ADRC-based air-cooled PEMFC temperature control method and system

Abstract

This invention discloses an ADRC-based temperature control method and system for air-cooled PEMFCs, relating to the field of fuel cell control technology. The method includes the following steps: inputting the current actual temperature and the temperature control quantity from the previous time step into a linear extended state observer to obtain the estimated temperature and estimated total disturbance for the next time step; inputting the estimated temperature error into a linear state error feedback controller to obtain the preliminary control quantity for the next time step; feedforward compensating the estimated total disturbance for the next time step to the preliminary control quantity for the next time step to obtain the temperature control quantity for the next time step; optimizing the gain of the temperature control quantity for the next time step through extreme value search during the feedforward compensation process; and controlling the temperature of the air-cooled proton exchange membrane fuel cell using the temperature control quantity for the next time step. The ADRC controller of this invention has strong disturbance rejection capability and good control effect, and can solve the model mismatch problem.

Description

Technical Field

[0001] This invention relates to the field of fuel cell control technology, and in particular to a temperature control method and system for air-cooled PEMFCs based on ADRC. Background Technology

[0002] With the increasing severity of environmental pollution and the energy crisis, the development of clean energy is urgently needed. Among them, proton exchange membrane fuel cells (PEMFCs) are considered the most promising clean energy conversion devices due to their advantages such as high energy conversion efficiency, high specific power, fast start-up, and zero pollution. In low-power fuel cell applications (such as drones and forklifts), air-cooled PEMFCs are widely used due to their simple and compact structure. However, because their fans need to simultaneously supply air and cool the fuel cell stack, the complexity of temperature control is increased.

[0003] In the temperature control of air-cooled PEMFCs, PID (Proportional-Integral-Derivative) control is currently the most widely used method. However, the air-cooled PEMFC temperature control system is a strongly nonlinear system, and the application of PID control is prone to problems such as low regulation accuracy, slow settling time, and large overshoot. To overcome the shortcomings of PID control, some researchers have adopted advanced model-based control methods to achieve temperature control of air-cooled PEMFCs, such as Model Predictive Control (MPC) and Linear Quadratic Regulator (LQR). Although these advanced control methods can achieve satisfactory control results in specific scenarios, they rely too heavily on the accuracy of the model and often struggle to handle external disturbances caused by environmental interference and measurement noise, or internal disturbances caused by inaccurate modeling. Therefore, they are prone to model mismatch problems, and due to their computational complexity, they are difficult to implement and maintain in practical engineering, thus limiting their application scenarios. Summary of the Invention

[0004] Based on the shortcomings of the existing technology, the present invention provides an air-cooled PEMFC temperature control method and system based on ADRC. This solves the problem that although the existing advanced control methods can achieve satisfactory control results in specific scenarios, they rely too much on the accuracy of the model and often have difficulty in handling external disturbances caused by environmental interference and measurement noise or internal disturbances caused by inaccurate modeling. Therefore, they are prone to model mismatch problems. Furthermore, due to the complexity of calculation, they are difficult to implement and maintain in actual engineering, thus limiting their application scenarios.

[0005] The present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a temperature control method for air-cooled PEMFCs based on ADRC, comprising the following steps:

[0007] Collect the actual temperature of the air-cooled proton exchange membrane fuel cell at the current moment and the temperature control value at the previous moment;

[0008] Construct an Active Disturbance Rejection Controller (ADRC), including a linear extended state observer and a linear state error feedback controller;

[0009] The current actual temperature and the temperature control quantity from the previous moment are input into the linear extended state observer to obtain the estimated temperature and estimated total disturbance for the next moment; the difference between the target temperature of the air-cooled proton exchange membrane fuel cell and the estimated temperature for the next moment is used to obtain the estimated temperature error, which is then input into the linear state error feedback controller to obtain the preliminary control quantity for the next moment.

[0010] The estimated total disturbance at the next time step is fed forward to compensate the preliminary control quantity at the next time step, thus obtaining the temperature control quantity at the next time step; the temperature of the air-cooled proton exchange membrane fuel cell is controlled by the temperature control quantity at the next time step.

[0011] 7. Preferably, the construction of the Active Disturbance Rejection Controller (ADRC) includes the following steps:

[0012] Obtain the ADRC's anti-interference paradigm, which is as follows:

[0013] ;

[0014] In the formula, For the controlled quantity, The derivative of the controlled variable. For temperature control quantity, For temperature control gain, The total disturbance is composed of both internal and external disturbances.

[0015] The disturbance rejection paradigm of ADRC is transformed into the state-space equation of ADRC, and the total disturbance is estimated by a linear extended state observer, which is specifically shown below:

[0016] ;

[0017] In the formula, and To estimate the parameters, Used for estimation , Used for estimation , To estimate the temperature, for The derivative of for The derivative, and These are adjustable parameters for the observer;

[0018] The linear extended state observer is input into the state-space equations of ADRC to obtain the control equations, which are shown below:

[0019] ;

[0020] ;

[0021] In the formula, The initial control quantity generated by the linear state error feedback controller;

[0022] Control of the single integral element is achieved through proportional control:

[0023] ;

[0024] In the formula, This is the proportionality coefficient. r The target temperature.

[0025] Preferably, the Active Disturbance Rejection Controller (ADRC) includes multiple adjustable parameters, and the bandwidths corresponding to these adjustable parameters are as follows:

[0026] ;

[0027] In the formula, The bandwidth of the linear state error feedback controller. is the bandwidth of the linearly extended state observer.

[0028] Preferably, the step of feeding forward the estimated total disturbance of the next time step to the preliminary control quantity of the next time step to obtain the temperature control quantity of the next time step includes the following steps:

[0029] During the feedforward compensation process, the gain of the temperature control quantity at the next moment is optimized through extreme value search;

[0030] The optimized temperature control gain for the next time step is combined with the compensated initial control value for the next time step to obtain the temperature control value for the next time step.

[0031] Preferably, the optimization of the temperature control gain at the next moment through extreme value search includes the following steps:

[0032] Obtain the actual temperature error between the current actual temperature and the target temperature, and define an objective function based on the actual temperature error, as shown below:

[0033] ;

[0034] In the formula, for t The objective function at time t, T To control process time, This represents the actual temperature error.

[0035] Apply a sinusoidal perturbation to the gain of the temperature control quantity at the current moment to obtain the objective function at the current moment;

[0036] Obtain the gradient of the objective function at the current moment, and optimize the temperature control gain along the gradient direction, as shown below:

[0037] ;

[0038] In the formula, Let be the derivative of the temperature control gain at the current moment. k To adjust the step size, This represents gradient information.

[0039] Preferably, controlling the temperature of the air-cooled proton exchange membrane fuel cell by controlling the temperature at the next moment specifically includes the following steps:

[0040] A thermal management system model for an air-cooled proton exchange membrane fuel cell was constructed, including a stack model and a thermal analysis module.

[0041] The thermal analysis module is used to calculate the temperature change rate of the battery stack model, as shown below:

[0042] ;

[0043] In the formula, Represents the total input energy. Represents the output of electrical energy. This means that the cooling air carries away the heat. Represents heat loss through radiation and natural convection. Represents heat capacity, t For time;

[0044] The temperature control quantity is the fan duty cycle; the temperature control quantity at the next moment is input to the thermal analysis module to change the air flow rate, change the surface heat transfer coefficient of the cathode channel, and control the temperature of the battery stack.

[0045] Secondly, the present invention provides an air-cooled PEMFC temperature control system based on ADRC, comprising:

[0046] The data acquisition module is used to acquire the actual temperature of the air-cooled proton exchange membrane fuel cell at the current moment and the temperature control value at the previous moment.

[0047] The building blocks are used to construct Active Disturbance Rejection Controllers (ADRCs), including a linear extended state observer and a linear state error feedback controller.

[0048] The input module is used to input the actual temperature at the current moment and the temperature control quantity at the previous moment into the linear extended state observer to obtain the estimated temperature and estimated total disturbance at the next moment; the difference between the target temperature and the estimated temperature at the next moment is used to obtain the estimated temperature error, and the estimated temperature error is input into the linear state error feedback controller to obtain the preliminary control quantity at the next moment.

[0049] The control module is used to feedforward the estimated total disturbance of the next time step to the preliminary control quantity of the next time step, so as to obtain the temperature control quantity of the next time step; and to control the temperature of the air-cooled proton exchange membrane fuel cell by the temperature control quantity of the next time step.

[0050] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0051] This invention provides a temperature control method for air-cooled PEMFCs based on Active Disturbance Rejection Controller (ADRC). For complex nonlinear systems like air-cooled PEMFCs, PID control is prone to drawbacks such as large overshoot and long settling time. This invention employs an ADRC to control the temperature of the PEMFC. First, an ADRC is constructed, comprising a linear extended state observer and a linear state error feedback controller. In the specific control process, the linear extended state observer estimates the total disturbance and temperature. The linear state error feedback controller outputs a preliminary control quantity based on the estimated temperature error, while the estimated total disturbance is feedforward to compensate for the preliminary control quantity, ultimately yielding the temperature control quantity. The ADRC controller handles both internal and external disturbances through the total disturbance term, exhibiting strong disturbance rejection capability and good control performance. It can solve the model mismatch problem and, compared to advanced control methods, has a simpler structure, making it easier to implement and maintain in practical engineering and applicable to a wide range of scenarios. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the air-cooled PEMFC thermal analysis module of the present invention;

[0054] Figure 2 This is a structural diagram of the first-order linear ADRC controller of the present invention;

[0055] Figure 3 This is a diagram of the applied current disturbance according to the present invention;

[0056] Figure 4 This is a comparison chart of the effects of PID and ADRC on battery stack temperature control in an embodiment of the present invention;

[0057] Figure 5 This is a diagram of the extreme value search structure of the present invention;

[0058] Figure 6 This is a structural diagram of the ES-ADRC controller of the present invention;

[0059] Figure 7 This is a comparison chart of the effects of ADRC and ES-ADRC on battery stack temperature control in embodiments of the present invention;

[0060] Figure 8 The control action gain of the present invention The changes;

[0061] Figure 9 This is a comparison chart of the objective functions in the ES optimization of this invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] I. Explanatory and Illustrative Embodiments. To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory and illustrative description of the embodiments described in the claims.

[0064] This invention provides a temperature control method for air-cooled PEMFC based on adaptive ADRC (Active Disturbance Rejection Controller), comprising the following steps:

[0065] S1: Based on the physical characteristics of the air-cooled PEMFC thermal management system, a dynamic model of it is built using the Matlab / Simulink simulation platform.

[0066] The dynamic model of the air-cooled PEMFC thermal management system mainly consists of two parts: the PEMFC battery stack and the thermal analysis module. The PEMFC battery stack is modeled using semi-empirical equations to calculate the output voltage. Semi-empirical PEMFC models are often used for simulation studies at the PEMFC system level due to their fast calculation speed and high accuracy.

[0067] (1);

[0068] in,

[0069] (2);

[0070] (3);

[0071] (4);

[0072] (5);

[0073] (6);

[0074] (7);

[0075] (8);

[0076] In the formula, , , , , , , λ , b and There are 9 coefficients, which can be calibrated through experimental data. For the reaction entropy, The number of electrons transferred in the reaction. It is Faraday's constant. Standard temperature The gas constant is For battery temperature, For hydrogen partial pressure, The partial pressure of oxygen. For water vapor partial pressure, Oxygen concentration, This is the operating current. For the total current, The area of ​​the reaction activation. For total internal resistance, For ohmic internal resistance, Resistivity This is the concentration loss coefficient. For the thickness of the exchange membrane, denoted as current density.

[0077] The thermal analysis module uses the energy conservation equation to calculate the temperature change rate of the PEMFC battery stack. Its main input and output energy forms are as follows: Figure 1 As shown, the temperature change rate of PEMFC is as follows:

[0078] (9);

[0079] In the formula, Represents the total input energy of the PEMFC. This represents the electrical energy output of PEMFC. This means that the cooling air carries away the heat. Represents heat loss through radiation and natural convection. Represents the heat capacity of PEMFC. t For time.

[0080] The total input energy of a fuel cell is the total energy obtained from the combustion of hydrogen, while the amount of hydrogen consumed depends on the current and the number of cells. The calculation is as follows:

[0081] (10);

[0082] In the formula, This represents the amount of hydrogen consumed. F Represents Faraday's constant. The enthalpy of hydrogen combustion. This represents the number of individual cells.

[0083] The output electrical energy is the product of the battery stack voltage and current. The calculation is as follows:

[0084] (11);

[0085] In the formula, This represents the voltage of the battery stack.

[0086] Forced convection of air is defined as the heat carried away by the cooling air:

[0087] (12);

[0088] In the formula, The surface heat transfer coefficient, For ambient temperature, This represents the total area of ​​the cathode flow channel.

[0089] Natural convection and radiative heat transfer of air are defined as the surface heat dissipation of the battery stack:

[0090] (13);

[0091] In the formula, This is the equivalent thermal resistance.

[0092] S2: Based on the dynamic model of the air-cooled PEMFC thermal management system established in S1, design a temperature control strategy and use PID to initially achieve temperature control.

[0093] The temperature control strategy for air-cooled PEMFCs involves adjusting the fan's PWM (Pulse Width Modulation) signal to change the airflow velocity, thereby altering the surface heat transfer coefficient of the cathode channel and ultimately achieving precise temperature control of the fuel cell stack. Based on the general structure and operating conditions of the fuel cell, the airflow is determined to be within the laminar flow range. Therefore, the relationship between the airflow velocity and the surface heat transfer coefficient of the cathode channel is determined using the Zidl-Tate equation:

[0094] (14);

[0095] In the formula, The Nucher number represents the cathode flow channel. Represents the Reynolds number, Representing Prandtl numbers, Represents feature size, Represents pipe diameter, Represents the dynamic viscosity of air. This represents the dynamic viscosity of the wall surface.

[0096] PID controllers are the most widely used controllers in practical engineering, characterized by their simple structure, ease of implementation, and strong robustness. This invention first employs a PID controller to achieve temperature control of an air-cooled PEMFC. The PID controller will adjust the temperature based on the temperature error... The control quantity is calculated using the following formula to adjust the fan PWM:

[0097] (15);

[0098] In the formula, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. To control the amount of action, This refers to the temperature error between the given value and the actual value. In the temperature control of the battery stack within an air-cooled PEMFC thermal management system, This is the fan PWM signal. This is the difference between the given and actual temperature of the battery stack. The PID controller will adjust the settings based on this error. The control input is continuously generated until the battery stack temperature is controlled to a given value.

[0099] S3: Design an ADRC controller to achieve temperature control of the air-cooled PEMFC thermal management system and compare the temperature control effect with that of the PID controller in S2.

[0100] Temperature control in S3 employs the same strategy as in S2, namely, controlling the battery stack temperature by adjusting the fan's PWM signal. However, because the PID controller in S2 generates control inputs solely based on temperature error, it struggles to achieve good control performance, often exhibiting large overshoot and long settling times. While advanced controllers based on model information demonstrate outstanding control performance in specific scenarios, they are overly reliant on model accuracy, often struggling to handle external disturbances such as environmental interference and measurement noise, or internal disturbances caused by inaccurate modeling. Furthermore, advanced controllers involve enormous computational demands, making them difficult to implement and maintain in practical engineering. To overcome the shortcomings of PID and MPC control, this invention employs Active Disturbance Rejection Control (ADRC) to achieve temperature control in the air-cooled PEMFC thermal management system.

[0101] ADRC is an improved version of PID control, with advantages of simple structure and low computational cost. Furthermore, ADRC can handle both internal and external disturbances and compensate for them through the final control action, thus exhibiting good control performance and disturbance rejection capability. This invention employs a first-order linear ADRC, mainly composed of a Linear State Error Feedback (LSEF) and a Linear Extended State Observer (LESO). Its controller structure diagram is shown below. Figure 2 As shown.

[0102] The disturbance resistance paradigm of a first-order linear ADRC is:

[0103] (16);

[0104] In the formula, This represents the amount under control. The derivative of the controlled variable. Represents the control action quantity. The gain represents the control action quantity. This represents the total disturbance consisting of both internal and external disturbances. In this invention, For battery stack temperature, This is the PWM signal for the fan. To facilitate the design of LESO, the above equation needs to be transformed into a state-space equation:

[0105] (17);

[0106] The meanings of each term in the formula are as follows:

[0107] (18);

[0108] In the formula, State variables The derivative, State variables The derivative of For battery stack temperature, For total disturbance f The derivative of .

[0109] Due to the total disturbance term Since it cannot be directly measured and needs to be estimated through a state observer, LESO is introduced, with the following structure:

[0110] (19);

[0111] In the formula, , Used to estimate , Used to estimate , To estimate the temperature, The derivative of z For the output matrix, Matrix and The matrix is ​​the same as that in equation (18). To design the parameters for the observer, we can obtain the following by expanding equation (19):

[0112] (20);

[0113] In the formula, and To estimate the parameters, for The derivative of for The derivative, and These are the adjustable parameters of the observer.

[0114] The total disturbance term can be accurately estimated using LESO. Substituting equation (17) into the equation, we can transform it into a single integral:

[0115] (twenty one);

[0116] (twenty two);

[0117] In other words, by using the ADRC controller, the complex air-cooled PEMFC thermal management system can ultimately be systematized into a simple single integral element. , The control action generated for LSEF (Linear State Error Feedback).

[0118] Right now Figure 2 The entire process from the middle part to the output is simplified, which greatly reduces the difficulty of controller design and achieves satisfactory control results. This invention achieves control of a single integral element only through proportional control.

[0119] (twenty three);

[0120] In the formula, The target temperature.

[0121] It can be seen that the first-order linear ADRC has 3 adjustable parameters: , and This invention uses the bandwidth method for adjustment. To achieve LESO convergence, it is necessary to make equation (20) ... If all eigenvalues ​​of the matrix are less than 0, then we can obtain:

[0122] (twenty four);

[0123] (25);

[0124] In the formula, These are the eigenvalues ​​of the matrix.

[0125] Using pole placement, that is, Then we can obtain the following relation:

[0126] (26);

[0127] In the formula, and These are two specific solutions to the eigenvalues ​​of the matrix.

[0128] To ensure that LESO has a faster convergence speed than LSEF, we generally let:

[0129] (27);

[0130] In the formula, For the bandwidth of LSEF, This refers to the bandwidth of LESO.

[0131] Apply Figure 3 The current disturbance shown is compared with the temperature control effect of the PID controller and the ADRC controller. Figure 4As shown, except for the initial disturbance phase, the temperature fluctuation amplitude caused by the current disturbance is smaller in the ADRC controller than in the PID controller, thus highlighting the superiority of ADRC control. In the initial stage of the control process, due to the LESO function of ADRC... Need to track from 0 y LESO failed to converge in time, resulting in a large temperature fluctuation during the first current disturbance.

[0132] S4: The ADRC controller in S3 is optimized using Elastic Search (ES) to enable its control parameters to adapt to changes in the system state.

[0133] To further improve the control effect of ADRC, the gain of its control action can be optimized. This coefficient affects the total disturbance. The magnitude of this value, if chosen appropriately, can reduce the total disturbance, thereby achieving better control.

[0134] In order to achieve This invention employs an extreme value search (ES) method for online optimization, adapting to changes in the system state. The value of is shown in the structure diagram of the ES optimization method. Figure 5 As shown. ES is a model-free online optimization method suitable for dynamic system optimization. It requires a predefined objective function. The objective function selected in this invention is as follows:

[0135] (28);

[0136] In the formula, for t The objective function at time t, T To control process time, This represents the actual temperature error.

[0137] It can be seen that the objective function of ES optimization is the integral of the square of the temperature error, and the temperature error is related to... The value of is closely related, therefore it can be considered that and There is a non-linear functional relationship:

[0138] (29);

[0139] In the formula, It is a nonlinear function. Let be the temperature control gain at time t.

[0140] Because the air-cooled PEMFC thermal management system combined with the ADRC controller is very complex, it is difficult to obtain... Therefore, the precise expression for ES (Elasticsearch) is well-suited for model-free optimization. Figure 6 The diagram illustrates the structure of the ES-optimized ADRC method in this invention. The core of ES lies in addressing high-frequency perturbations of the variable to be optimized. This invention utilizes... By applying a high-frequency sinusoidal signal perturbation, the objective function of the system output can be determined. The gradient of the objective function is calculated from the oscillations. Finally, optimize along the gradient direction. This minimizes the objective function and improves temperature control performance. The following details the ES optimization method used in this invention. The process, first of all, is Control action gain at time High-frequency gain is obtained by applying a high-frequency sinusoidal perturbation. :

[0141] (30);

[0142] In the formula, The amplitude of the disturbance signal, The frequency of the disturbance signal. Substitute (17) to design ADRC controller and obtain the corresponding objective function Taylor expansion of it:

[0143] (31);

[0144] In the formula, is the derivative of the nonlinear function.

[0145] To obtain gradient information, the objective function is multiplied by the same sine signal:

[0146] (32);

[0147] In the formula, This is an intermediate variable that contains gradient information.

[0148] Substituting into equation (31), we get:

[0149] (33);

[0150] We can simplify using the trigonometric double-angle formula to obtain:

[0151] (34);

[0152] Filtered by a low-pass filter Gradient information can then be obtained. :

[0153] (35);

[0154] Finally let Along the current t Adjusting the gradient direction at each time step optimizes the objective function.

[0155] (36);

[0156] In the formula, Let be the derivative of the temperature control gain at the current moment. k To adjust the step size, This represents gradient information.

[0157] Applying the same current disturbance as in S3, the temperature control effect is as follows: Figure 7 As shown, ES-ADRC has a smaller overshoot and a shorter settling time compared to ADRC. Figure 8 As shown, the improved temperature control performance is due to ES implementing control over the ADRC controller. The adaptive optimization reduces the total perturbation term. f This improves the convergence speed of LESO. Figure 9 This demonstrates the objective function before and after ES optimization throughout the entire control process. It can be seen that the objective function after ES optimization is significantly smaller. As shown in equation (28), the smaller the objective function, the better. This corresponds to better control.

[0158] Based on the same concept, the present invention also provides an air-cooled PEMFC temperature control system based on ADRC, including a data acquisition module, a construction module, an input module and a control module.

[0159] The data acquisition module is used to collect the actual temperature of the air-cooled proton exchange membrane fuel cell at the current moment and the temperature control value at the previous moment.

[0160] The building blocks are used to construct Active Disturbance Rejection Controllers (ADRCs), including a linear extended state observer and a linear state error feedback controller.

[0161] The input module is used to input the actual temperature at the current moment and the temperature control quantity at the previous moment into the linear extended state observer to obtain the estimated temperature and estimated total disturbance at the next moment; the difference between the target temperature and the estimated temperature at the next moment is used to obtain the estimated temperature error, and the estimated temperature error is input into the linear state error feedback controller to obtain the preliminary control quantity at the next moment.

[0162] The control module is used to feedforward the estimated total disturbance of the next time step to the preliminary control quantity of the next time step, so as to obtain the temperature control quantity of the next time step; and to control the temperature of the air-cooled proton exchange membrane fuel cell by the temperature control quantity of the next time step.

[0163] This invention provides a method for controlling the temperature of an air-cooled PEMFC thermal management system using an ADRC controller. For complex nonlinear systems like air-cooled PEMFC thermal management systems, PID control is prone to drawbacks such as large overshoot and long settling times. Advanced model-based control methods, on the other hand, rely heavily on model accuracy and often struggle to handle external and internal disturbances. Furthermore, their massive computational load makes them difficult to implement and maintain in practical engineering. The ADRC controller, inherited from PID, offers advantages such as flexible structure and low computational load, making it easy to implement in real-world engineering projects. Moreover, the ADRC controller can handle both internal and external disturbances through the total disturbance term, exhibiting strong disturbance rejection capability and good control performance, overcoming the shortcomings of PID control and advanced control methods.

[0164] This invention also provides a method for adaptively optimizing an ADRC controller using ES. The control action gain in the ADRC controller... While the dynamic factors influence the final control outcome, it is difficult to precisely describe their functional relationship with the final control outcome. Therefore, employing the model-free optimization method ES, suitable for dynamic systems, is a good choice, as it can achieve... Online adjustments are made to optimize control performance.

[0165] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0166] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A temperature control method for air-cooled PEMFC based on ADRC, characterized in that, Includes the following steps: Collect the actual temperature of the air-cooled proton exchange membrane fuel cell at the current moment and the temperature control value at the previous moment; Construct an Active Disturbance Rejection Controller (ADRC), including a linear extended state observer and a linear state error feedback controller; The current actual temperature and the temperature control value from the previous time are input into the linear extended state observer to obtain the estimated temperature and estimated total disturbance at the next time. The difference between the target temperature of the air-cooled proton exchange membrane fuel cell and the estimated temperature at the next moment is used to obtain the estimated temperature error. The estimated temperature error is then input into the linear state error feedback controller to obtain the preliminary control quantity at the next moment. The estimated total disturbance at the next time step is fed forward to compensate the preliminary control quantity at the next time step, and the temperature control quantity at the next time step is obtained. The temperature of the air-cooled proton exchange membrane fuel cell is controlled by the temperature control quantity at the next time step. The construction of the Active Disturbance Rejection Controller (ADRC) includes the following steps: Obtain the ADRC's anti-interference paradigm, which is as follows: ； In the formula, For controlled quantity, The derivative of the controlled variable. For temperature control quantity, For temperature control quantity gain, The total disturbance is composed of both internal and external disturbances. The disturbance rejection paradigm of ADRC is transformed into the state-space equation of ADRC, and the total disturbance is estimated by a linear extended state observer, which is specifically shown below: ； In the formula, and To estimate the parameters, Used for estimation , Used for estimation , To estimate the temperature, for The derivative, for The derivative, and These are adjustable parameters for the observer; The linear extended state observer is input into the state-space equations of ADRC to obtain the control equations, which are shown below: ； ； In the formula, The initial control quantity generated by the linear state error feedback controller; Control of the single integral element is achieved through proportional control: ； In the formula, This is the proportionality coefficient. r The target temperature.

2. The air-cooled PEMFC temperature control method based on ADRC as described in claim 1, characterized in that, The Active Disturbance Rejection Controller (ADRC) includes multiple adjustable parameters, and the bandwidths corresponding to these adjustable parameters are as follows: ； In the formula, The bandwidth of the linear state error feedback controller. is the bandwidth of the linearly extended state observer.

3. The air-cooled PEMFC temperature control method based on ADRC as described in claim 1, characterized in that, The step of feeding forward the estimated total disturbance at the next time step to the preliminary control quantity at the next time step, thereby obtaining the temperature control quantity at the next time step, includes the following steps: During the feedforward compensation process, the gain of the temperature control quantity at the next moment is optimized through extreme value search; The optimized temperature control gain for the next time step is combined with the compensated initial control value for the next time step to obtain the temperature control value for the next time step.

4. The air-cooled PEMFC temperature control method based on ADRC as described in claim 3, characterized in that, The optimization of the temperature control gain at the next moment through extreme value search includes the following steps: Obtain the actual temperature error between the current actual temperature and the target temperature, and define an objective function based on the actual temperature error, as shown below: ； In the formula, for t The objective function at time t, T To control process time, This represents the actual temperature error. Apply a sinusoidal perturbation to the gain of the temperature control quantity at the current moment to obtain the objective function at the current moment; Obtain the gradient of the objective function at the current moment, and optimize the temperature control gain along the gradient direction, as shown below: ； In the formula, Let be the derivative of the temperature control gain at the current moment. k To adjust the step size, This represents gradient information.

5. The air-cooled PEMFC temperature control method based on ADRC as described in claim 1, characterized in that, The control of the temperature of the air-cooled proton exchange membrane fuel cell by the temperature control quantity at the next moment specifically includes the following steps: A thermal management system model for an air-cooled proton exchange membrane fuel cell was constructed, including a stack model and a thermal analysis module. The thermal analysis module is used to calculate the temperature change rate of the battery stack model, as shown below: ； In the formula, Represents the total input energy. Represents the output of electrical energy. This means that the cooling air carries away the heat. Represents heat loss through radiation and natural convection. Represents heat capacity, t For time; The temperature control quantity is the fan duty cycle; the temperature control quantity for the next moment is input into the thermal analysis module to change the airflow rate, change the surface heat transfer coefficient of the cathode channel, and control the temperature of the battery stack.

6. A control system for the air-cooled PEMFC temperature control method based on ADRC as described in claim 1, characterized in that, include: The data acquisition module is used to acquire the actual temperature of the air-cooled proton exchange membrane fuel cell at the current moment and the temperature control value at the previous moment. The building blocks are used to construct Active Disturbance Rejection Controllers (ADRCs), including a linear extended state observer and a linear state error feedback controller. The input module is used to input the actual temperature at the current moment and the temperature control quantity at the previous moment into the linear extended state observer to obtain the estimated temperature and estimated total disturbance at the next moment. The difference between the target temperature and the estimated temperature at the next moment is used to obtain the estimated temperature error. The estimated temperature error is then input into the linear state error feedback controller to obtain the preliminary control quantity at the next moment. The control module is used to feedforward the estimated total disturbance of the next time step to the preliminary control quantity of the next time step, so as to obtain the temperature control quantity of the next time step; and to control the temperature of the air-cooled proton exchange membrane fuel cell by the temperature control quantity of the next time step.

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