Multi-stack fuel cell output power distribution method and system based on water activity correction

Abstract

The present invention relates to a method and system for distributing the output power of multiple fuel cells based on water activity correction, which belongs to the technical field of power distribution of multiple fuel cells. The method first collects the relative temperature of each sub-stack in the multiple fuel cells to obtain the real-time water activity, then calculates the real-time health factor of each sub-stack according to the real-time water activity, and finally distributes the power to be distributed according to the real-time health factor of each sub-stack to determine the output power of each sub-stack. Thus, the corresponding power distribution is performed on each sub-stack through the real-time health factor calculated based on the real-time water activity, and the latter half of the fuel cell polarization curve can be regulated, which is more targeted, does not generate oscillation, and has good stability.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-stack fuel cell power distribution, and in particular to a multi-stack fuel cell output power distribution method and system based on water activity correction. Background Art

[0002] As the world's environmental problems become increasingly serious, countries around the world are trying to reduce the emission of harmful gases in the current energy use and explore new clean energy. The international energy transformation is moving along the path from high carbon to low carbon. Hydrogen is currently recognized as the most ideal energy carrier and clean energy provider, and is known as the "ultimate energy of the 21st century." At present, the common use of hydrogen is to generate electricity through proton exchange membrane fuel cells, which release electrical energy through the chemical reaction of hydrogen and oxygen. It is not limited by the Carnot cycle and has the advantages of high energy conversion rate, high energy density, fast startup speed, low noise and zero emissions.

[0003] However, the power output of the fuel cell is smooth, the power level is not high, and the output voltage drops rapidly under high current conditions, resulting in reduced performance and reduced lifespan. Therefore, many researchers are currently focusing on coordinating multiple single-stack fuel cells and building multi-stack fuel cells to solve the problem of high power demand and durability. The prior art discloses a multi-stack distributed control method based on fuel cell health correction, which collects voltage and current signals on the demand side, evaluates the real-time operating performance of the fuel cell by obtaining the voltage, current and power at the output end of the fuel cell, and quantifies the health of each fuel cell. According to the calculation results of the health of the fuel cell, the real-time self-tuning factor related to the current performance state of each fuel cell is calculated in combination with the circuit and current characteristics of the DC power supply network. Finally, under the change of the real-time self-tuning factor, the voltage outer loop and the current inner loop are quickly corrected to complete the adaptive adjustment of the fuel cell output power, and the distributed control between the multi-stack fuel cells is realized, but it is adjusted in the full range. Due to the hysteresis of the stack adjustment, it may cause oscillation in the adjustment process and system instability. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for distributing the output power of multiple fuel cells based on water activity correction. The real-time health factor of each sub-stack is calculated by real-time water activity, and corresponding power distribution is performed on each sub-stack based on the real-time health factor, so as to regulate the second half of the fuel cell polarization curve, which is more targeted, will not produce oscillation, and has good stability.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for distributing output power of multiple fuel cell stacks based on water activity correction, the method comprising:

[0007] Collect the relative humidity of each sub-stack in multiple fuel cells to obtain real-time water activity;

[0008] For each of the sub-stacks, calculating a real-time health factor of the sub-stack according to the real-time water activity;

[0009] The power to be allocated is allocated according to the real-time health factor of each sub-stack, and the output power of each sub-stack is determined.

[0010] A multi-stack fuel cell output power distribution system based on water activity correction, the distribution system comprising:

[0011] An information collection module is used to collect the relative humidity of each sub-stack in multiple fuel cells to obtain real-time water activity;

[0012] A health factor calculation module, configured to calculate the real-time health factor of each sub-stack according to the real-time water activity;

[0013] The allocation module is used to allocate the power to be allocated according to the real-time health factor of each sub-stack and determine the output power of each sub-stack.

[0014] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0015] The present invention is used to provide a method and system for distributing the output power of multiple fuel cells based on water activity correction. The method first collects the relative temperature of each sub-stack in the multiple fuel cells to obtain real-time water activity, then calculates the real-time health factor of each sub-stack according to the real-time water activity, and finally distributes the power to be distributed according to the real-time health factor of each sub-stack to determine the output power of each sub-stack. Thus, the corresponding power distribution is performed on each sub-stack through the real-time health factor calculated based on the real-time water activity, and the latter half of the fuel cell polarization curve can be regulated, which is more targeted, does not generate oscillation, and has good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A method flow chart of the allocation method provided in Example 1 of the present invention;

[0018] Figure 2A control principle diagram of the distribution method provided in Example 1 of the present invention;

[0019] Figure 3 A schematic diagram of an application scenario of the allocation method provided in Embodiment 1 of the present invention;

[0020] Figure 4 A control principle diagram of power distribution provided in Embodiment 1 of the present invention;

[0021] Figure 5 This is a system block diagram of the distribution system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The purpose of the present invention is to provide a method and system for distributing the output power of multiple fuel cells based on water activity correction. The real-time health factor of each sub-stack is calculated by real-time water activity, and corresponding power distribution is performed on each sub-stack based on the real-time health factor, so as to regulate the second half of the fuel cell polarization curve (i.e., the part where the output voltage drops faster), which is more targeted, will not produce oscillation, and has good stability.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Embodiment 1:

[0026] On the one hand, when the power request of hydrogen and oxygen fuel cells increases, the temperature of the stack will increase, the water activity will decrease, and the water content in the membrane will decrease, which will in turn lead to a decrease in membrane conductivity, an increase in ohmic loss, and thus a decrease in output voltage and a decrease in stack performance. On the other hand, the decrease in water activity is related to the concentration of the product. As the reaction proceeds, the reactants in the catalyst layer are consumed, the concentration of the reactants decreases, while the concentration of the reactants in the flow channel remains unchanged, and a concentration difference is formed inside and outside the electrode. Under the action of the concentration gradient, the reactants in the external area diffuse to the catalyst layer. Similarly, the products accumulate in the catalyst layer, the concentration increases, and diffuse outward from the catalyst layer. Due to the progress of the electrochemical reaction, the actual concentration of reactants and products in the catalyst layer is different from that in the flow channel. According to the Nernst equation, the decrease in the concentration of reactants and the increase in the concentration of products will cause the reversible voltage of the battery to decrease, resulting in Nernst losses, which are part of the concentration loss. The other part of the concentration loss is activation loss. The concentration of reactants in the catalyst layer decreases, the concentration of products increases, and the activation overpotential increases. In summary, both ohmic loss and concentration loss are related to water activity. According to the current water activity, there will be a better output voltage, or when the temperature is too high (water activity is too low), the output voltage drops rapidly. Therefore, the health factor is derived according to the water activity to adjust the output power so that the water activity is in a better range and the performance of the fuel cell stack is maintained. The water activity can be used to establish a connection with the output voltage drop of the fuel cell stack, and the health factor can be derived as the basis for power distribution between multiple fuel cell stacks, so as to maintain good performance of the fuel cell stack and realize the distribution management and control of the output power of multiple fuel cell stacks.

[0027] Based on this, this embodiment is used to provide a method for distributing the output power of multiple fuel cells based on water activity correction, such as Figure 1 As shown, the allocation method includes:

[0028] S1: Collect the relative humidity of each sub-stack in multiple fuel cells to obtain real-time water activity;

[0029] Specifically, a humidity sensor can be used to collect the relative temperature of each sub-stack, and the relative humidity is the real-time water activity.

[0030] The definition formula of water activity (essentially relative humidity) is as follows:

[0031]

[0032] Where a is water activity; is the current actual water vapor partial pressure; P sat is the saturated water vapor partial pressure at the current temperature.

[0033] S2: For each of the sub-stacks, calculating a real-time health factor of the sub-stack according to the real-time water activity;

[0034] In this embodiment, the calculation formula for the real-time health factor used to measure the performance of the fuel cell is as follows:

[0035]

[0036] Among them, H is the real-time health factor; a is the real-time water activity; a acute is the preset value of water activity; Δa is the difference between the real-time water activity a and the preset value of water activity a acute The difference is equal to aa acute .

[0037] The preset value of water activity in this embodiment is the water activity at the water activity extreme point determined based on the offline test. The definition of the water activity extreme point is: when the temperature rise interval is N°C, the output voltage drop between the latter point and the current point is at least a preset multiple of the output voltage drop between the current point and the previous point. At this time, the current point is the water activity extreme point.

[0038] Furthermore, in this embodiment, the off-line test process of the water activity acute point of the fuel cell includes the following steps:

[0039] (1) Performing a voltage-current test on a fuel cell with good performance, obtaining the output voltage and water activity of the fuel cell at multiple different temperatures, and constructing a first output voltage set and a first water activity set; the interval between adjacent temperatures is N degrees Celsius.

[0040] (2) The first output voltage set and the first water activity set are interpolated respectively by using the Lagrange interpolation method to obtain a second output voltage set and a second water activity set, wherein the temperatures corresponding to adjacent output voltages of the second output voltage set are spaced by M degrees Celsius, and the temperatures corresponding to adjacent water activities of the second water activity set are spaced by M degrees Celsius, where M is less than N.

[0041] In this embodiment, the first output voltage set and the second output voltage set are both sets obtained by sorting all output voltages in order of temperature from small to large, except that the adjacent output voltages of the first output voltage set are spaced N degrees Celsius, while the adjacent output voltages of the second output voltage set are spaced M degrees Celsius; the first water activity set and the second water activity set are both sets obtained by sorting all water activities in order of temperature from small to large, except that the adjacent water activities of the first water activity set are spaced N degrees Celsius, while the adjacent water activities of the second water activity set are spaced M degrees Celsius.

[0042] For the fuel cell system, the Lagrange interpolation method is expressed as: y(k) = f(x(k), x S ,y s), where y(k) is the interpolation function value, corresponding to the output voltage and water activity of the fuel cell at intervals of M degrees Celsius; x(k) is the data to be interpolated, corresponding to the points at intervals of M degrees Celsius, s =[x0, x1, x2, ..., x i-1 , x i , x i+1 ,...x n ] is the node variable data, corresponding to the temperature data with interval N degrees Celsius; s =[y0,y1,y2,...,y i-1 ,y i ,y i+1 , ..., y n ] are node function values, corresponding to the output voltage and water activity data of the fuel cell at intervals of N degrees Celsius.

[0043] Basis function l of Lagrange interpolation method i The expression of (x(k)) is:

[0044]

[0045] Then the expression of the interpolation polynomial y(k) is:

[0046]

[0047] Based on the above interpolation polynomial, the output voltage and water activity can be interpolated.

[0048] (3) For each first output voltage of the second output voltage set, determine a second temperature and a third temperature that are N degrees Celsius apart from the first temperature based on the first temperature corresponding to the first output voltage, calculate a first voltage drop between the first output voltage and the second output voltage corresponding to the second temperature, and calculate a second voltage drop between the first output voltage and the third output voltage corresponding to the third temperature; determine whether a ratio of the first voltage drop to the second voltage drop is greater than or equal to a preset ratio; if so, the first temperature is a water activity peak point, and the water activity corresponding to the first temperature is a preset water activity value.

[0049] The second temperature is a temperature lower than the first temperature and spaced N degrees Celsius from the first temperature, and the third temperature is a temperature higher than the first temperature and spaced N degrees Celsius from the first temperature.

[0050] In this embodiment, N may be 1 degree Celsius, M may be 0.1 degree Celsius, and the preset ratio may be 1.8, that is, the collected output voltage and water activity are spaced 1 degree Celsius apart, and the output voltage and water activity after interpolation are spaced 0.1°C apart. By subtracting the output voltage signals spaced 1°C apart before and after, multiple voltage drops can be formed. The process of traversing and retrieving the points of intense water activity may include: subtracting the output voltage data after Lagrange interpolation every 10 points (i.e., every 1°C) to obtain the voltage drop, and each voltage drop is divided by the previous voltage drop to obtain a ratio that can characterize the speed of the voltage drop. If the ratio is greater than or equal to 1.8, then the point is a point of intense water activity, and the water activity corresponding to the point is the preset value of water activity. For example, for the voltage drop between the 10th point and the 20th point, its previous voltage drop refers to the voltage drop between the 10th point and the 0th point. If the ratio of these two voltage drops is greater than or equal to 1.8, the 10th point is the water activity acute point, and the water activity corresponding to the 10th point is the water activity preset value. The traversal search process of this embodiment is performed in order from low to high temperature. When the water activity acute point is determined, the traversal search is stopped, and each sub-stack will perform the above-mentioned offline test process accordingly, and then the unique water activity acute point of each sub-stack can be determined through the above steps.

[0051] Through the above operation, the problem of inaccurate detection caused by too small interval of collected signals can be avoided, and the collection points can be reduced, reducing the workload of offline testing. Compared with the existing technology that requires offline testing of a large number of data points to identify the performance characterization parameters of the fuel cell, so as to obtain the ideal voltage-current relationship as the basis for good performance points, and the testing work is complicated, the water activity acute point of this embodiment is offline testing, with fewer test points and simple work. In actual operation, the water activity can be calculated by obtaining temperature and humidity signals through temperature and humidity sensors, which is easy to implement.

[0052] The above code for traversing and retrieving the water activity extreme point is as follows:

[0053]

[0054]

[0055]

[0056] This embodiment quantifies the real-time health factor characterizing the real-time operating performance of each sub-stack based on the collected real-time water activity of each sub-stack and the water activity severity point of each sub-stack determined by offline testing.

[0057] S3: Allocate the power to be allocated according to the real-time health factor of each of the sub-stacks, and determine the output power of each of the sub-stacks.

[0058] Preferably, Figure 2 As shown, S3 may include:

[0059] (1) For each sub-stack, determine whether the real-time health factor is 1; if not, use the sub-stack as the first sub-stack; if so, use the sub-stack as the second sub-stack;

[0060] (2) For each first sub-stack, the real-time health factor of the first sub-stack is used as input, an adaptive fuzzy tuning PID control algorithm is used to drive a PWM signal generator to generate a PWM signal, and a DC-DC converter is adjusted based on the PWM signal to determine the output power of the first sub-stack;

[0061] like Figure 3 As shown, each sub-stack of multiple fuel cells is connected to the voltage bus in parallel through a DC-DC converter. In this embodiment, the real-time health factor is calculated by the relative humidity collected at the output end of the sub-stack, and the error and error change rate are obtained by subtracting the relative humidity from the reference value 1. The proportional coefficient k is obtained by the adaptive fuzzy setting PID control algorithm. p , integral action coefficient k i , differential action coefficient k d Three parameters are used to control the PWM signal generator to generate a PWM signal, and the DC-DC converter is adjusted by the PWM signal to adjust and control the output power of each sub-stack.

[0062] Specifically, Figure 4 As shown, the adaptive fuzzy tuning PID control algorithm includes a fuzzy controller and a PID controller. Then, taking the real-time health factor of the first sub-stack as input, using the adaptive fuzzy tuning PID control algorithm to drive the PWM signal generator to generate a PWM signal may include:

[0063] 1) Calculate the difference between the real-time health factor of the first sub-stack and 1 to obtain the error and error change rate;

[0064] The error is the difference between the health factor and 1. The error change rate is the difference between the error at the current moment and the error at the previous moment divided by the time interval between the current moment and the previous moment, that is, the error change rate is equal to [e(τ)-e(τ-1)] / Δτ, where e(τ) is the error at moment τ; e(τ-1) is the error at moment τ-1; Δτ is the time interval between moment τ and moment τ-1.

[0065] 2) Taking the error and the error change rate as input, the fuzzy controller is used to calculate the control parameters of the PID controller, which include the proportional coefficient, the integral action coefficient and the differential action coefficient;

[0066] In this embodiment, the variation ranges of the error e and the error variation rate ec are defined as the domain on the fuzzy set, the domain of e is {-1, 0, 1}, and the domain of ec is {-1, 0, 1}.

[0067] In order to simplify the testing work and make it easier to implement, the fuzzy subset used by the fuzzy controller is {NB, ZE, PB}, where NB represents negative (<0), ZE represents zero (=0), and PB represents positive (>0), that is, the elements in the subset represent negative, zero, and positive, respectively.

[0068] The membership function of each fuzzy subset can be selected from Sigmoid function and triangular function.

[0069] Based on the above fuzzy subsets, there are 9 control rules for fuzzy control, that is, the control rules used by the fuzzy controller include:

[0070] If the error is NB and the error change rate is NB, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is ZE;

[0071] If the error is NB and the error change rate is ZE, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is NB;

[0072] If the error is NB and the error change rate is PB, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is ZE;

[0073] If the error is ZE and the error change rate is NB, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is ZE;

[0074] If the error is ZE and the error change rate is ZE, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is ZE;

[0075] If the error is ZE and the error change rate is PB, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is ZE;

[0076] If the error is PB and the error change rate is NB, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is PB;

[0077] If the error is PB and the error change rate is ZE, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is PB;

[0078] If the error is PB and the error change rate is PB, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is PB.

[0079] The above 9 fuzzy rules are expressed as follows:

[0080] If(e is NB)and(ec is NB),then(k p is PB)(k i is NB)(k d is ZE);

[0081] If(e is NB)and(ec is ZE),then(k p is PB)(k i is NB)(k d is NB);

[0082] If(e is NB)and(ec is PB),then(k p is ZE)(k i is ZE)(k d is ZE);

[0083] If(e is ZE)and(ec is NB),then(k p is PB)(k i is NB)(k d is ZE);

[0084] If(e is ZE)and(ec is ZE),then(k p is ZE)(k i is ZE)(k d is ZE);

[0085] If(e is ZE)and(ec is PB),then(k p is NB)(k i is PB)(k d is ZE);

[0086] If(e is PB)and(ec is NB),then(k p is ZE)(k i is ZE)(k d is PB);

[0087] If(e is PB)and(ec is ZE),then(k p is NB)(k i is PB)(k d is PB);

[0088] If(e is PB)and(ec is PB),then(k p is NB)(k i is PB)(k d is PB);

[0089] Based on the above domain, fuzzy subsets, membership functions and fuzzy rules, with error and error change rate as input, using a fuzzy controller to calculate the control parameters of the PID controller may include: fuzzifying the error and the error change rate through a membership function to obtain a fuzzified result, calculating the membership of the control parameter according to the fuzzified result and a pre-established fuzzy rule, and then defuzzifying the membership of the control parameter to obtain a corrected value of the control parameter. The defuzzification may be performed using the area center method, the area bisection method, or the maximum membership method (maximum value, minimum value or average value). The above process may be completed using a fuzzy inference engine, that is, the proportional coefficient k is obtained through the fuzzy inference engine. p , integral action coefficient k i , differential action coefficient k d Correction value Δk p (e(k),ec(k)),Δk i (e(k), ec(k)) and Δk d (e(k), ec(k)), and substitute it into the following formula to get the optimal output k of the control parameter p (k), k i (k), k d (k)

[0090] k p (k) = k p (0)+Δk p (e(k),ec(k));

[0091] k i (k) = k i (0)+Δk i (e(k),ec(k));

[0092] k d (k) = k d (0)+Δk d (e(k),ec(k));

[0093] Among them, k p (0), k i (0), k d (0) respectively k p , k i , k d The initial value of .

[0094] 3) Based on the control parameters, a PID controller is used to drive a PWM signal generator to generate a PWM signal.

[0095] The pulse width modulation (PWM) method (such as SPWM, SVPWM, etc.) is to obtain the desired pulse output by controlling the on and off of the switch tube (i.e., on and off), and controlling the on and off of the switch tube requires designing a corresponding control strategy. The PID controller used in this embodiment is used to generate a control strategy, and the PID controller outputs a modulation wave to control the on and off of the switch tube, thereby forming a PWM signal, and the high and low levels of the PWM signal are used to control the on and off of the MOSFET tube in the DC-DC controller to control the output power of the first sub-stack, which has a constant voltage mode and a constant current mode, and is quickly controlled by a voltage outer loop and a current inner loop. In this embodiment, a PWM signal can be generated by commercial software such as matlab, and the fuzzy control part can be quickly built by the Fuzzy toolbox.

[0096] This embodiment uses an adaptive fuzzy tuning PID control algorithm to control the DC-DC converter, which can avoid repeated debugging work, and the control parameters can be continuously adjusted as the characteristics of the controlled system and the environment change, and an adaptive control law can be formed under the premise of ensuring system stability.

[0097] (3) Calculate the difference between the power to be allocated and the sum of the output powers of all the first sub-stacks to obtain the remaining power; and evenly distribute the remaining power to obtain the output power of each second sub-stack.

[0098] After controlling each first sub-stack, the output voltage and output current of each first sub-stack can be obtained by using multiple current sensors and voltage sensors installed on the voltage bus. The product of the output voltage and the output current can be calculated to obtain the output power of each first sub-stack. The sum of the output powers of all first sub-stacks is calculated, and then the remaining power is subtracted from the power to be distributed. The output power of each second sub-stack can be obtained by evenly distributing the remaining power. At this time, the output power of each second sub-stack is still adjusted by the PWM signal generator and the DC-DC converter. Specifically, the PWM signal generator generates a corresponding PWM signal based on the output power of the second sub-stack, and the DC-DC converter is controlled by the PWM signal to complete the output power adjustment process of the second sub-stack.

[0099] This embodiment starts to allocate when receiving the power to be allocated, calculates the health factor in real time, and allocates power in real time. After the allocation is completed, the health factor is calculated again at the next allocation moment, and the power allocation is performed in real time. This allocation process continues. The power allocation strategy proposed in this embodiment, according to the size of the health factor, for the sub-stack that deviates from the set health factor of 1, its health factor is compared with the reference value 1 to obtain the error and error change rate, and k is obtained through the adaptive fuzzy tuning PID algorithm. p , k i , k d The optimized value is used to regulate the PWM signal generator to obtain the PWM signal, and then the DC-DC converter is adjusted by the PWM signal. The remaining sub-stacks are averagely distributed to achieve adaptive distribution of the fuel cell output power, so that each sub-stack works in the linear segment and maintains good performance of each sub-stack.

[0100] In order to solve the problem of multi-stack fuel cell performance and durability, this embodiment proposes a multi-stack fuel cell output power distribution method based on fuel cell water activity correction. This method monitors the water activity of the fuel cell system, calculates the real-time health factor, and uses this as the basis for correction to adjust the power of the part where the fuel cell performance decreases faster (the second half of the polarization curve), so as to keep the fuel cell stack working in the linear section to maintain good performance of the stack and increase the system life. Considering the changes in the working environment and state of the fuel cell, the PID parameter online optimization tuning algorithm based on adaptive fuzzy tuning is used to adjust k p , k i , k d The three parameters are used to adaptively adjust the PWM signal transmitted to the DC-DC controller to realize the output power distribution of multiple fuel cell stacks. Through power distribution, each sub-stack is restored to the set health factor of 1, so that each sub-stack can operate stably under appropriate conditions and extend its service life.

[0101] In this embodiment, the health factor is used to determine the degree to which the stack deviates from the water activity drastic point (i.e., the critical point where the output voltage drops rapidly), and then the health factor is used as the input of the fuzzy allocation algorithm to correct the three control parameters, which are used to regulate the PWM signal to complete the allocation process, mainly for the second half of the polarization curve where the fuel cell performance drops rapidly, which is conducive to adjusting the performance under the premise of maintaining the stable operation of the stack and avoiding adjustment oscillation. The reason for not oscillating is that the allocation algorithm will respond only when the critical point is exceeded, and it will maintain stable operation after adjustment. Compared with the allocation strategy of the optimal allocation class, they are adjusted according to the optimal path under a certain working condition. Due to the hysteresis of the fuel cell output power response, the working point may be changed due to the effect of the adjustment at the previous time point (if the starting working points of two adjustments are the working points after the adjustment of each other or are close to each other), which will then trigger another adjustment, and the cycle will repeat.

[0102] Embodiment 2:

[0103] This embodiment is used to provide a multi-stack fuel cell output power distribution system based on water activity correction, such as Figure 5 As shown, the distribution system includes:

[0104] The information collection module M1 is used to collect the relative humidity of each sub-stack in the multiple fuel cells to obtain the real-time water activity;

[0105] A health factor calculation module M2 is used to calculate the real-time health factor of each sub-stack according to the real-time water activity;

[0106] The allocation module M3 is used to allocate the power to be allocated according to the real-time health factor of each of the sub-stacks, and determine the output power of each of the sub-stacks.

[0107] This embodiment collects real-time water activity to evaluate the performance of the current fuel cell working stage, and derives the health factor as the basis for power control; combined with the adaptive fuzzy tuning PID algorithm, with the health factor deviation as input, k p , k i , k d In order to output the regulation PWM signal and then control the DC-DC converter to achieve adaptive regulation of the output power, the control is mainly carried out on the second half of the polarization curve where the performance of the fuel cell decreases faster. This is beneficial to adjust the performance while maintaining the stable operation of the fuel cell stack and avoid regulation oscillation.

[0108] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0109] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for distributing output power of multiple fuel cells based on water activity correction, characterized in that: The allocation method includes: Collect the relative humidity of each sub-stack in multiple fuel cells to obtain real-time water activity; For each of the sub-stacks, calculating a real-time health factor of the sub-stack according to the real-time water activity; Allocate the power to be allocated according to the real-time health factor of each of the sub-stacks to determine the output power of each of the sub-stacks; The calculation formula of the real-time health factor is: Among them, H is the real-time health factor; a is the real-time water activity; a acute is the preset value of water activity; Δa is the difference between the real-time water activity and the preset value of water activity; The preset water activity value is the water activity at the water activity peak point determined based on the off-line test; The offline test includes: Performing a voltage-current test on the fuel cell, obtaining the output voltage and water activity of the fuel cell at multiple temperatures, and constructing a first output voltage set and a first water activity set; the interval between adjacent temperatures is N degrees Celsius; The first output voltage set and the first water activity set are interpolated respectively by using Lagrange interpolation method to obtain a second output voltage set and a second water activity set; the interval between the temperatures corresponding to the adjacent output voltages of the second output voltage set is M degrees Celsius, and the interval between the temperatures corresponding to the adjacent water activities of the second water activity set is M degrees Celsius; M is less than N; For each first output voltage of the second output voltage set, determine a second temperature and a third temperature N degrees Celsius apart from the first temperature according to a first temperature corresponding to the first output voltage, calculate a first voltage drop between the first output voltage and the second output voltage corresponding to the second temperature, and a second voltage drop between the first output voltage and the third output voltage corresponding to the third temperature; determine whether a ratio of the first voltage drop to the second voltage drop is greater than or equal to a preset ratio; if so, the first temperature is a water activity peak point; and the water activity corresponding to the first temperature is the preset water activity value; The allocating the power to be allocated according to the real-time health factor of each of the sub-stacks and determining the output power of each of the sub-stacks specifically includes: For each of the sub-stacks, determining whether the real-time health factor is 1; if not, using the sub-stack as the first sub-stack; if yes, using the sub-stack as the second sub-stack; For each of the first sub-stacks, taking the real-time health factor of the first sub-stack as input, using an adaptive fuzzy tuning PID control algorithm to drive a PWM signal generator to generate a PWM signal, and adjusting a DC-DC converter based on the PWM signal to determine the output power of the first sub-stack; The difference between the power to be distributed and the sum of the output powers of all the first sub-stacks is calculated to obtain the remaining power; and the remaining power is evenly distributed to obtain the output power of each of the second sub-stacks.

2. The distribution method according to claim 1, characterized in that: The adaptive fuzzy tuning PID control algorithm includes a fuzzy controller and a PID controller, and the method of using the real-time health factor of the first sub-stack as input and using the adaptive fuzzy tuning PID control algorithm to drive the PWM signal generator to generate a PWM signal specifically includes: Calculate the difference between the real-time health factor of the first sub-stack and 1 to obtain an error and an error change rate; The error and the error change rate are used as inputs, and the control parameters of the PID controller are calculated by the fuzzy controller; the control parameters include a proportional coefficient, an integral action coefficient, and a differential action coefficient; Based on the control parameters, the PID controller is used to drive a PWM signal generator to generate a PWM signal.

3. The distribution method according to claim 2, characterized in that: The fuzzy subset used by the fuzzy controller is {NB, ZE, PB}, NB represents negative, ZE represents zero, and PB represents positive; The control rules used by the fuzzy controller include: If the error is NB and the error change rate is NB, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is ZE; If the error is NB and the error change rate is ZE, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is NB; If the error is NB and the error change rate is PB, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is ZE; If the error is ZE and the error change rate is NB, then the proportional coefficient is PB, the integral action coefficient is NB, and the differential action coefficient is ZE; If the error is ZE and the error change rate is ZE, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is ZE; If the error is ZE and the error change rate is PB, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is ZE; If the error is PB and the error change rate is NB, then the proportional coefficient is ZE, the integral action coefficient is ZE, and the differential action coefficient is PB; If the error is PB and the error change rate is ZE, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is PB; If the error is PB and the error change rate is PB, then the proportional coefficient is NB, the integral action coefficient is PB, and the differential action coefficient is PB.

4. A distribution system for implementing the multi-stack fuel cell output power distribution method based on water activity correction as described in claim 1, characterized in that: The dispensing system comprises: An information collection module is used to collect the relative humidity of each sub-stack in multiple fuel cells to obtain real-time water activity; A health factor calculation module, configured to calculate the real-time health factor of each sub-stack according to the real-time water activity; The allocation module is used to allocate the power to be allocated according to the real-time health factor of each sub-stack and determine the output power of each sub-stack.

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