A hybrid system energy management method considering platinum degradation

Abstract

The present application belongs to the field of fuel cell system, and particularly relates to a hybrid system energy management method considering platinum degradation, which takes the SOC of lithium battery as the state variable of the system, takes the output power of the fuel cell as the control variable of the system, takes the minimum fuel consumption and life attenuation as the optimization target, constructs an energy management optimization model, solves the optimization problem by using the minimum value principle, the loss function includes two parts of hydrogen consumption and platinum degradation, the platinum degradation is decomposed into the degradation caused by the change of working voltage and the degradation caused by high potential, the former degradation is estimated by using the change amount of the current voltage and the voltage at the previous moment; the latter is estimated in the form of exponential; the weight coefficients of each part are adjusted according to the actual management tendency, so that the energy management method has a certain tendency, and the present application improves the traditional strategy which only considers the fuel optimization to comprehensively consider the two targets of the minimum fuel and the minimum fuel cell life attenuation, and delays the performance degradation of the power source.

Description

Technical Field

[0001] This invention belongs to the field of fuel cell systems, and more specifically, relates to an energy management method for a hybrid power system that takes into account platinum degradation. Background Technology

[0002] Hydrogen energy, as a green energy source to replace fossil fuels, is considered a viable solution to the global energy crisis. As a representative of hydrogen energy, proton exchange membrane fuel cells (PEMFCs) have better start-up performance and the advantage of operating at room temperature compared to other types of fuel cells, and therefore have been widely used in fuel cell vehicles (FCVs).

[0003] However, fuel cells suffer from slow dynamic response, inability to handle high-frequency, large-scale load changes, and inability to recover braking energy. Using them as the primary power source, combined with lithium-ion batteries as auxiliary energy, to form a hybrid power system is an effective way to address these issues. Currently, many scholars are researching hybrid power systems, primarily focusing on energy management strategies. However, traditional energy distribution strategies often only consider economics and power performance, pursuing efficient energy allocation and numerical optimization, without adequately addressing the performance degradation and internal degradation mechanisms of hydrogen fuel cells. Since performance degradation is a key factor limiting the lifespan of hybrid power systems, optimizing control strategies during power distribution based on internal degradation mechanisms is of great significance to avoid or delay the performance degradation of the power source.

[0004] In PEMFCs, the catalyst consists of platinum (Pt) metal and carbon (C) support, and is typically used to catalyze slow redox reactions, where platinum ions (Pt) play a crucial role. 2+ Platinum provides catalytic activity, while the carbon support provides high electrical conductivity. Due to its scarcity, platinum is expensive. To reduce platinum loading and lower costs while improving the performance of platinum catalysts, metallic platinum is fabricated into nanoparticles dispersed on a carbon support to achieve maximum catalytically active surface area. However, frequent oxidation and reduction reactions cause platinum nanoparticle degradation, leading to electrochemical surface area (ECSA) loss, which in turn causes voltage loss in fuel cells, ultimately limiting the lifespan of PEMFCs. Currently, there are few studies characterizing fuel cell performance degradation from the perspective of platinum degradation. To better understand the platinum degradation mechanism and mitigate ECSA loss, it is crucial to construct a mathematical model describing the degradation process and further analyze the degradation patterns of fuel cells under different voltages, thereby providing insights for optimizing energy management strategies. Summary of the Invention

[0005] To address the shortcomings and improvement needs of existing technologies, this invention provides an energy management method for hybrid power systems that considers platinum degradation. The purpose is to solve the problem that existing energy management methods for hybrid power systems do not fully consider fuel cell performance degradation and lack an understanding of the intrinsic decay mechanism of fuel cells, thus limiting their energy management capabilities.

[0006] To achieve the above objectives, according to one aspect of the present invention, an energy management method for a hybrid power system considering platinum degradation is provided, comprising:

[0007] S1. Taking the SOC of the power battery as the state variable x(t) of the hybrid power system, and the output power P of the fuel cell as... fc For the control variable u(t) of the hybrid power system, with the optimization objectives of minimizing fuel consumption and lifespan degradation, an energy management optimization model is constructed, expressed as: Where L[x(t),u(t),t]=f1(t)+f2(t), f1(t) represents hydrogen consumption, and f2(t) represents platinum stack degradation, and This indicates that the platinum stack degradation index is decomposed into degradation caused by changes in operating voltage and degradation caused by high potential. RUL indicates the hydrogen consumption of the fuel cell, and U indicates the remaining lifespan of the fuel cell. fc (t) represents the current output voltage of the fuel cell; c1, c2, and c3 represent the weight coefficients of the corresponding terms. The relative sizes of the three are set according to actual management preferences. Increasing the coefficient of a certain term will cause the solution algorithm to consider suppressing the loss caused by that term more during the optimization process, thereby relaxing the constraints on other terms.

[0008] S2. Solve the energy management optimization model to obtain the optimal fuel cell output power curve of the system, and complete the energy management of the hybrid power system.

[0009] Furthermore, the equivalent hydrogen consumption of the fuel cell includes the amount of hydrogen consumed by the fuel cell stack itself. Equivalent hydrogen consumption for lithium battery discharge Represented as

[0010]

[0011] In the formula, I b U represents the current of a lithium battery. b The voltage of the lithium battery is represented by LHV, the lower heating value of hydrogen is represented by η. fc This represents the average efficiency of the fuel cell, and n represents the number of fuel cell cells. I represents the molar mass of hydrogen gas. fcThis represents the output current of the fuel cell, where F represents the Faraday constant. This indicates the utilization rate of hydrogen.

[0012] Furthermore, the energy management optimization model is solved using the minimum principle, specifically as follows:

[0013] Initial conditions:

[0014] SOC(t0)=SOC(t f ) = 0.6

[0015] System state equations:

[0016]

[0017] Constraints:

[0018]

[0019] The Hamilton function is as follows:

[0020]

[0021] In the formula, λ T (t) is a costate variable; P fc (t) represents the output power of the fuel cell, P fc,max P represents the maximum output power of the fuel cell. req (t) represents the power demand of the hybrid power system, P bat,min P represents the maximum charging power of a lithium battery. bat,max This indicates the maximum discharge power of the lithium battery;

[0022] The state equations and co-state equations of the optimal trajectory line satisfy the following equations:

[0023] Combined with open circuit voltage V oc The relationship with SOC is used to calculate the rate of change of the costate variable. And determine the initial value λ0 of the costate variable based on the boundary conditions;

[0024] The objective function taking its minimum value is also the Hamiltonian function taking its local minimum value, and the condition for the Hamiltonian function to take its local minimum value is: Then the optimal control variable u*(t) is one of the feasible regions u∈U that minimizes the Hamiltonian function:

[0025] Furthermore, the energy management optimization model is constructed as follows:

[0026] (1) Construct a one-dimensional dynamic model of Pt degradation in the cathode catalyst layer of a proton exchange membrane fuel cell in a hybrid power system.

[0027] (2) Based on the one-dimensional dynamic model of Pt degradation, the influence of different voltage parameters on ECSA is studied. The different voltage parameters include different voltage cycles, different voltage cycle limits, and different voltage fluctuation amplitudes. The degradation law of Pt is summarized, that is, the high voltage working state is not conducive to the degradation of the catalyst layer and will increase the loss of platinum mass in the catalyst layer. At the same time, frequent load changes have an adverse effect on the lifetime of the catalyst layer and will increase the degradation caused by aging. Among them, the power mode is used to study the influence of different voltage parameters on ECSA, that is, by adjusting the duty cycle of the potential cycle, the average power output of the potential cycle with different voltage characteristics is fixed at the same level.

[0028] (3) Based on the Pt degradation decay law, the energy management optimization model is constructed, so that the energy management optimization model considers the platinum degradation of the stack caused by high potential and the platinum degradation of the stack caused by voltage change.

[0029] Furthermore, the one-dimensional dynamic model of Pt degradation includes: a platinum dissolution-redeposition / redox model and a platinum ion diffusion model;

[0030] When constructing the one-dimensional dynamic model of Pt degradation, the entire catalyst layer is divided into different partitions along the thickness direction of the cathode catalyst layer. A platinum particle dissolution and redeposition / redox model is established for each partition, and a platinum ion transfer model is established between adjacent partitions.

[0031] Furthermore, the platinum dissolution-redeposition / redox model is constructed as follows:

[0032] Assume that there are M discrete particle size groups in each partition region i, where j represents the number of one of the particle groups, and the diameter of each particle group is d. i,j Oxide coverage θ for each particle group i,j The number of platinum particles in each particle group (Num) i,j The platinum ion concentration in each particle group is

[0033] Construct platinum particle dissolution-deposition / redox models for each particle group, where the net platinum dissolution rate r of the platinum particles in each particle group is... net,Pt (i,j) and net oxidation rate r net,oxide (i,j) are as follows:

[0034]

[0035] In the formula, v1 represents the forward dissolution rate factor. The expression represents the activation enthalpy of platinum dissolution under fully humidified conditions, where R represents the ideal gas constant, T represents the temperature, n1 represents the electrons transferred during platinum dissolution, F represents the Faraday constant, β1 represents the Butler-Volmer transfer coefficient of platinum dissolution, and U... eq γ represents the equilibrium voltage of platinum solution, Ω represents the molar volume of platinum, and γ represents the equilibrium voltage of platinum solution. total (i,j) represents the total surface tension of this particle group, U fc v1 represents the operating voltage of the fuel cell, and v2 represents the reverse dissolution rate factor. The reference platinum ion concentration is represented by Γ, and the platinum surface position density is represented by Γ.

[0036]

[0037] In the formula, This represents the rate constant for the formation of platinum oxide. U represents the partial molar activation enthalpy of oxide formation, λ represents the kinetic barrier constant dependent on platinum oxide, n2 represents the electrons transferred during platinum oxide formation, β2 represents the Butler-Volmer transfer coefficient of platinum oxide, and U fit ω represents the equilibrium voltage of platinum oxide formation, and ω represents the oxide-oxide interaction energy. This represents the reverse platinum formation rate constant, and pH represents the system pH.

[0038] Among them, the total surface tension γ of this particle group total (i,j) represents the following:

[0039]

[0040] In the formula, γ Pt Indicates the surface tension of Pt

[111] ;

[0041] The rate of change of platinum particle diameter and the rate of change of oxide coverage for each discrete group can be calculated by the following equations:

[0042]

[0043]

[0044] Furthermore, the platinum ion diffusion model is constructed as follows:

[0045] The platinum ion concentration varies in different zones of the catalyst layer, and platinum ion diffusion occurs between these zones. The diffusion equation is as follows: In the formula, and D eff Pt 2+ Concentration in the partition and effective diffusion coefficient in the electrolyte, initial Pt in each partition 2+When the concentration is 0, the effective diffusion coefficient is calculated as follows: This represents the diffusion coefficient of platinum ions in water. Indicates the volume fraction of water in the ionomer phase;

[0046] The platinum particle dissolution source term is calculated based on the sum of all particle dissolution effects in the partition. Used to calculate the dissolution of all platinum particles in the partition for partition Pt 2+ Effect of concentration:

[0047]

[0048] In the formula, N particle This indicates the number of platinum particles per unit volume.

[0049] Pt 2+ Two boundary conditions for diffusion in the catalyst layer: The boundary condition in the GDL / CL plane is... Under the condition H2|Air(anode|cathode), the boundary conditions of the CL / PEM plane are: In the formula, L represents the thickness of the cathode catalyst layer, ε represents the increase in the volume fraction of ionomers in the electrode, and δ Pt This represents the distance the platinum band reaches the CL / PEM plane. This indicates the H2 permeability of the membrane at 100% relative humidity. Indicates the partial pressure of H2, δ m Indicates the thickness of the proton exchange membrane. This indicates the O2 permeability of the membrane at 100% relative humidity. This indicates the partial pressure of O2 in the air.

[0050] Furthermore, the Euler method was used to iteratively solve the platinum degradation model. During the iterative calculation, a judgment on the diameter of the particle group was added to the iterative calculation of particle partitioning and particle grouping. If the diameter of some particle groups is less than a certain value, it is assumed that they are completely dissolved in this cycle. After that, the diameter of the particle group becomes 0 and no longer participates in the iterative calculation.

[0051] The present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed by a processor, it controls the device in which the storage medium is located to perform an energy management method for a hybrid power system considering platinum degradation as described above.

[0052] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0053] (1) This invention proposes an energy management method for a hybrid power system considering platinum degradation, using the SOC of the power battery as the state variable x(t) of the hybrid power system, and the output power P of the fuel cell as the state variable x(t). fc For the control variable u(t) of the hybrid power system, an energy management optimization model is constructed with the optimization objectives of minimizing fuel consumption and lifespan degradation. The management model is a sum of two parts: hydrogen consumption and fuel cell stack platinum degradation. The fuel cell stack platinum degradation part is further decomposed into degradation caused by changes in operating voltage and degradation caused by high potential. For degradation caused by voltage changes, the magnitude of the change between the current voltage and the previous voltage is used as the cost function. According to previous research, the impact of degradation caused by high potential is small when the voltage is low, but the impact increases rapidly with the increase of voltage. Therefore, an exponential form is used to estimate this phenomenon. It should be noted that the weighted summation of the various parts in the management model will give the energy management a certain bias. The relative magnitudes of the three factors are set according to the actual management bias. Increasing the coefficient of a certain term will cause the solution algorithm to consider suppressing the loss caused by that term more during the optimization process, thereby relaxing the constraints on other terms. The method of this invention fully considers the performance loss and internal degradation mechanism of hydrogen fuel cells, and can avoid or delay the performance degradation of the power source, which has practical engineering significance.

[0054] (2) This invention establishes a one-dimensional dynamic model of Pt degradation in the cathode catalyst layer. This model can be used to analyze the impact of different voltage parameters on Pt degradation in fuel cells. By summarizing the laws governing degradation, it is found that high-voltage operation is detrimental to catalyst layer degradation, increasing platinum mass loss. Simultaneously, frequent load changes negatively impact catalyst layer lifespan, increasing degradation due to aging. This provides guidance for optimizing energy management strategies. Specifically, a power model is used to study the impact of different voltage parameters on the ECSA (Electrochemically Selected Anode Subsystem), i.e., by adjusting the duty cycle of the potential cycle to fix the average power output of potential cycles with different voltage characteristics at the same level. This construction method of the energy management optimization model ensures that the energy management method for the hybrid power system proposed in this invention effectively suppresses Pt degradation while meeting power requirements, improves fuel cell durability, and significantly reduces the overall system cost. Attached Figure Description

[0055] Figure 1 A flowchart illustrating the implementation of the hybrid power system energy management method of the present invention is provided for an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of Pt degradation in the cathode catalyst layer of PEMFC;

[0057] Figure 3This is a simulation flowchart of the Pt degradation model provided in an embodiment of the present invention;

[0058] Figure 4 The following is a comparison of the simulation and experimental results of the Pt degradation model provided in the embodiments of the present invention; wherein, (a) is a triangular wave cyclic voltage diagram, (b) is the change curve of ECSA under different number of cycles, (c) is the relationship between the remaining particle mass distribution of different partitions and the distance from the partition to the GDL / CL interface, and (d) is the Pt particle diameter distribution diagram obtained from simulation and experimental observations at the initial stage and after 10,000 cycles.

[0059] Figure 5 This is a diagram illustrating the effect of different voltage fluctuation amplitudes on Pt degradation, provided by an embodiment of the present invention.

[0060] Figure 6 This is a diagram illustrating the effect of different voltage cycle limits on Pt degradation, provided in an embodiment of the present invention.

[0061] Figure 7 This is a diagram illustrating the effect of different voltage cycles on Pt degradation, provided in an embodiment of the present invention; wherein, (a) shows the Pt in the catalyst layer under different cycle voltages. 2+ The graphs show the changes in concentration, (b) the relationship between cycle length and platinum mass loss, (c) the ECSA change curve over time, and (d) the ECSA degradation curves after excluding mass loss under different cycle voltages.

[0062] Figure 8 The power system characteristic diagram provided in the embodiments of the present invention includes (a) the load demand power curve; (b) the hydrogen consumption and fuel cell net output power curve; (c) the efficiency and fuel cell net output power curve; and (d) the lithium battery Rint model.

[0063] Figure 9 A flowchart illustrating the implementation of the PMP algorithm provided in this embodiment of the invention;

[0064] Figure 10 The following are fuel cell output power curves under three different optimization methods provided in the embodiments of the present invention: (a) is the fuel cell output power curve corresponding to the preferred consideration of efficiency, (b) is the fuel cell output power curve corresponding to the limitation of voltage change, and (c) is the fuel cell output power curve corresponding to the prevention of high potential.

[0065] Figure 11 The graphs show the changes in SOC of the lithium battery and ECSA of the fuel cell in the hybrid power system under different optimization methods provided in the embodiments of the present invention. (a) is the curve of SOC change of the lithium battery, and (b) is the curve of ECSA change of the fuel cell. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0067] Example 1

[0068] An energy management method for a hybrid power system considering platinum degradation includes:

[0069] S1. Taking the SOC of the power battery as the state variable x(t) of the hybrid power system, and the output power P of the fuel cell as... fc For the control variable u(t) of the hybrid power system, with the optimization objectives of minimizing fuel consumption and lifespan degradation, an energy management optimization model is constructed, expressed as: Where L[x(t),u(t),t]=f1(t)+f2(t), f1(t) represents hydrogen consumption, and f2(t) represents platinum stack degradation, and This indicates that the platinum stack degradation index is decomposed into degradation caused by changes in operating voltage and degradation caused by high potential. RUL indicates the hydrogen consumption of the fuel cell, and U indicates the remaining lifespan of the fuel cell. fc (t) represents the current output voltage of the fuel cell; c1, c2, and c3 represent the weight coefficients of the corresponding terms. The relative sizes of the three are set according to actual management preferences. Increasing the coefficient of a certain term will cause the solution algorithm to consider suppressing the loss caused by that term more during the optimization process, thereby relaxing the constraints on other terms.

[0070] S2. Solve the energy management optimization model to obtain the optimal fuel cell output power curve of the system, and complete the energy management of the hybrid power system.

[0071] As a preferred embodiment, such as Figure 1 As shown, this embodiment first establishes a one-dimensional dynamic model of Pt degradation in the cathode catalyst layer of a proton exchange membrane fuel cell (PEMFC). Based on the one-dimensional Pt degradation model, the influence of different voltage parameters on ECSA is studied, and the Pt degradation decay law is summarized, thereby guiding the construction of an energy management optimization model.

[0072] A schematic diagram of the Pt degradation model is shown below. Figure 2 As shown, during operation, platinum ions (Pt) in the cathode catalyst layer (CCL) increase. 2+ The Pt diffuses towards the center and encounters hydrogen gas inside the membrane. 2+Platinum ions are reduced to form metallic platinum, which then precipitates within the membrane, eventually forming a platinum band. Due to the reduction of some platinum ions, the platinum ion concentration is lower near the cathode catalyst layer-proton exchange membrane interface, while it is relatively higher on the other side (gas diffusion layer-catalyst layer side). This means that the platinum ion concentration in the catalyst layer exhibits a gradient along its thickness. Therefore, simply setting a uniform concentration parameter for the entire catalyst layer is unreasonable. Consequently, we divide the entire catalyst layer into different zones along its thickness and establish a platinum particle dissolution-deposition / redox model for each zone (Pt dissolution-deposition / redox causes Oswald ripening (particle growth)). A platinum ion transfer model is also established between adjacent zones. The platinum dissolution-deposition / redox reaction equations are as follows:

[0073]

[0074]

[0075] The catalyst layer is divided into N regions along its thickness. Assume that each region i contains M discrete particle size groups, and j represents the number of one of these particle groups. The diameter of each particle group is d. i,j Oxide coverage θ for each particle group i,j The number of platinum particles in each particle group (Num) i,j The platinum ion concentration in each particle group is

[0076] 1) Platinum particle dissolution-deposition / redox model for each particle group

[0077] The net oxidation rate r of the platinum particles in this particle group net,oxide (i,j)[mol / (cm 2 ·s)] and net platinum dissolution rate r net,Pt (i,j)[mol / (cm 2 ·s)] as follows:

[0078]

[0079]

[0080] Among them, the total surface tension γ of the particle group total (i,j) represents the following:

[0081]

[0082] The rate of change of platinum particle diameter and the rate of change of oxide coverage for each discrete group can be calculated by the following equations:

[0083]

[0084]

[0085] 2) Platinum ion diffusion model

[0086] The platinum ion concentration in each zone of the catalyst layer has a certain concentration gradient, and platinum ion diffusion exists between the zones. The diffusion equation is as follows:

[0087] and D eff It is Pt 2+ Concentration in the partition and effective diffusion coefficient in the electrolyte, The initial value is 0. The effective diffusion coefficient is calculated as follows:

[0088] The diffusion equation is discretized and solved using the implicit Euler method and the central difference method.

[0089] Among them, the source term generated by the dissolution of platinum particles It needs to be calculated based on the sum of the dissolution effects of all particles in the partition:

[0090] Pt 2+ Two boundary conditions for diffusion in the catalyst layer, and boundary conditions at the diffusion layer / catalyst layer (i.e., CL) interface:

[0091] Boundary conditions of the CL / PEM interface under H2|air conditions:

[0092] Where δ Pt The distance between the platinum band formed in the proton exchange membrane and the CL / PEM interface:

[0093]

[0094] The platinum particle dissolution and deposition / redox model and the platinum ion diffusion model constitute the platinum degradation model. The parameters and values ​​involved in the platinum degradation model are shown in the table below.

[0095] Table 1. Parameters of the Pt degradation model

[0096]

[0097]

[0098] From the above platinum degradation models, especially the Pt dissolution-deposition / redox model, it can be seen that platinum particle diameter, oxide coverage, platinum ion concentration, and various reaction rates are interdependent, making it difficult to obtain an analytical solution. Therefore, the Euler method is used to iteratively solve the discretized problem. The specific iterative steps are as follows: Figure 3 As shown. It is worth noting that d i,j If the value is greater than 0, during the iterative calculation, the diameter of some particle groups may gradually degenerate to near 0. If the calculation continues at this point, it may lead to outlier values ​​for d, which will interfere with the results of the entire model. Therefore, a judgment on the diameter of particle groups is added to the iterative calculation of particle partitioning and particle grouping. If the diameter of some particle groups is less than a certain value (in this embodiment, it is set to 1e-7, that is, 10 to the power of -7), it is assumed that they have completely dissolved in this iteration. After this, the diameter of the particle group becomes 0 and no longer participates in the iterative calculation.

[0099] Furthermore, to determine the effectiveness of the model, this embodiment selects the experimental results of Ferreira et al. (reference "Instability of Pt / C electrocatalysts in proton exchange membrane fuelcells: a mechanistic investigation") for verification.

[0100] 1) Commonly used comparison indicators

[0101] The output of the iterative calculation includes the diameter d after the discrete group evolution. i，j and the number of particles in the discrete group Num i,j From the following formula, we can obtain the geometrical surface area (GSA) of the platinum particle catalyst in the catalyst layer as a function of time, calculated by the model.

[0102]

[0103] In experiments, ECSA (catalytically active surface area) is usually measured by cyclic voltammetry. It's important to note that GSA is not entirely equivalent to ECSA, and not all GSA can be used for hydrogen adsorption. GSA and ECSA typically exhibit a fixed ratio, satisfying the following equation:

[0104]

[0105] Therefore, unless otherwise specified, this value will be used as the scaling factor between GSA and ECSA in the following text. Furthermore, in experiments, the unit for ECSA is usually meters (m). 2 / g PtTherefore, when comparing with the experiment, the ECSA calculated by the above two formulas needs to be divided by the initial platinum load in order to compare with the experimental results.

[0106] 2) Simulation matching test conditions

[0107] In Ferreira et al.'s experiments, the experimental subject was a catalyst-covered membrane structure consisting of a perfluorosulfonic acid (Nafion) membrane and a 46 wt% Pt / C-Nafion solution. The electrode thickness was approximately 13 μm, the membrane thickness was approximately 50 μm, and the platinum loading was approximately 0.4 mg / cm². 2 At a temperature of 353 K, the anode is H2 and the cathode is N2, both fully wetted. The applied voltage is a triangular wave cyclic voltage with an amplitude between 0.6 V and 1.0 V and a scan rate of 20 mV / s, such as... Figure 4 As shown in Figure a.

[0108] 3) Analysis of Experimental Results

[0109] Figure 4 Figure b in the figure shows the variation curves of ECSA under different number of cycles, and it can be seen that the ECSA evolution trend obtained by the model simulation is in good agreement with the experimental results. Figure 4 Figure c shows the relationship between the remaining particle mass distribution in different partitions and the distance from the partition to the GDL / CL interface. The mass distribution curve output by the model is basically consistent with the experimental measurement. In addition, it can be seen that the greater the distance, the smaller the remaining particle mass. This is consistent with the aforementioned reduction of platinum ions by hydrogen to form platinum bands, which makes the platinum ion concentration in the partitions near the PEM / CL interface relatively lower, resulting in more platinum mass loss in these partitions. This indicates that the one-dimensional model can explain the platinum mass loss phenomenon in the catalyst layer well. Figure 4 The figure d in the diagram shows the initial and experimental Pt particle diameter distributions obtained from simulation and observation. According to experimental observations, the platinum particle diameter exhibits a normal distribution in the initial state, with a mean μ = 3.0 nm and a variance σ0. 2 =0.22. After 10,000 cycles, the experimentally measured diameter of the platinum particles still follows a normal distribution, with an average diameter μ = 5.9 nm and a variance σ. 2 =2.8. The distribution curve obtained from the simulation differs from the experimental results to some extent. The particle distribution in the medium particle size (3-5nm) range is higher than the experimental observation, while the distribution in the larger particle size (6-10nm) range is lower than the experimental observation. This may be due to the deviation caused by not considering the aggregation of platinum particles and carbon corrosion.

[0110] S2. Based on the Pt degradation model, the impact of different voltage parameters on the ECSA (Energy Capacity Synthetic Acid) is studied and analyzed. Under the operating conditions of fuel cell hybrid electric vehicles, the impact on fuel cell lifespan mainly stems from variable load operating conditions, i.e., voltage changes cause ECSA degradation. To explore the influence of these factors on fuel cell lifespan, referring to the accelerated stress test (AST) experimental conditions, the degradation of the Pt degradation model was tested using cyclic square wave voltages with different voltage fluctuation amplitudes, different voltage lower limits, and different periods. This provides guidance for developing corresponding energy management strategies to mitigate degradation.

[0111] Furthermore, for experiments with different voltage characteristics, simply modifying a single parameter may result in different average output power of the fuel cell corresponding to different voltage cycle potentials. Clearly, such a comparison is unreasonable because, in actual operation, the system's required average power is fixed over a sufficiently long period. In this case, simply changing a single parameter of the voltage cycle potential is insufficient to adjust the average output power to a single value throughout the entire cycle. Therefore, in the following experiments, we will use a power mode to ensure that the output power of different voltage cycle potentials remains at the same level, thus ensuring that the experimental comparison is more practically valuable. Detailed cyclic square wave design is shown in Table 2.

[0112] Table 2. Cyclic square waves with different voltage characteristics

[0113]

[0114] 1) The effect of different voltage fluctuation amplitudes on Pt degradation

[0115] Figure 5 This demonstrates the effect of different voltage fluctuation amplitudes on Pt degradation. A period of 10 seconds was used, with each voltage cycle consisting of 2000 cycles and a duty cycle of 50%. To ensure consistent output power, each voltage cycle corresponded to a different upper voltage limit E. H and lower voltage limit E L The average output power corresponding to each cycle voltage was adjusted to 50% of the fuel cell's maximum output power. Based on the upper and lower voltage limits corresponding to different voltage cycle fluctuation amplitudes, it can be seen that, while maintaining consistent power output, the range of variation in the upper voltage limit is greater than that in the lower voltage limit. This is mainly because the fuel cell operates at high power output at low voltage, where power fluctuations have a smaller impact on voltage; conversely, high voltage output corresponds to low power output, where voltage is more significantly affected by power fluctuations. The experimental conclusion is also clear: when the voltage fluctuation amplitude is less than 200mV, the ECSA loss is extremely low, with the corresponding upper voltage limit not exceeding 0.82V. The larger the voltage fluctuation amplitude, the greater the ECSA loss.

[0116] 2) Effect of different voltage cycle limits on Pt degradation

[0117] Figure 6 The effect of different lower voltage limits on the mass loss of ECSA and platinum was demonstrated. Experimental conditions were set with a fixed upper voltage limit of 0.95 V and a cycle length of 10 s. Different high-voltage cycles were recorded at 2 × 10⁻⁶ Hz. 4 ECSA losses after s. Since the upper voltage limit is fixed at 0.95V, the fuel cell basically does not output power at this voltage. Therefore, correspondingly, as the lower voltage limit increases, the output power of the fuel cell at the lower voltage limit will decrease. To ensure consistent output power, the duty cycle of the lower voltage limit also increases. It can be seen that when the lower voltage limit is less than 0.7V, the ECSA loss hardly changes, while the mass loss increases continuously. This is because as the lower voltage limit increases, the voltage cycle fluctuation amplitude decreases. Corresponding to the previous conclusion, this leads to a decrease in ECSA losses caused by ripening. However, due to the overall increase in potential, due to Pt 2+ The mass loss due to transfer will increase again. However, once the lower voltage limit exceeds 0.7V, the ECSA loss and mass loss decrease. This is because as the lower voltage limit continues to increase, the duty cycle corresponding to that voltage also increases, causing the duty cycle corresponding to the upper voltage limit (0.95V) to decrease rapidly. This indicates that for fuel cells, operating at high voltage leads to increased ECSA loss. Furthermore, the higher the voltage, the greater the impact on degradation. Avoiding high-voltage standby states can effectively reduce degradation.

[0118] 3) Effect of different periodic voltages on Pt degradation

[0119] Figure 7 The effect of different periodic voltage cycles on Pt degradation was demonstrated. The experimental cycle voltages had different periods, with a lower voltage limit of 0.6 V, an upper voltage limit of 0.95 V, a duty cycle of 50%, and a duration of 2 × 10⁻⁶. 4 Choose 1s, 10s, 50s, and 100s as the voltage cycle period.

[0120] Figure 7 Figure a in the diagram shows the Pt content in the catalyst layer under different cycle voltages. 2+The concentration change curves show that at a period of 1 second, the platinum ion concentration fluctuates slightly around 0.05 μM with no significant overall change. Similarly, at a period of 10 seconds, the platinum ion concentration fluctuates more widely but remains around 0.05 μM. Therefore, in terms of the resulting platinum mass loss, there is no significant difference in the two periods. However, for periods longer than 50 seconds, it can be seen that during the Pt dissolution phase, due to the longer time spent at high potential, the platinum ion concentration rises rapidly to a high level. This leads to an increase in platinum ions diffusing into the proton exchange membrane, resulting in an increased platinum mass loss. Figure 7 As shown in Figure b, longer cycling voltages result in greater platinum mass loss. This is because the platinum mass loss in the catalyst layer primarily originates from Pt. 2+ Transfer towards the proton exchange membrane, Pt 2+ The higher the concentration, the more platinum ions transfer to the PEM / CL interface, resulting in a greater loss of platinum mass. For example... Figure 7 As shown in Figure c, with a larger voltage cycling period, the overall ECSA decreases more rapidly due to greater Pt mass loss. However, it can also be seen that the curves represented by the 1s period overlap with those represented by the 10s period. This is because shorter cycles lead to more frequent fluctuations in the platinum ion concentration in the catalyst layer, which is extremely detrimental to smaller diameter particles. This results in more platinum transferring from smaller diameter particles to larger diameter particles (Ostwald ripening phenomenon), thus causing greater ECSA loss due to ripening (particle growth). To demonstrate this, this embodiment... Figure 7 The d-plot in the figure shows the ECSA degradation curves after excluding mass loss under different cycle voltages. It is found that the smaller the cycle, the greater the ECSA loss. This is because, after excluding the influence of mass loss, the ECSA loss mainly comes from the Ostwald ripening phenomenon; the smaller the cycle, i.e., the more frequent the voltage changes, the more severe the ripening phenomenon.

[0121] In summary, high-voltage operation is extremely detrimental to the degradation of the catalyst layer and will greatly increase the loss of platinum mass in the catalyst layer; at the same time, appropriate cycle is crucial, and frequent load changes also have an adverse effect on the life of the catalyst layer, as they will increase degradation caused by aging.

[0122] Finally, the degradation and decay patterns of Pt were analyzed and used to guide the construction of an energy management optimization model.

[0123] Hybrid power systems have multiple energy sources, necessitating power allocation among these sources according to an Energy Management Strategy (EMS). This embodiment proposes an improved EMS that considers both fuel minimization and fuel consumption minimization, building upon traditional strategies that only prioritize fuel efficiency. The power output curve corresponding to the China Light Commercial Vehicle Driving Cycle (CLTC) standard test condition is used, as shown below. Figure 8 Figure a in the diagram represents the load demand power P. req The energy management strategy of the hybrid power system was tested.

[0124] (1) Fuel cell model: the relationship between net output power of the fuel cell stack and hydrogen consumption, and the relationship between net output power and efficiency, such as Figure 8 Figures b and c in the diagram illustrate this. The net output power of the fuel cell is obtained by subtracting the output power of the fuel cell stack from the power of the auxiliary systems, which include air compressors, humidifiers, and coolers. As shown in the figures, for economic reasons, the fuel cell should be operated within its high-efficiency range as much as possible to reduce hydrogen consumption. Furthermore, as previously studied on fuel cell degradation mechanisms, adjusting the characteristics of the fuel cell output voltage can slow down degradation. Therefore, designing a reasonable EMS (Energy Management System) is crucial to avoid operations that exacerbate ECSA (Excessive Electrolytic Stabilization) degradation in the fuel cell and to extend system life.

[0125] (2) Power Battery Model: To simplify calculations, the power battery adopts the Rint internal resistance model, such as... Figure 8 As shown in Figure d, the power battery pack uses 190 cells with a capacity of Q. bat It is composed of single ternary lithium batteries with a capacity of 12A·h connected in series. The internal resistance of a single battery cell is R. bat =12mΩ. The state of charge (SOC) of the battery is estimated using the Ampere integral method, and the relevant formula is as follows:

[0126]

[0127] Here, Q bat Indicates battery capacity; P bat This indicates the battery power; a positive value indicates discharging, and a negative value indicates charging; V oc This represents the open-circuit voltage. The open-circuit voltage V of a battery. oc Regarding SOC, to simplify the process, a polynomial is used to fit the relationship between the two, and the fitted relationship can be expressed as:

[0128] V oc =1.323×SOC 3 -1.547×SOC 2 -1.077×SOC+3.419.

[0129] b) Performance evaluation indicators

[0130] To more objectively demonstrate the differences between various EMS effects, we designed evaluation indicators for hybrid power systems from the perspectives of efficiency and degradation.

[0131] (1) Fuel Consumption: In a hybrid power system, all electricity theoretically comes from the fuel cell. Even though the lithium battery also supplies power to the outside, its source is still the storage of excess electricity from the fuel cell stack when the load demand is low. Therefore, preferably, the calculation of hydrogen consumption should not only consider the amount of hydrogen consumed by the fuel cell stack itself, but also the equivalent hydrogen consumption during lithium battery discharge.

[0132] (2) Lifetime degradation: Studies have found that the degradation process of the ECSA catalyst layer can be approximated as a logarithmic curve decline, so the remaining useful life (RUL) of the fuel cell can be calculated using the following equation:

[0133]

[0134] In the formula, ECSA low This represents the lower limit of ECSA tolerance, approximately 60% of the initial value. Below this limit, the fuel cell stack must be replaced. ECSA degradation does not continue indefinitely to zero; as the ECSA value decreases, the degradation trend gradually slows, eventually approaching a stable ECSA value. min It is approximately 0.1.

[0135] c) Energy management and control based on the minimum principle

[0136] For energy control of hybrid power systems, the problem can be described as "taking the SOC of the power battery as the system's state variable x(t), and the output power P of the fuel cell as..." fc Let u(t) be the control variable of the system, and let u(t) be the optimization problem with the goal of minimizing fuel consumption and lifespan degradation. The objective function is expressed as:

[0137]

[0138] To reliably maintain battery power, the final state of charge (SOC) of the battery is generally required to be equal to its initial value, i.e.: SOC(t0) = SOC(t0) f =0.6;

[0139] System state equations:

[0140] Subject to constraints:

[0141] The Pontryagin Minimum Principle (PMP) algorithm is a classic method for solving optimal control problems, derived from the classical variational method. For the optimization problem described above, the minimum principle is used for solution. The Hamiltonian function is constructed as follows:

[0142] H[x(t),u(t),λ(t),t]=L[x(t),u(t),t]+λ T (t)f[x(t),u(t),t];

[0143] Where, λ T (t) is a costate variable.

[0144] The state equations and co-state equations of the optimal trajectory line satisfy the following equations:

[0145]

[0146] The objective function taking its minimum value is also the Hamiltonian function taking its local minimum value, and the condition for the Hamiltonian function to take its local minimum value is:

[0147]

[0148] According to the minimum principle, the optimal control variable u*(t) is the one in all feasible regions u∈U that minimizes the Hamiltonian function.

[0149]

[0150] d) Implementation of the PMP algorithm:

[0151] The actual Hamiltonian function can be expressed as:

[0152]

[0153] Based on the co-state equation and the ampere-hour integral calculated above, the rate of change of SOC is combined with the open-circuit voltage V. oc The relationship with SOC allows us to calculate the rate of change of the costate variable:

[0154]

[0155] The costate equation only provides the rate of change of the costate variable. The initial value λ0 of the costate variable needs to be determined based on the boundary conditions. The specific process is as follows: Figure 9 As shown.

[0156] For the formulation of the loss function L[x(t),u(t),t], based on the previously established performance evaluation indicators and considering hydrogen consumption and platinum degradation of the fuel cell stack, the loss function is divided into two parts:

[0157] L[x(t),u(t),t]=f1(t)+f2(t)

[0158] Where f1(t) represents hydrogen consumption and f2(t) represents platinum stack degradation:

[0159]

[0160]

[0161] The degree of fuel cell degradation is related not only to the current operating conditions but also to preceding operations. Furthermore, the impact of a single operation on degradation is very limited, requiring a considerable amount of time before a significant difference appears. Real-time calculation of the fuel cell's RUL during the iterative process is difficult to achieve, thus necessitating the use of other features for estimation. Based on previous research, voltage operating conditions have a significant impact on fuel cell degradation. Reducing the fuel cell's operation at high potentials and minimizing variations in the stack's operating voltage during output power distribution can effectively suppress platinum degradation, resulting in a longer fuel cell lifespan. Therefore, this study decomposes the degradation based on voltage characteristics, developing a loss function according to different voltage characteristics during power distribution. This further decomposes the stack platinum degradation into degradation caused by operating voltage variations and degradation caused by high potentials.

[0162]

[0163] For degradation caused by voltage changes, the magnitude of the change between the current voltage and the previous voltage is used as the cost function. Based on previous research, degradation caused by high potential has a smaller impact when the voltage is low, but the impact increases rapidly with increasing voltage. Therefore, an exponential form is used to estimate this phenomenon. Coefficients c2 and c3 represent the weights of the two phenomena, respectively. These weighting coefficients give EMS a certain bias; increasing the coefficient corresponding to a particular phenomenon will cause the algorithm to consider suppressing the loss caused by that phenomenon more during the optimization process, thus relaxing the constraints on other phenomena. Therefore, based on the different coefficients, EMS can be divided into three different biases, as shown in Table 3:

[0164] Table 3. Energy Management Strategy Tendencies

[0165]

[0166] e) Discussion of results based on the PMP algorithm:

[0167] 1) Fuel cell output power curves under different strategies

[0168] Figure 10The output power curves of fuel cells using three different strategies are shown. Under the efficiency-first strategy, the output power of the fuel cell is frequently adjusted, and it often operates at extremely low power. Under the voltage-limiting strategy, the output power variation of the fuel cell is reduced, but low power output cannot be completely avoided. Under the strategy of preventing high potential, the fuel cell almost never operates at low power.

[0169] 2) Curves of lithium battery SOC and fuel cell ECSA variation under different strategies

[0170] Figure 11 The table shows the SOC (State of Charge) changes of the lithium-ion battery and the ECSA (Energy Capacity Scale) changes of the fuel cell in a hybrid power system under different strategies. It can be seen that under the strategy of preventing high potential, the ECSA degradation of the fuel cell is slower, but the fluctuation of the battery SOC is more drastic. Similarly, Table 4 shows that adding more restrictions to the energy management strategy increases the equivalent hydrogen consumption of the system and the fluctuation range of the lithium-ion battery SOC is also larger, which means that the manufacturing and operating costs of the system will increase. Considering that the new strategy has a significant inhibitory effect on fuel cell Pt degradation, enabling the hybrid system to have a longer service life, this embodiment believes that the strategy of limiting voltage fluctuations and preventing high potential is valuable.

[0171] Table 4. Comparison of Energy Management Strategies

[0172]

[0173] In summary, this embodiment analyzes the impact of high voltage and rapid voltage changes on the degradation of the ECSA (which is also a mathematical description of platinum degradation), a characterizing factor of fuel cell life, based on the platinum degradation model. The energy management method provides the optimal fuel cell output power while considering the goals of minimizing fuel consumption and minimizing life degradation. Whether the given result is optimal or what the actual effect is can be verified by analyzing the changes in ECSA using the platinum degradation model, thus closely linking platinum degradation with the energy management method.

[0174] This embodiment establishes a one-dimensional dynamic model of platinum (Pt) degradation in the cathode catalyst layer (CCL) of a proton exchange membrane fuel cell (PEMFC) based on the mechanisms of platinum dissolution / redeposition, platinum oxidation / reduction, and platinum ion migration in the catalyst layer. Comparison with experimental results in existing literature demonstrates that this degradation model can effectively reflect Pt degradation in the CCL during accelerated aging tests (AST). Based on this model, the influence of voltage on Pt degradation under different cycles, cycle limits, and fluctuation amplitudes is summarized. Furthermore, the energy management strategy of the PEFMC-lithium battery hybrid system is optimized, improving the traditional strategy from solely considering fuel optimization to comprehensively considering both fuel minimization and fuel cell lifetime degradation. Results show that while the optimization methods for limiting voltage fluctuations and preventing high potentials have some impact on system efficiency, they effectively suppress Pt degradation. Therefore, this method can meet power requirements while extending the fuel cell system life and effectively reducing system operating costs.

[0175] Example 2

[0176] A computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed by a processor, it controls the device on which the storage medium is located to perform an energy management method for a hybrid power system considering platinum degradation as described above.

[0177] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0178] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An energy management method for a hybrid power system considering platinum degradation, characterized in that, include: S1. The SOC of the power battery is used as the state variable of the hybrid power system. x (t), with the output power of the fuel cell P fc Control variables for hybrid power systems u(t) With the goal of minimizing fuel consumption and lifespan degradation, an energy management optimization model is constructed, expressed as: in, , f 1(t) represents hydrogen consumption. f 2(t) represents the degradation of the platinum stack, and , This indicates that the platinum stack degradation index is decomposed into degradation caused by changes in operating voltage and degradation caused by high potential. This indicates the hydrogen consumption of the fuel cell. Indicates the remaining lifespan of the fuel cell. This indicates the current output voltage of the fuel cell; , , These represent the weight coefficients of the corresponding terms. The relative sizes of the three are set according to the actual management preferences. Increasing the coefficient of a certain term will cause the solution algorithm to consider suppressing the loss caused by that term more during the optimization process, thereby relaxing the constraints on other terms. S2. Solve the energy management optimization model to obtain the optimal fuel cell output power curve of the system, and complete the energy management of the hybrid power system; The energy management optimization model is solved using the minimum principle, and the specific implementation method is as follows: Initial conditions: SOC(t 0 ) = SOC(t f ) = 0.6 System state equations: Constraints: The Hamilton function is constructed as follows: ; In the formula, For costate variables; Indicates the output power of the fuel cell. This indicates the maximum output power of the fuel cell. Indicates the power demand of the hybrid power system. This indicates the maximum charging power of the lithium battery. This indicates the maximum discharge power of the lithium battery; The state equations and co-state equations of the optimal trajectory line satisfy the following equations: ; Combined with open circuit voltage V oc and SOC Find the rate of change of the costate variable based on the relationship. : And determine the initial value λ0 of the costate variable based on the boundary conditions; The objective function taking its minimum value is also the Hamiltonian function taking its local minimum value, and the condition for the Hamiltonian function to take its local minimum value is: Then the optimal control variable u*(t) It is one of all feasible regions u∈U that minimizes the Hamiltonian function: .

2. The energy management method for a hybrid power system according to claim 1, characterized in that, The equivalent hydrogen consumption of the fuel cell includes the amount of hydrogen consumed by the fuel cell stack itself. Equivalent hydrogen consumption for lithium battery discharge , represented as ; In the formula, This indicates the current of the lithium battery. This indicates the voltage of the lithium battery. This indicates the lower calorific value of hydrogen. This indicates the average efficiency of the fuel cell. This indicates the number of cells in a fuel cell. This indicates the molar mass of hydrogen. This indicates the output current of the fuel cell. Denotes Faraday's constant. This indicates the utilization rate of hydrogen.

3. The energy management method for a hybrid power system according to claim 1 or 2, characterized in that, The energy management optimization model is constructed as follows: (1) Construct a one-dimensional dynamic model of Pt degradation in the cathode catalyst layer of a proton exchange membrane fuel cell in a hybrid power system; (2) Based on the one-dimensional dynamic model of Pt degradation, the influence of different voltage parameters on ECSA is studied. The different voltage parameters include different voltage cycles, different voltage cycle limits and different voltage fluctuation amplitudes. The degradation law of Pt is summarized, that is, the high voltage working state is not conducive to the degradation of the catalyst layer and will increase the loss of platinum mass in the catalyst layer. At the same time, frequent load changes have an adverse effect on the lifetime of the catalyst layer and will increase the degradation caused by the aging phenomenon. Among them, the power mode is used to study the influence of different voltage parameters on ECSA, that is, by adjusting the duty cycle of the potential cycle, the average power output of the potential cycle with different voltage characteristics is fixed at the same level. (3) Based on the degradation law of Pt, the energy management optimization model is constructed, so that the energy management optimization model considers the degradation of platinum stack caused by high potential and the degradation of platinum stack caused by voltage change.

4. The energy management method for a hybrid power system according to claim 3, characterized in that, The one-dimensional dynamic model of Pt degradation includes: a platinum dissolution-redeposition / redox model and a platinum ion diffusion model; When constructing the one-dimensional dynamic model of Pt degradation, the entire catalyst layer is divided into different partitions along the thickness direction of the cathode catalyst layer. A platinum particle dissolution and redeposition / redox model is established for each partition, and a platinum ion transfer model is established between adjacent partitions.

5. The energy management method for a hybrid power system according to claim 4, characterized in that, The platinum dissolution-redeposition / redox model is constructed as follows: Assuming in each partition region i There is M A discrete group of particle sizes, j This represents the number of one of the particle groups, and the diameter of each particle group is... d i,j Oxide coverage of each particle group θ i,j Number of platinum particles in each particle group Num i,j The platinum ion concentration in each particle group is c Pt 2+ i,j ; A platinum particle dissolution-deposition / redox model was constructed for each particle group, where the net platinum dissolution rate of the platinum particles in each particle group was determined. and net oxidation rate as follows: ； In the formula, Indicates the forward dissolution rate factor. This represents the enthalpy of platinum dissolution activation under fully humidified conditions. Represents the ideal gas constant. Indicates temperature. This represents the electrons transferred during the dissolution of platinum. Denotes Faraday's constant. The Butler-Volmer transfer coefficient represents the platinum dissolution rate. This represents the equilibrium voltage of the platinum solution. This represents the molar volume of platinum. This represents the total surface tension of the particle group. Indicates the operating voltage of the fuel cell. Indicates the reverse dissolution rate factor. Indicates the reference platinum ion concentration. Indicates the surface position density of platinum; ； In the formula, This represents the rate constant for the formation of platinum oxide. This represents the activation enthalpy of partial molar oxide formation. This represents the kinetic barrier constant that depends on the platinum oxide. This represents the electrons transferred during the formation of platinum oxide. The Butler-Volmer transfer coefficient for platinum oxidation is represented. This represents the equilibrium voltage of the platinum oxide forming organism. Represents the oxide-oxide interaction energy. This represents the reverse platinum formation rate constant. Indicates the system pH; Among them, the total surface tension of this particle group It is expressed as follows: ； In the formula, Indicates the surface tension of Pt [111]; The rate of change of platinum particle diameter and the rate of change of oxide coverage for each discrete group can be calculated by the following equations: ； 。 6. The energy management method for a hybrid power system according to claim 4, characterized in that, The platinum ion diffusion model is constructed as follows: The platinum ion concentration varies in different zones of the catalyst layer, and platinum ion diffusion occurs between these zones. The diffusion equation is as follows: In the formula, and They represent The concentration in the partition and the effective diffusion coefficient in the electrolyte, the initial concentration in each partition When the concentration is 0, the effective diffusion coefficient is calculated as follows: ; This represents the diffusion coefficient of platinum ions in water. Indicates the volume fraction of water in the ionomer phase; The platinum particle dissolution source term is calculated based on the sum of all particle dissolution effects in the partition. This is used to calculate the dissolution of all platinum particles in the partition for the partition. Effect of concentration: ； In the formula, This indicates the number of platinum particles per unit volume. Two boundary conditions for diffusion in the catalyst layer: The boundary condition in the GDL / CL plane is... Under the condition of H2|Air (anode | cathode), the boundary conditions of the CL / PEM plane are: In the formula, Indicates the thickness of the cathode catalyst layer. This indicates an increase in the volume fraction of ionomers in the electrode. This represents the distance the platinum band reaches the CL / PEM plane. , This indicates the H2 permeability of the membrane at 100% relative humidity. This indicates the partial pressure of H2. Indicates the thickness of the proton exchange membrane. This indicates the O2 permeability of the membrane at 100% relative humidity. This indicates the partial pressure of O2 in the air.

7. The energy management method for a hybrid power system according to claim 4, characterized in that, The Euler method was used to iteratively solve the platinum degradation model. During the iterative calculation, a judgment on the diameter of the particle group was added to the iterative calculation of particle partitioning and particle grouping. If the diameter of some particle groups is less than a certain value, it is assumed that they have completely dissolved in this cycle. After that, the diameter of the particle group becomes 0 and no longer participates in the iterative calculation.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed by a processor, it controls the device in which the storage medium is located to perform an energy management method for a hybrid power system considering platinum degradation as described in any one of claims 1 to 7.

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