Boost current loop control method and system for sodium ion battery energy storage system, medium and processor

Abstract

The invention relates to the technical field of direct current conversion control, and discloses a Boost current loop control method and system for a sodium ion battery energy storage system, a medium and a processor, which are applied to a Boost circuit, a transfer function between an inductive current and a duty ratio is obtained based on the Boost circuit, a double-loop control structure of a current inner loop and a voltage outer loop is established according to the transfer function, and the current inner loop and the voltage outer loop are controlled. Constructing a PI controller based on a BP neural network according to the double-loop control structure; the control method comprises the following steps: acquiring initial parameters set by a current inner loop PI controller; according to the initial parameters, collecting circuit parameters of a Boost circuit, calculating an output value of a voltage outer loop PI controller, and updating parameters of a current inner loop PI controller through BP neural network processing; and optimizing the dynamic characteristics of the current inner loop PI controller according to the updated parameters. The PI controller parameters are updated in real time based on the BP neural network, and the dynamic characteristics of the controller are optimized.

Description

Technical Field

[0001] The present invention relates to the field of direct current conversion control technology, and in particular to a Boost current loop control method, system, medium and processor for a sodium ion battery energy storage system. Background Art

[0002] The DC boost circuit is often used as the pre-stage circuit of the DC / AC conversion circuit and is widely used in renewable energy power generation systems.

[0003] With the development of new energy storage and material technologies, new energy storage sodium ion energy storage batteries have also been developed and produced. Sodium ion energy storage batteries also use DC boost circuits as the front-end circuits of DC / AC conversion circuits, but traditional boost circuits use voltage and current dual-loop PI control, and the control parameters are all fixed values. When the input DC voltage is relatively stable, it can basically meet the requirements. However, since sodium ion energy storage batteries may exhibit wide voltage input characteristics, the output of the boost circuit is unstable and the dynamic performance is poor. Summary of the invention

[0004] In view of the problem that the Boost circuit in the prior art has a wide voltage input characteristic, which leads to unstable output and poor dynamic performance of the Boost circuit, the present invention provides a Boost current loop control method, system, medium and processor for a sodium ion battery energy storage system, which can update the parameters of the PI controller in real time based on the BP neural network, optimize the dynamic characteristics of the controller, and solve the problem that the Boost circuit in the prior art has unstable output and poor dynamic performance. The specific technical solution is as follows: The present invention provides a Boost current loop control method for a sodium ion battery energy storage system, which is applied to a Boost circuit of the sodium ion battery energy storage system. Based on the Boost circuit, a transfer function between an inductor current and a duty cycle is obtained, a dual-loop control structure of a current inner loop and a voltage outer loop is established according to the transfer function, and a PI controller based on a BP neural network is constructed according to the dual-loop control structure; based on the Boost circuit and the PI controller of the BP neural network, the control method comprises: Get the initial parameters set by the current inner loop PI controller; According to the initial parameters, the circuit parameters of the Boost circuit are collected, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after being processed by the BP neural network; According to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized.

[0005] Preferably, the initial parameters of the PI controller include proportional gain and integral gain .

[0006] Preferably, the collecting of the circuit parameters of the Boost circuit according to the initial parameters, calculating the output value of the voltage outer loop PI controller, and updating the parameters of the current inner loop PI controller through BP neural network processing includes: According to the current inner loop PI controller, the initial and , collect the inductor current and capacitor voltage of the Boost circuit; According to the collected capacitor voltage and the preset voltage reference value, the output value of the voltage outer loop PI controller is calculated and set as the current reference value; The current reference value, the deviation value of the collected inductor current, and the current reference value are input into the BP neural network for processing to obtain the latest current inner loop PI controller. and ; According to the latest and , get the output value of the current inner loop PI controller, and through PWM modulation, get the latest inductor current and capacitor voltage of the Boost circuit, and continuously update the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated.

[0007] Preferably, the current reference value and the deviation value of the collected inductor current, as well as the current reference value, are input into the BP neural network for processing to obtain the latest current inner loop PI controller. and include: Input the current reference value, the deviation value of the inductor current collected in real time, and the current reference value into the BP neural network with set corresponding parameters and structure; By performing forward propagation calculation in the BP neural network, the input deviation value is combined with the weight matrix and bias matrix of each layer of the neural network, and the calculation result is processed by the preset activation function to obtain the final output; According to the final output, the latest current inner loop PI controller is obtained and .

[0008] Preferably, the forward propagation calculation is performed in the BP neural network, the input deviation value is combined with the weight matrix and the bias matrix of each layer of the neural network, and the calculation result is processed by a preset activation function to obtain the final output including: By performing forward propagation calculation in the BP neural network, the input deviation value is passed to the hidden layer through the input layer; Multiply the input deviation value with the weight matrix updated by the hidden layer, add the preset bias matrix, perform nonlinear transformation through the preset activation function, and obtain the output of the hidden layer; The output of the hidden layer is multiplied by the updated weight matrix of the output layer, added with the preset bias matrix, and then nonlinearly transformed through the preset activation function to obtain the output of the output layer.

[0009] Preferably, the updating process of the updated weight matrix includes: An error performance function is established according to a current reference value and a deviation value of the inductor current collected in real time; Using the steepest descent method and the set learning rate and inertia coefficient, the weight matrices of the hidden layer and the output layer are updated along the negative gradient direction of the error performance function.

[0010] The embodiment of the present invention further provides a Boost current loop control system for a sodium ion battery energy storage system, which is applied to the aforementioned Boost current loop control method for a sodium ion battery energy storage system, comprising: The boost circuit includes a low-voltage DC power supply, a power switch device, a freewheeling diode, a filter capacitor and a load resistor, and is used to perform voltage boost conversion and control the output voltage of the circuit by adjusting the duty cycle of the power switch device; The PI control module based on BP neural network is used to obtain the initial parameters set by the current inner loop PI controller; according to the initial parameters, the circuit parameters of the Boost circuit are collected, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after being processed by the BP neural network; according to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized.

[0011] Preferably, the PI control module based on BP neural network includes: The acquisition module is used to set the initial current according to the current inner loop PI controller. and , collect the inductor current and capacitor voltage of the Boost circuit; The voltage outer loop PI controller is used to calculate the output value of the voltage outer loop PI controller according to the collected capacitor voltage and the preset voltage reference value, and set it as the current reference value; the deviation value between the current reference value and the collected inductor current, as well as the current reference value, are input into the BP neural network; The BP neural network module is used to process the input current reference value, the deviation value of the collected inductor current, and the current reference value to obtain the latest current inner loop PI controller. and , and continuously updates the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated; The current inner loop PI controller is used to and , and obtain the output value of the current inner loop PI controller.

[0012] The present invention also provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the aforementioned Boost current loop control method for a sodium-ion battery energy storage system.

[0013] The present invention also provides a processor, which is used to run a program, wherein the program executes the aforementioned Boost current loop control method for a sodium ion battery energy storage system when running.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The Boost current loop control method for the sodium ion battery energy storage system of the present invention realizes flexible adjustment of input voltage and output voltage by constructing a Boost circuit and using PWM to control the power switch device, establishes a state average model, performs small signal analysis, and establishes a dual-loop control structure of the current inner loop and the voltage outer loop, thereby improving the dynamic response speed and stability of the system. By collecting the circuit parameters of the Boost circuit, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after BP neural network processing; according to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized. The PI controller of the BP neural network is constructed and the parameters are updated in real time to optimize the dynamic characteristics of the controller, thereby solving the problems of unstable output and poor dynamic performance of the Boost circuit in the prior art. At the same time, through real-time learning and parameter optimization of the BP neural network, combined with PWM modulation, accurate current loop and voltage loop control is achieved, and control errors are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0016] Figure 1 This is a flow chart of a Boost current loop control method for a sodium ion battery energy storage system according to an embodiment of the present invention.

[0017] Figure 2A Boost circuit diagram of a Boost current loop control method for a sodium ion battery energy storage system according to an embodiment of the present invention.

[0018] Figure 3 This is a Boost circuit topology diagram of a Boost current loop control method for a sodium ion battery energy storage system according to an embodiment of the present invention.

[0019] Figure 4 Schematic diagram of two working modes of the Boost circuit of the Boost current loop control method for a sodium ion battery energy storage system according to an embodiment of the present invention.

[0020] Figure 5 This is a Boost dual-loop control structure diagram of a Boost current loop control method for a sodium-ion battery energy storage system according to an embodiment of the present invention.

[0021] Figure 6 Schematic diagram of forward propagation of a Boost current loop control method for a sodium-ion battery energy storage system according to an embodiment of the present invention.

[0022] Figure 7 This is a control flow chart of a Boost circuit current loop PI controller based on a BP neural network for a Boost current loop control method for a sodium ion battery energy storage system according to an embodiment of the present invention.

[0023] Figure 8 A current loop Bode diagram of a Boost current loop control method for a sodium ion battery energy storage system provided in an embodiment of the present invention.

[0024] Fig. 9 The voltage loop Bode diagram of the Boost current loop control method for a sodium ion battery energy storage system provided in an embodiment of the present invention.

[0025] Fig.10 A main circuit diagram of a Boost current loop control method for a sodium ion battery energy storage system provided in an embodiment of the present invention.

[0026] Fig.11 A control circuit diagram b of the Boost current loop control method for a sodium ion battery energy storage system provided in an embodiment of the present invention.

[0027] Fig.12 The PI controller of the Boost current loop control method for a sodium-ion battery energy storage system provided in an embodiment of the present invention when the input voltage fluctuates by ±600V and the control effect diagram of the present invention.

[0028] Fig.13 A schematic diagram of a Boost current loop control system for a sodium ion battery energy storage system provided in an embodiment of the present invention.

[0029] Fig.14 A schematic diagram of a PI control module of a BP neural network for a Boost current loop control system for a sodium-ion battery energy storage system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when used in this specification, the terms "include" and "comprising" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0032] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0033] It should be further understood that the term “and / or” used in the specification of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0034] See the following examples Figures 1 to 14 .

[0035] The present invention provides a Boost current loop control method for a sodium ion battery energy storage system, which is applied to a Boost circuit in the sodium ion battery energy storage system. Based on the Boost circuit, a transfer function between an inductor current and a duty cycle is obtained, a dual-loop control structure of a current inner loop and a voltage outer loop is established according to the transfer function, and a PI controller based on a BP neural network is constructed according to the dual-loop control structure; based on the Boost circuit and the PI controller of the BP neural network, the control method comprises: Step S1, obtaining the initial parameters set by the current inner loop PI controller; the initial parameters / parameters of the PI controller include proportional gain and integral gain ; Step S2, according to the initial parameters, collecting the circuit parameters of the Boost circuit, calculating the output value of the voltage outer loop PI controller, and updating the parameters of the current inner loop PI controller after BP neural network processing; specifically including: According to the current inner loop PI controller, the initial and , collect the inductor current and capacitor voltage of the Boost circuit; According to the collected capacitor voltage and the preset voltage reference value, the output value of the voltage outer loop PI controller is calculated and set as the current reference value; The current reference value, the deviation value of the collected inductor current, and the current reference value are input into the BP neural network for processing to obtain the latest current inner loop PI controller. and ; According to the latest and , get the output value of the current inner loop PI controller, and through PWM modulation, get the latest inductor current and capacitor voltage of the Boost circuit, and continuously update the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated.

[0036] Step S3: Optimize the dynamic characteristics of the current inner loop PI controller according to the updated parameters.

[0037] The dynamic characteristics of the controller are optimized based on the real-time updated PI parameters, and the voltage loop control parameters are set according to the circuit parameters through the Boost circuit current loop control.

[0038] In this embodiment, the BP neural network can continuously update the proportional gain and integral gain of the current inner loop PI controller based on the collected real-time circuit parameters such as the inductor current and capacitor voltage. When facing wide voltage input or load changes, the controller can quickly adjust the parameters to adapt to the dynamic changes of the system, thereby improving the system's adaptability to different working conditions. And by continuously iteratively updating the PI parameters, setting the output of the voltage outer loop PI controller as the current reference value, and processing the current reference value and the inductor current deviation, the output of the Boost circuit can be accurately controlled. Continuous optimization makes the output inductor current and capacitor voltage closer to the expected value, reduces the steady-state error, and achieves precise control of the output voltage and current.

[0039] For details, please refer to Figure 7 In one embodiment of the present application, the current reference value and the deviation value of the collected inductor current, as well as the current reference value, are input into the BP neural network for processing to obtain the latest current inner loop PI controller and include: Input the current reference value, the deviation value of the inductor current collected in real time, and the current reference value into the BP neural network with set corresponding parameters and structure; (2) By performing forward propagation calculations in the BP neural network, the input deviation value is combined with the weight matrix and bias matrix of each layer of the neural network, and the calculation results are processed by a preset activation function to obtain the final output; specifically, it includes: By performing forward propagation calculation in the BP neural network, the input deviation value is passed to the hidden layer through the input layer; Multiply the input deviation value with the weight matrix updated by the hidden layer, add the preset bias matrix, perform nonlinear transformation through the preset activation function, and obtain the output of the hidden layer; The output of the hidden layer is multiplied by the updated weight matrix of the output layer, and then the preset bias matrix is ​​added. Then, a nonlinear transformation is performed through the preset activation function to obtain the output of the output layer. (3) Obtain the latest value of the current inner loop PI controller based on the final output and .

[0040] By inputting the current reference value and its deviation from the inductor current into the BP neural network, the network can integrate the system set target and actual current state information. Through the forward propagation calculation within the network, including the operation with the weight matrix, bias matrix and nonlinear transformation of the activation function, the relevant information is effectively extracted and processed, and the latest proportional gain and integral gain parameters of the PI controller that are adapted to the current system state are accurately output.

[0041] In this example, see Figure 7 , set the voltage loop PI controller parameters according to the circuit parameters, and adjust the inductor current and capacitor voltage Perform real-time sampling to calculate the output value of the voltage outer loop PI controller , calculate the current loop and , output duty cycle, PWM modulation, output capacitor voltage , determine whether k reaches the upper limit (k is a measurement value, indicating the number of iteration terminations). When k does not reach the upper limit, k=k+1, and the voltage loop PI controller parameters are updated and reset in real time. Repeat the process. When k reaches the upper limit, the control process ends.

[0042] It should be noted that, assuming that the BP neural network has an input column vector of m×1 dimension X, and the weight matrix of the kth hidden layer (in this embodiment, k is 1, and one hidden layer is used), that is, the coefficient matrix is ​​n×n dimension , the bias matrix is ​​n×1 dimensional , activation function f, the output layer is a 1×n-dimensional column vector O, then the output of the first hidden layer of the forward propagation is:

[0043]

[0044] The weight matrix corresponding to the output layer O is , the bias matrix , then:

[0045] Among them, the first layer weight matrix , the second layer weight matrix They are:

[0046]

[0047] First layer bias matrix , the second layer bias matrix They are:

[0048]

[0049] The activation function f(x) is:

[0050] Among them, h is the hidden layer output. The forward propagation diagram is as follows Figure 6 shown.

[0051] According to the input current reference value at the current moment, the parameters of the current inner loop PI controller corresponding to the output layer are and :

[0052] Specifically, the updating process of the updated weight matrix includes: An error performance function is established according to a current reference value and a deviation value of the inductor current collected in real time; Using the steepest descent method and the set learning rate and inertia coefficient, the weight matrices of the hidden layer and the output layer are updated along the negative gradient direction of the error performance function.

[0053] In this embodiment, the number of hidden layers of the BP neural network, the number of elements in each hidden layer, and the activation function are set, and the output of the discrete system PI controller is expressed as , assuming that the output reference value at time k (indicating real-time acquisition) is The actual output is , the error is = , define the error performance function :

[0054] Use the steepest descent method to find the optimal solution of the weight matrix, and update the weight matrix along the negative gradient direction of the error function. :

[0055] in:

[0056] This simplifies to a symbolic function:

[0057] The PI controller output expression and the PI parameters corresponding to the output layer and , the output of the PI controller based on the BP neural network is expressed as:

[0058] but: ,

[0059] , Respectively by , Find:

[0060] The gradient of the error function is:

[0061] The weight matrix element update needs to be based on the set learning rate xite and inertia coefficient alpha:

[0062] It should be noted that the BP neural network data composition includes input layer, hidden layer and output layer. The algorithm execution process includes forward propagation and back propagation. The upper layer data of the forward propagation is calculated according to the weight matrix, bias matrix and activation function to obtain the lower layer data, and the lower layer data is deduced in the same way to obtain the final output. Backward propagation uses the defined error function to calculate the partial derivative of the weight matrix to the error function layer by layer from the output layer data, and updates the weight matrix using the steepest descent method. When the number of iterations of the forward propagation and back propagation cycles reaches the upper limit, or when the error function value is within the set range, the iteration can be terminated and the final result can be output.

[0063] The Boost current loop control method for the sodium ion battery energy storage system of the present invention realizes flexible adjustment of input voltage and output voltage by constructing a Boost circuit and using PWM to control the power switch device, establishes a state average model, performs small signal analysis, and establishes a dual-loop control structure of the current inner loop and the voltage outer loop, thereby improving the dynamic response speed and stability of the system. By collecting the circuit parameters of the Boost circuit, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after BP neural network processing; according to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized. The PI controller of the BP neural network is constructed and the parameters are updated in real time to optimize the dynamic characteristics of the controller, thereby solving the problems of unstable output and poor dynamic performance of the Boost circuit in the prior art. At the same time, through real-time learning and parameter optimization of the BP neural network, combined with PWM modulation, accurate current loop and voltage loop control is achieved, and control errors are reduced.

[0064] In one embodiment, based on the Boost circuit, a transfer function between the inductor current and the duty cycle is obtained, and a dual-loop control structure of a current inner loop and a voltage outer loop is established according to the transfer function, which specifically includes: The first step is to use PWM to control the on and off of the power switch device according to the Boost circuit, adjust the input voltage and output voltage, control the output voltage by adjusting the duty cycle, and establish a state average model of the Boost circuit; In one embodiment, the Boost circuit is powered by a low voltage DC power supply. , power switch device T, freewheeling diode , filter capacitor C and load resistor R; Figure 3 As shown, the power switch device T is controlled by a square wave (usually generated by PWM) to realize different working modes of the circuit; When the power switch device T is turned off, the power Charge the filter capacitor C, and the output voltage rise; When the power switch device T is turned on, the power , inductor L, power switch device T form a loop, filter capacitor C discharges to load resistor R, output voltage reduce; Input voltage With output voltage The relationship between the transformation ratio and the square wave duty cycle D satisfies:

[0065] By changing the square wave duty cycle D, the output voltage control, where L is the inductance value.

[0066] It should be noted that if Figure 4 As shown, the Boost circuit state average model is:

[0067] in, is the inductor current; is the input voltage; is the capacitor voltage, that is = ; L is the inductance value; r is the equivalent DC resistance of the inductor; let the left side of the equal sign be 0 to obtain the steady-state solution:

[0068] Using the small disturbance analysis method, the state quantities of the Boost circuit state average model can be written as the sum of the steady-state component and the transient component:

[0069] In the formula, is the small signal change of the inductor current; is the small signal change of the input voltage; is the small signal change of capacitor voltage; Small signal change in the duty cycle of a square wave.

[0070] In the steady-state process, the changes in the inductor current and capacitor voltage are considered to be 0, and there are:

[0071] in, , , , They are the inductor current disturbance, capacitor voltage disturbance, input voltage disturbance, and duty cycle change respectively; is the steady-state solution of the inductor current, is the steady-state solution for the capacitor voltage.

[0072] The second step is to perform small signal analysis, derive the transfer function between the inductor current and the duty cycle, and establish a dual-loop control structure with a current inner loop and a voltage outer loop; Considering that r is usually small, r can be ignored in the process of designing the control law, that is, r=0. At the same time, when the disturbance value is small, the multiplication of the two disturbances can be ignored. Then, the small signal model of the Boost circuit integrated according to the state average model of the Boost circuit is:

[0073] Performing Laplace transform on the second equation of the Boost circuit small signal model yields:

[0074] Substituting the result obtained by Laplace transform into the first equation of the Boost circuit small signal model, we get:

[0075] Where s is the symbol of the frequency domain variable, that is, s=jw; Substitute the Boost circuit state average model into The transfer function from inductor current to duty cycle D is obtained as:

[0076] Substituting the first equation of the boost circuit small signal model into the second equation, we can eliminate for:

[0077] Substituting the steady-state solution into the elimination The transfer function from capacitor voltage to inductor current is obtained from the equation:

[0078] The inductor current is used as the input of the inner current loop and the output of the outer voltage loop. The duty cycle is used as the output of the inner current loop and the capacitor voltage is used as the input of the outer voltage loop. The PI controller is used as the controller of the inner current loop and the outer voltage loop to obtain the dual-loop control structure as shown in the attached figure. Figure 5 shown.

[0079] The embodiment of the present invention further provides a Boost current loop control system for a sodium ion battery energy storage system, which is applied to the aforementioned Boost current loop control method for a sodium ion battery energy storage system, comprising: The boost circuit includes a low-voltage DC power supply, a power switch device, a freewheeling diode, a filter capacitor and a load resistor, and is used to perform voltage boost conversion and control the output voltage of the circuit by adjusting the duty cycle of the power switch device; The PI control module based on BP neural network is used to obtain the initial parameters set by the current inner loop PI controller; according to the initial parameters, the circuit parameters of the Boost circuit are collected, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after being processed by the BP neural network; according to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized.

[0080] Specifically, the PI control module of the BP neural network includes: The acquisition module is used to set the initial current according to the current inner loop PI controller. and , collect the inductor current and capacitor voltage of the Boost circuit; The voltage outer loop PI controller is used to calculate the output value of the voltage outer loop PI controller according to the collected capacitor voltage and the preset voltage reference value, and set it as the current reference value; the deviation value between the current reference value and the collected inductor current, as well as the current reference value, are input into the BP neural network; The BP neural network module is used to process the input current reference value, the deviation value of the collected inductor current, and the current reference value to obtain the latest current inner loop PI controller. and , and continuously updates the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated; The current inner loop PI controller is used to and , and obtain the output value of the current inner loop PI controller.

[0081] The technical effect of the Boost current loop control system for the sodium ion battery energy storage system in this embodiment is the same as the technical effect of the Boost current loop control method for the sodium ion battery energy storage system in this embodiment. The functions of the corresponding modules correspond to the methods and will not be repeated here.

[0082] The present invention also provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the aforementioned Boost current loop control method for a sodium-ion battery energy storage system.

[0083] The technical effect of this embodiment is the same as the technical effect of the Boost current loop control method for a sodium ion battery energy storage system of the embodiment, and will not be repeated here.

[0084] The present invention can be used in many general or special computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0085] The present invention also provides a processor, which is used to run a program, wherein the program executes the aforementioned Boost current loop control method for a sodium ion battery energy storage system when running.

[0086] The technical effect of this embodiment is the same as the Boost current loop control method for the sodium ion battery energy storage system, and will not be repeated here.

[0087] The processor in this embodiment may be a central processing unit (CPU), a controller, a microcontroller, or other data processing chips.

[0088] In order to verify the beneficial effects of the present invention, scientific demonstration was carried out through experiments. Figure 3 Taking the circuit shown in the figure as an example, the parameters are shown in Table 1, and a current loop PI controller based on BP neural network is designed.

[0089] Table 1 Boost circuit parameters parameter value Inductance L 2.5mH Capacitor C 500μF Load resistance R 100Ω Input voltage Uin 800V Output voltage target value 1000V Switching frequency fs 10kHz First, adjust the voltage and current loop PI controller parameters. Set the current loop open-loop transfer function Gio crossover frequency f ic is the switching frequency f s 1 / 20 of f ic =500Hz, in order to make the current loop have good dynamic characteristics, according to the principle of automatic control, the Gio phase margin is set to 45°, and the parameter k of the current loop PI controller is pi , k ii For the quantity to be determined, construct the equation system:

[0090] Solve to get k pi =5.29678, k ii =4587.67459; Similarly, the voltage loop open-loop transfer function crossover frequency f is specified uc =0.1fic, parameter k of voltage loop PI controller pu , k iu As the quantity to be determined, let the current loop open-loop transfer function be:

[0091] Solve to get k pu =0.13108, k iu =51.61707.

[0092] Corresponding to the current loop Bode diagram and voltage loop Bode diagram, such as Figure 8 and Fig. 9 As shown; build the simulation main circuit and control circuit, as shown Fig.10 and Fig.11 shown.

[0093] The input voltage is set to produce a wide voltage fluctuation of ±600V. The control method set by the present invention is compared with the traditional constant PI parameter control method. The output result is as shown in the attached figure. Fig.12As shown, the horizontal axis is the time sampling point; the vertical axis is the voltage. There are two curves in the figure. The dotted line represents the Boost current loop control method (BPPI) for the sodium-ion battery energy storage system, and the solid line represents the traditional constant PI parameter control method (PI). In the face of wide voltage fluctuations, the fluctuation amplitude of the BPPI control method curve is relatively larger, with an obvious drop at the time sampling point 2, and a sharp rise and then a rapid drop at about the time sampling point 4.5; the PI control method curve is relatively stable and has small fluctuations. The BPPI curve fluctuates greatly, indicating that it can respond to voltage fluctuations and adjust the output more quickly, and quickly return to a stable state close to the initial state; while the PI control method curve fluctuates little, indicating that the adjustment speed is slow. Therefore, the BPPI control method of the present invention has better control effect when dealing with wide voltage fluctuations, can more effectively adjust the output to adapt to input voltage changes, and is practical.

[0094] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0095] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0096] In addition, each functional unit / module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the specification of the present invention.

Claims

1. A boost current loop control method for a sodium ion battery energy storage system, characterized in that: The boost circuit is applied to the sodium-ion battery energy storage system. Based on the boost circuit, the transfer function between the inductor current and the duty cycle is obtained. According to the transfer function, a dual-loop control structure of the current inner loop and the voltage outer loop is established. According to the dual-loop control structure, a PI controller based on the BP neural network is constructed. Based on the Boost circuit and the PI controller of the BP neural network, the control method includes: Get the initial parameters set by the current inner loop PI controller; According to the initial parameters, the circuit parameters of the Boost circuit are collected, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after being processed by the BP neural network; According to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized.

2. The Boost current loop control method for a sodium ion battery energy storage system according to claim 1, characterized in that: The initial parameters of the PI controller include the proportional gain and integral gain .

3. The Boost current loop control method for a sodium ion battery energy storage system according to claim 2, characterized in that: The method of collecting the circuit parameters of the Boost circuit according to the initial parameters, calculating the output value of the voltage outer loop PI controller, and updating the parameters of the current inner loop PI controller through BP neural network processing includes: According to the current inner loop PI controller, the initial and , collect the inductor current and capacitor voltage of the Boost circuit; According to the collected capacitor voltage and the preset voltage reference value, the output value of the voltage outer loop PI controller is calculated and set as the current reference value; The current reference value, the deviation value of the collected inductor current, and the current reference value are input into the BP neural network for processing to obtain the latest current inner loop PI controller. and ; According to the latest and , get the output value of the current inner loop PI controller, and through PWM modulation, get the latest inductor current and capacitor voltage of the Boost circuit, and continuously update the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated.

4. The Boost current loop control method for a sodium ion battery energy storage system according to claim 3, characterized in that: The current reference value and the deviation value of the collected inductor current, as well as the current reference value, are input into the BP neural network for processing to obtain the latest current inner loop PI controller. and include: Input the current reference value, the deviation value of the inductor current collected in real time, and the current reference value into a BP neural network with set corresponding parameters and structure; By performing forward propagation calculations in the BP neural network, the input deviation value is combined with the weight matrix and bias matrix of each layer of the neural network, and the calculation results are processed by a preset activation function to obtain the final output; According to the final output, the latest current inner loop PI controller is obtained and .

5. The Boost current loop control method for a sodium ion battery energy storage system according to claim 4, characterized in that: The forward propagation calculation is performed on the BP neural network, the input deviation value is combined with the weight matrix and bias matrix of each layer of the neural network, and the calculation result is processed by a preset activation function to obtain the final output including: By performing forward propagation calculation in the BP neural network, the input deviation value is passed to the hidden layer through the input layer; Multiply the input deviation value with the weight matrix updated by the hidden layer, add the preset bias matrix, perform nonlinear transformation through the preset activation function, and obtain the output of the hidden layer; The output of the hidden layer is multiplied by the updated weight matrix of the output layer, added with the preset bias matrix, and then nonlinearly transformed through the preset activation function to obtain the output of the output layer.

6. The Boost current loop control method for a sodium ion battery energy storage system according to claim 5, characterized in that: The updating process of the updated weight matrix includes: An error performance function is established according to a current reference value and a deviation value of the inductor current collected in real time; Using the steepest descent method and the set learning rate and inertia coefficient, the weight matrices of the hidden layer and the output layer are updated along the negative gradient direction of the error performance function.

7. A Boost current loop control system for a sodium ion battery energy storage system, characterized in that: The Boost current loop control method for a sodium ion battery energy storage system as claimed in any one of claims 1 to 6 comprises: The boost circuit includes a low-voltage DC power supply, a power switch device, a freewheeling diode, a filter capacitor and a load resistor, and is used to perform voltage boost conversion and control the output voltage of the circuit by adjusting the duty cycle of the power switch device; The PI control module based on BP neural network is used to obtain the initial parameters set by the current inner loop PI controller; according to the initial parameters, the circuit parameters of the Boost circuit are collected, the output value of the voltage outer loop PI controller is calculated, and the parameters of the current inner loop PI controller are updated after being processed by the BP neural network; according to the updated parameters, the dynamic characteristics of the current inner loop PI controller are optimized.

8. The Boost current loop control system for a sodium ion battery energy storage system according to claim 7, characterized in that: The PI control module based on BP neural network includes: The acquisition module is used to set the initial current according to the current inner loop PI controller. and , collect the inductor current and capacitor voltage of the Boost circuit; The voltage outer loop PI controller is used to calculate the output value of the voltage outer loop PI controller according to the collected capacitor voltage and the preset voltage reference value, and set it as the current reference value; the deviation value between the current reference value and the collected inductor current, as well as the current reference value, are input into the BP neural network; The BP neural network module is used to process the input current reference value, the deviation value of the collected inductor current, and the current reference value to obtain the latest current inner loop PI controller. and , and continuously updates the current inner loop PI controller and , until the number of loop iterations of the BP neural network reaches the upper limit or the error function value is within the set range, the iteration is terminated; The current inner loop PI controller is used to and , and obtain the output value of the current inner loop PI controller.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the Boost current loop control method for a sodium-ion battery energy storage system according to any one of claims 1 to 6.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the Boost current loop control method for a sodium ion battery energy storage system according to any one of claims 1 to 6.

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