A DCDC control circuit module applied to a hydrogen fuel cell two-wheeled vehicle

Abstract

The application discloses a DCDC control circuit module applied to a hydrogen fuel cell two-wheeled vehicle, relates to the technical field of power electronics, and comprises a core control module and a current-limiting constant-voltage control module; a control algorithm module comprises the following specific steps: step one, parameter initialization; step two, state estimation; step three, predicting future behavior; step four, solving an optimization problem; step five, robust control; step six, feedforward compensation; step seven, comprehensive control; step eight, executing control; and step nine, repeating the steps, in the application, the core control module can control the output of the current-limiting constant-voltage control module, so that the stability of the voltage and current output of the DCDC control circuit module is realized; in the optimization problem in the control algorithm module, a slack variable is introduced, some constraints are allowed to be violated within a certain range, so that the situation of no solution is avoided.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, specifically to a DC-DC control circuit module for use in hydrogen fuel cell two-wheeled vehicles. Background Technology

[0002] A hydrogen fuel cell is a power generation device that uses hydrogen energy through a chemical reaction, producing only water during operation, thus achieving true zero emissions. Compared to traditional fuel combustion methods, hydrogen fuel cells have higher energy conversion efficiency and lower emissions, making them one of the important technologies for future environmentally friendly energy. The application of hydrogen fuel cells is also increasing, with hydrogen fuel cell two-wheelers being the most common application.

[0003] In hydrogen fuel cell two-wheeled vehicles, the DC-DC control circuit plays a particularly crucial role, as it needs to ensure a stable operating voltage for the hydrogen fuel cell. Therefore, improving the output power of the hydrogen fuel cell is of paramount importance.

[0004] In summary, a DC-DC control circuit module for hydrogen fuel cell two-wheeled vehicles was designed. Summary of the Invention

[0005] To overcome the above-mentioned shortcomings, the present invention provides a DC-DC control circuit module for use in hydrogen fuel cell two-wheeled vehicles.

[0006] The present invention achieves the above objectives through the following technical solution: a DC-DC control circuit module for hydrogen fuel cell two-wheeled vehicles, comprising a core control module and a current-limiting constant-voltage control module, wherein the core control module is electrically connected to the current-limiting constant-voltage control module, and the core control module includes a control algorithm module;

[0007] The control algorithm module includes the following specific steps:

[0008] Step 1: Parameter initialization, initialize module state and external disturbances At the same time, set optimization parameters;

[0009] Step 2: State estimation. Use an extended Kalman filter to estimate and update the state. ;

[0010] Step 3: Predict future behavior. Use the state-space module to predict the output voltage at the next N sampling points. ;

[0011] Step 4: Solve the optimization problem using quadratic programming with slack variables. The optimization problem is to obtain the optimal control sequence. , ;

[0012] Step 5: Robust control, calculation Output ;

[0013] Step 6: Feedforward compensation, calculate the feedforward compensation signal. ;

[0014] Step 7: Integrated control, combining predictive control, robust control, and feedforward compensation to form the final control input. ;

[0015] Step 8: Execute control, execute the first control action. Update the current status ;

[0016] Step 9: Repeat the steps to form a rolling optimization process.

[0017] Preferably, the core control module is composed of a core control circuit based on STM32F072, and the core control module is electrically connected to a communication module, a sampling current module, and a feedback current module.

[0018] Preferably, the current-limiting and constant-voltage control module is a current-limiting and constant-voltage control circuit mainly composed of LM5176.

[0019] Preferably, a communication module is also included, which is electrically connected to a sampling current module, a voltage acquisition module, and a temperature detection module.

[0020] Preferably, in step one, a state-space model of the DC-DC converter is established:

[0021] + , ;

[0022] in, It is a state vector (such as inductor current and output voltage). It is the control input (such as PWM duty cycle). It is an external disturbance. This refers to the output (e.g., output voltage). A, B, C, and D are the system matrix. It is the perturbation matrix.

[0023] Preferably, in step two, an extended Kalman filter is used for state estimation:

[0024] +

[0025] in, It is a state estimate. It is the Kalman gain.

[0026] Preferably, in step three, a slack variable is defined. The optimization problem is as follows:

[0027]

[0028]

[0029]

[0030] 0

[0031] in, Let J be the slack variable, and J be the cost function, which includes the output error, the penalty for changes in control input, and the slack variable. For reference output, To control changes in input, Weighting coefficients to control input variations, These are the weighting coefficients of the slack variables. , These are the upper and lower limits of the output voltage. , These are the upper and lower limits of the inductor current.

[0032] Preferably, in step five, the design This ensures that the system maintains good performance even in the presence of uncertainties and external disturbances.

[0033]

[0034] in, It is an error signal. yes The gain matrix.

[0035] Preferably, in step six, the formula for calculating the feedforward compensation signal is as follows:

[0036]

[0037] in, It is an external disturbance. It is the feedforward gain matrix.

[0038] Preferably, in step seven, predictive control, robust control, and feedforward compensation are combined to form the final control law. + + .

[0039] The beneficial effects of this invention are: In the DC-DC control circuit module applied to hydrogen fuel cell two-wheeled vehicles:

[0040] 1. The core control module can control the output of the current-limiting constant voltage control module, thereby achieving the stability of the voltage and current output of the DC-DC control circuit module;

[0041] 2. In the control algorithm module, slack variables are introduced into the optimization problem to allow certain constraints to be violated within a certain range, in order to avoid situations where there is no solution.

[0042] 3. H∞ control is used in the control algorithm module, and a robust controller is designed to ensure that the DC-DC control circuit module can maintain good performance in the presence of uncertainties and external disturbances. Attached Figure Description

[0043] The present invention will be described by way of example and with reference to the accompanying drawings, wherein:

[0044] Figure 1 This is a flowchart of the control algorithm module of the present invention;

[0045] Figure 2 This is a schematic diagram of the present invention;

[0046] Figure 3 This is a circuit diagram of the current-limiting constant voltage control module of the present invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0048] like Figures 1-3 As shown, a DC-DC control circuit module for a hydrogen fuel cell two-wheeled vehicle includes a core control module and a current-limiting constant-voltage control module. The core control module is electrically connected to the current-limiting constant-voltage control module, and the core control module includes a control algorithm module.

[0049] The control algorithm module includes the following specific steps:

[0050] Step 1: Parameter initialization, initialize module state and external disturbances At the same time, set optimization parameters;

[0051] Step 2: State estimation. Use an extended Kalman filter to estimate and update the state. ;

[0052] Step 3: Predict future behavior. Use the state-space module to predict the output voltage at the next N sampling points. ;

[0053] Step 4: Solve the optimization problem using quadratic programming with slack variables. The optimization problem is to obtain the optimal control sequence. , ;

[0054] Step 5: Robust control, calculation Output ;

[0055] Step 6: Feedforward compensation, calculate the feedforward compensation signal. ;

[0056] Step 7: Integrated control, combining predictive control, robust control, and feedforward compensation to form the final control input. ;

[0057] Step 8: Execute control, execute the first control action. Update the current status ;

[0058] Step 9: Repeat the steps to form a rolling optimization process.

[0059] Specifically, the core control module is composed of a core control circuit based on an STM32F072, and is electrically connected to a communication module, a sampling current module, and a feedback current module.

[0060] Specifically, the current-limiting and constant-voltage control module is a current-limiting and constant-voltage control circuit mainly composed of LM5176.

[0061] A specific implementation example of a current-limiting constant voltage control module is as follows. The current-limiting constant voltage control circuit includes a first integrated circuit U1, a first resistor R1, a second resistor R2, a third resistor R3, a fourth resistor R4, a first capacitor C1, a second capacitor C2, a third capacitor C3, a fourth capacitor C4, a fifth capacitor C5, a sixth capacitor C6, a seventh capacitor C7, an eighth capacitor C8, a ninth capacitor C9, a tenth capacitor C10, a first diode VD1, a second diode VD2, and a third diode VD3. The first integrated circuit U1 is an LM5176. The second terminal of the first integrated circuit U1 is connected to a 5V DC voltage through anti-series connection with the first diode VD1. The cathode of the first diode VD1 is grounded through the first capacitor C1. The third terminal of the first integrated circuit U1 is connected to a 5V DC voltage through the first resistor R1. The eighth terminal of the first integrated circuit U1 is electrically connected to the ADC signal output terminal of the STM32F072 in the core control module through the second resistor R2. The eighth terminal of the first integrated circuit U1 is grounded through the third capacitor C3. The ninth terminal of the first integrated circuit U1 is connected to the first... Four capacitors C4 are grounded. The ninth terminal of the first integrated circuit U1 is grounded through the fifth capacitor C5 and the fourth resistor R4. The seventh terminal of the first integrated circuit U1 is grounded through the sixth capacitor C6. The sixth terminal of the first integrated circuit U1 is grounded through the third resistor R3. The sixth terminal of the first integrated circuit U1 is connected to a current transformer through the second capacitor C2. The tenth, twenty-second, and twenty-ninth terminals of the first integrated circuit U1 are all grounded. The twenty-third terminal of the first integrated circuit U1 is grounded through the seventh capacitor C7. The twenty-third terminal of the first integrated circuit U1 is connected to an external 5V power supply. The twenty-sixth terminal of the first integrated circuit U1 is connected to an external 5V power supply through the second diode VD2 in reverse series. The eighth capacitor C8 is electrically connected to the twenty-sixth and twenty-eighth terminals of the first integrated circuit U1. The ninth capacitor C9 is electrically connected to the twentieth and eighteenth terminals of the first integrated circuit U1. The twentieth terminal of the first integrated circuit U1 is connected to an external 5V power supply through the third diode VD3 in reverse series. The two ends of the tenth capacitor C10 are electrically connected to the thirteenth and fourteenth terminals of the first integrated circuit U1.

[0062] Specifically, it also includes a communication module, which is electrically connected to a sampling current module, a voltage acquisition module, and a temperature detection module.

[0063] Specifically, in step one, a state-space model of the DC-DC converter is established:

[0064] + , ;

[0065] in, It is a state vector (such as inductor current and output voltage). It is the control input (such as PWM duty cycle). It is an external disturbance. This refers to the output (e.g., output voltage). A, B, C, and D are the system matrix. It is the perturbation matrix.

[0066] Specifically, in step two, an extended Kalman filter is used for state estimation:

[0067] +

[0068] in, It is a state estimate. It is the Kalman gain.

[0069] Specifically, in step three, slack variables are defined. The optimization problem is as follows:

[0070]

[0071]

[0072]

[0073] 0

[0074] in, Let J be the slack variable, and J be the cost function, which includes the output error, the penalty for changes in control input, and the slack variable. For reference output, To control changes in input, Weighting coefficients to control input variations, These are the weighting coefficients of the slack variables. , These are the upper and lower limits of the output voltage. , These are the upper and lower limits of the inductor current.

[0075] Specifically, in step five, the design... This ensures that the system maintains good performance even in the presence of uncertainties and external disturbances.

[0076]

[0077] in, It is an error signal. yes The gain matrix.

[0078] Specifically, in step six, the formula for calculating the feedforward compensation signal is as follows:

[0079]

[0080] in, It is an external disturbance. It is the feedforward gain matrix.

[0081] Specifically, in step seven, predictive control, robust control, and feedforward compensation are combined to form the final control law. + + .

[0082] Example 1: Handling constraints in an optimization problem.

[0083] In DC-DC converters, the upper and lower limits of the output voltage and inductor current are important constraints. To ensure that the optimization problem always has a solution, relaxation variables are introduced.

[0084] 1. System Modeling: ,

[0085] 2. Optimize the problem definition:

[0086]

[0087]

[0088]

[0089] 0

[0090] 3. Parameter settings =11V, =13V, =0A, =10A, =0.1, =100;

[0091] 4. Solve optimization problems using quadratic programming with slack variables. The optimization problem is to obtain the optimal control sequence. and slack variables ;

[0092] 5. Execute control, perform the first control action. Update status .

[0093] Example 2: Handling External Disturbances

[0094] In DC-DC converters, external disturbances (such as load changes) can affect system performance. To improve the system's immunity to disturbances, an H∞ controller is introduced.

[0095] 1. System Modeling: + ,

[0096] 2. H∞ Controller Design: Design an H∞ controller to ensure that the system maintains good performance under uncertainties and external disturbances.

[0097] in, It is an error signal. yes The gain matrix;

[0098] 3. Parameter settings, via Control Theory Design This ensures the robustness of the system.

[0099] 4. Overall control rate + .

[0100] 5. Execute control, perform the first control action. Update status .

[0101] Example 3: Combining state estimation and feedforward compensation

[0102] In DC-DC converters, external disturbances (such as load variations) and model uncertainties can affect system performance. To improve the accuracy and stability of the system, state estimation and feedforward compensation are combined.

[0103] 1. System Modeling: + , ;

[0104] 2. State estimation: State estimation is performed using an extended Kalman filter.

[0105] +

[0106] in, It is a state estimate. It is the Kalman gain.

[0107] 3. Feedforward compensation: Calculate the feedforward compensation signal.

[0108] in, It is an external disturbance. It is the feedforward gain matrix.

[0109] 4. Overall control rate + + .

[0110] 5. Execute control, perform the first control action. Update status .

[0111] In summary, the DCDC control circuit module applied to hydrogen fuel cell two-wheeled vehicles introduces slack variables to handle constraints in the optimization problem, ensuring that the optimization problem always has a solution. Robust control is introduced to improve the system's anti-interference capability, ensuring good performance even under uncertainties and external disturbances. Combining state estimation and feedforward compensation improves the system's accuracy and stability, reducing the impact of model uncertainties. Therefore, the performance and stability of the DCDC control circuit module are effectively improved, ensuring efficient operation under various working conditions.

[0112] Based on the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of this invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A DC-DC control circuit module for use in hydrogen fuel cell two-wheeled vehicles, characterized in that: It includes a core control module and a current-limiting constant voltage control module. The core control module is electrically connected to the current-limiting constant voltage control module. The core control module includes a control algorithm module. The control algorithm module includes the following specific steps: Step 1: Parameter initialization, initialize module state and external disturbances At the same time, set optimization parameters; Step 2: State estimation. Use an extended Kalman filter to estimate and update the state. ; Step 3: Predict future behavior. Use the state-space module to predict the output voltage at the next N sampling points. ; Step 4: Solve the optimization problem using quadratic programming with slack variables. The optimization problem is to obtain the optimal control sequence. , ; Step 5: Robust control, calculation Output ; Step 6: Feedforward compensation, calculate the feedforward compensation signal. ; Step 7: Integrated control, combining predictive control, robust control, and feedforward compensation to form the final control input. ; Step 8: Execute control, execute the first control action. Update the current status ; Step 9: Repeat the steps to form a rolling optimization process; In step one, a state-space model of the DC-DC converter is established: + ， ； in, It is a state vector, including inductor current and output voltage. It refers to the control input, including the PWM duty cycle. It is an external disturbance. This refers to the output, including the output voltage. A, B, C, and D are the system matrix. It is the perturbation matrix; In step two, extended Kalman filtering is used for state estimation: + in, It is a state estimate. It is the Kalman gain; In step three, slack variables are defined. The optimization problem is as follows: 0 in, Let J be the slack variable, and J be the cost function, which includes the output error, the penalty for changes in control input, and the slack variable. For reference output, To control changes in input, Weighting coefficients to control input variations, These are the weighting coefficients of the slack variables. , These are the upper and lower limits of the output voltage. , These are the upper and lower limits of the inductor current; In step five, design This ensures that the system maintains good performance even in the presence of uncertainties and external disturbances. in, It is an error signal. yes The gain matrix; In step six, the formula for calculating the feedforward compensation signal is as follows: in, It is an external disturbance. It is the feedforward gain matrix; In step seven, predictive control, robust control, and feedforward compensation are combined to form the final control law. + + .

2. The DC-DC control circuit module for hydrogen fuel cell two-wheeled vehicles according to claim 1, characterized in that: The core control module is based on the STM32F072 as the core control circuit. The core control module is electrically connected to the communication module, the sampling current module, and the feedback current module.

3. The DC-DC control circuit module for a hydrogen fuel cell two-wheeled vehicle according to claim 1, characterized in that: The current-limiting and constant-voltage control module is a current-limiting and constant-voltage control circuit mainly composed of LM5176.

4. The DC-DC control circuit module for hydrogen fuel cell two-wheeled vehicles according to claim 2, characterized in that: It also includes a communication module, which is electrically connected to a sampling current module, a voltage acquisition module, and a temperature detection module.

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