Adaptive control method, computer and medium for fuel cell flow and pressure

Abstract

The present invention discloses a method, computer, and medium for adaptive control of fuel cell flow and pressure, and relates to the field of fuel cell technology. The specific adaptive control method is as follows: obtaining a target power of the fuel cell; then, based on the difference between the calibrated power and the target power of the fuel cell, using a flow and pressure adaptive model established based on the calibrated speed and actual speed of the air compressor at the calibrated power, as well as the calibrated opening and actual opening of the back pressure valve, to adaptively predict the target speed of the air compressor and the target opening of the back pressure valve at the target power. The present invention establishes rules for the flow and pressure adaptive model based on the calibrated speed and actual speed of the air compressor at the calibrated power, as well as the calibrated opening and actual opening of the back pressure valve. Based on the difference between the calibrated power and the target power, the flow and pressure adaptive model is used to correct the air compressor speed and the back pressure valve opening at the target power, thereby achieving reasonable correction of the calibration parameters at the moment of load change.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a method, computer and medium for adaptively controlling flow and pressure of a fuel cell. Background Art

[0002] Fuel cell vehicles are an important branch of new energy vehicles. Due to their advantages such as fast refueling speed, high efficiency, low noise and zero emissions, they are considered to be one of the ultimate solutions for future vehicles.

[0003] At present, the existing fuel cell parameter calibration method is difficult to adapt the calibration parameters to the system operation requirements when the air circuit flow resistance changes or the ambient temperature changes significantly. In particular, short-term cathode air deficiency or cathode pressure overshoot is prone to occur at the loading moment, resulting in poor performance and durability of the fuel cell. Therefore, the calibration parameters need to be reasonably corrected at the load change moment. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, computer and medium for adaptive control of fuel cell flow and pressure, which can reasonably correct calibration parameters at the moment of load change.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for adaptively controlling fuel cell flow and pressure, the method being applied to a fuel cell system comprising a fuel cell, an air compressor, and a back pressure valve; the method comprising the following steps:

[0007] Step 11: Obtaining the target power of the fuel cell;

[0008] Step 12: Based on the difference between the rated power of the fuel cell and the target power, adaptively predict the target speed of the air compressor and the target opening of the backpressure valve at the target power using a flow and pressure adaptive model; the rules of the flow and pressure adaptive model are established based on the rated speed and actual speed of the air compressor at the rated power, and the rated opening and actual opening of the backpressure valve;

[0009] Step 13: Controlling the speed of the air compressor according to the target speed of the air compressor;

[0010] Step 14: Control the opening of the back-pressure valve according to the target opening of the back-pressure valve.

[0011] Optionally, the adaptive control method further includes:

[0012] The flow and pressure adaptive model is constructed using a fuzzy algorithm, specifically including:

[0013] Calculating the difference between the rated speed and the actual speed of the air compressor at the rated power to obtain a speed difference;

[0014] Performing fuzzy quantization on the speed difference to obtain a speed output factor;

[0015] Determining a fuzzy control rule for the speed of the air compressor according to the speed difference and the speed output factor corresponding to the speed difference;

[0016] Calculating the difference between the calibrated opening and the actual opening of the back pressure valve under the calibrated power to obtain the opening difference;

[0017] Performing fuzzy quantization on the opening difference to obtain an opening output factor;

[0018] The fuzzy control rule of the opening of the back pressure valve is determined according to the opening difference and the opening output factor corresponding to the opening difference.

[0019] Optionally, fuzzy quantization is performed on the speed difference to obtain a speed output factor, specifically including:

[0020] according to determining the speed output factor;

[0021] Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e N-ρ (t) represents the speed output factor, e N (t) represents the speed deviation input.

[0022] Optionally, fuzzy quantization is performed on the opening difference to obtain an opening output factor, specifically including:

[0023] according to determining the opening output factor;

[0024] Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e Deg-ρ (t) represents the opening output factor, e Deg (t) represents the opening deviation input.

[0025] Optionally, based on the difference between the rated power and the target power, a flow and pressure adaptive model is used to adaptively predict the target speed of the air compressor under the target power, specifically including:

[0026] According to N trgt =N ref +e N-ρ (t)*(P trgt -Pref ) / P ref Determining a target speed of the air compressor;

[0027] Among them, N trgt Indicates the target speed of the air compressor, N ref Indicates the rated speed of the air compressor, e N-ρ (t) represents the speed output factor, P trgt Indicates the target power, P ref Indicates the rated power.

[0028] Optionally, based on the difference between the calibrated power and the target power, a flow and pressure adaptive model is used to adaptively predict the target opening of the back pressure valve under the target power, specifically including:

[0029] According to Deg trgt =Deg ref +e Deg-ρ (t)*(P trgt -P ref ) / P ref Determining a target speed of the air compressor;

[0030] Among them, Deg trgt Indicates the target speed of the back pressure valve, Deg ref Indicates the back pressure valve calibrated speed, e Deg-ρ (t) represents the opening output factor, P trgt Indicates the target power, P ref Indicates the rated power.

[0031] Optionally, step 13 specifically includes:

[0032] According to the target speed of the air compressor, the speed of the air compressor is controlled by using a PID algorithm.

[0033] Optionally, step 14 specifically includes:

[0034] The opening of the back-pressure valve is controlled by a PID algorithm according to the target opening of the back-pressure valve.

[0035] The present invention also provides a computer comprising: a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for adaptively controlling the flow and pressure of a fuel cell when executing the computer program.

[0036] The present invention also provides a storage medium, in which a computer program is stored. When a processor executes the computer program, the above-mentioned method for adaptively controlling the flow and pressure of a fuel cell is implemented.

[0037] According to the specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides an adaptive control method, computer and medium for fuel cell flow and pressure, wherein the adaptive control method is applied to a fuel cell system containing a fuel cell, an air compressor and a back-pressure valve, and the specific adaptive control method is: first, obtaining the target power of the fuel cell; then, based on the difference between the calibrated power and the target power of the fuel cell, using a flow and pressure adaptive model established based on the calibrated speed and actual speed of the air compressor under the calibrated power, and the calibrated opening and actual opening of the back-pressure valve, adaptively predict the target speed of the air compressor and the target opening of the back-pressure valve under the target power; finally, according to the predicted target speed of the air compressor, the speed of the air compressor is controlled; according to the predicted target opening of the back-pressure valve, the opening of the back-pressure valve is controlled. The present invention establishes the rules of the flow and pressure adaptive model based on the calibrated speed and actual speed of the air compressor at the calibrated power, as well as the calibrated opening and actual opening of the back-pressure valve. Then, based on the difference between the calibrated power and the target power, the flow and pressure adaptive model is used to correct the air compressor speed and the back-pressure valve opening at the target power, thereby realizing reasonable correction of the calibration parameters at the moment of load change. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flow chart of a method for adaptively controlling fuel cell flow and pressure according to an embodiment of the present invention;

[0040] Figure 2 Flowchart of a method for constructing a traffic adaptive model in an embodiment of the present invention;

[0041] Figure 3 This is a flow chart of a method for constructing a pressure adaptive model in an embodiment of the present invention;

[0042] Figure 4 : is a diagram of the adaptive model framework in an embodiment of the present invention;

[0043] Figure 5 This is a model framework diagram of the anti-surge control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0045] The purpose of the present invention is to provide a method, computer and medium for adaptive control of fuel cell flow and pressure, so as to realize reasonable correction of calibration parameters at the moment of load change.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown, the present invention provides an adaptive control method for fuel cell flow and pressure, the adaptive control method is applied to a fuel cell system, the fuel cell system includes a fuel cell, an air compressor and a back pressure valve; the adaptive control method includes the following steps:

[0048] Step 11: Obtain the target power of the fuel cell.

[0049] Step 12: Based on the difference between the calibrated power of the fuel cell and the target power, a flow and pressure adaptive model is used to adaptively predict the target speed of the air compressor and the target opening of the back pressure valve under the target power; the rules of the flow and pressure adaptive model are established based on the calibrated speed and actual speed of the air compressor under the calibrated power, as well as the calibrated opening and actual opening of the back pressure valve.

[0050] Step 13: Control the speed of the air compressor according to the target speed of the air compressor.

[0051] Step 14: Control the opening of the back-pressure valve according to the target opening of the back-pressure valve.

[0052] In some embodiments, the adaptive control method further includes using a fuzzy algorithm to construct a flow and pressure adaptive model.

[0053] like Figure 2 As shown, the method of constructing the flow adaptive model in the flow and pressure adaptive model using the fuzzy algorithm can be as follows:

[0054] Step 21: Obtain the rated speed and actual speed of the air compressor at the rated power.

[0055] Step 22: According to the formula N=N act -N refCalculate the speed deviation between the rated speed and the actual speed of the air compressor under rated power, where N represents the speed deviation, N act Indicates the actual speed of the air compressor, N ref Indicates the rated speed of the air compressor.

[0056] Step 23: Input the calculated speed deviation N into the fuzzy module for fuzzy quantization to obtain the speed output factor e N-ρ (t), speed difference input e N (t) and the speed output factor e N-ρ The fuzzy control of the speed (t) is Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e N-ρ (t) represents the speed output factor, e N (t) represents the speed deviation input.

[0057] like Figure 3 As shown, the method of constructing the pressure adaptive model in the flow and pressure adaptive model using fuzzy algorithm can be as follows:

[0058] Step 31: Obtain the calibrated opening and actual opening of the back pressure valve under the calibrated power.

[0059] Step 32: According to the formula Deg = Deg act -Deg ref Calculate the opening deviation between the calibrated opening and the actual opening of the back pressure valve under the calibrated power, where Deg represents the opening deviation, Deg act Indicates the actual opening of the back pressure valve, Deg ref Indicates the calibrated opening of the back pressure valve.

[0060] Step 33: Input the calculated opening deviation Deg into the fuzzy module for fuzzy quantization to obtain the opening output factor e Deg-ρ (t), opening deviation input e Deg (t) and opening output factor e Deg-ρ The fuzzy control of the opening of (t) is Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e Deg-ρ (t) represents the opening output factor, e Deg (t) represents the opening deviation input.

[0061] In some embodiments, step 12 may be implemented by:

[0062] like Figure 4As shown, the calibrated power and target power of the fuel cell, the calibrated speed of the air compressor, the calibrated opening of the back pressure valve, the actual speed of the air compressor and the actual opening of the back pressure valve are input into the flow and pressure adaptive model.

[0063] Based on the difference between the rated speed and the actual speed of the air compressor at the rated power, according to N trgt =N ref +e N-ρ (t)*(P trgt -P ref ) / P ref Determine the target speed N of the air compressor trgt ; Among them, N trgt Indicates the target speed of the air compressor, N ref Indicates the rated speed of the air compressor, e N-ρ (t) represents the speed output factor, P trgt Indicates the target power, P ref Indicates the rated power.

[0064] Based on the difference between the calibrated opening and the actual opening of the back pressure valve under the calibrated power, according to Deg trgt =Deg ref +e Deg-ρ (t)*(P trgt -P ref ) / P ref Determine the target speed of the air compressor Deg trgt ; Among them, Deg trgt Indicates the target speed of the back pressure valve, Deg ref Indicates the back pressure valve calibrated speed, e Deg-ρ (t) represents the opening output factor, P trg t Indicates the target power, P ref Indicates the rated power.

[0065] In some embodiments, step 13 may be implemented by:

[0066] like Figure 5 As shown, the adaptive model used in the present invention is corrected on the basis of calibration parameters. The air compressor speed mainly controls the cathode inlet flow. The initial value of the flow PID algorithm is the air compressor calibrated speed value. After calculation by the PID algorithm, the speed correction value of the adaptive model is added and input into the air compressor controller. The speed of the air compressor is controlled according to the target speed of the air compressor. Figure 5 Middle Q trgt Indicates the target air flow rate, P trgt Indicates the target air intake pressure, Q fdbk Indicates the feedback air flow, P fdbk Indicates the feedback air inlet pressure.

[0067] The main control parameter of the fuel air circuit is the target air flow Q trgt and the target air intake pressure P trgt Insufficient flow or pressure will cause gas shortage in the fuel cell stack, affecting its lifespan; excessive instantaneous flow or pressure overshoot will affect its durability.

[0068] In some embodiments, step 14 may be implemented by:

[0069] like Figure 3 As shown, the adaptive model used in the present invention is corrected on the basis of calibration parameters. The back-pressure valve angle mainly regulates the cathode inlet pressure. The initial value of the flow PID algorithm is the calibrated opening value of the back-pressure valve. After calculation by the PID algorithm, the opening correction value of the adaptive model is added and input into the back-pressure valve actuator. According to the target speed of the air compressor, the PID algorithm is used to control the speed of the air compressor.

[0070] The present invention also provides a computer, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor implements a method for adaptively controlling the flow and pressure of a fuel cell when executing the computer program.

[0071] The present invention also provides a storage medium, in which a computer program is stored. When a processor executes the computer program, a method for adaptively controlling the flow and pressure of a fuel cell is implemented.

[0072] In summary, the present invention has the following advantages:

[0073] (1) The present invention establishes the rules of the flow and pressure adaptive model based on the calibrated speed and actual speed of the air compressor at the calibrated power, and the calibrated opening and actual opening of the back pressure valve. Then, based on the difference between the calibrated power and the target power, the flow and pressure adaptive model is used to correct the air compressor speed and the back pressure valve opening at the target power, thereby realizing reasonable correction of the calibration parameters at the moment of load change.

[0074] (2) The adaptive control method for fuel cell flow and pressure proposed in the present invention does not require the establishment of an air compressor model, thus avoiding the tedious process of model identification, resulting in faster response speed and higher robustness;

[0075] (3) The present invention adopts an adaptive logic algorithm, which can be adjusted at any time according to the engineer's experience and test process, making the control more flexible and more adaptable;

[0076] (4) The present invention adopts an adaptive fuel cell flow and pressure control method. Compared with the calibration parameters, it can avoid the problem of short-term cathode gas shortage or cathode pressure overshoot at the moment of loading, thereby improving system stability and durability.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0078] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for adaptively controlling flow and pressure of a fuel cell, wherein the method is applied to a fuel cell system comprising a fuel cell, an air compressor, and a back pressure valve; characterized in that: The adaptive control method comprises the following steps: Step 11: Obtaining the target power of the fuel cell; Step 12: Based on the difference between the rated power of the fuel cell and the target power, adaptively predict the target speed of the air compressor and the target opening of the backpressure valve at the target power using a flow and pressure adaptive model; the rules of the flow and pressure adaptive model are established based on the rated speed and actual speed of the air compressor at the rated power, and the rated opening and actual opening of the backpressure valve; Step 13: Controlling the speed of the air compressor according to the target speed of the air compressor; Step 14: Controlling the opening of the back-pressure valve according to the target opening of the back-pressure valve; The adaptive control method further comprises: The flow and pressure adaptive model is constructed using a fuzzy algorithm, specifically including: Calculating the difference between the rated speed and the actual speed of the air compressor at the rated power to obtain a speed difference; Performing fuzzy quantization on the speed difference to obtain a speed output factor; Determining a fuzzy control rule for the speed of the air compressor according to the speed difference and the speed output factor corresponding to the speed difference; Calculating the difference between the calibrated opening and the actual opening of the back pressure valve under the calibrated power to obtain the opening difference; Performing fuzzy quantization on the opening difference to obtain an opening output factor; Determining a fuzzy control rule for the opening of the back pressure valve according to the opening difference and the opening output factor corresponding to the opening difference; Based on the difference between the rated power and the target power, the flow and pressure adaptive model is used to adaptively predict the target speed of the air compressor under the target power, specifically including: According to N trgt =N ref +e N-ρ (t)*(P trgt -P ref ) / P ref Determining a target speed of the air compressor; Among them, N trgt Indicates the target speed of the air compressor, N ref Indicates the rated speed of the air compressor, e N-ρ (t) represents the speed output factor, P trgt Indicates the target power, P ref Indicates the rated power; According to Deg trgt =Deg ref +e Deg-ρ (t)*(P trgt -P ref ) / P ref Determining a target speed of the air compressor; Among them, Deg trgt Indicates the target speed of the back pressure valve, Deg ref Indicates the back pressure valve calibrated speed, e Deg-ρ (t) represents the opening output factor, P trgt Indicates the target power, P ref Indicates the rated power.

2. The adaptive control method for fuel cell flow and pressure according to claim 1, characterized in that: The speed difference is fuzzy quantized to obtain a speed output factor, which specifically includes: according to determining the speed output factor; Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e N-ρ (t) represents the speed output factor, e N (t) represents the speed deviation input.

3. The adaptive control method for fuel cell flow and pressure according to claim 1, characterized in that: The opening difference is fuzzy quantized to obtain an opening output factor, which specifically includes: according to determining the opening output factor; Among them, NB means negative big, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, PB means positive big, e Deg-ρ (t) represents the opening output factor, e Deg (t) represents the opening deviation input.

4. The adaptive control method for fuel cell flow and pressure according to claim 1, characterized in that: The step 13 specifically includes: According to the target speed of the air compressor, the speed of the air compressor is controlled by using a PID algorithm.

5. The adaptive control method for fuel cell flow and pressure according to claim 1, characterized in that: The step 14 specifically includes: The opening of the back-pressure valve is controlled by a PID algorithm according to the target opening of the back-pressure valve.

6. A computer, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

7. A storage medium, characterized in that: The storage medium stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

Images