A method for real-time power optimization of a variable altitude fuel cell system

Abstract

This invention provides a real-time power optimization method for a variable altitude fuel cell system. The method modifies the fuel cell cathode air supply system model based on environmental changes at different altitudes, effectively improving the model's accuracy and applicability. A feedforward-based particle swarm optimization algorithm for finding the optimal control rate of the air compressor's operating voltage is designed, making the method highly practical. The method optimizes the net power of the fuel cell system through a perturbation-observation approach. Its algorithm structure and implementation are relatively simple, and the feedforward-based optimal control rate can quickly match the altitude environment, greatly shortening the algorithm's optimization time and enabling the fuel cell system to operate at its maximum net power point as quickly as possible.

Description

Technical Field

[0001] This invention belongs to the field of fuel cell system control technology, specifically relating to a method for real-time power optimization of variable altitude fuel cell systems based on perturbation observation and particle swarm feedforward. Background Technology

[0002] Currently, some existing technologies have proposed control models for fuel cell air supply systems to address issues such as internal dynamic changes and air supply system control. However, most of these models are based on sea-level conditions and are not well-suited for fuel cell applications at high altitudes. Due to the thin air and low oxygen partial pressure at high altitudes, fuel cell stack cathodes experience insufficient gas supply. Furthermore, fuel cell stack performance is highly sensitive to cathode operating conditions (including air pressure and oxygen ratio), especially under high-power output conditions where fuel cell performance is significantly affected by concentration polarization. In such conditions, meeting the required air flow and pressure for the fuel cell reaction often leads to increased air compressor energy consumption and a substantial decrease in fuel cell system efficiency. Therefore, providing a fuel cell air supply strategy that is more adaptable to different altitude environments to achieve real-time power optimization is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0003] In view of this, and to address the technical problems existing in this field, the present invention provides a method for real-time power optimization of a variable altitude fuel cell system, mainly based on perturbation observation and particle swarm optimization. The method specifically includes the following steps:

[0004] Step 1: Establish a model of the fuel cell cathode air supply system and a model of the fuel cell electrochemical output characteristics;

[0005] Step 2: Use sensors to measure altitude information in real time and determine relevant environmental parameters. Use these environmental parameters to correct the air compressor operating performance parameters corresponding to different altitude environments in the model established in Step 1.

[0006] Step 3: Based on the modified model, design a particle swarm optimization algorithm. Sequentially set the net power calculation of the fuel cell system as the fitness function and set appropriate control law constraints. Take the air compressor operating voltage as a particle and the optimal air compressor operating voltage under different altitudes and load currents as the optimization problem. Combine the fitness function to update the optimal solutions of the particles and the swarm.

[0007] Step 4: Design a perturbation-observation algorithm to observe the impact of the air compressor operating voltage on the net power of the fuel cell system, and correct the optimal operating voltage of the air compressor by tracking the maximum net power point; the initial value of the perturbation-observation algorithm is provided by the feedforward of the optimal solution of the air compressor operating voltage at the same altitude obtained in Step 3.

[0008] Furthermore, the fuel cell cathode air supply system model established in step one specifically includes: a cathode supply manifold model, an air compressor model, a cathode model, a return manifold model, and a back pressure valve model.

[0009] Furthermore, step two specifically involves using an international standard atmospheric model to reflect the relationship between different altitudes and ambient temperature, pressure, and air density:

[0010]

[0011] Where h is altitude, T h P h and ρ h Let represent the ambient temperature, pressure, and air density at an altitude of h, respectively; T0 be the air temperature at sea level; P0 be the air pressure at sea level; Z be the compressibility factor; R be the ideal gas constant; and x be the air pressure at sea level. v M is the mole fraction of water vapor. a and M v These are the molar masses of air and water vapor, respectively.

[0012] Based on the above relationships, the operating performance parameters of the air compressor are then adjusted:

[0013] The dynamic characteristics of the air compressor output are related to the compression ratio, the supply manifold pressure, and the altitude. Its outlet flow rate is:

[0014]

[0015] Among them, W cp ω is the output flow rate of the air compressor. cp Where A is the air compressor speed, A is the air compressor impeller eye area, L is the air compressor gas transmission pipe length, and P is the air compressor speed. sm The pressure is the gas supply manifold pressure, ψ is the compression ratio, and t is the time variable;

[0016] Assuming isentropic gas compression, and considering the impact of energy transfer on air compressor performance, the air compressor compression characteristics are corrected based on ambient temperature, pressure, and density calculated at different altitudes as follows:

[0017]

[0018] Where, Δh s P represents the actual enthalpy increment. cp T is the outlet pressure of the air compressor. cp,out The air compressor outlet temperature is C, r is the ratio of air specific heat, and C is the ratio of air specific heat. p This is the specific heat capacity of air.

[0019] Furthermore, the particle swarm optimization algorithm in step three is specifically designed to execute the following processes sequentially:

[0020] (1) Initialize the population size of the air compressor working voltage, the air compressor voltage update rate and working voltage value of each particle; set the fitness function for calculating the net power of the fuel cell system and the control law constraint; use linear decreasing inertia to update the weights, and set the acceleration constant and the maximum number of iterations;

[0021] (2) Calculate the current voltage update rate and voltage value of each particle using the following formula:

[0022]

[0023]

[0024] Where w is the inertia weight, Let be the voltage update rate of the i-th particle in the d-th iteration; Let be the voltage value of the i-th particle at the d-th iteration; c1 and c2 are learning factors, r1 and r2 are uniformly random numbers in [0,1]; pBest and gBest are the individual extreme value and the global extreme value, respectively;

[0025] (3) Calculate the fitness function of each particle, compare and update the best historical position of the particle and the best historical position of the population;

[0026] (4) If the maximum number of iterations D is reached max Stop the search and obtain the optimal solution for the working voltage of the air compressor; otherwise, let D = D + 1 and return to step (2) to continue the solution.

[0027] The optimal operating voltage of the air compressor at various operating points in the fuel cell system is obtained based on the particle swarm optimization algorithm. The optimal control rate expression is obtained under different altitudes and load currents through fitting.

[0028]

[0029] In the formula, a ij This is an empirical constant. st This is the load current;

[0030] Furthermore, step four involves designing the perturbation-observation algorithm through the following steps:

[0031] First, set the initial net power P. net (0), Adaptive step size ΔU cm Error threshold d net And update time Δt; then run for Δt time, and start collecting the net power P of the fuel cell. net (t) and air compressor operating voltage U cm (t), the difference ΔP between the current cycle output power and the previous cycle output power is calculated using the following formula.net (t) yields the trend of output power change in the current cycle.

[0032] ΔP net (t)=P net (t)-P net (t-Δt);

[0033] After the algorithm is designed, the following steps are performed to track the maximum net power and correct the air compressor operating voltage:

[0034] (1) Compare the net power difference ΔP of the system net (t) Whether it is within the given error threshold d net Within the specified range, if it is within the range, the algorithm stops; otherwise, it continues.

[0035] (2) If the system net power error is not within the error threshold, then further determine whether ΔP net (t)>d net If so, then based on the air compressor's operating voltage U cm Is (t) greater than U at the previous moment? cm (t-Δt) determines whether the current power point is to the left or right of the peak value. If it is to the left (or right) of the peak value, the air compressor operating voltage is increased (or decreased) to compensate for positive (negative) disturbances.

[0036] (3) If ΔP net (t)<-d net Use the same method to determine the current net power point location and peak value, and adjust the air compressor operating voltage accordingly.

[0037] The above tracking process can be represented as:

[0038]

[0039] Until U cm If the system is repeatedly disturbed around a certain value, it means that the system has found the maximum net power point and is running stably.

[0040] Furthermore, the net power of the fuel cell is specifically the output power of the fuel cell stack minus the power consumed by the air compressor.

[0041] Furthermore, in step three, the control rate constraint conditions are set considering the boundary of the cathode oxygen ratio and the surge and blockage boundary constraints of the air compressor.

[0042] Furthermore, the adaptive step size in the perturbation observation algorithm is specifically the air compressor voltage perturbation step size adjusted based on the percentage change in the current system power relative to the power of the previous cycle.

[0043] The real-time power optimization method for variable altitude fuel cell systems provided by the present invention modifies the fuel cell cathode air supply system model based on environmental changes corresponding to different altitudes, effectively improving the model's accuracy and applicability. The designed method for solving the optimal control rate of the air compressor operating voltage based on particle swarm optimization algorithm feedforward makes the method highly practical. The method optimizes the net power of the fuel cell system through perturbation observation, and its algorithm structure and implementation are relatively simple. At the same time, the optimal control rate of feedforward can quickly match the altitude environment, greatly shortening the algorithm's optimization time and enabling the fuel cell system to operate at the maximum net power point as quickly as possible. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the real-time power optimization process for a variable altitude fuel cell system using the method provided by this invention.

[0045] Figure 2 The air compressor output characteristics at altitudes of 0m and 3000m are shown in the examples of this invention.

[0046] Figure 3 The optimal control law obtained by the particle swarm optimization algorithm in the embodiments of the present invention;

[0047] Figure 4 This is an example of the real-time power optimization result under a certain operating condition in the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0049] The real-time power optimization method for variable altitude fuel cell systems provided by this invention, such as... Figure 1 As shown, it specifically includes the following steps:

[0050] Step 1: Establish a model of the fuel cell cathode air supply system and a model of the fuel cell electrochemical output characteristics;

[0051] Step 2: Use sensors to measure altitude information in real time and determine relevant environmental parameters. Use these environmental parameters to correct the air compressor operating performance parameters corresponding to different altitude environments in the model established in Step 1.

[0052] Step 3: Based on the modified model, design a particle swarm optimization algorithm. Sequentially set the net power calculation of the fuel cell system as the fitness function and set appropriate control law constraints. Take the air compressor operating voltage as a particle and the optimal air compressor operating voltage under different altitudes and load currents as the optimization problem. Combine the fitness function to update the optimal solutions of the particles and the swarm.

[0053] Step 4: Design a perturbation-observation algorithm to observe the impact of the air compressor operating voltage on the net power of the fuel cell system, and correct the optimal operating voltage of the air compressor by tracking the maximum net power point; the initial value of the perturbation-observation algorithm is provided by the feedforward of the optimal solution of the air compressor operating voltage at the same altitude obtained in Step 3.

[0054] Furthermore, the fuel cell cathode air supply system model established in step one specifically includes: a cathode supply manifold model, an air compressor model, a cathode model, a return manifold model, and a back pressure valve model.

[0055] In a preferred embodiment of the present invention, step two specifically involves first using an international standard atmospheric model to reflect the relationship between different altitudes and ambient temperature, pressure, and air density:

[0056]

[0057] Where h is altitude, T h P h and ρ h Let represent the ambient temperature, pressure, and air density at an altitude of h, respectively; T0 be the air temperature at sea level; P0 be the pressure at sea level; Z be the compressibility factor; R be the ideal gas constant; and x be the air pressure at sea level. v M is the mole fraction of water vapor. a and M v These are the molar masses of air and water vapor, respectively.

[0058] Based on the above relationships, the operating performance parameters of the air compressor are then adjusted:

[0059] The dynamic characteristics of the air compressor output are related to the compression ratio, the supply manifold pressure, and the altitude. Its outlet flow rate is:

[0060]

[0061] Among them, W cp ω is the output flow rate of the air compressor. cp Where A is the air compressor speed, A is the air compressor impeller eye area, L is the air compressor gas transmission pipe length, and P is the air compressor speed. sm The pressure is the supply manifold pressure, ψ is the compression ratio, and t is the time variable;

[0062] Assuming isentropic gas compression, and considering the impact of energy transfer on air compressor performance, the air compressor compression characteristics are corrected based on ambient temperature, pressure, and density calculated at different altitudes as follows:

[0063]

[0064] Where, Δh s P represents the actual enthalpy increment. cp T is the outlet pressure of the air compressor. cp,out The air compressor outlet temperature is C, r is the ratio of air specific heat, and C is the ratio of air specific heat. p This is the specific heat capacity of air.

[0065] In a preferred embodiment of the present invention, the particle swarm algorithm in step three is specifically designed to execute the following processes sequentially:

[0066] (1) Initialize the population size of the air compressor working voltage, the air compressor voltage update rate and working voltage value of each particle; set the fitness function for calculating the net power of the fuel cell system and the control law constraint; use linear decreasing inertia to update the weights, and set the acceleration constant and the maximum number of iterations;

[0067] (2) Calculate the current voltage update rate and voltage value of each particle using the following formula:

[0068]

[0069]

[0070] Where w is the inertia weight, Let be the voltage update rate of the i-th particle in the d-th iteration; Let be the voltage value of the i-th particle at the d-th iteration; c1 and c2 are learning factors, r1 and r2 are uniformly random numbers in [0,1]; pBest and gBest are the individual extreme value and the global extreme value, respectively;

[0071] (3) Calculate the fitness function of each particle, compare and update the best historical position of the particle and the best historical position of the population;

[0072] (4) If the maximum number of iterations D is reached max Stop the search and obtain the optimal solution for the working voltage of the air compressor; otherwise, let D = D + 1 and return to step (2) to continue the solution.

[0073] The optimal operating voltage of the air compressor at various operating points in the fuel cell system is obtained based on the particle swarm optimization algorithm. The optimal control rate expression is obtained under different altitudes and load currents through fitting.

[0074]

[0075] In the formula, a ij This is an empirical constant. st This is the load current;

[0076] In a preferred embodiment of the present invention, step four specifically involves designing the perturbation observation algorithm through the following steps:

[0077] First, set the initial net power P. net (0), Adaptive step size ΔU cm Error threshold d net And update time Δt; then run for Δt time, and start collecting the net power P of the fuel cell. net (t) and air compressor operating voltage U cm (t), the difference ΔP between the current cycle output power and the previous cycle output power is calculated using the following formula. net (t) yields the trend of output power change in the current cycle.

[0078] ΔP net (t)=P net (t)-P net (t-Δt);

[0079] After the algorithm is designed, the following steps are performed to track the maximum net power and correct the air compressor operating voltage:

[0080] (1) Compare the net power difference ΔP of the system net (t) Whether it is within the given error threshold d net Within the specified range, if it is within the range, the algorithm stops; otherwise, it continues.

[0081] (2) If the system net power error is not within the error threshold, then further determine whether ΔP net (t)>d net If so, then based on the air compressor's operating voltage U cm Is (t) greater than U at the previous moment? cm (t-Δt) determines whether the current power point is to the left or right of the peak value. If it is to the left (or right) of the peak value, the air compressor operating voltage is increased (or decreased) to compensate for positive (negative) disturbances.

[0082] (3) If ΔP net (t)<-d net Use the same method to determine the current net power point and peak value, and adjust the air compressor operating voltage accordingly.

[0083] The above tracking process can be represented as:

[0084]

[0085] Until U cm If the system is repeatedly disturbed around a certain value, it means that the system has found the maximum net power point and is running stably.

[0086] In an embodiment of the present invention, the net power of the fuel cell is specifically the output power of the fuel cell stack minus the power consumed by the air compressor.

[0087] Specifically, the error threshold is 30W, and the update time is 1 second.

[0088] In step three, the control rate constraint conditions are set considering the boundary of the cathode oxygen ratio and the surge and blockage boundary constraints of the air compressor.

[0089] The adaptive step size in the perturbation observation algorithm is specifically the air compressor voltage perturbation step size, which is adjusted based on the percentage change in the current system power relative to the power of the previous cycle.

[0090] To verify the effectiveness of the real-time power optimization method for variable altitude fuel cell systems based on perturbation observation and particle swarm optimization, this embodiment uses a custom variable altitude operating condition to simulate and verify the designed real-time power optimization method for variable altitude fuel cell systems based on perturbation observation and particle swarm optimization. Figure 2 These are the air compressor output characteristics at altitudes of 0m and 3000m. Figure 2 (a) shows the air compressor output characteristics at an altitude of 0m. Figure 2 (b) is the corrected output characteristics of the air compressor at an altitude of 3000m. Figure 3 This is the result of solving the load current condition of 120A at an altitude of 0m using the particle swarm optimization algorithm. Figure 3 (a) is the calculated working voltage of the air compressor, which is 103V. Figure 3 (b) is the maximum net output power of the fuel cell under this operating condition, which is 32.5 kW. Figure 4 This is the result of real-time power optimization of a variable altitude fuel cell system based on perturbation observation and particle swarm feedforward. Figure 4 (a) is a custom operating condition. Figure 4 (b) shows the optimization results of the algorithm proposed in this invention.

[0091] It should be understood that the sequence number of each step in the embodiments of the present invention does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time power optimization of a variable altitude fuel cell system, characterized in that: Specifically, the following steps are included: Step 1: Establish a model of the fuel cell cathode air supply system and a model of the fuel cell electrochemical output characteristics; Step 2: Use sensors to measure altitude information in real time and determine relevant environmental parameters. Use these environmental parameters to correct the air compressor operating performance parameters corresponding to different altitude environments in the model established in Step 1. Step 3: Based on the modified model, design a particle swarm optimization algorithm. Sequentially set the net power calculation of the fuel cell system as the fitness function and set appropriate control law constraints. Take the air compressor operating voltage as a particle and the optimal air compressor operating voltage under different altitudes and load currents as the optimization problem. Combine the fitness function to update the optimal solutions of the particles and the swarm. Step 4: Design a perturbation-observation algorithm to observe the impact of the air compressor operating voltage on the net power of the fuel cell system, and correct the optimal operating voltage of the air compressor by tracking the maximum net power point; the initial value of the perturbation-observation algorithm is provided by the feedforward of the optimal solution of the air compressor operating voltage at the same altitude obtained in Step 3.

2. The method as described in claim 1, characterized in that: The fuel cell cathode air supply system model established in step one specifically includes: cathode supply manifold model, air compressor model, cathode model, return manifold model, and back pressure valve model.

3. The method as described in claim 1, characterized in that: Step two specifically involves using an international standard atmospheric model to reflect the relationship between different altitudes and ambient temperature, pressure, and air density: Where h is altitude, T h P h and ρ h Let represent the ambient temperature, pressure, and air density at an altitude of h, respectively; T0 be the air temperature at sea level; P0 be the pressure at sea level; Z be the compressibility factor; R be the ideal gas constant; and x be the air pressure at sea level. v M is the mole fraction of water vapor. a and M v These are the molar masses of air and water vapor, respectively. Based on the above relationships, the operating performance parameters of the air compressor are then adjusted: The dynamic characteristics of the air compressor output are related to the compression ratio, the supply manifold pressure, and the altitude. Its outlet flow rate is: Among them, W cp ω is the output flow rate of the air compressor. cp Where A is the air compressor speed, A is the air compressor impeller eye area, L is the air compressor gas transmission pipe length, and P is the air compressor speed. sm The pressure is the gas supply manifold pressure, ψ is the compression ratio, and t is the time variable; Assuming isentropic gas compression, and considering the impact of energy transfer on air compressor performance, the air compressor compression characteristics are corrected based on ambient temperature, pressure, and density calculated at different altitudes as follows: Where, Δh s P represents the actual enthalpy increment. cp T is the outlet pressure of the air compressor. cp,out The air compressor outlet temperature is C, r is the ratio of air specific heat, and C is the ratio of air specific heat. p This is the specific heat capacity of air.

4. The method as described in claim 1, characterized in that: The particle swarm optimization algorithm in step three is specifically designed to execute the following processes sequentially: (1) Initialize the population size of the air compressor working voltage, the air compressor voltage update rate and working voltage value of each particle; set the fitness function for calculating the net power of the fuel cell system and the control law constraint; use linear decreasing inertia to update the weights, and set the acceleration constant and the maximum number of iterations; (2) Calculate the current voltage update rate and voltage value of each particle using the following formula: Where w is the inertia weight, Let be the voltage update rate of the i-th particle in the d-th iteration; Let be the voltage value of the i-th particle at the d-th iteration; c1 and c2 are learning factors, r1 and r2 are uniformly random numbers in [0,1]; pBest and gBest are the individual extreme value and the global extreme value, respectively; (3) Calculate the fitness function of each particle, compare and update the best historical position of the particle and the best historical position of the population; (4) If the maximum number of iterations D is reached max Stop the search and obtain the optimal solution for the working voltage of the air compressor; otherwise, let D = D + 1 and return to step (2) to continue the solution. The optimal operating voltage of the air compressor at various operating points in the fuel cell system is obtained based on the particle swarm optimization algorithm. The optimal control rate expression is obtained under different altitudes and load currents through fitting. In the formula, a ij I is an empirical constant. st This is the load current.

5. The method as described in claim 4, characterized in that: Step four involves designing the perturbation-observation algorithm through the following steps: First, set the initial net power P. net (0), Adaptive step size ΔU cm Error threshold d net And update time Δt; then run for Δt time, and start collecting the net power P of the fuel cell. net (t) and air compressor operating voltage U cm (t), the difference ΔP between the current cycle output power and the previous cycle output power is calculated using the following formula. net (t) yields the trend of output power change in the current cycle. ΔP net (t)=P net (t)-P net (t-Δt); After the algorithm is designed, the following steps are performed to track the maximum net power and correct the air compressor operating voltage: (1) Compare the net power difference ΔP of the system net (t) Whether it is within the given error threshold d net Within the specified range, if it is within the range, the algorithm stops; otherwise, it continues. (2) If the system net power error is not within the error threshold, then further determine whether ΔP net (t)>d net If so, then based on the air compressor's operating voltage U cm Is (t) greater than U at the previous moment? cm (t-Δt) determines whether the current power point is to the left or right of the peak value. If it is to the left of the peak value, the air compressor operating voltage is increased to compensate for the positive disturbance. If it is to the right of the peak value, the air compressor operating voltage is decreased to compensate for the negative disturbance. (3) If ΔP net (t)<-d net Use the same method to determine the current net power point and peak value, and adjust the air compressor operating voltage accordingly. The above tracking process is represented as follows: Until U cm If the system is repeatedly disturbed around a certain value, it means that the system has found the maximum net power point and is running stably.

6. The method as described in claim 1, characterized in that: The net power of the fuel cell system is specifically the output power of the fuel cell stack minus the power consumed by the air compressor.

7. The method as described in claim 1, characterized in that: In step three, the control rate constraint conditions are set considering the boundary of the cathode oxygen ratio and the surge and blockage boundary constraints of the air compressor.

8. The method as described in claim 1, characterized in that: The adaptive step size in the perturbation observation algorithm is specifically the air compressor voltage perturbation step size, which is adjusted based on the percentage change in the current system power relative to the power of the previous cycle.

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