SOFC-lithium battery hybrid power supply control system and method

Abstract

The invention relates to the technical field of solid oxide fuel cells, in particular to an SOFC-lithium battery hybrid power supply control system and method, and the method comprises the following steps: S1, data collection: a data collection device collects prototype operation data from an SOFC stack; s2, data decoupling: taking the collected hydrogen utilization rate HU and air excess ratio AR as the input of a feed-forward compensation decoupling controller, and taking the stack temperature T and power P as the output to obtain the matching relationship among a plurality of single inputs and single outputs; s3, data optimization: performing multi-target optimization on the acquired data by using an improved non-dominated sorting genetic algorithm according to the decoupled matching relationship to obtain the maximum power and the safe temperature; s4, cooperative power supply: the fuzzy controller regulates and controls the charging and discharging mode of the lithium battery pack according to the power difference between the maximum power obtained through optimization and the power required by the load equipment and the power change rate; according to the invention, thermal health and safe operation of the SOFC stack can be guaranteed, and effective cooperative power supply with the lithium battery pack in a stable state can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of solid oxide fuel cells, and in particular to a SOFC-lithium battery hybrid power supply control system and method. Background Art

[0002] In the fields of power system engineering, fuel cell technology and lithium battery technology, fuel cells (SOFC) and lithium batteries are both important components of modern energy technology. Fuel cells can directly convert the chemical energy of fuel into electrical energy, and have the advantages of high efficiency and environmental protection. They are widely used in distributed energy systems and transportation. As an efficient and environmentally friendly energy storage device, lithium batteries are widely used in solar power generation, wind power generation and other systems to achieve energy storage and release.

[0003] At present, most power supply systems use fuel cells or lithium batteries to provide electricity alone. For example, in distributed energy systems, fuel cells are usually used as the main power source, and lithium batteries are used as backup power sources or energy storage devices. Fuel cells and lithium batteries usually operate independently of each other, lacking effective coordination and control, resulting in low efficiency and serious energy waste in the power supply system. In addition, the performance of fuel cells is easily affected by many factors (such as stack temperature, current, voltage, air utilization, and hydrogen flow rate, etc.). How to optimize the operating parameters of the SOFC system and improve the reliability, practicality and stability of the SOFC system is also a key challenge facing current technology. Therefore, it is very necessary to develop a control system and method that can not only ensure the thermal health and safe operation of the SOFC system, but also effectively cooperate with lithium batteries to provide power in a stable state. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide a SOFC-lithium battery hybrid power supply control system and method that can not only ensure the thermal health and safe operation of the SOFC stack, but also effectively coordinate power supply with the lithium battery pack under stable conditions.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a SOFC-lithium battery hybrid power supply control method, comprising the following steps:

[0006] S1. Data acquisition: The data acquisition device collects the operation data of the prototype from the SOFC stack. The operation data includes the stack temperature T, current I, voltage V, hydrogen utilization rate HU and air excess ratio AR;

[0007] S2. Data decoupling: The collected hydrogen utilization rate HU and air excess ratio AR are used as the input of the feedforward compensation decoupling controller, and the stack temperature T and power P are used as the output of the feedforward compensation decoupling controller to obtain the matching relationship between multiple single input and single output;

[0008] S3. Data optimization: Based on the matching relationship after decoupling, the collected stack temperature T, power P, hydrogen utilization rate HU and air excess ratio AR are optimized using an improved non-dominated sorting genetic algorithm to obtain the maximum power and safe temperature;

[0009] S4. Collaborative power supply: The fuzzy controller regulates the charge and discharge mode of the lithium battery pack according to the power difference and power change rate between the maximum power obtained by optimization and the power required by the load equipment, so that the SOFC stack and the lithium battery pack can collaboratively supply power to the load equipment.

[0010] Furthermore, the feedforward compensation decoupling controller in step S2 is designed as:

[0011] Z p11 (s)D 12 (s)+Z p12 (s)=0

[0012] Z p22 (s)D 21 (s)+Z p21 (s)=0

[0013] Among them, D ij (s) represents the transfer function of ui affecting the output yj in the feedforward loop, Z pij (s) represents the transfer function of ui affecting yj in the feedback loop, ui represents the input signal, which is the hydrogen utilization rate HU or the air excess ratio AR, yj represents the output signal, which is the stack temperature T or power P, i=1,2, j=1,2.

[0014] Furthermore, the specific steps of step S2 are as follows:

[0015] S21. The hydrogen utilization rate HU and the air excess ratio AR are input into the feedforward control module of the feedforward compensation decoupling controller and are connected through their respective transfer functions D ij (s) process;

[0016] S22. The hydrogen utilization rate HU and the air excess ratio AR processed by the feedforward control module are input into the decoupling compensation module of the feedforward compensation decoupling controller, and are converted into the decoupling compensation module through their respective transfer functions Z pij (s) undergoing reprocessing;

[0017] S23. Output the stack temperature T and power P.

[0018] Furthermore, the multi-objective optimization function formula in step S3 is as follows:

[0019] g1=G1(HU,AR)

[0020] g2=G2(HU,AR)

[0021] Wherein, g1 represents the output power of the SOFC stack, g2 represents the stack temperature of the SOFC stack, G1 represents the functional relationship between the parameter and g1, and G2 represents the functional relationship between the parameter and g2.

[0022] Furthermore, the specific steps of improving the non-dominated sorting genetic algorithm in step S3 are as follows:

[0023] S31. Population initialization, randomly generate N individuals to form the initial population P t ;

[0024] S32. For population P t Perform non-dominated sorting, use the σ-truncation selection method to select individual reproduction, and apply adaptive crossover and adaptive mutation operations to the offspring population Q t ;

[0025] S33. Set the initial population P t and the offspring population Q t Merge to get a new population R t , for the new population R t Perform non-dominated sorting to determine the non-dominated fronts g1 and g2 of different levels, and use the crowding comparison operator to extract the population R from the new population t Select the next generation population P t+1 , the next generation population P t+1 The number of individuals is equal to the initial population P t The number of individuals;

[0026] S34. For the next generation population P t+1 Repeat the genetic algorithm operation to obtain a new population Q t+1 ;

[0027] S35. Repeat steps S32 to S34 until the set evolutionary generation is reached, and the final solution set is taken as the optimal solution set.

[0028] Furthermore, the formula of the truncation standard τ in the σ-truncation selection operation method is as follows:

[0029]

[0030] In the formula,

[0031] Among them, α represents the truncation coefficient, P(I i ) indicates the first i The fitness of each individual, N represents the size of the population, μ represents the mean fitness, and σ represents the standard deviation of fitness;

[0032] The crossover probability p in the adaptive crossover c The formula for (i) is as follows:

[0033]

[0034] The crossover formula in the adaptive crossover is as follows:

[0035]

[0036] Among them, i represents the i-th individual in the population, a represents the adaptive adjustment factor of the nonlinear function, and F i represents the fitness of the i-th individual in the population, avg F represents the average fitness of the population, s1 represents the saturation coefficient, p c represents the initial crossover probability, and p c =0.5, γ represents the random number seed, and γ∈[0,1], Represents chromosome X m The gene value at position p, Represents chromosome X n The gene value at position p;

[0037] The mutation probability p in the adaptive mutation b The formula for (i) is as follows:

[0038]

[0039] The mutation formula in the adaptive mutation is as follows:

[0040]

[0041] Among them, b represents the adaptive adjustment factor of the nonlinear function, s2 represents the saturation coefficient, and p b represents the initial mutation probability, and p b =0.45, represents the nth gene on the mth chromosome, Indicates gene The upper bound of Indicates gene The lower bound of , h represents the number of iterations, H represents the maximum number of iterations, ζ represents the random seed, and ζ∈[-1,1].

[0042] Furthermore, in step S4, the fuzzy controller integrates an active disturbance rejection control algorithm, and the extended state observer in the active disturbance rejection control algorithm is as follows:

[0043]

[0044] In the formula,

[0045] Where e represents the error signal, y represents the output signal, z1 represents the tracking signal of y, z2 represents the differential signal of z1, z3 represents the tracking signal of the total disturbance, δ represents the set value, and δ>0, β1, β2 and β3 represent controllers, α1 and α2 represent adjustable parameters, and 0<α1<1<α2, fal(e,α i ,δ) represents a nonlinear function, i=1,2.

[0046] A control system adopting the above-mentioned SOFC-lithium battery hybrid power supply control method comprises a SOFC stack, a lithium battery group, a load device, a data acquisition device, a feedforward compensation decoupling controller, an optimization module, a fuzzy controller and a DC-DC conversion circuit, wherein the SOFC stack, the lithium battery group and the load device are respectively connected to the DC-DC conversion circuit, the SOFC stack is connected to the lithium battery group through the data acquisition device, the feedforward compensation decoupling controller, the optimization module and the fuzzy controller in sequence, and the load device is connected to the fuzzy controller.

[0047] Furthermore, the DC-DC conversion circuit includes a constant voltage power supply E, an energy storage inductor L, a capacitor C, diodes D1, D2, switching elements T1, T2 and a resistor R, wherein the positive electrode of the constant voltage power supply E is connected to one end of the resistor R through the switching element T1, the energy storage inductor L and the diode D1 in sequence, and its negative electrode is connected to the other end of the resistor R, one end of the diode D2 is connected to the common end of the switching element T1 and the energy storage inductor L, and the other end of the diode D2 is connected to the common end of the constant voltage power supply E and the resistor R, one end of the switching element T2 is connected to the common end of the energy storage inductor L and the diode D1, and the other end of the diode D2 is connected to the common end of the constant voltage power supply E and the resistor R, one end of the capacitor C is connected to the common end of the diode D1 and the resistor R, and the other end of the capacitor C is connected to the common end of the constant voltage power supply E and the resistor R.

[0048] Furthermore, the data acquisition device is provided with a voltage detection circuit, a current detection circuit, a temperature sensor, a hydrogen flow sensor and an air flow sensor.

[0049] The beneficial effects of the present invention are:

[0050] (1) The present invention uses data acquisition equipment to detect key parameters of the SOFC stack, such as stack temperature, current, voltage, air utilization rate and hydrogen flow rate, in real time, and uses a feedforward compensation decoupling controller to process the key parameters, output the stack temperature and power, and convert the originally complex multi-input multi-output system into multiple single-input single-output systems, eliminating the mutual interference between multiple control loops and effectively reducing the bad coupling in the system. Then, an improved non-dominated sorting genetic algorithm is used to perform multi-objective optimization on the stack temperature and output power of the SOFC stack to obtain the maximum power and safe temperature. Finally, the fuzzy controller regulates the charging and discharging mode of the lithium battery pack according to the power difference and power change rate between the maximum power obtained by optimization and the power required by the load device, so as to realize the effective coordination of the SOFC stack and the lithium battery pack to supply power to the load device, while improving the reliability, practicality and stability of the system in complex and uncertain environments.

[0051] (2) The fuzzy controller in the present invention integrates an anti-disturbance control algorithm to achieve precise dynamic regulation of the charging and discharging mode of the lithium battery pack, thereby enhancing the system's adaptability and robustness, thereby further improving the system's reliability, practicality and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0053] Figure 1 It is a flow chart of the SOFC-lithium battery hybrid power supply control method of the present invention;

[0054] Figure 2 Schematic diagram of the network structure of the feedforward compensation decoupling controller in the present invention;

[0055] Figure 3 It is a flow chart of the improved non-dominated sorting genetic algorithm in the present invention;

[0056] Figure 4 It is a structural diagram of the active disturbance rejection control algorithm in the present invention;

[0057] Figure 5 It is a framework diagram of the SOFC-lithium battery hybrid power supply control system of the present invention;

[0058] Figure 6 It is a circuit principle diagram of the DC-DC conversion circuit in the present invention. DETAILED DESCRIPTION

[0059] The present invention will now be further described in conjunction with the accompanying drawings and preferred embodiments. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0060] Example 1

[0061] like Figure 1 As shown, a SOFC-lithium battery hybrid power supply control method comprises the following steps:

[0062] S1. Data acquisition: The data acquisition equipment collects the prototype operation data from the SOFC stack. The operation data includes the stack temperature T, current I, voltage V, hydrogen utilization rate HU and air excess ratio AR.

[0063] Specifically, the calculation formulas for hydrogen utilization HU and air excess ratio AR are as follows:

[0064]

[0065] in, is the hydrogen reaction rate, is the reaction rate of oxygen, is the hydrogen molar flow rate, is the molar flow rate of oxygen.

[0066] S2. Data decoupling: The collected hydrogen utilization rate HU and air excess ratio AR are used as the input of the feedforward compensation decoupling controller, and the stack temperature T and power P are used as the output of the feedforward compensation decoupling controller to obtain the matching relationship between multiple single input and single output.

[0067] Specifically, the value range of HU is 0.6 to 0.9, the value range of AR is 6 to 12, the control range of the stack temperature T is 600°C to 800°C, and the power P is not greater than 5kw.

[0068] S3. Data optimization: According to the matching relationship after decoupling, the collected stack temperature T, power P, hydrogen utilization rate HU and air excess ratio AR are optimized using an improved non-dominated sorting genetic algorithm to obtain the maximum power and safe temperature.

[0069] S4. Collaborative power supply: The fuzzy controller regulates the charge and discharge mode of the lithium battery pack according to the power difference and power change rate between the maximum power obtained by optimization and the power required by the load equipment, so that the SOFC stack and the lithium battery pack can collaboratively supply power to the load equipment.

[0070] The key parameters of the SOFC stack, such as the stack temperature T, current I, voltage V, hydrogen utilization rate HU and air excess ratio AR, are detected in real time by data acquisition equipment, and a feedforward compensation decoupling controller is used to process the key parameters, output the stack temperature T and power P, and convert the originally complex multi-input multi-output system into multiple single-input single-output systems, eliminating the mutual interference between multiple control loops and effectively reducing the bad coupling in the system. Then, an improved non-dominated sorting genetic algorithm is used to perform multi-objective optimization on the stack temperature T and power P of the SOFC stack to obtain the maximum power and safe temperature. Finally, the fuzzy controller regulates the charge and discharge mode of the lithium battery pack according to the power difference and power change rate between the maximum power obtained by optimization and the power required by the load device, so as to realize the effective coordination of the SOFC stack and the lithium battery pack to power the load device, while improving the reliability, practicality and stability of the system in complex and uncertain environments.

[0071] like Figure 2 As shown, in step S2, according to the invariance principle, the feedforward compensation decoupling controller is designed as:

[0072] Z p11 (s)D 12 (s)+Z p12 (s)=0

[0073] Z p22 (s)D 21 (s)+Z p21 (s)=0

[0074] Then we have:

[0075]

[0076] D 11 (s) = D 22 (s)=1

[0077] Among them, D ij (s) represents the transfer function of ui affecting the output yj in the feedforward loop, Z pij (s) represents the transfer function of ui affecting yj in the feedback loop, ui represents the input signal, which is the hydrogen utilization rate HU or the air excess ratio AR, yj represents the output signal, which is the stack temperature T or power P, i=1,2, j=1,2.

[0078] After using feedforward compensation decoupling, the result is still a diagonal matrix, which completely eliminates the correlation between multiple loops. The diagonal matrix is ​​as follows:

[0079]

[0080] The specific steps of step S2 are as follows:

[0081] S21. The hydrogen utilization rate HU and the air excess ratio AR are input into the feedforward control module of the feedforward compensation decoupling controller and are connected through their respective transfer functions D ij (s) process;

[0082] S22. The hydrogen utilization rate HU and the air excess ratio AR processed by the feedforward control module are input into the decoupling compensation module of the feedforward compensation decoupling controller, and are converted into the decoupling compensation module through their respective transfer functions Z pij (s) undergoing reprocessing;

[0083] S23. Output the stack temperature T and power P.

[0084] The multi-objective optimization function formula in step S3 is as follows:

[0085] g1=G1(HU,AR)

[0086] g2=G2(HU,AR)

[0087] Wherein, g1 represents the output power of the SOFC stack, g2 represents the stack temperature of the SOFC stack, G1 represents the functional relationship between the parameter and g1, and G2 represents the functional relationship between the parameter and g2.

[0088] like Figure 3 As shown, the specific steps of improving the non-dominated sorting genetic algorithm in step S3 are as follows:

[0089] S31. Population initialization, randomly generate N individuals to form the initial population P t ;

[0090] S32. For population P t Perform non-dominated sorting, use the σ-truncation selection method to select individual reproduction, and apply adaptive crossover and adaptive mutation operations to the offspring population Q t ;

[0091] S33. Set the initial population P t and the offspring population Q t Merge to get a new population R t , for the new population R t Perform non-dominated sorting to determine the non-dominated fronts g1 and g2 of different levels, and use the crowding comparison operator to extract the population R from the new population t Select the next generation population P t+1 , the next generation population P t+1 The number of individuals is equal to the initial population P t The number of individuals;

[0092] S34. For the next generation population P t+1 Repeat the genetic algorithm operation to obtain a new population Q t+1 ;

[0093] S35. Repeat steps S32 to S34 until the set evolutionary generation is reached, and the final solution set is taken as the optimal solution set.

[0094] According to the improved non-dominated sorting genetic algorithm, an initial population is generated, and new offspring are generated through genetic operators such as selection, crossover and mutation. These offspring participate in non-dominated sorting together with their parents to determine which individuals will enter the next generation. After multiple rounds of iterative optimization, the genetic algorithm finally converges to a set of non-dominated solutions, which represent a set of operating points that achieve the best balance between maximum power and safe temperature.

[0095] Specifically, the initial population P t , offspring population Q t and the next generation population P t+1 The size of the new population R is 200; t The population size is 400; each individual in the population represents a different setting of a set of operating variables (such as stack temperature T, power P, hydrogen utilization HU and air excess ratio AR).

[0096] The formula for the cutoff criterion τ in the σ-cutoff selection operation method is as follows:

[0097]

[0098] In the formula,

[0099] Among them, α represents the truncation coefficient, P(I i ) indicates the first i N represents the fitness of each individual, N represents the size of the population, μ represents the mean fitness, and σ represents the standard deviation of the fitness.

[0100] σ-truncation selection can more accurately screen out individuals with excellent performance by setting a standard based on the fitness distribution of the population. Only those individuals with fitness P(I i ) will be selected to enter the next generation population, which can not only significantly improve the proportion of excellent individuals, but also maintain the diversity of the population to a certain extent, thereby accelerating the overall convergence speed of the genetic algorithm.

[0101] Crossover probability p in adaptive crossover c The formula for (i) is as follows:

[0102]

[0103] The crossover formula in adaptive crossover is as follows:

[0104]

[0105] Among them, i represents the i-th individual in the population, a represents the adaptive adjustment factor of the nonlinear function, and F i represents the fitness of the i-th individual in the population, avg F represents the average fitness of the population, s1 represents the saturation coefficient, p c represents the initial crossover probability, and p c =0.5, γ represents the random number seed, and γ∈[0,1], Represents chromosome X m The gene value at position p, Represents chromosome X n The value of the gene at position p.

[0106] By introducing the adaptive adjustment factor a to dynamically adjust the crossover probability, the overall performance of the genetic algorithm is enhanced, which means that individuals with high fitness are more likely to be selected to participate in the crossover operation, thereby accelerating the convergence of the population to a better solution. In addition, in order to prevent the fitness from being too high, causing the crossover probability to approach 1, thereby reducing the diversity of the population, the saturation coefficient s1 is introduced and combined with a nonlinear function (such as the sigmoid function). The role of the saturation coefficient s1 is to limit the gain effect of fitness on the crossover probability, ensuring that even for individuals with very high fitness, the crossover probability will not increase infinitely. The nonlinear function can smoothly adjust the crossover probability so that it changes with the change of fitness, but always remains within a reasonable range.

[0107] Mutation probability p in adaptive mutation b The formula for (i) is as follows:

[0108]

[0109] The mutation formula in adaptive mutation is as follows:

[0110]

[0111] Among them, b represents the adaptive adjustment factor of the nonlinear function, s2 represents the saturation coefficient, and p b represents the initial mutation probability, and p b =0.45, represents the nth gene on the mth chromosome, Indicates gene The upper bound of Indicates gene The lower bound of , h represents the number of iterations, H represents the maximum number of iterations, ζ represents the random seed, and ζ∈[-1,1].

[0112] Through the adaptive adjustment factor b and the saturation coefficient s2, individuals with high fitness are given a greater chance to perform mutation operations, thereby approaching the global optimal solution more quickly. At the same time, due to the intervention of the saturation coefficient s2, it is ensured that even the best performing individuals will not have unlimited growth in mutation probability, effectively preventing the risk of falling into local extreme values ​​in advance and avoiding the problem of premature convergence to the local optimal solution.

[0113] like Figure 4 As shown, in step S4, the fuzzy controller integrates the active disturbance rejection control algorithm, and the extended state observer in the active disturbance rejection control algorithm is as follows:

[0114]

[0115] In the formula,

[0116] Where e represents the error signal, y represents the output signal, z1 represents the tracking signal of y, z2 represents the differential signal of z1, z3 represents the tracking signal of the total disturbance, δ represents the set value, and δ>0, β1, β2 and β3 represent controllers, α1 and α2 represent adjustable parameters, and 0<α1<1<α2, fal(e,α i ,δ) represents a nonlinear function, i=1,2.

[0117] The ADRC algorithm uses the extended state observer (ESO) to estimate and compensate for disturbances in real time, thereby constructing a fuzzy controller with ADRC function. Specifically, the architecture of the ADRC algorithm is the existing architecture; the nonlinear function fal(e,α i ,δ) is actually a mathematical fit to an empirical knowledge in the control world (i.e., large error, small gain; small error, large gain), which can reduce the control error.

[0118] The input variables of the fuzzy controller are the difference ΔP between the power required by the load device and the maximum power of the SOFC stack and the rate of change of the power difference dΔP / dt, which determine the power difference domain and the power difference rate of change domain of the fuzzy controller. Among them, the power difference domain is a power range, and the power difference rate of change domain is a range of power change rate over time. If the maximum power of the SOFC stack is greater than the power required by the load device, the fuzzy controller controls the lithium battery pack to enter the charging mode; if the maximum power of the SOFC stack is less than the power required by the load device, the fuzzy controller controls the lithium battery pack to enter the discharge mode.

[0119] Example 2

[0120] like Figure 5As shown, a control system using the SOFC-lithium battery hybrid power supply control method in Example 1 includes a SOFC stack, a lithium battery group, a load device, a data acquisition device, a feedforward compensation decoupling controller, an optimization module, a fuzzy controller and a DC-DC conversion circuit. The SOFC stack, the lithium battery group and the load device are respectively connected to the DC-DC conversion circuit, and the SOFC stack is connected to the lithium battery group through the data acquisition device, the feedforward compensation decoupling controller, the optimization module and the fuzzy controller in turn, and the load device is connected to the fuzzy controller. Specifically, the SOFC stack is a prior art; the data acquisition device is provided with a voltage detection circuit, a current detection circuit, a temperature sensor, a hydrogen flow sensor and an air flow sensor.

[0121] like Figure 6 As shown, the DC-DC conversion circuit includes a constant voltage power supply E, an energy storage inductor L, a capacitor C, diodes D1, D2, switch elements T1, T2 and a resistor R. The positive electrode of the constant voltage power supply E is connected to one end of the resistor R through the switch element T1, the energy storage inductor L and the diode D1 in sequence, and its negative electrode is connected to the other end of the resistor R. One end of the diode D2 is connected to the common end of the switch element T1 and the energy storage inductor L, and the other end is connected to the common end of the constant voltage power supply E and the resistor R. One end of the switch element T2 is connected to the common end of the energy storage inductor L and the diode D1, and the other end is connected to the common end of the constant voltage power supply E and the resistor R. One end of the capacitor C is connected to the common end of the diode D1 and the resistor R, and the other end is connected to the common end of the constant voltage power supply E and the resistor R. The voltage of the SOFC stack is bucked and stepped up by precisely controlling the states of the switch elements T1 and T2.

[0122] During operation, the fuzzy controller regulates the charge and discharge mode of the lithium battery pack, while the DC-DC conversion circuit performs step-up and step-down conversion on the voltage of the SOFC stack, so that the SOFC stack and the lithium battery pack can work together to supply power to the load equipment.

[0123] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with this technology to understand the content of the present invention and implement it. They cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A SOFC-lithium battery hybrid power supply control method, characterized in that: The following steps are involved: S1. Data acquisition: The data acquisition device collects the operation data of the prototype from the SOFC stack. The operation data includes the stack temperature T, current I, voltage V, hydrogen utilization rate HU and air excess ratio AR; S2. Data decoupling: The collected hydrogen utilization rate HU and air excess ratio AR are used as the input of the feedforward compensation decoupling controller, and the stack temperature T and power P are used as the output of the feedforward compensation decoupling controller to obtain the matching relationship between multiple single input and single output; S3. Data optimization: Based on the matching relationship after decoupling, the collected stack temperature T, power P, hydrogen utilization rate HU and air excess ratio AR are optimized using an improved non-dominated sorting genetic algorithm to obtain the maximum power and safe temperature; S4. Collaborative power supply: The fuzzy controller regulates the charge and discharge mode of the lithium battery pack according to the power difference and power change rate between the maximum power obtained by optimization and the power required by the load equipment, so that the SOFC stack and the lithium battery pack can collaboratively supply power to the load equipment.

2. The SOFC-lithium battery hybrid power supply control method according to claim 1, characterized in that: The feedforward compensation decoupling controller in step S2 is designed as: Z p11 (s)D 12 (s)+Z p12 (s)=0 Z p22 (s)D 21 (s)+Z p21 (s)=0 Among them, D ij (s) represents the transfer function of ui affecting the output yj in the feedforward loop, Z pij (s) represents the transfer function of ui affecting yj in the feedback loop, ui represents the input signal, which is the hydrogen utilization rate HU or the air excess ratio AR, yj represents the output signal, which is the stack temperature T or power P, i=1,2, j=1,2.

3. The SOFC-lithium battery hybrid power supply control method according to claim 2, characterized in that: The specific steps of step S2 are as follows: S21. The hydrogen utilization rate HU and the air excess ratio AR are input into the feedforward control module of the feedforward compensation decoupling controller and are connected through their respective transfer functions D ij (s) process; S22. The hydrogen utilization rate HU and the air excess ratio AR processed by the feedforward control module are input into the decoupling compensation module of the feedforward compensation decoupling controller, and are converted into the decoupling compensation module through their respective transfer functions Z pij (s) undergoing reprocessing; S23. Output the stack temperature T and power P.

4. The SOFC-lithium battery hybrid power supply control method according to claim 1, characterized in that: The multi-objective optimization function formula in step S3 is as follows: g1=G1(HU,AR) g2=G2(HU,AR) Wherein, g1 represents the output power of the SOFC stack, g2 represents the stack temperature of the SOFC stack, G1 represents the functional relationship between the parameter and g1, and G2 represents the functional relationship between the parameter and g2.

5. The SOFC-lithium battery hybrid power supply control method according to claim 1, characterized in that: The specific steps of improving the non-dominated sorting genetic algorithm in step S3 are as follows: S31. Population initialization, randomly generate N individuals to form the initial population P t ; S32. For population P t Perform non-dominated sorting, use the σ-truncation selection method to select individual reproduction, and apply adaptive crossover and adaptive mutation operations to the offspring population Q t ; S33. Set the initial population P t and the offspring population Q t Merge to get a new population R t , for the new population R t Perform non-dominated sorting to determine the non-dominated fronts g1 and g2 of different levels, and use the crowding comparison operator to extract the population R from the new population t Select the next generation population P t+1 , the next generation population P t+1 The number of individuals is equal to the initial population P t The number of individuals; S34. For the next generation population P t+1 Repeat the genetic algorithm operation to obtain a new population Q t+1 ; S35. Repeat steps S32 to S34 until the set evolutionary generation is reached, and take the final solution set as the optimal solution set.

6. The SOFC-lithium battery hybrid power supply control method according to claim 5, characterized in that: The formula of the truncation standard τ in the σ-truncation selection operation method is as follows: In the formula, Among them, α represents the truncation coefficient, P(I i ) indicates the first i The fitness of each individual, N represents the size of the population, μ represents the mean fitness, and σ represents the standard deviation of fitness; The crossover probability p in the adaptive crossover c The formula for (i) is as follows: The crossover formula in the adaptive crossover is as follows: Among them, i represents the i-th individual in the population, a represents the adaptive adjustment factor of the nonlinear function, and F i represents the fitness of the i-th individual in the population, avg F represents the average fitness of the population, s1 represents the saturation coefficient, and p c represents the initial crossover probability, and p c =0.5, γ represents the random number seed, and γ∈[0,1], Represents chromosome X m The gene value at position p, Represents chromosome X n The gene value at position p; The mutation probability p in the adaptive mutation b The formula for (i) is as follows: The mutation formula in the adaptive mutation is as follows: Among them, b represents the adaptive adjustment factor of the nonlinear function, s2 represents the saturation coefficient, and p b represents the initial mutation probability, and p b =0.45, represents the nth gene on the mth chromosome, Indicates gene The upper bound of Indicates gene , h represents the number of iterations, H represents the maximum number of iterations, ζ represents the random seed, and ζ∈[-1,1].

7. The SOFC-lithium battery hybrid power supply control method according to claim 1, characterized in that: In step S4, the fuzzy controller is integrated with an active disturbance rejection control algorithm, and the extended state observer in the active disturbance rejection control algorithm is as follows: In the formula, Where e represents the error signal, y represents the output signal, z1 represents the tracking signal of y, z2 represents the differential signal of z1, z3 represents the tracking signal of the total disturbance, δ represents the set value, and δ>0, β1, β2 and β3 represent controllers, α1 and α2 represent adjustable parameters, and 0<α1<1<α2, fal(e,α i ,δ) represents a nonlinear function, i=1,2.

8. A control system using the SOFC-lithium battery hybrid power supply control method according to any one of claims 1 to 7, characterized in that: The invention comprises a SOFC stack, a lithium battery group, a load device, a data acquisition device, a feedforward compensation decoupling controller, an optimization module, a fuzzy controller and a DC-DC conversion circuit. The SOFC stack, the lithium battery group and the load device are respectively connected to the DC-DC conversion circuit. The SOFC stack is connected to the lithium battery group through the data acquisition device, the feedforward compensation decoupling controller, the optimization module and the fuzzy controller in sequence. The load device is connected to the fuzzy controller.

9. The SOFC-lithium battery hybrid power supply control system according to claim 8, characterized in that: The DC-DC conversion circuit includes a constant voltage power supply E, an energy storage inductor L, a capacitor C, diodes D1, D2, switching elements T1, T2 and a resistor R. The positive electrode of the constant voltage power supply E is connected to one end of the resistor R through the switching element T1, the energy storage inductor L and the diode D1 in sequence, and its negative electrode is connected to the other end of the resistor R. One end of the diode D2 is connected to the common end of the switching element T1 and the energy storage inductor L, and the other end is connected to the common end of the constant voltage power supply E and the resistor R. One end of the switching element T2 is connected to the common end of the energy storage inductor L and the diode D1, and the other end is connected to the common end of the constant voltage power supply E and the resistor R. One end of the capacitor C is connected to the common end of the diode D1 and the resistor R, and the other end is connected to the common end of the constant voltage power supply E and the resistor R.

10. The SOFC-lithium battery hybrid power supply control system according to claim 8, characterized in that: The data acquisition device is provided with a voltage detection circuit, a current detection circuit, a temperature sensor, a hydrogen flow sensor and an air flow sensor.

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