DC current signals.

[0069] The expected value of the DC signal refers to the value of the DC signal in the instruction.

[0070] The actual value of the DC signal refers to the value of the DC signal collected from the energy storage system through each submodule.

[0071] S201 can be implemented as follows: first, obtain the expected value and actual value of the DC signal output by the energy storage system, then determine the difference between the expected value and the actual value of the DC signal as the error of the DC signal; and then determine the sliding surface based on the error of the DC signal and the error derivative.

[0072] S202. The error of the DC signal is adjusted proportionally and integrally by the target controller, and the sliding surface is adjusted by sliding mode to determine the adjustment value of the DC signal.

[0073] The target controller can be configured as a proportional-integral sliding mode controller.

[0074] The sliding mode adjustment (also known as sliding mode control) here can be either simple sliding mode adjustment or fuzzy sliding mode adjustment.

[0075] The specific expression of the target controller can be configured according to actual needs, and is not uniquely limited here.

[0076] S202 can be implemented as follows: the error of the DC signal is adjusted proportionally and integrally by the target controller, the sliding surface is adjusted by sliding mode, and the results of the proportional-integral adjustment and the sliding mode adjustment are fused to obtain the adjustment value of the DC signal.

[0077] For example, the error of the DC signal is adjusted by proportional-integral control of the target controller, and sliding mode adjustment is performed on the sliding surface. The results of proportional-integral adjustment and sliding mode adjustment are summed to obtain the adjustment value of the DC signal.

[0078] S203. Determine the number of modules N to be put into operation, based at least on the reference DC signal and the average electrical signal of one sub-module in the energy storage system.

[0079] The reference DC signal is the sum of the adjusted value of the DC signal and the expected value of the DC signal.

[0080] N is an integer greater than zero. This application does not limit the value of N and can be configured according to actual circumstances.

[0081] S203 can be implemented as follows: first, sum the adjustment value of the DC signal with the expected value of the DC signal to obtain the reference DC signal; then, based at least on the reference DC signal and the average signal, determine the number of modules N to be put into operation.

[0082] For the implementation of determining the number N of input modules based at least on a reference DC signal and an average electrical signal:

[0083] In one possible implementation, the reference DC signal is divided by the average signal to obtain an integer part and a fractional part. The integer part is then directly determined as the number of modules N to be put into operation; or the integer part is directly added by one to determine the number of modules N to be put into operation; or the fractional part is rounded down based on the rounding principle and then the integer part is added to obtain the number of modules N to be put into operation.

[0084] In another possible implementation, the reference DC signal is divided by the average signal to obtain an integer part and a fractional part. The fractional part is then compared with a reference waveform (e.g., a triangular wave) to obtain the number of input modules N.

[0085] S204. The energy storage valve control module of the energy storage system controls the conduction of N sub-modules in the energy storage system so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0086] In one possible implementation, when the electrical signal is a voltage signal, the energy storage valve control module of the energy storage system controls the conduction of N sub-modules in the energy storage system, so that the energy storage system outputs DC voltage signals according to the output capabilities of the N sub-modules. Specifically, after receiving the number N of modules in operation, the controller in the energy storage valve control module outputs N to the energy storage valve, which then controls the conduction of the N sub-modules in the energy storage system, so that the energy storage system outputs DC voltage signals according to the output capabilities of the N sub-modules.

[0087] When the electrical signal is a current signal, the implementation process is similar to that of a voltage signal, and will not be repeated here.

[0088] The electrical signal control method for an energy storage system provided in this application includes: determining the error and sliding surface of the DC signal based on the expected and actual values ​​of the DC signal output by the energy storage system; adjusting the error proportionally and integrally by a target controller and adjusting the sliding surface to determine the adjustment value of the DC signal; determining the number of modules N to be engaged based on at least a reference DC signal and the average electrical signal of one sub-module in the energy storage system; the reference DC signal is the sum of the adjustment value and the expected value of the DC signal; N is an integer greater than zero; and controlling the N sub-modules in the energy storage system to conduct through the energy storage valve control module of the energy storage system, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0089] As can be seen, in the process of controlling the electrical signal, since the actual electrical signal value is the DC signal output by the output capability of N sub-modules, it may not be completely consistent with the expected value of the DC signal. Traditional PID control may suffer from instability. The target controller of this application integrates PI control and sliding mode control, determining the adjustment value of the electrical signal through PI adjustment and sliding mode adjustment. This control scheme can be applied to linear, nonlinear, and unlocking scenarios. PI adjustment achieves fast response; sliding mode control meets the requirements of stability and robustness. This scheme combines the advantages of PI control and sliding mode control, and features wide application scenarios, fast response, good stability, and high robustness.

[0090] The following explains the process in S202 of using the target controller to perform proportional-integral adjustment of the error, sliding mode adjustment of the sliding surface, and determination of the adjustment value of the DC signal.

[0091] refer to Figure 3 The process may include, but is not limited to, S2021 to S2025 below.

[0092] S2021. Determine the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller.

[0093] The values ​​of the proportional gain, integral gain, sliding mode gain, and saturation function here can be empirical values ​​or dynamically adjusted values.

[0094] S2022. The product of the proportional gain and the error is determined as the proportional adjustment value.

[0095] S2023. The product of the integral operation on the error and the integral gain is determined as the integral adjustment value.

[0096] S2024. Determine the sliding mode adjustment value based on the sliding surface, saturation function, and sliding mode gain.

[0097] The method for determining the sliding mode adjustment value based on the sliding surface, saturation function, and sliding mode gain is not specifically limited here and can be configured according to actual needs.

[0098] For example, the sliding mode adjustment value can be determined according to the following first formula. The first formula includes: A = K s |s| r sat(s). In the first formula, A represents the sliding mode adjustment value; K s denoted as sliding mode gain; sat(s) represents the saturation function; s represents the sliding surface; r is a coefficient.

[0099] S2025. Based on the proportional adjustment value, integral adjustment value, and sliding mode adjustment value, determine the adjustment value of the DC signal.

[0100] In one possible implementation, the proportional adjustment value, integral adjustment value, and sliding mode adjustment value are summed to obtain the adjustment value of the DC signal.

[0101] In another possible implementation, the weights corresponding to the proportional adjustment value, integral adjustment value, and sliding mode adjustment value are first determined, and then a weighted sum is performed to obtain the adjustment value of the DC signal.

[0102] As can be seen, in this possible real-time method, the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller are first determined. Then, based on these gains and saturation functions, the proportional adjustment value, integral adjustment value, and sliding mode adjustment value are determined respectively. Finally, the adjustment value of the DC signal is obtained from the proportional adjustment value, integral adjustment value, and sliding mode adjustment value. The adjustment value can be obtained by direct summation, or weighted summation can be performed by adding weight coefficients to various adjustment values ​​according to actual needs. It has the characteristics of flexible implementation and reliability.

[0103] The process of determining the proportional gain in the target controller in S2021 will be explained below.

[0104] refer to Figure 4 The process may include, but is not limited to, S401 to S403 described below.

[0105] S401. Determine whether the error of the DC signal is greater than the first error threshold.

[0106] The embodiments of this application do not limit the value of the first error threshold, which can be determined according to the actual situation.

[0107] S402. When the error of the DC signal is greater than the first error threshold, determine the integral gain based on the error of the DC signal.

[0108] The larger the absolute value of the error of the DC signal, the closer the integral gain is to zero; the smaller the absolute value of the error of the DC signal, the closer the integral gain is to the first integral gain.

[0109] The implementation of error determination of integral gain based on DC signal can be configured according to actual needs. For example, it can be implemented based on inverse proportional function and exponential function. Of course, it can also be implemented using other functions, which will not be listed here.

[0110] S403. When the error of the DC signal is less than or equal to the first error threshold, the integral gain is determined to be zero.

[0111] As can be seen, by setting a first error threshold, when the error of the DC signal is less than or equal to the first error threshold, the integral gain is set to zero to achieve integral separation, thereby avoiding the generation of a large integral value of the system voltage near the equilibrium point, which would lead to poor system stability; thus further improving stability.

[0112] The following explains the process of determining the integral gain based on the DC signal error in S402.

[0113] Referring to the content shown in 5, this process may include, but is not limited to, S4021 to S4024.

[0114] S4021. Determine the inverse proportional function and the first exponential function used for integral adjustment.

[0115] S4022. Determine the inverse ratio adjustment value based on the absolute value of the error of the DC signal and the inverse ratio function.

[0116] Among them, the larger the absolute value of the error of the DC signal, the closer the inverse adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the inverse adjustment value is to the first value.

[0117] For example, the inverse proportional function can be referred to in the second formula.

[0118] The second formula includes: In the second formula, B represents the inverse adjustment value; |e| represents the absolute value of the error of the DC signal; a1 and b1 are the coefficients of the inverse function; a1 and b1 can be determined based on empirical values.

[0119] In the second formula, the first value is 1 / a1.

[0120] S4023. Determine the first exponential adjustment value based on the absolute value of the error of the DC signal and the first exponential function.

[0121] The larger the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to the second value.

[0122] The first exponential function can be found in the third formula.

[0123] The third formula includes: C = c1 × e -d1×|e| In the third formula, C represents the first exponential adjustment value; |e| represents the absolute value of the error of the DC signal; c1 and d1 are the coefficients of the first exponential function, respectively; c1 and d1 can be determined based on empirical values.

[0124] In the third formula, the second value is c1.

[0125] S4024. After summing the inverse adjustment value and the first exponential adjustment value, multiply by the reference integral coefficient to obtain the integral gain.

[0126] The first integral gain is the sum of the first and second values ​​multiplied by the reference integral coefficient.

[0127] The determination process for integral gain can be found in Formula 4.

[0128] The fourth formula includes: K I =K i ×(B+C); In the fourth formula, K I K represents the integral gain; i B represents the reference integral coefficient; C represents the inverse adjustment value; and D represents the first index adjustment value.

[0129] For example, the first integral gain is K i ×(1 / a1+c1).

[0130] It can be seen that when the absolute value of the error is large, the function value will approach 0, thereby reducing the integral gain and preventing integral saturation. When the absolute value of the error is small, the coefficient value will approach the first integral gain, maintaining a high integral gain and achieving fast response. Furthermore, determining the integral gain using inverse proportional and exponential functions is simple and reliable to implement.

[0131] The process of determining the saturation function in the target controller in S2021 will be explained below.

[0132] refer to Figure 6 The process may include, but is not limited to, S601 to S604 described below.

[0133] S601. Determine the second exponential function, proportional function, and hyperbolic tangent function used for sliding mode adjustment.

[0134] The second exponential function, proportional function, and hyperbolic tangent function can be configured based on actual needs.

[0135] S602. Determine the first coefficient based on the first reference parameter of the saturation function, the sliding surface, and the exponential function.

[0136] For example, the first coefficient can be determined by referring to the fifth formula.

[0137] The fifth formula may include: In the fifth formula, K represents the first coefficient; s represents the sliding surface; k2 represents the first reference parameter of the saturation function; a2 represents the adjustment coefficient; the value of a2 can be configured empirically.

[0138] S603. The second coefficient is determined based on the second reference parameter of the saturation function, the sliding surface, and the proportional function.

[0139] For example, the second coefficient can be determined by referring to the sixth formula.

[0140] The sixth formula may include: L=L2×(1+b2×|s|); in the sixth formula, L represents the second coefficient; s represents the sliding surface; L2 represents the second reference parameter of the saturation function; b2 represents the adjustment coefficient; the value of b2 can be configured based on experience.

[0141] S604. Determine the saturation function based on the first coefficient, the second coefficient, the sliding surface, and the hyperbolic tangent function.

[0142] Among them, the larger the absolute value of the sliding surface, the smaller the value of the first coefficient and the larger the value of the second coefficient; the smaller the absolute value of the sliding surface, the larger the value of the first coefficient and the smaller the value of the second coefficient; the value range of the saturation function is between the third and fourth values.

[0143] For example, the saturation function can be determined according to the seventh formula. The seventh formula may include: In the seventh formula, K represents the first coefficient; L represents the second coefficient; s represents the sliding mode; and tanh represents the hyperbolic tangent function.

[0144] It can be seen that when the sliding surface s is large, the first coefficient decreases and the second coefficient increases, improving stability; when the sliding surface s is small, the first coefficient increases and the second coefficient decreases, improving response speed. This saturation function, implemented using exponential, proportional, and hyperbolic tangent functions, can achieve smooth transitions, avoid abrupt changes, and enhance overall robustness. The adaptive saturation function can dynamically adjust the control characteristics according to the actual operating state of the sliding mode controller, improving the performance of sliding mode control.

[0145] The process of determining the sliding mode gain in the target controller in S2021 is explained below.

[0146] refer to Figure 7 The process may include, but is not limited to, S701 to S704 described below.

[0147] S701. Based on the error of the DC signal, determine the error derivative of the DC signal.

[0148] The error derivative of the DC signal is obtained by differentiating the error of the DC signal.

[0149] S702. Fuzzyenize the error and the error derivative to obtain the fuzzy input of the error and the fuzzy input of the error derivative.

[0150] The method of fuzzification is not limited here and can be configured according to actual needs. For example, fuzzification can be performed using the tiered fuzzy set method or the membership value method.

[0151] S703. Search in the fuzzy rule base for the fuzzy output of the sliding mode gain that corresponds to the fuzzy input of the error and the fuzzy input of the error derivative.

[0152] The fuzzy rule base stores the fuzzy output corresponding to each set of error fuzzy input and error derivative fuzzy input. It is limited to the error fuzzy input and error derivative fuzzy input obtained in S702, and then searches for the fuzzy output corresponding to this error fuzzy input and error derivative fuzzy input; this fuzzy output is then determined as the fuzzy output of the sliding mode gain.

[0153] S704. Defuzzify the fuzzy output of the sliding mode gain to obtain the sliding mode gain.

[0154] There are no restrictions on the defuzzification method here; it can be configured according to actual needs. For example, the maximum membership method, centroid method, or weighted average method can be used for defuzzification to obtain the sliding mode gain.

[0155] In sliding mode control, larger disturbances require a larger switching gain to ensure the stability of the closed-loop system, which leads to chattering. Fuzzy controllers offer better robustness and adaptability compared to traditional controllers. In practical applications, fuzzy controllers can be combined with sliding mode control, and the sliding gain can be optimized by adjusting the parameters of the fuzzy set and the rule base, thereby eliminating oscillations.

[0156] The following explains the process of determining the number of modules N to be put into operation in S203, based at least on the reference DC electrical signal and the average electrical signal of one sub-module in the energy storage system.

[0157] refer to Figure 8 The process may include, but is not limited to, S801 to S804 described below.

[0158] S801. Divide the reference DC signal by the average signal to obtain the integer part and the fractional part.

[0159] S802, Obtain the current amplitude of the reference triangular wave.

[0160] The amplitude range of the reference triangular wave is zero to one.

[0161] The frequency of the reference triangle wave can be configured according to actual needs.

[0162] For example, the frequency of the reference triangular wave is related to one or more of the following parameters: control period, IGBT turn-on frequency, and electrical signal effect.

[0163] The control period refers to the duration of a complete process of an electrical signal. The period corresponding to the frequency of the reference triangle can be set to be longer than the control period. For example, if a control period is 50 microseconds, then the period corresponding to the frequency of the reference triangle wave needs to be greater than 50 microseconds.

[0164] The IGBT's turn-on frequency refers to the turn-on frequency of the IGBT device in the submodule.

[0165] When the frequency of the reference triangular wave is too high, rapid switching of a single submodule may cause the trigger frequency to exceed the limit, resulting in the tripping of the energy storage valve control module (e.g., the energy storage valve). Therefore, the frequency of the reference triangular wave should be lower than the conduction frequency of the IGBT device, thereby reducing the conduction frequency and mitigating the problem of rapid switching of a single submodule potentially causing the trigger frequency to exceed the limit.

[0166] The frequency of the reference triangular wave can be adjusted according to the actual effect of the electrical signal to achieve a good electrical signal effect at the reference triangular wave frequency.

[0167] S803. When the decimal part is greater than or equal to the magnitude, determine N as the integer part plus 1.

[0168] The amplitude here refers to the amplitude of the reference triangular wave at the current moment.

[0169] S804. If the fractional part is less than the magnitude, determine N as the integer part.

[0170] It should be noted that if the decimal part is zero, that is, there is no decimal part, then the integer part is directly determined as N.

[0171] In practice, dividing the reference DC signal by the average signal does not necessarily yield an integer value. When the result includes a decimal part, it is usually rounded down directly. This can lead to stability issues. In this implementation, the number of modules N is determined based on a triangular wave method. The system can be controlled to change back and forth between N and N+1 according to the carrier period, thus improving stability.

[0172] The following section explains the process of determining the error of the DC signal and the sliding surface based on the expected and actual values ​​of the DC signal output by the energy storage system in S201.

[0173] refer to Figure 9 The process may include, but is not limited to, S901 to S903 described below.

[0174] S901. Determine the error of the DC signal based on the expected and actual values ​​of the DC signal.

[0175] The difference between the expected value and the actual value of the DC signal is defined as the error of the DC signal.

[0176] S902. Based on the error of the DC signal, determine the error derivative of the DC signal.

[0177] The error derivative of the DC signal is obtained by differentiating the error of the DC signal.

[0178] S903. Determine the sliding surface based on the error of the DC signal and the derivative of the DC signal error.

[0179] For example, the error of the DC signal and the sum of the error derivative of the DC signal can be used to determine the sliding surface.

[0180] For example, the error of the DC signal can be multiplied by a coefficient and then summed with the error derivative to obtain the sliding surface.

[0181] As can be seen, in this embodiment, the sliding surface is determined based on the error of the current signal and the error derivative of the DC signal. On the basis of realizing sliding mode control, the weight can be increased according to actual needs, which has the characteristics of flexible implementation.

[0182] When the DC signal is a DC voltage, the reference Figure 10 The process may include, but is not limited to, S1001 to S1005 below.

[0183] S1001. Determine that the energy storage system is in constant voltage control mode.

[0184] Read the voltage control configuration information to obtain the voltage control mode. If the voltage control mode is constant voltage control mode, determine that the energy storage system is in constant voltage control mode.

[0185] S1002. When it is determined that the energy storage valve in the energy storage system is unlocked, the error of the DC voltage signal and the sliding surface are determined based on the expected value and the actual value of the DC voltage signal output by the energy storage system.

[0186] The implementation of S1002 can be referred to the description of S201 above, and will not be repeated here.

[0187] S1003. The error is adjusted proportionally and integrally by the target controller, and the sliding surface is adjusted by sliding mode to determine the adjustment value of the DC voltage signal.

[0188] The implementation of S1003 can be referred to the description of S202 above, and will not be repeated here.

[0189] S1004. Determine the number of modules N to be put into operation, based at least on the reference DC voltage signal and the average voltage signal of one sub-module in the energy storage system.

[0190] The implementation of S1004 can be referred to the description of S203 above, and will not be repeated here.

[0191] S1005. The energy storage valve control module of the energy storage system controls the conduction of N sub-modules in the energy storage system so that the energy storage system outputs DC voltage signals according to the output capacity of the N sub-modules.

[0192] The implementation of S1005 can be referred to the description of S204 above, and will not be repeated here.

[0193] It can be seen that the electrical signal control method of this energy storage system can be applied to the energy storage valve unlocking scenario in constant voltage mode. In energy storage systems, the system voltage changes at the moment of unlocking due to variations in the number of engaged sub-modules (changes in average capacitor voltage), resulting in disturbances. This target controller can improve the stability of the voltage at the moment of unlocking.

[0194] The control method provided in the embodiments of this application will be described below.

[0195] Energy storage systems are playing an increasingly important role in renewable energy generation, grid peak shaving, and electric vehicle charging. Energy storage technology enables smooth power system operation, improves energy efficiency, and enhances grid flexibility. Among these technologies, DC voltage control is a crucial component of energy storage systems, directly affecting the safety, reliability, and lifespan of the storage units.

[0196] DC voltage control has wide applications in various industrial and energy sectors, such as grid energy storage systems, power systems, and renewable energy systems. Traditional DC voltage control methods typically employ proportional-integral-derivative (PID) feedback control. However, due to the nonlinear, time-varying, and uncertain characteristics of the system, PID controllers struggle to meet the requirements for control accuracy, fast response, and anti-interference capabilities.

[0197] In recent years, Adaptive Fuzzy Sliding Mode Control (AFSMC) has attracted widespread attention in the field of DC voltage control due to its excellent robustness and adaptability. AFSMC combines the fuzzy inference of fuzzy logic control with the strong robustness of sliding mode control, effectively suppressing the impact of system parameter variations and external disturbances on control performance. However, existing AFSMC methods still have some problems in practical applications, such as high controller design complexity and strong dependence on system models.

[0198] Therefore, this application proposes an adaptive fuzzy sliding mode control method for DC voltage based on energy storage valve control. This method fully utilizes the dynamic characteristics of the energy storage valve control system and employs a simplified AFSMC algorithm to achieve rapid and accurate control of the DC voltage. Compared with methods in related technologies, this method has advantages such as high control accuracy, strong anti-interference capability, and ease of engineering implementation, and can meet the stringent requirements for DC voltage control in industrial production and energy applications.

[0199] Related technologies provide a DC voltage control method for a Static Synchronous Compensator (STATCOM) integrated energy storage system, including: acquiring the DC voltage value (UDCX) of the power module in the system; performing a logical subtraction operation between the DC voltage value UDCX and a DC voltage reference value (Udcref); and integrating the result of the logical subtraction operation using a PI controller to obtain a current reference value (Idref1X). Embodiments of this application can achieve fast and flexible active power control of the STATCOM integrated energy storage system.

[0200] The proposed solution has the following problems: It cannot effectively address system voltage variations caused by changes in the number of engaged submodules (average capacitor voltage changes) during the unlocking of the energy storage valve base controller (VBC) and voltage source coverter (VSC). Furthermore, this method only uses conventional PI control, which may present challenges in terms of stability and robustness under system uncertainties, disturbances, or faults.

[0201] The embodiments of this application can solve the following problems: solve the problem of system voltage change caused by the change in the number of engaged sub-modules (average capacitor voltage change) at the moment of VBC (energy storage valve control) and VSC (polar control); solve the problem of slow system dynamic response; solve the problem of poor system voltage anti-interference capability and weak robustness when the system is running stably.

[0202] This embodiment designs an adaptive fuzzy sliding mode controller suitable for DC voltage control in energy storage systems. An adaptive adjustment strategy for integral gain, saturation function, and sliding mode gain is designed. A carrier comparison method to enhance system robustness is also proposed.

[0203] The technical advantages of this embodiment are as follows: The fuzzy sliding mode control method for energy storage valves provided in this embodiment combines the advantages of fuzzy control, sliding mode control, and PI control. It is very suitable for systems with high nonlinearity, such as energy storage valve control, and has high adaptability. The parameters are easy to adjust, and the integral gain, sliding mode gain, and saturation function can be adaptively adjusted according to different operating conditions and voltage errors. The control method provided in this embodiment has strong robustness and can effectively cope with electrical fluctuations caused by VBC and VSC unlocking and improve the anti-interference capability of the system in steady state. The integral gain tuning method in the controller provided in this embodiment, combined with the characteristics of inverse proportional and exponential functions, achieves smoother adjustment. The designed integral separation method can prevent the system from experiencing integral oversaturation near the steady state point. The carrier comparison function designed in this embodiment can improve the dynamic response and robustness of the system in steady state under voltage control, avoid saturation and overcharging, and allow the system to oscillate near the equilibrium point to achieve better performance.

[0204] refer to Figure 11 The system architecture shown in this embodiment may include a VSC valve 1101, an energy storage valve VBC 1102, and an LC 1103. VBC is an energy storage valve controlled by the ducting valve, and VSC is a converter valve controlled by the ducting valve. The energy storage valve VBC controls the system voltage, while the system current is controlled by the VSC. A voltage command value 1104 is sent from the VSC to the VBC to achieve voltage control. Unlocking refers to the system transitioning from a non-operating state to an operating state. The system unlocking logic follows the order of unlocking the constant voltage side first, followed by the constant power side. This is to ensure that a stable voltage control loop can be established first during system startup, guaranteeing system voltage stability and safe equipment operation. However, when VBC and VSC unlock, the controller needs to adjust its output to adapt to the new operating requirements, which may lead to voltage and current instability. To reduce this fluctuation, it is necessary to ensure coordination and smooth switching between VBC and VSC to ensure system stability.

[0205] The principle of the control system voltage in this embodiment is as follows: by receiving the voltage command value (equivalent to the expected value of the above electrical signal) from the VSC and the current controller's calculated Δ (equivalent to the adjustment value of the above electrical signal), the input reference value Ndc_ref (equivalent to the above reference electrical signal) is obtained. The number of system input modules is calculated by dividing Ndc_ref by the average capacitor voltage of the sub-module. The voltage of the system is controlled by switching the sub-modules on and off, and the actual voltage value is measured in real time to achieve closed-loop control.

[0206] Fuzzy control is a control method based on fuzzy logic theory. It transforms the precise calculations and deterministic rules of traditional control into calculations and rules based on experience and fuzzy concepts. Compared to traditional control methods, fuzzy control has stronger adaptability and robustness, and performs exceptionally well in nonlinear systems where control rules are difficult to establish or accurately describe. Figure 12 shows the basic principle block diagram of fuzzy control. Simply put, after fuzzifying the data according to a set method, it is compared and calculated with a set rule base. The result is then defuzzified, and the output is obtained.

[0207] refer to Figure 12 The content shown is as follows: input 1201 is transmitted to fuzzification interface 1202, fuzzification is performed by fuzzification interface 1202, and then transmitted to inference engine 1203. Inference engine 1203 compares and calculates the fuzzified data with knowledge base 1204 (database 12041 and rule base 12042) (equivalent to fuzzy rule base), and then defuzzifies it through defuzzification interface 1205 before outputting 1206.

[0208] Sliding mode control, also known as variable structure control, can be divided into two parts: sliding surface design and sliding mode control law design. Its main idea is to introduce a sliding variable to divide the control task of the controlled object into several different subsystems, allowing the controller to switch between these subsystems to achieve system control. Fuzzy sliding mode control (FSMC) is a control algorithm that combines fuzzy logic and sliding mode control. During the control process, the system first uses fuzzy logic to perform fuzzy inference based on the current state and control input to obtain a fuzzy controller output, which is then input into the sliding mode controller for further processing. Fuzzy sliding mode control has a simple structure, is model-independent, and has strong robustness, making it very suitable for voltage control in this system architecture.

[0209] In this embodiment, the voltage error and its derivative are used as inputs to the fuzzy controller. The sliding mode gain coefficient of the sliding mode controller is calculated and updated in real time according to set rules. (Reference) Figure 13 The content shown involves subtracting the Uord voltage 1301 and the Umea voltage 1302 to obtain the error 1303. The error 1303 and its derivative 1304 are then input to the sliding mode controller 1305. This sliding mode controller 1305 includes an adaptive integral function 13051, a fuzzy controller 13052, and an adaptive saturation function 13053. The sliding mode controller 1305 uses an integral gain K... IThe saturation function sat(s) and sliding mode gain Ks are processed to obtain Δ, resulting in the Δ function difference and error. This Δ is then summed with the Uord voltage 1301 and input to the carrier comparator 1307 for further processing to obtain the number of operational modules N. Voltage control is then performed based on these N operational modules.

[0210] For example, the sliding mode controller designed in this embodiment uses a commonly used standard-type linear sliding mode surface as the sliding mode surface, which can be referred to in formula (1-1); the sliding mode reaching law adopts a power reaching law, which can be referred to in formula (1-2); and the designed sliding mode controller can be referred to in formula (1-3).

[0211] This controller combines PI control and fuzzy sliding mode control. The PI controller provides a fast dynamic response, thereby improving the system's tracking performance. The fuzzy sliding mode controller can effectively resist external disturbances and uncertainties in system parameters, improving the robustness of the entire control system. The introduced saturation function can make the changes in the control quantity smoother.

[0212]

[0213] u=K P e+K I ∫e+K s |s| r sat(s), 0 <r<1(1-3);

[0214] In formulas (1-1) to (1-3), s(t) represents the sliding surface, e(t) represents the error function, and c, r, and k represent coefficients; The derivative of the error is represented by sgn(s), the sign function is represented by sgn(s), a is the control coefficient, k is the control coefficient, u is the control function, and K is the control factor. P K represents the proportional gain. I K represents the integral gain. s represents the sliding mode gain, and sat(s) represents the saturation function.

[0215] Understandably, sliding surfaces can also be linear sliding surfaces, saturated sliding surfaces, or second-order sliding surfaces; sliding convergence laws can also be saturated convergence laws, linear convergence laws, etc.

[0216] In sliding mode control, the approach rate design determines the speed and manner in which the system moves from the initial state to the ideal sliding surface.

[0217] Power-law approach rates have the following functions:

[0218] 1. Regulation of control force: |s| in power-law approach law aThe term can provide a corresponding control force based on the magnitude of the sliding surface error s. When |s| is large, |s| a When |s| is relatively large, the controller output will be larger, causing the system to quickly approach the sliding surface. When |s| is relatively small, |s| a The output is also relatively small, so the control output will be appropriately reduced to avoid the system oscillating near the sliding surface.

[0219] 2. Adjustment of Approaching Dynamic Characteristics: The exponent 'a' in the power-law approach rate determines the dynamic characteristics of the system approaching the sliding surface. When 'a' is large, the system approaches the sliding surface faster, but may produce a large overshoot. When 'a' is small, the system approaches the sliding surface relatively slowly, but a smoother transition can be achieved.

[0220] 3. Anti-interference capability: The sign(s) term in the power-law approach rate can provide constant anti-interference capability, overcoming the influence of system parameters and external interference.

[0221] This embodiment also includes K. I K s And sat(s) has been optimized.

[0222] 1. Regarding the integral gain K I Optimization.

[0223] K I The optimization can be performed according to the following formula (2).

[0224]

[0225] In formula (2), K I K represents the integral gain; i The reference integral coefficient is represented by |e|; the absolute value of the error of the DC signal is represented by |e|; the parameters a1, b1, c1, d1, and Δ can be preset based on experience.

[0226] Design an adaptive integral gain function that combines the characteristics of inverse proportional and exponential functions to achieve smoother adjustment. When the error is large, the exponential function will quickly reduce the gain, and when the error is small, the gain will quickly recover.

[0227] Specifically, when the error is large, |e| is large, and the function value will approach 0, thereby reducing the integral gain and preventing integral saturation. When the error is small, |e| is small, and the coefficient value will approach 1 / a1+c1, maintaining a high integral gain.

[0228] Specifically, an error threshold of Δ is set. When the error is less than the threshold Δ, the integral gain is set to 0 to achieve integral separation and avoid the system voltage from generating a large integral value near the equilibrium point, which would lead to poor system stability.

[0229] Integral gain K I The optimization process can be referenced. Figure 14 The contents shown include, but are not limited to, S1401 to S1404 below.

[0230] S1401, Integral gain tuning;

[0231] S1402. Determine whether e is less than Δ.

[0232] If yes, proceed with S1403 below; if no, proceed with S1404 below.

[0233] S1403, K I Set to 0 for integral separation.

[0234]

[0235] 2. Optimization of the saturation function sat(s).

[0236] The sliding mode controller uses an adaptive sat(s) function, which adjusts the parameters of the sat(s) function in real time according to the system state.

[0237] First, configure the k and delta parameters in the sat(s) function according to the following formulas (3-2) and (3-3). Then, substitute the k and delta parameters into formula (3-1) to obtain the sat(s) function.

[0238]

[0239] delta=delta2×(1+b2×|s|)(3-3);

[0240] In formulas (3-1) to (3-3), s represents the sliding surface; k2 and delta2 are the baseline parameters of the sat function; a2 and b2 are adjustment coefficients that control the adaptive speed and amplitude. The four parameters k2, delta2, a2, and b2 in formulas (3-1) to (3-3) can also be preset based on experience.

[0241] By designing this adaptive SAT function based on the sliding surface s, we can achieve the following: when the sliding surface s is large, k decreases and delta increases, improving stability; when the sliding surface s is small, k increases and delta decreases, improving response speed; smooth transition, avoiding abrupt changes, and enhancing overall robustness. This adaptive SAT function can dynamically adjust the control characteristics according to the actual operating state of the sliding controller, thereby improving the performance of sliding control.

[0242] 3. Optimization of sliding mode gain Ks.

[0243] In sliding mode control, a large switching gain is required to ensure the stability of the closed-loop system in the face of large disturbances, which leads to chattering. Fuzzy controllers, compared to traditional controllers, offer better robustness and adaptability. In practical applications, fuzzy controllers can be combined with sliding mode control to optimize the control effect by adjusting the parameters of the fuzzy set and the rule base, thereby eliminating oscillations.

[0244] The conditions for the existence of sliding mode are: Ks is the gain that drives the system state to move to the sliding surface, provided that... Under these conditions, a smaller gain Ks should be used as much as possible to reduce chattering. Secondly, when the system moves to a position far from the switching surface, i.e. When the value of Ks is large, the larger the Ks value, the better the system's responsiveness; when the system is close to the switching surface, i.e. When the value of Ks is relatively small, the smaller the value of Ks, the better the stability of the system.

[0245] In fuzzy control, positive large (PB), positive medium (PM), positive small (PS), zero (ZO), negative small (NS), negative medium (NM), and negative large (NB) are commonly used to distinguish fuzzy states. The following figure shows an example of the membership function of a triangular waveform.

[0246] Based on the above principles, the formula will be used to... Convert to e and To design a fuzzy rule base to estimate ΔKs, please refer to the contents of Table 1.

[0247] Table 1 Examples of Fuzzy Rule Bases

[0248]

[0249]

[0250] Finally, using defuzzification strategies such as the area centroid method, the upper bound of Ks is estimated using the following equation (4):

[0251] K s =R∫ΔK s R>0 (4);

[0252] In formula (4), K s Represents the sliding mode gain, R is a coefficient; ΔK s This is the fuzzy output in Table 1.

[0253] In summary, the voltage control of this embodiment can be referred to Figure 15 The content shown may include, but is not limited to, S1501 to S1508 below. Wherein:

[0254] S1501, constant voltage control mode.

[0255] Simply put, the current operating mode of the energy storage system is determined to be constant voltage operating mode.

[0256] S1502, VBC unlock.

[0257] Perform the VBC unlock action.

[0258] S1503, Using an adaptive integral gain limiting strategy, for controller K I The value is controlled.

[0259] The adaptive integral gain limiting strategy here is equivalent to substituting the real-time error into the above formula (2) to obtain the current integral gain K. I Based on the current integral gain K I Implement integral control.

[0260] S1504. Determine if the voltage error value is less than the set threshold Δ1. If it is less, set K. I =0.

[0261] S1505. Use fuzzy control and adaptive adjustment of sliding mode gain based on expert database logic.

[0262] The expert database here is equivalent to the rule base of the aforementioned module. The fuzzy rule base stores the fuzzy output (corresponding to the sliding mode gain of the fuzzy input) corresponding to each set of error fuzzy input and error derivative fuzzy input.

[0263] In simple terms, after fuzzing the current error and its derivative, the algorithm searches the expert database and then defuzzifies the result to obtain the current sliding mode gain.

[0264] S1506. Use the adaptive SAT function to adjust the parameters of the SAT function in real time according to the coefficient status.

[0265] S1507, VBC unlock.

[0266] S1508. Determine if the voltage error value is less than the set threshold Δ2. If it is less, set K. I =0.

[0267] 4. Carrier Comparison Function

[0268] Due to the nonlinearity of the system, in order to increase the stability and anti-interference capability of the system during the steady state process, a carrier comparison function is designed to allow the number of sub-modules to be put into the system to vary between n and n+1 according to a set frequency.

[0269] refer to Figure 16As shown, the comparison process of the carrier comparison module may include: inputting the number of input modules N1601 and the triangular wave 1602 to the carrier comparison module 1603, and the carrier comparison module 1603 obtaining the number of input modules N1 (1604) through the carrier comparison function.

[0270] The reference number of modules to be put into operation, n, is obtained by adding the voltage command value to ΔNdc_ref calculated by the adaptive fuzzy sliding mode controller and dividing the result by the average capacitor voltage of the available submodules. The carrier comparison module generates a carrier wave with an amplitude between 0 and 1 and a period of X, such as a triangular wave, and compares it with the decimal part of the reference number of modules to be put into operation, to determine whether to keep or discard the decimal part. At the same time, the frequency at which the final number of modules to be put into operation, N, changes between n and n+1 can be controlled according to the carrier period X.

[0271] refer to Figure 17 The carrier comparison function shown may include, but is not limited to, S1701 to S1708 below.

[0272] S1701, ΔNdc_ref is calculated through adaptive fuzzy sliding mode control.

[0273] S1702. Ndc_ref=Uord+ΔNdc_ref.

[0274] Summing ΔNdc_ref with Uord (voltage command value) yields the input reference value Ndc_ref.

[0275] S1703, The reference number of input modules n is Ndc_ref divided by the average capacitor voltage of available submodules.

[0276] Divide Ndc_ref by the average capacitor voltage of available submodules to obtain the reference number of modules n.

[0277] S1704. Calculate the integer and fractional parts of the reference input module number n as int(N) and frac(N), respectively.

[0278] S1705 generates a triangular wave with an amplitude of 0-1.

[0279] The amplitude of the triangular wave here ranges from 0 to 1.

[0280] S1706. Determine whether frac(N) is greater than |Wave|.

[0281] Determine whether frac(N) is greater than the current amplitude of the triangular wave, |Wave|.

[0282] If yes, proceed with S1707 below; otherwise, proceed with S1708 below.

[0283] S1707, N = int(N) + 1.

[0284] The final number of modules N to be invested is determined as int(N)+1.

[0285] S1708, N = int(N).

[0286] The final number of modules to be deployed, N, is determined as int(N).

[0287] Secondly, embodiments of this application provide an electrical signal control device for an energy storage system, such as... Figure 18 As shown, the electrical signal control device 180 of the energy storage system includes a first determining unit 1801, a second determining unit 1802, a third determining unit 1803, and a control unit 1804.

[0288] in:

[0289] The first determining unit 1801 is used to determine the error of the DC signal and the sliding surface based on the expected value and the actual value of the DC signal output by the energy storage system.

[0290] The second determining unit 1802 is used to perform proportional-integral adjustment of the error through the target controller, perform sliding adjustment of the sliding surface, and determine the adjustment value of the DC signal.

[0291] The third determining unit 1803 is used to determine the number of modules N to be put into operation based at least on a reference DC signal and the average electrical signal of a sub-module in the energy storage system; the reference DC signal is the sum of the adjusted value of the DC signal and the expected value of the DC signal; N is an integer greater than zero.

[0292] The control unit 1804 is used to control the conduction of N sub-modules in the energy storage system through the energy storage valve control module of the energy storage system, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

[0293] In some embodiments, the second determining unit 1802 is further configured to: determine the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller; determine the proportional adjustment value by multiplying the proportional gain by the error; determine the integral adjustment value by integrating the error and multiplying it by the integral gain; determine the sliding mode adjustment value based on the sliding surface, the saturation function, and the sliding mode gain; and determine the adjustment value of the DC signal based on the proportional adjustment value, the integral adjustment value, and the sliding mode adjustment value.

[0294] In some embodiments, the second determining unit 1802 is further configured to: determine whether the error of the DC signal is greater than a first error threshold; if the error of the DC signal is greater than the first error threshold, determine the integration gain based on the error of the DC signal; wherein, the larger the absolute value of the error of the DC signal, the closer the integration gain is to zero; the smaller the absolute value of the error of the DC signal, the closer the integration gain is to the first integration gain; and if the error of the DC signal is less than or equal to the first error threshold, determine that the integration gain is zero.

[0295] In some embodiments, the second determining unit 1802 is further configured to: determine an inverse proportional function and a first exponential function for integral adjustment; determine an inverse proportional adjustment value based on the absolute value of the error of the DC signal and the inverse proportional function; wherein, the larger the absolute value of the error of the DC signal, the closer the inverse proportional adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the inverse proportional adjustment value is to a first value; determine a first exponential adjustment value based on the absolute value of the error of the DC signal and the first exponential function; wherein, the larger the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to a second value; sum the inverse proportional adjustment value and the first exponential adjustment value, and multiply by a reference integral coefficient to obtain the integral gain; wherein, the first integral gain is the result of multiplying the sum of the first value and the second value by the reference integral coefficient.

[0296] In some embodiments, the second determining unit 1802 is further configured to: determine a second exponential function, a proportional function, and a hyperbolic tangent function for sliding mode adjustment; determine a first coefficient based on a first reference parameter of the saturation function, the sliding surface, and the exponential function; determine a second coefficient based on the second reference parameter of the saturation function, the sliding surface, and the proportional function; and determine the saturation function based on the first coefficient, the second coefficient, the sliding surface, and the hyperbolic tangent function; wherein, the larger the absolute value of the sliding surface, the smaller the value of the first coefficient and the larger the value of the second coefficient; the smaller the absolute value of the sliding surface, the larger the value of the first coefficient and the smaller the value of the second coefficient; and the value range of the saturation function is between a third value and a fourth value.

[0297] In some embodiments, the second determining unit 1802 is further configured to: determine the error derivative of the DC signal based on the error of the DC signal; perform fuzzification processing on the error and the error derivative to obtain a fuzzy input of the error and a fuzzy input of the error derivative; search in a fuzzy rule base for a fuzzy output of a sliding mode gain corresponding to the fuzzy input of the error and the fuzzy input of the error derivative; and perform defuzzification processing on the fuzzy output of the sliding mode gain to obtain the sliding mode gain.

[0298] In some embodiments, the third determining unit 1803 is further configured to: divide the reference DC signal by the average signal to obtain an integer part and a fractional part; obtain the current amplitude of the reference triangular wave; the amplitude range of the reference triangular wave is zero to one; if the fractional part is greater than or equal to the amplitude, determine that N is the integer part plus 1; if the fractional part is less than the amplitude, determine that N is the integer part.

[0299] In some embodiments, the first determining unit 1801 is further configured to: determine the error of the DC signal based on the expected value and the actual value of the DC signal; determine the error derivative of the DC signal based on the error of the DC signal; and determine the sliding surface based on the error of the DC signal and the error derivative of the DC signal.

[0300] In some embodiments, when the DC signal is a DC voltage, the electrical signal control device 180 of the energy storage system may further include a fourth determining unit, which is used to determine that the energy storage system is in a constant voltage control mode.

[0301] If the energy storage valve in the energy storage system is determined to be unlocked, the expected value and actual value of the DC signal output by the energy storage system are calculated to determine the error of the DC signal and the sliding surface.

[0302] It should be noted that the device provided in this application embodiment includes all the units included, which can be implemented by a processor in an electronic device; of course, it can also be implemented by specific logic circuits; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field-programmable gate array (FPGA), etc.

[0303] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0304] It should be noted that, in the embodiments of this application, if the above methods are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0305] Thirdly, embodiments of this application provide an electronic device including a memory and a processor. The memory stores computer programs or instructions, which, when executed by the processor, implement the method described in the first aspect.

[0306] In one example, reference Figure 19 As shown, the electronic device 190 includes: a processor 1901, at least one communication bus 1902, a user interface 1903, at least one external communication interface 1904, and a memory 1905. The communication bus 1902 is configured to enable communication between these components. The user interface 1903 may include features for receiving user input, and the external communication interface 1904 may include standard wired and wireless interfaces.

[0307] The memory 1905 is configured to store instructions and applications executable by the processor 1901, and can also cache data to be processed or already processed by the processor 1901 and various modules in the electronic device (e.g., image data, audio data, voice communication data and video communication data), which can be implemented by flash memory or random access memory (RAM).

[0308] Fourthly, embodiments of this application provide a storage medium, namely a computer-readable storage medium, on which a computer program or instructions are stored, which, when executed by a processor, implement any of the methods provided in the first aspect of the above embodiments.

[0309] Fifthly, embodiments of this application provide a computer program product, which includes a computer program or instructions that, when executed by a processor, implement any of the methods provided in the first aspect of the above embodiments.

[0310] It should be noted that the descriptions of the above embodiments of storage media, devices, apparatuses, and program products are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of storage media, devices, apparatuses, and program products of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0311] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0312] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0313] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0314] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0315] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0316] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0317] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0318] The above are merely embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling electrical signals in an energy storage system, characterized in that, The method includes: Based on the expected and actual values ​​of the DC signal output by the energy storage system, the error of the DC signal and the sliding surface are determined. The error is proportional-integral adjustment is performed by the target controller, the sliding surface is adjusted by sliding mode adjustment, and the adjustment value of the DC signal is determined. The number of modules N to be put into operation is determined based at least on a reference DC signal and the average electrical signal of one submodule in the energy storage system; the reference DC signal is the sum of the adjusted value of the DC signal and the expected value of the DC signal; and N is an integer greater than zero. The energy storage valve control module controls the conduction of N sub-modules in the energy storage system, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

2. The method according to claim 1, characterized in that, The step of adjusting the error through proportional-integral control using a target controller, adjusting the sliding surface, and determining the adjustment value of the DC signal includes: Determine the proportional gain, integral gain, sliding mode gain, and saturation function in the target controller; The product of the proportional gain and the error is determined as the proportional adjustment value; The product of the integral operation on the error and the integral gain is determined as the integral adjustment value; Based on the sliding surface, the saturation function, and the sliding gain, determine the sliding adjustment value; The adjustment value of the DC signal is determined based on the proportional adjustment value, the integral adjustment value, and the sliding mode adjustment value.

3. The method according to claim 1 or 2, characterized in that, Determining the integral gain in the target controller includes: Determine whether the error of the DC signal is greater than a first error threshold; If the error of the DC signal is greater than the first error threshold, the integral gain is determined based on the error of the DC signal; wherein, the larger the absolute value of the error of the DC signal, the closer the integral gain is to zero; the smaller the absolute value of the error of the DC signal, the closer the integral gain is to the first integral gain. If the error of the DC signal is less than or equal to the first error threshold, the integral gain is determined to be zero.

4. The method according to claim 3, characterized in that, Determining the integral gain based on the error of the DC signal includes: Determine the inverse proportional function and the first exponential function used for integral adjustment; Based on the absolute value of the error of the DC signal and the inverse proportional function, an inverse proportional adjustment value is determined; wherein, the larger the absolute value of the error of the DC signal, the closer the inverse proportional adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the inverse proportional adjustment value is to a first value. Based on the absolute value of the error of the DC signal and the first exponential function, a first exponential adjustment value is determined; wherein, the larger the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to zero; the smaller the absolute value of the error of the DC signal, the closer the first exponential adjustment value is to a second value. The integral gain is obtained by summing the inverse adjustment value and the first exponential adjustment value and multiplying it by the reference integral coefficient. Wherein, the first integral gain is the sum of the first value and the second value multiplied by the reference integral coefficient.

5. The method according to claim 1 or 2, characterized in that, Determining the saturation function in the target controller includes: Determine the second exponential function, proportional function, and hyperbolic tangent function used for sliding mode adjustment; The first coefficient is determined based on the first reference parameter of the saturation function, the sliding surface, and the exponential function; The second coefficient is determined based on the second reference parameter of the saturation function, the sliding surface, and the proportional function; The saturation function is determined based on the first coefficient, the second coefficient, the sliding surface, and the hyperbolic tangent function; Wherein, the larger the absolute value of the sliding surface, the smaller the value of the first coefficient and the larger the value of the second coefficient; the smaller the absolute value of the sliding surface, the larger the value of the first coefficient and the smaller the value of the second coefficient; the value range of the saturation function is between the third and fourth values.

6. The method according to claim 1 or 2, characterized in that, Determining the sliding mode gain in the target controller includes: Based on the error of the DC signal, determine the error derivative of the DC signal; The error and the error derivative are fuzzified to obtain the fuzzy input of the error and the fuzzy input of the error derivative; Search in the fuzzy rule base for the fuzzy output of the sliding mode gain that corresponds to the fuzzy input of the error and the fuzzy input of the error derivative; The fuzzy output of the sliding mode gain is defuzzified to obtain the sliding mode gain.

7. The method according to claim 1 or 2, characterized in that, The determination of the number N of modules to be put into operation, based at least on a reference DC signal and the average electrical signal of one submodule in the energy storage system, includes: Divide the reference DC signal by the average signal to obtain the integer part and the fractional part; Obtain the current amplitude of the reference triangular wave; the amplitude range of the reference triangular wave is zero to one. If the fractional part is greater than or equal to the amplitude, then N is determined to be the integer part plus 1; If the fractional part is less than the magnitude, then N is determined to be the integer part.

8. The method according to claim 1 or 2, characterized in that, The determination of the error of the DC signal and the sliding surface based on the expected and actual values ​​of the DC signal output by the energy storage system includes: Based on the expected and actual values ​​of the DC signal, the error of the DC signal is determined; Based on the error of the DC signal, determine the error derivative of the DC signal; The sliding surface is determined based on the error of the DC signal and the derivative of the DC signal error.

9. The method according to claim 1, characterized in that, When the DC signal is a DC voltage, the method further includes: The energy storage system is determined to be in constant voltage control mode; If the energy storage valve in the energy storage system is determined to be unlocked, the expected value and actual value of the DC signal output by the energy storage system are calculated to determine the error of the DC signal and the sliding surface.

10. An electrical signal control device for an energy storage system, characterized in that, The device includes: The first determining unit is used to determine the error of the DC signal and the sliding surface based on the expected value and the actual value of the DC signal output by the energy storage system. The second determining unit is used to perform proportional-integral adjustment on the error through the target controller, perform sliding adjustment on the sliding surface, and determine the adjustment value of the DC signal; The third determining unit is used to determine the number N of modules to be put into operation based at least on a reference DC signal and the average electrical signal of one sub-module in the energy storage system; the reference DC signal is the sum of the adjusted value of the DC signal and the expected value of the DC signal; and N is an integer greater than zero. The control unit is used to control the conduction of N sub-modules in the energy storage system through the energy storage valve control module of the energy storage system, so that the energy storage system outputs DC signals according to the output capacity of the N sub-modules.

11. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program or instructions, which, when executed by the processor, implement the method described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a processor, implement the method described in any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes a computer program or instructions, which, when executed by a processor, implement the method described in any one of claims 1 to 9.

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