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

Abstract

The invention discloses a DCDC control circuit module applied to a hydrogen fuel cell two-wheeled vehicle, and relates to the technical field of power electronics, the DCDC control circuit module comprises a core control module and a current-limiting constant-voltage control module, and a control algorithm module comprises the following specific steps: step 1, initializing parameters; step 2, state estimation; 3, predicting future behaviors; 4, solving an optimization problem; 5, robust control is carried out; step 6, feedforward compensation; 7, comprehensive control is carried out; step 8, executing control; according to the invention, the output of the current-limiting constant-voltage control module can be controlled through the core control module, so that the stability of the voltage and current output of the DCDC control circuit module is realized; a slack variable is introduced into an optimization problem in the control algorithm module, and certain constraints are allowed to be violated within a certain range so as to avoid the condition of no solution.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and particularly to a DCDC control circuit module applied to a two-wheel vehicle powered by a hydrogen fuel cell. Background Art

[0002] A hydrogen fuel cell is a power generation device that utilizes hydrogen energy through a chemical reaction. During the operation process, only water is generated, achieving true zero emissions. Compared with the traditional method of burning fuels, hydrogen fuel cells have higher energy conversion efficiency and lower emissions, becoming one of the important technologies for future environmental protection energy. Moreover, the applications of hydrogen fuel cells are continuously increasing, and two-wheel vehicles powered by hydrogen fuel cells are the most common applications.

[0003] In a two-wheel vehicle powered by a hydrogen fuel cell, the role of the DCDC control circuit is particularly crucial. It is necessary to ensure that the hydrogen fuel cell outputs a stable working voltage, and how to improve the output power of the hydrogen fuel cell is of utmost importance.

[0004] In summary, a DCDC control circuit module applied to a two-wheel vehicle powered by a hydrogen fuel cell is designed. Summary of the Invention

[0005] In order to overcome the above deficiencies, the present invention provides a DCDC control circuit module applied to a two-wheel vehicle powered by a hydrogen fuel cell.

[0006] The present invention realizes the above object through the following technical solutions: A DCDC control circuit module applied to a two-wheel vehicle powered by a hydrogen fuel cell 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;

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

[0008] Step 1: Parameter initialization, initializing the module state X(0) and the external disturbance d(0), and simultaneously setting the optimization parameters;

[0009] Step 2: State estimation, using the extended Kalman filter for state estimation and updating X(k|k);

[0010] Step 3: Predicting future behavior, using the state space module to predict the output voltages y(k + 1|k), y(k + 2|k),..., y(k + N|k) at the next N sampling points;

[0011] Step 4: Solving the optimization problem, using quadratic programming to solve the optimization problem with slack variables i to obtain the optimal control sequence u MPC (k), u MPC (k + 1),..., u MPC(k + N - 1);

[0012] Step Five: Robust control, calculate the output u of the H∞ controller robust (k);

[0013] Step Six: Feedforward compensation, calculate the feedforward compensation signal u f (k);

[0014] Step Seven: Comprehensive control, combine predictive control, robust control and feedforward compensation to form the final control input u(k);

[0015] Step Eight: Execute control, execute the first control action u(k) and update the current state X(k + 1);

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

[0017] Preferably, the core control module is composed of a core control circuit mainly based on STM32F072. The core control module is electrically connected to a communication module, and the core control module is electrically connected to 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, it further includes a communication module, and the communication module is electrically connected to a sampling current module, a voltage acquisition module and a temperature detection module.

[0020] Preferably, in Step One, establish the state space model of the DC-DC converter:

[0021]

[0022] where X(t) is the state vector (such as inductor current and output voltage), u(t) is the control input (such as PWM duty cycle), d(t) is the external disturbance, y(t) is the output (such as output voltage), and A, B, C, and D are system matrices, and B d is the disturbance matrix.

[0023] Preferably, in Step Two, use the extended Kalman filter for state estimation:

[0024]

[0025] where is the state estimate value, and K(k) is the Kalman gain.

[0026] Preferably, in Step Three, define the optimization problem with a slack variable i, and the formula is as follows:

[0027]

[0028] Among them, u(k) is the control input sequence, i is the slack variable, J is the cost function, including the output error, the change of the control input, and the penalty of the slack variable, y ref (k + i) is the reference output, Δμ(k + i) is the change of the control input, λ is the weight coefficient of the control input change, ρ is the weight coefficient of the slack variable, ymin, y max are the upper and lower limits of the output voltage, i min 、i max are the upper and lower limits of the inductor current.

[0029] Preferably, in the fifth step, an H∞ controller is designed to ensure that the system can still maintain good performance in the presence of uncertainties and external disturbances:

[0030] u robust (k) = K H∞ e(k)

[0031] Among them, e(k) = y ref (k) - y(k) is the error signal, K H∞ is the gain matrix of the H∞ controller.

[0032] Preferably, in the sixth step, the calculation formula of the feedforward compensation signal is as follows:

[0033] u feedforward (k) = K f d(k)

[0034] Among them, d(k) is the external disturbance, K f is the feedforward gain matrix.

[0035] Preferably, in the seventh step, predictive control, robust control, and feedforward compensation are combined to form the final control law, u(k) = u MPC (k) + u robust (k) + u feedforward (k).

[0036] The beneficial effects of the present invention are as follows: In the DCDC control circuit module applied to hydrogen fuel cell two-wheel vehicles:

[0037] 1. The output of the current limiting constant voltage control module can be controlled through the core control module, thereby realizing the stability of the voltage and current output of the DCDC control circuit module;

[0038] 2. In the control algorithm module, slack variables are introduced into the optimization problem, allowing some constraints to be violated within a certain range to avoid the situation of no solution;

[0039] 3. In the control algorithm module, H∞ control is used to design a robust controller to ensure that the DCDC control circuit module can still maintain good performance in the presence of uncertainties and external disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be described by way of examples with reference to the accompanying drawings, wherein:

[0041] Figure 1 is the step diagram of the control algorithm module of the present invention;

[0042] Figure 2 is the schematic diagram of the present invention;

[0043] Figure 3 is the circuit schematic diagram of the current-limiting constant-voltage control module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, and therefore only showing the components related to the present invention.

[0045] As Figures 1 - 3 shown, a DCDC control circuit module applied to a two-wheeled hydrogen fuel cell 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;

[0046] The control algorithm module includes the following specific steps:

[0047] Step 1: Parameter initialization, initialize the module state X(0) and the external disturbance d(0), and at the same time set the optimization parameters;

[0048] Step 2: State estimation, use the extended Kalman filter for state estimation and update

[0049] Step 3: Predict future behavior, use the state space module to predict the output voltages y(k + 1|k), y(k + 2|k),..., y(k + N|k) at the next N sampling points;

[0050] Step 4: Solve the optimization problem, use quadratic programming to solve the optimization problem with slack variables i to obtain the optimal control sequence u MPC (k), u MPC (k + 1),..., u MPC (k + N - 1);

[0051] Step 5: Robust control, calculate the output u robust (k) of the H∞ controller;

[0052] Step Six: Feedforward Compensation, calculating the feedforward compensation signal u f (k);

[0053] Step Seven: Comprehensive Control, combining predictive control, robust control and feedforward compensation to form the final control input u(k);

[0054] Step Eight: Execute Control, executing the first control action u(k) and updating the current state X(k + 1);

[0055] Step Nine: Repeat the steps to form a rolling optimization process.

[0056] Specifically, the core control module is composed of a core control circuit mainly based on STM32F072. The core control module is electrically connected to a communication module, and the core control module is electrically connected to a sampled current module and a feedback current module.

[0057] Specifically, the current limiting and constant voltage control module is a current limiting and constant voltage control circuit mainly composed of LM5176.

[0058] A specific implementation case of the 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 model of the first integrated circuit U1 is LM5176. The second terminal of the first integrated circuit U1 is externally connected to a 5V DC voltage in 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 externally 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 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 grounded through the fourth capacitor C4. 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 externally connected to a 5V voltage source. The twenty-sixth terminal of the first integrated circuit U1 is externally connected to a 5V voltage source in anti-series connection with the second diode VD2. The eighth capacitor C8 is electrically connected to the twenty-sixth and twenty-eighth terminals of the first integrated circuit U1 respectively. The ninth capacitor C9 is electrically connected to the twentieth and eighteenth terminals of the first integrated circuit U1 respectively. The twentieth terminal of the first integrated circuit U1 is externally connected to a 5V voltage source in anti-series connection with the third diode VD3. The two ends of the tenth capacitor C10 are electrically connected to the thirteenth and fourteenth terminals of the first integrated circuit U1 respectively.

[0059] Specifically, it further includes a communication module, and the communication module is electrically connected to a sampling current module, a voltage acquisition module, and a temperature detection module.

[0060] Specifically, in step one, a state-space model of the DC-DC converter is established:

[0061]

[0062] Among them, X(t) is the state vector (such as inductor current and output voltage), u(t) is the control input (such as PWM duty cycle), d(t) is the external disturbance, y(t) is the output (such as output voltage), and A, B, C, and D are system matrices, B dis the perturbation matrix.

[0063] Specifically, in the second step, the extended Kalman filter is used for state estimation:

[0064]

[0065] Wherein, is the state estimate value, and K(k) is the Kalman gain.

[0066] Specifically, in the third step, an optimization problem with slack variables i is defined, and the formula is as follows:

[0067]

[0068]

[0069] Wherein, u(k) is the control input sequence, i is the slack variable, J is the cost function, including the output error, the change of the control input, and the penalty of the slack variable, y ref (k + i) is the reference output, Δμ(k + i) is the change of the control input, λ is the weight coefficient of the control input change, ρ is the weight coefficient of the slack variable, ymin, y max are the upper and lower limits of the output voltage, i min and i max are the upper and lower limits of the inductor current.

[0070] Specifically, in the fifth step, an H∞ controller is designed to ensure that the system can still maintain good performance in the presence of uncertainties and external perturbations:

[0071] u robust (k) = K H∞ e(k)

[0072] Wherein, e(k) = y ref (k) - y(k) is the error signal, and K H∞ is the gain matrix of the H∞ controller.

[0073] Specifically, in the sixth step, the formula for calculating the feedforward compensation signal is as follows:

[0074] u feedforward (k) = K f d(k)

[0075] Wherein, d(k) is the external perturbation, and K f is the feedforward gain matrix.

[0076] Specifically, in the seventh step, predictive control, robust control, and feedforward compensation are combined to form the final control law, u(k) = u MPC (k) + urobust (k) + u feedforward (k).

[0077] Example 1, Constraints for Handling Optimization Problems

[0078] In a DC - DC converter, 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, slack variables are introduced.

[0079] 1. System Modeling: x(k + 1) = A d x(k) + B d u(k), y(k) = C d x(k)

[0080] 2. Definition of Optimization Problem:

[0081]

[0082]

[0083] 3. Parameter Setting, ymin = 11V, y max = 13V, i min = 0A, i max = 10A, λ = 0.1, ρ = 100;

[0084] 4. Solve the Optimization Problem, use quadratic programming to solve the optimization problem with the slack variable i, and obtain the optimal control sequence u MPC (k) and the slack variable ∈(k);

[0085] 5. Execute Control, execute the first control action u(k) and update the state u(k + 1).

[0086] Example 2: Handling External Disturbances

[0087] In a DC - DC converter, external disturbances (such as load changes) can affect the performance of the system. To improve the anti - disturbance ability of the system, an H∞ controller is introduced.

[0088] 1. System Modeling: x(k + 1) = A d x(k) + B d u(k) + B d d(k), y(k) = C d x(k)

[0089] 2. H∞ Controller Design: Design an H∞ controller to ensure that the system can still maintain good performance in the presence of uncertainties and external disturbances, u robust (k) = K H∞ e(k)

[0090] where, e(k) = y ref (k) - y(k) is the error signal, and K H∞ is the gain matrix of the H∞ controller;

[0091] 3. Parameter setting, design K H∞ through the H∞ control theory to ensure the robustness of the system.

[0092] 4. Comprehensive control law, u(k) = u MPC (k) + u robust (k).

[0093] 5. Execute control, execute the first control action u(k) and update the state u(k + 1).

[0094] Embodiment 3: Combining state estimation and feedforward compensation

[0095] In a DC - DC converter, external disturbances (such as load changes) and model uncertainties can affect the performance of the system. To improve the accuracy and stability of the system, state estimation and feedforward compensation are combined.

[0096] 1. System modeling: x(k + 1) = A d x(k) + B d u(k) + B d d(k), y(k) = C d x(k);

[0097] 2. State estimation: Use the extended Kalman filter for state estimation:

[0098]

[0099] where, is the state estimate value, and K(k) is the Kalman gain.

[0100] 3. Feedforward compensation, calculate the feedforward compensation signal: u feedforward (k) = K f d(k)

[0101] where, d(k) is the external disturbance, and K f is the feedforward gain matrix.

[0102] 4. Comprehensive control law, u(k) = u MPC (k) + u robust (k) + u feedforward (k).

[0103] 5. Execute control, execute the first control action u(k) and update the state u(k + 1).

[0104] In summary, slack variables are introduced in the DCDC control circuit module applied to hydrogen fuel cell two-wheelers: to handle the constraints of the optimization problem and ensure that the optimization problem always has a solution. Robust control is introduced: to improve the anti-interference ability of the system and ensure good performance in the presence of uncertainties and external disturbances. Combining state estimation and feedforward compensation: to improve the accuracy and stability of the system and reduce the impact of model uncertainties. Thus, the performance and stability of the DCDC control circuit module are effectively improved, ensuring that the DCDC control circuit module can operate efficiently under various working conditions.

[0105] Based on the inspiration of the present invention, through the above description, relevant staff can make various changes and modifications completely within the scope without departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A DCDC control circuit module applied to a two-wheeled vehicle with a hydrogen fuel cell, 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 the module state X(0) and the external disturbance d(0), and at the same time set the optimization parameters; Step 2: State estimation. Use the extended Kalman filter for state estimation and update X(k|k); Step 3: Predict future behavior. Use the state space module to predict the output voltages y(k + 1|k), y(k + 2|k), …, y(k + N|k) at the next N sampling points; Step 4: Solve the optimization problem, and use quadratic programming to solve the optimization problem with slack variables i to obtain the optimal control sequence u MPC (k), u MPC (k + 1), …, u MPC (k + N - 1); Step 5: Robust control, calculate the output u of the H∞ controller robust (k); Step 6: Feedforward compensation, calculate the feedforward compensation signal u feedforward (k); Step 7: Comprehensive control. Combine predictive control, robust control, and feedforward compensation to form the final control input u(k); Step 8: Execute control. Execute the first control action u(k) and update the current state X(k + 1); Step 9: Repeat the steps to form a rolling optimization process.

2. The DCDC control circuit module applied to a two-wheeled vehicle with a hydrogen fuel cell according to claim 1, wherein: The core control module is composed of a core control circuit mainly based on STM32F072. The core control module is electrically connected to a communication module, a sampling current module, and a feedback current module.

3. The DCDC control circuit module applied to the hydrogen fuel cell two-wheeler according to claim 1, characterized in that: The current-limiting constant-voltage control module is a current-limiting constant-voltage control circuit mainly composed of LM5176.

4. The DCDC control circuit module applied to a two-wheeled vehicle with a hydrogen fuel cell according to claim 2, wherein: It also includes a communication module, which is electrically connected to a sampling current module, a voltage acquisition module, and a temperature detection module.

5. The DCDC control circuit module applied to a two-wheeled vehicle with a hydrogen fuel cell according to claim 1, characterized in that: In the said Step 1, establish the state space model of the DC-DC converter: where, X(t) is the state vector (such as inductor current and output voltage), u(t) is the control input (such as PWM duty cycle), d(t) is the external disturbance, y(t) is the output (such as output voltage), A, B, C, and D are system matrices, and B d is the disturbance matrix.

6. The DCDC control circuit module applied to a hydrogen fuel cell two-wheeler according to claim 1, wherein: In the said Step 2, use the extended Kalman filter for state estimation: where, is the state estimate value, and K(k) is the Kalman gain.

7. The DCDC control circuit module applied to a hydrogen fuel cell two-wheeler according to claim 1, characterized in that: In the third step, an optimization problem with slack variables i is defined, and the formula is as follows: s.t.y min -∈1≤y(k+i|k)≤y max +∈1 i min -∈2 ≤ i(k + 1|k) ≤ i max +∈2 i ≥0 where u(k) is the control input sequence, i is the slack variable, J is the cost function, including the output error, the change in the control input, and the penalty of the slack variable, y ref (k + i) is the reference output, Δμ(k + i) is the change in the control input, λ is the weight coefficient of the control input change, ρ is the weight coefficient of the slack variable, y min 、y max are the upper and lower limits of the output voltage, i min 、i max are the upper and lower limits of the inductor current.

8. The DCDC control circuit module applied to a two-wheeled hydrogen fuel cell vehicle according to claim 1, characterized in that: In the said Step 5, design an H∞ controller to ensure that the system can still maintain good performance in the presence of uncertainties and external disturbances: u robust (k) = K H∞ e(k) where e(k) = y ref (k) - y(k) is the error signal, and K H∞ is the gain matrix of the H∞ controller.

9. The DCDC control circuit module applied to a hydrogen fuel cell two-wheeler according to claim 1, wherein: In the said Step 6, the calculation formula of the feedforward compensation signal is as follows: u feedforward (k) = K f d(k) where d(k) is an external disturbance, and K f is a feedforward gain matrix.

10. The DCDC control circuit module applied to the hydrogen fuel cell two-wheeler according to claim 1, characterized in that: In step seven, predictive control, robust control, and feedforward compensation are combined to form the final control law, u(k) = u MPC (k) + u robust (k) + u feedforward (k).

Images