Fuel cell anode in-stack pressure and flow control method

Abstract

The invention relates to the technical field of fuel cells, in particular to a fuel cell anode in-pile pressure and flow control method. The method comprises the specific steps that disturbance inside and outside a system is estimated and compensated in real time through a second-order fuzzy ADRC method, and stable control over pressure and flow is achieved in combination with sensor data and adjustment of an executing mechanism; the second-order fuzzy ADRC method comprises a differential tracker, a nonlinear state error feedback control law and an expansion state observer. Through the second-order fuzzy ADRC method, the pressure and the flow of the anode inlet of the fuel cell are controlled, and stable cooperative control of the pressure and the flow of the hydrogen supply system can be realized, so that the stability of the output performance of the fuel cell is ensured, and a safety accident of membrane rupture is avoided.

Description

TECHNICAL FIELD

[0002] The present application relates to the technical field of fuel cell, in particular to a fuel cell anode pressure and flow control method. BACKGROUND

[0004] As a key device for hydrogen energy utilization, proton exchange membrane fuel cell is widely considered as one of the important technical paths to achieve vehicle zero emission. The pressure flow coordinated control technology of hydrogen supply system is of great significance to improve the performance and efficiency of fuel cell system. Pressure flow fluctuation and excessively high inlet pressure can lead to reduced output performance of fuel cell, and even cause safety accidents such as membrane rupture. To realize the pressure flow coordinated control of hydrogen supply system, effective and reliable control strategy needs to be developed. SUMMARY

[0006] In order to overcome the shortcomings of the existing fuel cell hydrogen supply system, which can easily lead to reduced performance of fuel cell, and even cause membrane rupture, the present application provides a fuel cell anode pressure and flow control method.

[0007] The technical scheme adopted by the present application to solve its technical problems is: a fuel cell anode pressure and flow control method, the specific steps of which are: through a second-order fuzzy ADRC method to estimate and compensate the disturbance inside and outside the system in real time, combined with sensor data and adjustment of actuator, to realize stable control of pressure and flow.

[0008] The second-order fuzzy ADRC method includes a differential tracker, a nonlinear state error feedback control law, and an extended state observer.

[0009] According to another embodiment of the present application, the algorithm parameters of the second-order fuzzy ADRC method are further adapted by a fuzzy control algorithm, and the parameters are optimized and adjusted in real time according to the deviation and rate of change of the deviation between the target value and the actual value.

[0010] The fuzzy control algorithm includes fuzzification, fuzzy reasoning, and defuzzification.

[0011] According to another embodiment of the present application, the differential tracker further generates a transition signal according to the steepest principle arranging a smooth and gradual transition signal , and generating a corresponding differential signal , so as to realize rapid response of the input signal while suppressing the amplification effect of high-frequency noise, and avoid overreaction of the control object when the input signal changes sharply.

[0012] The expression of the discrete form of the differential tracker is:

[0013]

[0014]

[0015] in For the first The input signal at any given moment; For the first Transition signals at any given moment; No. The differential signal of the time transition signal; For the first The error between the transition signal and the input signal at any given time; The sampling period; The speed factor determines the speed at which the transition signal tracks the input signal; The fastest control synthesis function is expressed as follows:

[0016]

[0017]

[0018] ; ; ; .

[0019] According to another embodiment of the present invention, the nonlinear state error feedback control law further includes nonlinearly combining the error information to generate a control signal, thereby decaying the error at an exponential rate. The nonlinear state error feedback control law calculates the initial control quantity based on the error between the state quantity fed back by the extended state observer, the transition signal output by the differential tracker, and the differential signal. The control law expression is as follows:

[0020]

[0021]

[0022] in, The error between the transition signal and the state observation; The error between the differential signal of the transition signal and the observed differential state quantity; This is the integral information of the transition signal and the state observation error; For state observations; For differential state observations; For control parameters, satisfy , ; This is the error threshold; For controller parameters;

[0023] nonlinear functions Represented as:

[0024] .

[0025] According to another embodiment of the present invention, the extended state observer further includes expanding the internal unobservable state and the external unknown disturbance into a new state variable. When the extended state observer converges, the extended state also converges to the disturbance information of the real system. The discrete form of the equation is as follows:

[0026]

[0027]

[0028] For system output values With state observations The error; Input values ​​for the system; They are respectively the first Time-state observations, differential signal observations, and disturbance compensation observations; The sampling period; For control signals; These are the observer parameters; To compensate for the gain;

[0029] The extended state observer parameters are represented by the pole placement method, which uses bandwidth to represent the parameters. The pole placement method ensures observer stability by placing the observer poles in the left half-plane. The second-order fuzzy ADRC method is expressed as follows:

[0030]

[0031] Among the disturbances For the third state quantity The extended state observer is then represented as:

[0032]

[0033] matrix The eigenvalues ​​are:

[0034]

[0035] Observer parameters Represented as ; ; ,in To expand the bandwidth of the state observer.

[0036] According to another embodiment of the present invention, the fuzzification of the fuzzy control algorithm in the second-order fuzzy ADRC method is achieved by dividing the input signal into various fuzzy subsets and calculating the corresponding membership degree using quantization factors and membership functions to form fuzzy input quantities.

[0037] The number of fuzzy subsets is 7, namely negative large, negative medium, negative small, zero, positive small, positive medium, and positive large;

[0038] The algorithm input signals are the anode inlet pressure control deviation and the rate of change of deviation, and the fuzzy control outputs are the controller parameters of the second-order fuzzy ADRC method, with triangular membership functions selected for all of them.

[0039] According to another embodiment of the present invention, the fuzzy control algorithm further includes adjusting the fuzzy rules based on the magnitude of the deviation and the rate of change.

[0040] According to another embodiment of the present invention, the defuzzification of the fuzzy control algorithm further includes calculating the geometric centroid position of the fuzzy output set and using it as the final sharp numerical output. The fuzzy universe of discourse is in discrete form, and the calculation of the output signal is expressed as follows:

[0041]

[0042] in, For discrete point locations; This represents the membership degree of that position; This is the precise value of the output signal.

[0043] According to another embodiment of the invention, the actuator further includes a proportional valve and a circulating pump.

[0044] The beneficial effect of this invention is that it controls the anode inlet pressure and flow rate of the fuel cell using the second-order fuzzy ADRC method, thereby achieving stable and coordinated control of the hydrogen supply system pressure and flow rate, ensuring the stability of the fuel cell output performance and avoiding safety accidents such as membrane rupture. Attached Figure Description

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] Figure 1 This is a schematic diagram of the structure of the present invention;

[0048] Figure 2 This is a diagram of the algorithm architecture of the present invention;

[0049] Figure 3 This is a schematic diagram of the membership functions of the input and output quantities of the fuzzy control algorithm of the present invention; Detailed Implementation

[0051] Figure 1 This is a schematic diagram of the structure of the present invention;

[0052] Figure 2 This is a diagram of the algorithm architecture of the present invention;

[0053] Figure 3 This is a schematic diagram of the membership functions of the input and output quantities of the fuzzy control algorithm of the present invention.

[0054] Combined with appendix Figure 1 Appendix Figure 2 and attached Figure 3 As shown, a method for controlling the pressure and flow rate of fuel cell anode feed into the stack is described. The specific steps of this method are as follows: using a second-order fuzzy ADRC method to estimate and compensate for disturbances inside and outside the system in real time, and combining sensor data with the adjustment of the actuator to achieve stable control of pressure and flow rate; the actuator includes a proportional valve and a circulating pump.

[0055] The second-order fuzzy ADRC method includes a differential tracker, a nonlinear state error feedback control law, and an extended state observer.

[0056] The algorithm parameters of the second-order fuzzy ADRC method are adaptively tuned through a fuzzy control algorithm, and the tuning parameters are optimized in real time based on the deviation between the target value and the actual value and the rate of change of the deviation.

[0057] Fuzzy control algorithms include fuzzification, fuzzy inference, and defuzzification.

[0058] The differential tracker uses the fastest principle to measure input pressure and flow rate. Arrange a smooth, gradual follow-up transition signal And generate the differential signal of the response. This enables a rapid response to input signals while suppressing the amplification effect of high-frequency noise, thus preventing the controlled object from overreacting when the input signal changes drastically.

[0059] The discrete form of the differential tracker is expressed as follows:

[0060]

[0061]

[0062] in For the first The input signal at any given moment; For the first Transition signals at any given moment; No. The differential signal of the time transition signal; For the first The error between the transition signal and the input signal at any given time; The sampling period; The speed factor determines the speed at which the transition signal tracks the input signal; The fastest control synthesis function is expressed as follows:

[0063]

[0064]

[0065] ; ; ; .

[0066] The nonlinear state error feedback control law nonlinearly combines error information to generate a control signal, decaying the error at an exponential rate. The nonlinear state error feedback control law calculates the initial control quantity based on the error between the state quantity fed back by the extended state observer, the transition signal output by the differential tracker, and the differential signal. The control law expression is as follows:

[0067]

[0068]

[0069] in, The error between the transition signal and the state observation; The error between the differential signal of the transition signal and the observed differential state quantity; This is the integral information of the transition signal and the state observation error; For state observations; For differential state observations; For control parameters, satisfy , ; This is the error threshold; For controller parameters;

[0070] nonlinear functions Represented as:

[0071] .

[0072] The extended state observer expands internal unobservable states and external unknown disturbances into new state variables. When the extended state observer converges, the extended state also converges to the disturbance information of the real system. The extended state observer has strong robustness and anti-interference ability. The discrete form of the equation is as follows:

[0073]

[0074]

[0075] For system output values With state observations The error; Input values ​​for the system; They are respectively the first Time-state observations, differential signal observations, and disturbance compensation observations; The sampling period; For control signals; These are the observer parameters; To compensate for the gain;

[0076] The extended state observer parameters are represented by the pole placement method, which uses bandwidth to represent the parameters. The pole placement method ensures observer stability by placing the observer poles in the left half-plane. The second-order fuzzy ADRC method is expressed as follows:

[0077]

[0078] Among the disturbances For the third state quantity The extended state observer is then represented as:

[0079]

[0080] matrix The eigenvalues ​​are:

[0081]

[0082] Observer parameters Represented as ; ; ,in To expand the bandwidth of the state observer.

[0083] In the second-order fuzzy ADRC method, fuzzification in the fuzzy control algorithm involves dividing the input signal into various fuzzy subsets and calculating the corresponding membership degrees using quantization factors and membership functions to form fuzzy input quantities.

[0084] The number of fuzzy subsets is 7, namely negative large, negative medium, negative small, zero, positive small, positive medium, and positive large;

[0085] Membership functions are a key component of fuzzification and defuzzification. The algorithm input signals are the anode inlet pressure control deviation and the rate of change of the deviation, and the fuzzy control outputs are the controller parameters of the second-order fuzzy ADRC method, with triangular membership functions selected for both.

[0086] Table 1. Input and output signals of the fuzzy control algorithm; fuzzy universe of discourse.

[0087]

[0088] The fuzzy rules in the fuzzy control algorithm are adjusted based on the magnitude of the deviation and the rate of change.

[0089] Among the parameters to be tuned, the parameters , and Increasing the value will improve the system response speed, but excessively large values ​​can easily cause the system to oscillate or overshoot; parameter Increasing the value will slow down the system response speed but improve the system's anti-interference capability; parameters Increasing the value makes the system more sensitive to changes in deviation, and excessively large values ​​can amplify the effects of high-frequency noise. Therefore, when the deviation changes rapidly and significantly, the parameter should be increased appropriately. , and Reduce parameters To enable the system to respond to pressure fluctuations as quickly as possible; when the deviation decreases and the rate of change slows down, the parameters should be appropriately reduced. , and Increase parameters This is to prevent system overshoot or oscillation. Parameters The deviation change rate is adjusted slightly to avoid amplifying noise interference. Therefore, the fuzzy rules are formulated as shown in Table 2.

[0090] Table 2 Fuzzy rules for parameters to be tuned

[0091]

[0092]

[0093] The fuzzy control algorithm for defuzzification calculates the geometric centroid position of the fuzzy output set and uses it as the final sharp numerical output. The fuzzy universe of discourse is in discrete form, and the output signal is calculated as follows:

[0094]

[0095] in, For discrete point locations; This represents the membership degree of that position; This is the precise value of the output signal.

Claims

1. A method for controlling the anode feed pressure and flow rate of a fuel cell, characterized in that, The specific steps of this method are as follows: using the second-order fuzzy ADRC method to estimate and compensate for disturbances inside and outside the system in real time, and combining sensor data with the adjustment of the actuator to achieve stable control of pressure and flow. The second-order fuzzy ADRC method includes a differential tracker, a nonlinear state error feedback control law, and an extended state observer.

2. The fuel cell anode feed pressure and flow control method according to claim 1, characterized in that, The algorithm parameters of the second-order fuzzy ADRC method are adaptively tuned through a fuzzy control algorithm, and the tuning parameters are optimized in real time based on the deviation between the target value and the actual value and the rate of change of the deviation. The fuzzy control algorithm includes fuzzification, fuzzy inference, and defuzzification.

3. The fuel cell anode feed pressure and flow control method according to claim 1, characterized in that, The differential tracker tracks input pressure and flow rate based on the fastest speed principle. Arrange a smooth, gradual follow-up transition signal And generate the differential signal of the response. This enables a rapid response to input signals while suppressing the amplification effect of high-frequency noise, thus preventing the controlled object from overreacting when the input signal changes drastically. Discrete form of the differential tracker expression; ； ； in For the first The input signal at any given moment; For the first Transition signals at any given moment; No. The differential signal of the time transition signal; For the first The error between the transition signal and the input signal at any given time; The sampling period; The speed factor determines the speed at which the transition signal tracks the input signal; The fastest control synthesis function is expressed as follows: ； ； ； ； ； 。 4. The fuel cell anode feed pressure and flow control method according to claim 1, characterized in that, The nonlinear state error feedback control law nonlinearly combines error information to generate a control signal, decaying the error at an exponential rate. The nonlinear state error feedback control law calculates the initial control quantity based on the error between the state quantity fed back by the extended state observer, the transition signal output by the differential tracker, and the differential signal. The control law expression is as follows: ； ； in, The error between the transition signal and the state observation; The error between the differential signal of the transition signal and the observed differential state quantity; This is the integral information of the transition signal and the state observation error; For state observations; For differential state observations; For control parameters, satisfy , ; This is the error threshold; For controller parameters; nonlinear functions Represented as; 。 5. The method for controlling the anode feed pressure and flow rate of a fuel cell according to claim 1, characterized in that, The extended state observer expands the internal unobservable state and the external unknown disturbance into new state variables. When the extended state observer converges, the extended state also converges to the disturbance information of the real system. The discrete form of the equation is as follows: ； ； For system output values With state observations The error; Input values ​​for the system; They are respectively the first Time-state observations, differential signal observations, and disturbance compensation observations; The sampling period; For control signals; These are the observer parameters; To compensate for the gain; The extended state observer parameters are represented by the pole placement method, which uses bandwidth to represent the extended state observer parameters. The pole placement method ensures the stability of the observer by placing the observer poles in the left half-plane. The second-order fuzzy ADRC method is expressed as follows: ； Among the disturbances For the third state quantity Then the extended state observer is represented as: ； matrix The eigenvalues ​​are: ； Observer parameters Represented as ; ; ,in To expand the bandwidth of the state observer.

6. The fuel cell anode feed pressure and flow control method according to claim 2, characterized in that, The fuzzy control algorithm in the second-order fuzzy ADRC method fuzzification is achieved by dividing the input signal into various fuzzy subsets and calculating the corresponding membership degree through quantization factors and membership functions to form fuzzy input quantities. The number of fuzzy subsets is 7, namely negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; The algorithm input signals are the anode inlet pressure control deviation and the rate of change of deviation, and the fuzzy control outputs are the controller parameters of the second-order fuzzy ADRC method, with triangular membership functions selected for all of them.

7. The fuel cell anode feed pressure and flow control method according to claim 2, characterized in that, The fuzzy rules of the fuzzy control algorithm are adjusted according to the magnitude of the deviation and the rate of change.

8. The fuel cell anode feed pressure and flow control method according to claim 2, characterized in that, The defuzzification process of the fuzzy control algorithm involves calculating the geometric centroid position of the fuzzy output set and using it as the final sharp numerical output. The fuzzy universe of discourse is in discrete form, and the output signal is calculated as follows: ； in, For discrete point locations; This represents the membership degree of that position; This is the precise value of the output signal.

9. The method for controlling the anode feed pressure and flow rate of a fuel cell according to claim 1, characterized in that, The actuator includes a proportional valve and a circulating pump.

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