Energy Management Method for Multi-Module Fuel Cell Parallel System

Abstract

This invention relates to an energy management method for a multi-module fuel cell parallel system, comprising the following steps: Step 1: Obtaining the total power request and initially calculating the number of packs to be started based on the total power request; Step 2: Identifying the power-efficiency curve of each pack online; Step 3: Allocating the total power based on the power-efficiency curve of the pack; Step 4: Obtaining the lifetime characteristic value of each pack; Step 5: Deploying the operating power of each pack based on the lifetime characteristic value; Step 6: Dynamically correcting the power allocation online. This invention can ensure both optimal overall efficiency of the multi-module fuel cell parallel power generation system and consistent stack lifetime, thereby minimizing the overall aging of the system.

Description

Technical Field

[0001] This invention belongs to the field of fuel cell technology and relates to an energy management method for a multi-module fuel cell parallel system, particularly a method for managing the energy of a multi-module fuel cell parallel system based on the power generation efficiency and lifespan management of the fuel cell system cluster. Background Technology

[0002] Currently, hydrogen fuel cells are widely used in power generation, and the installed capacity is gradually increasing. Most single fuel cell systems are in the hundreds of kilowatts range, while the installed capacity requirements of large power systems are generally in the megawatts or gigawatts range. A single fuel cell system cannot meet such a large power generation demand.

[0003] To meet the demands of large-capacity power systems, fuel cell systems can expand their capacity through parallel operation. However, the parallel connection of multiple fuel cell systems inevitably raises the issue of energy management and scheduling across these systems.

[0004] Current energy allocation and scheduling strategies do not start from the system itself, directly allocating the total energy demand mathematically. Because fuel cell systems are inherently fragile, energy allocation strategies that deviate from the system's design will inevitably have adverse effects on the system.

[0005] a. Although load balancing can ensure the consistency of system lifespan, the operating power of a single system is low under low power demand, the additional system loss is large, and the hydrogen utilization rate is low.

[0006] b. The daisy chain energy distribution method will cause some systems to work at full load all the time, resulting in severe lifespan degradation, while the modules at the end of the daisy chain will basically not work, resulting in poor lifespan consistency among different fuel cell systems.

[0007] c. Although the optimized daisy-chain method can overcome the problem of lifespan degradation caused by some modules in the daisy chain being under high load for a long time, it only reduces the rate of degradation and does not fundamentally solve the problem of lifespan consistency of the entire fuel cell system cluster. Summary of the Invention

[0008] To address the aforementioned technical problems, the purpose of this invention is to provide an energy management method for a multi-module fuel cell parallel system.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] An energy management method for a multi-module fuel cell parallel system includes the following steps:

[0011] Step 1: Obtain the total power request and calculate the number of packs to be powered on based on the total power request;

[0012] Based on the theoretical maximum power provided by each Pack, the minimum number of devices n required to meet the total power request is calculated using the following formula:

[0013]

[0014] Where [] represents the floor function, P total For the total power requirement, p max This represents the theoretical maximum power output of the Pack;

[0015] Step 2: Online identification of the power-efficiency curve for each pack. The online identification process is as follows:

[0016] First, the fuel cell system is modeled using semi-empirical parameters based on the following equation (2);

[0017] V FC =N Cell (E Nernst +V act +V ohmic +V con (2)

[0018] Among them, V FC N represents the output voltage of the fuel cell stack. Cell E represents the number of fuel cell stack plates. Nernst V is the open-circuit voltage. act For activation loss, V ohmic For ohmic loss, V con For concentration loss,

[0019] in

[0020]

[0021] V act =θ1+Tθ2+Tln(C(O2))θ3+Tln(I)θ4

[0022]

[0023] V ohmic =-IR internal

[0024]

[0025] In the formula, T represents temperature, and P represents temperature. H2 For hydrogen pressure, P O2 Let C(O2) be the oxygen pressure, C(O2) be the oxygen concentration, I be the electric current, and R be the oxygen pressure. internalB is the internal resistance of the membrane electrode, and I is the empirical parameter for concentration loss. Max For the through current parameters, η = [θ1, θ2, θ3, θ4, R] internal B, I max [] represents the parameter to be identified;

[0026] Next, the values ​​of the parameters to be identified are obtained by performing particle filtering estimation on the nonlinear system.

[0027] Step 3: Based on the power-efficiency curve of the pack, allocate the total power to make the overall efficiency of the multi-module fuel cell parallel system theoretically optimal;

[0028] Based on the minimum number of start-up units n calculated in step 1, and combined with the stack power efficiency curve identified in step 2, the power distribution and the corresponding comprehensive efficiency of the multi-module fuel cell parallel system are calculated for the number of start-up units n, (n+1) and (n+2). By comparing the comprehensive efficiency under different number of start-up units, the actual number of start-up units N and the corresponding power distribution sequence Pn (n=1, 2...N) are selected.

[0029] Step 4: Obtain the lifetime characteristic value for each Pack;

[0030] The process involves using the individual voltage information of the subsystem's fuel cell stack as a dataset to extract the stack's lifetime characteristic values ​​for identification.

[0031] Step 41: Filter the data, sort it according to the deviation, and remove the 10% of features with the largest deviation;

[0032] Step 42: Recalculate the ratio of the remaining average voltage of individual cells to the full-lifetime reference voltage as the expected value of the pack lifetime characteristic, and use the chi-square test to calculate the goodness of fit of the individual cell voltages. The chi-square test formula is as follows:

[0033]

[0034] Where vi is the single-chip voltage, v full The reference voltage for a single chip at full lifespan, v avg This represents the average voltage of a single chip.

[0035] Step 43: If the test result is greater than 0.05, reject the above expected value of Pack lifetime characteristics and repeat step 41 until the result that satisfies the expected frequency is obtained, which is used as the Pack lifetime prediction characteristic value.

[0036] Step 5: Deploy operating power for each Pack based on lifetime characteristic values;

[0037] After obtaining the Pack lifetime characteristic values, the lifetime prediction characteristic values ​​are sorted by Pack, and the power allocation sequence Pn obtained in step 3 is paired according to the Pack lifetime characteristic values ​​from largest to smallest.

[0038] Step 6: Dynamically adjust power distribution online to find the operating point that maximizes the overall efficiency of the multi-module fuel cell parallel system;

[0039] Based on the allocation sequence obtained in step 5, during the operation of the multi-module fuel cell parallel system, the power allocation of the packs is dynamically adjusted, and the overall system efficiency under the corresponding power allocation is calculated according to equation (9). The overall system efficiency η under different power allocations is then compared. SYS This yields the power allocation sequence that maximizes the overall system efficiency.

[0040]

[0041] Preferably, in the energy management method for a multi-module fuel cell parallel system, step 2 involves particle filtering estimation of the nonlinear system to obtain the values ​​of the parameters to be identified. The calculation process is as follows:

[0042] 1) State prediction Where the superscript i represents the estimated value of the i-th particle, k represents the k-th sampling step, and w represents the noise in the modeling process;

[0043] 2) Calculate the particle weights for i = 1, 2, 3...N:

[0044]

[0045]

[0046] Where q represents the likelihood probability of the current measurement value under the predicted value of the particle state. This likelihood probability is calculated by the probability distribution of the measurement noise v, i.e., p, y represent the measurement value, and h is the measurement matrix obtained by formula (2).

[0047] 3) Calculate the posterior expected value:

[0048]

[0049] 4) Resampling: Randomly generate a∈[0,1] for i=1,2…N.

[0050]

[0051] Where W represents the normalized particle weight, and a is a random number generated between 0 and 1;

[0052] This formula means that the weight W is accumulated starting from the first particle until the accumulated sum exceeds a for the first time. That is, the accumulated sum of the weights of particles from 1 to 1-1 is less than a, but the accumulated sum of particles from 1 to 1 is greater than a.

[0053] definition Go back to step 1);

[0054] The power curves and efficiency curves of each independent fuel cell system can be obtained from the parameters. The power curve can be obtained directly from equation (2), while the efficiency curve, considering the stack efficiency, accessory losses, and DC converter efficiency, is calculated from equation (7):

[0055]

[0056] Among them, V SFG P is the operating voltage of the fuel cell stack. FC For the fuel cell stack power, P aux η represents the power loss of the accessory. SFC For system efficiency, η FC For the stack efficiency, η aux The efficiency η is calculated by adjusting for accessory losses. ele This refers to DC power generation efficiency.

[0057] Preferably, the energy management method for a multi-module fuel cell parallel system includes energy management constraints that ensure the highest power generation efficiency of the entire fuel cell parallel system, consistent with the lifespan of the pack, and the lowest degree of aging.

[0058] By means of the above-described solution, the present invention has at least the following advantages:

[0059] This invention addresses the low power generation efficiency problem in traditional load balancing power distribution strategies. It also optimizes the handling of issues such as poor stack lifetime consistency and severe degradation in some subsystems of daisy-chain-based energy distribution strategies. This invention ensures optimal overall efficiency for multi-module fuel cell parallel power generation systems while maintaining consistent stack lifetime, thus minimizing the overall aging of the system.

[0060] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of the multi-module fuel cell parallel system in this invention;

[0063] Figure 2 This is a flowchart of the management strategy of the present invention;

[0064] Figure 3 This is the power-efficiency curve of the fuel cell system of the present invention;

[0065] Figure 4 This is a flowchart of the fuel cell power-efficiency curve identification process of the present invention;

[0066] Figure 5 This is a flowchart of the lifetime characteristic value identification process of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0068] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0069] Example

[0070] In the implementation of this invention, the smallest power generation module unit (Pack, hereinafter referred to as Pack) can be a single subsystem or a module composed of multiple subsystems. The Pack mentioned below refers to a single fuel cell system, such as... Figure 1 As shown.

[0071] The energy management constraints in this invention are as follows:

[0072] 1. The entire fuel cell parallel system has the highest power generation efficiency;

[0073] 2. Packs have a consistent lifespan and exhibit the lowest degree of aging.

[0074] like Figures 2 to 5 As shown, an energy management method for a multi-module fuel cell parallel system includes the following steps:

[0075] Step 1: Obtain the total power request and calculate the number of packs to be powered on based on the total power request;

[0076] Based on the theoretical maximum power provided by each Pack, the minimum number of devices n required to meet the total power request is calculated using the following formula:

[0077]

[0078] Where [] represents the floor function, P total For the total power requirement, p max This represents the theoretical maximum power output of the Pack;

[0079] Step 2: Online identification of the power-efficiency curve for each pack. The online identification process is as follows:

[0080] First, the fuel cell system is modeled using semi-empirical parameters based on the following equation (2);

[0081] V FC =N Cell (E Nernst +V act +V ohmic +V con (2)

[0082] Among them, V Fc N represents the output voltage of the fuel cell stack. Cell E represents the number of fuel cell stack plates. Nernst V is the open-circuit voltage. act For activation loss, V ohmic For ohmic loss, V con For concentration loss,

[0083] in

[0084]

[0085] V act =θ1+Tθ2+Tln(C(O2))θ3+Tln(I)θ4

[0086]

[0087] V ohmic =-IR internal

[0088]

[0089] In the formula, T represents temperature, and P represents temperature. H2 For hydrogen pressure, P O2 Let C(O2) be the oxygen pressure, C(O2) be the oxygen concentration, I be the electric current, and R be the oxygen pressure. internal B is the internal resistance of the membrane electrode, and I is the empirical parameter for concentration loss. Max For the through current parameters, η = [θ1, θ2, θ3, θ4, R] internal B, I max [] represents the parameter to be identified;

[0090] Next, the values ​​of the parameters to be identified are obtained by performing particle filtering estimation on the nonlinear system. The estimated values ​​are the current, voltage, and temperature of each fuel cell stack. The calculation process is as follows:

[0091] 1) State prediction Where the superscript i represents the estimated value of the i-th particle, k represents the k-th sampling step, and w represents the noise in the modeling process;

[0092] 2) Calculate the particle weights for i = 1, 2, 3…N:

[0093]

[0094]

[0095] Where q represents the likelihood probability of the current measurement value under the predicted particle state condition, and this likelihood probability is calculated from the probability distribution of the measurement noise v, i.e., p v y represents the measurement value, and h is the measurement matrix obtained using formula (2);

[0096] 3) Calculate the posterior expected value:

[0097]

[0098] 4) Resampling: Randomly generate a∈[0,1] for i=1,2…N.

[0099]

[0100] Where W represents the normalized particle weight, and a is a random number generated between 0 and 1;

[0101] This formula means that the weight W is accumulated starting from the first particle until the accumulated sum exceeds a for the first time. That is, the accumulated sum of the weights of particles 1 to 1-1 is less than a, but the accumulated sum of the weights of particles 1 to l is greater than a.

[0102] definition Go back to step 1);

[0103] The power curves and efficiency curves of each independent fuel cell system can be obtained from the parameters. The power curve can be obtained directly from equation (2), while the efficiency curve, considering the stack efficiency, accessory losses, and DC converter efficiency, is calculated from equation (7):

[0104]

[0105] Among them, V SFC P is the operating voltage of the fuel cell stack. FC For the fuel cell stack power, P aux η represents the power loss of the accessory. SFC For system efficiency, η FC For the stack efficiency, η aux The efficiency η is calculated by adjusting for accessory losses. ele DC power generation efficiency;

[0106] Step 3: Based on the power-efficiency curve of the pack, allocate the total power to make the overall efficiency of the multi-module fuel cell parallel system theoretically optimal;

[0107] Based on the minimum number of start-up units n calculated in step 1, and combined with the stack power efficiency curve identified in step 2, the power allocation and the corresponding comprehensive efficiency of the multi-module fuel cell parallel system are calculated for the number of start-up units n, (n+1) and (n+2). By comparing the comprehensive efficiency under different number of start-up units, the actual number of start-up units N and the corresponding power allocation sequence Pn(n1, 2...N) are selected.

[0108] Step 4: Obtain the lifetime characteristic value for each Pack;

[0109] The process involves using the individual voltage information of the subsystem's fuel cell stack as a dataset to extract the stack's lifetime characteristic values ​​for identification.

[0110] Step 41: Filter the data, sort it according to the deviation, and remove the 10% of features with the largest deviation;

[0111] Step 42: Recalculate the ratio of the remaining average voltage of individual cells to the full-lifetime reference voltage as the expected value of the pack lifetime characteristic, and use the chi-square test to calculate the goodness of fit of the individual cell voltages. The chi-square test formula is as follows:

[0112]

[0113] Where vi is the single-chip voltage, v full The reference voltage for a single chip at full lifespan, v avg This represents the average voltage of a single chip.

[0114] Step 43: If the test result is greater than 0.05, reject the above expected value of Pack lifetime characteristics and repeat step 41 until the result that satisfies the expected frequency is obtained, which is used as the Pack lifetime prediction characteristic value.

[0115] Step 5: Deploy operating power for each Pack based on lifetime characteristic values;

[0116] After obtaining the Pack lifetime characteristic values, the lifetime prediction characteristic values ​​are sorted by Pack, and the power allocation sequence Pn obtained in step 3 is paired according to the Pack lifetime characteristic values ​​from largest to smallest.

[0117] Step 6: Dynamically adjust power distribution online to find the operating point that maximizes the overall efficiency of the multi-module fuel cell parallel system;

[0118] Based on the allocation sequence obtained in step 5, during the operation of the multi-module fuel cell parallel system, the power allocation of the packs is dynamically adjusted, and the overall system efficiency under the corresponding power allocation is calculated according to equation (9). The overall system efficiency η under different power allocations is then compared. SYS This yields the power allocation sequence that maximizes the overall system efficiency.

[0119]

[0120] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0121] In the description of this application, it should be noted that the terms "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing this application and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0122] Furthermore, terms such as "horizontal" and "vertical" do not imply that components must be absolutely horizontal or vertical, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0123] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An energy management method for a multi-module fuel cell parallel system, Its features are, Includes the following steps: Step 1: Obtain the total power request and calculate the number of packs to be powered on based on the total power request; Based on the theoretical maximum power provided by each Pack, the minimum number of devices n required to meet the total power request is calculated using the following formula: （1） Where [ ] represents the floor function, P total For the total power requirement, p max This represents the theoretical maximum power output of the Pack; Step 2: Online identification of the power-efficiency curve for each pack. The online identification process is as follows: First, the fuel cell system is modeled using semi-empirical parameters based on the following equation (2); （2） Among them, V FC N represents the output voltage of the fuel cell stack. Cell E represents the number of fuel cell stack plates. Nernst V is the open-circuit voltage. act For activation loss, V ohmic For ohmic loss, V con For concentration loss, in In the formula, T represents temperature, and P represents temperature. H2 For hydrogen pressure, P O2 Let C(O2) be the oxygen pressure, C(O2) be the oxygen concentration, I be the electric current, and R be the oxygen pressure. internal B is the internal resistance of the membrane electrode, and I is the empirical parameter for concentration loss. Max For the through current parameters, The parameters to be identified; Next, the values ​​of the parameters to be identified are obtained by performing particle filtering estimation on the nonlinear system; Step 3: Allocate the total power based on the power-efficiency curve of the Pack; Based on the minimum number of start-up units n calculated in step 1, and combined with the stack power-efficiency curve identified in step 2, the power distribution and the corresponding comprehensive efficiency of the multi-module fuel cell parallel system are calculated for the number of start-up units n, (n+1) and (n+2). By comparing the comprehensive efficiency under different number of start-up units, the actual number of start-up units N and the corresponding power distribution sequence Pn (n=1, 2...N) are selected. Step 4: Obtain the lifetime characteristic value for each Pack; The process involves using the individual voltage information of the subsystem's fuel cell stack as a dataset to extract the stack's lifetime characteristic values ​​for identification. Step 41: Filter the data, sort it according to the deviation, and remove the 10% of features with the largest deviation; Step 42: Recalculate the ratio of the remaining average voltage of individual cells to the full-lifetime reference voltage as the expected value of the pack lifetime characteristic, and use the chi-square test to calculate the goodness of fit of the individual cell voltages. The chi-square test formula is as follows: （8） Where vi is the single-chip voltage, v full The reference voltage for a single chip at full lifespan, v avg This represents the average voltage of a single chip. Step 43: If the test result is greater than 0.05, reject the above expected value of Pack lifetime characteristic and repeat step 41 until the result that satisfies the expected frequency is obtained as the Pack lifetime characteristic value. Step 5: Deploy operating power for each Pack based on lifetime characteristic values; After obtaining the Pack lifetime characteristic value, the lifetime characteristic value is sorted by Pack, and the power allocation sequence Pn obtained in step 3 is paired according to the Pack lifetime characteristic value from largest to smallest. Step 6: Dynamically adjust power distribution online to find the operating point that maximizes the overall efficiency of the multi-module fuel cell parallel system; Based on the allocation sequence obtained in step 5, during the operation of the multi-module fuel cell parallel system, the power allocation of the packs is dynamically adjusted, and the overall system efficiency under the corresponding power allocation is calculated according to equation (9). The overall system efficiency under different power allocations is then compared. This yields the power allocation sequence that maximizes the overall system efficiency. （9）。 2. The energy management method for a multi-module fuel cell parallel system according to claim 1, characterized in that: In step 2, particle filter estimation is performed on the nonlinear system to obtain the values ​​of the parameters to be identified. The calculation process is as follows: 1) State prediction Where, the superscript i represents the estimated value of the i-th particle, k represents the k-th sampling step, and w represents the noise in the modeling process; 2) Calculate particle weights, for calculate: （3） （4） Where q represents the likelihood probability of the current measurement value under the predicted value of the particle state, p and y represent the measurement values, and h is the measurement matrix obtained using equation (2). 3) Calculate the posterior expected value: （5） 4) Resampling, for Randomly generated （6） Where W represents the normalized particle weight, and a is a random number generated between 0 and 1; definition Go back to step 1); The power curves and efficiency curves of each independent fuel cell system can be obtained from the parameters. The power curve can be obtained directly from equation (2), while the efficiency curve, considering the stack efficiency, accessory losses, and DC converter efficiency, is calculated from equation (7): （7） in, This is the operating voltage of the fuel cell stack. For fuel cell power, For the power loss of the accessory, For system efficiency, For fuel cell stack efficiency, The efficiency is calculated based on the loss of accessories. This refers to DC power generation efficiency.

3. The energy management method for a multi-module fuel cell parallel system according to claim 1 or 2, characterized in that: The constraints of energy management include maximizing the power generation efficiency of the entire fuel cell parallel system, matching the lifespan of the pack, and minimizing aging.

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