A method for optimizing the capacity configuration of a marine fuel cell power system

Abstract

The application discloses a kind of ship fuel cell power system capacity configuration optimization methods, including the following steps: by the energy management strategy of established ship fuel cell power system, energy distribution is carried out to ship power demand;Establish the mathematical model of ship fuel cell-accumulation battery hybrid power system;Based on the above strategy and model, the economy objective function of ship fuel cell hybrid power system, fuel cell system output stability objective function, hydrogen consumption objective function and multiple constraint conditions are constructed;Then, by the particle swarm algorithm of multi-objective optimization, ship fuel cell power system capacity configuration is optimized and solved.The present application can meet the demand of various loads and working conditions of ship;It can reduce the economic cost and hydrogen consumption of system, and improve the service life of fuel cell system.

Description

Technical Field

[0001] This invention relates to the field of marine fuel cell power system technology, and specifically to a method for optimizing the capacity configuration of a marine fuel cell power system. Background Technology

[0002] In recent years, low-carbon economy and green development have become a focus of attention across all industries globally. The shipping industry is a vital pillar of the global economy and trade, but the negative impact of its emissions on greenhouse gases also warrants attention. In recent years, the shipping industry has been identified as a significant source of greenhouse gas emissions, surpassing air transport to become the second largest source of NOx (nitrogenous pollutants) after road transportation. X SO X In addition to PM and CO2 emission sources, research on fuel cell power technology for ships is particularly important, and fuel cell-powered ships have begun to be developed, promoted, and demonstrated.

[0003] Fuel cells-storage batteries are the primary application form of fuel cell power in ships, and optimizing the capacity configuration of hybrid power systems is an important research direction for optimizing the performance of fuel cell-powered ship propulsion systems. Some studies configure the ship-engine-propeller capacity of a target ship based on its design parameters and operational requirements using ship dynamics. This capacity configuration method cannot perfectly utilize the ship's potential power, easily leading to power redundancy and high investment costs. Research on the capacity configuration of fuel cell-storage battery power systems should design corresponding objective functions for the actual operating conditions of the ship, and solve them using optimization algorithms, transforming the research problem of capacity configuration of ship fuel cell power systems into an optimization problem involving multiple objectives and multiple variables.

[0004] Traditional ship-engine-propeller capacity configuration matching methods are not suitable for fuel cell hybrid power systems, easily leading to power redundancy and high investment costs. This invention designs a corresponding objective function for actual ship operating conditions and solves it through an optimization algorithm. This transforms the research problem of ship fuel cell power system capacity configuration into an optimization problem involving multiple objectives and variables, allowing the fuel cell power system performance to be fully utilized. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the capacity configuration of a marine fuel cell power system to meet the load requirements of the ship; it can reduce the economic cost and hydrogen consumption of the system and improve the service life of the fuel cell system.

[0006] The technical solution adopted in this invention is: A method for optimizing the capacity configuration of a marine fuel cell power system, characterized by the following steps: Establish an energy management strategy for marine fuel cell power systems. Through this strategy, the power demand of the ship will be allocated to the fuel cells and energy storage batteries within the marine fuel cell power system. The marine fuel cell power system includes fuel cells and energy storage batteries. Establish a mathematical model for a marine fuel cell-energy storage battery hybrid power system; Based on the energy management strategy of the marine fuel cell power system and the mathematical model of the marine fuel cell-storage battery hybrid power system, we construct the economic objective function, the output stability objective function, the hydrogen consumption objective function, and various constraints of the marine fuel cell hybrid power system. Based on the economic objective function, the fuel cell output stability objective function, the hydrogen consumption objective function, and the constraints, the capacity configuration of the ship's fuel cell power system is optimized using a particle swarm optimization algorithm with multi-objective optimization.

[0007] Preferably, the energy management strategy for the marine fuel cell power system includes the following steps: S1, The original ship power demand signal (t), decomposed into several IMFs using the EMD algorithm. i component h i (t) and residual components res; S2. Based on MSAM, select the filter coefficient k and reconstruct the IMF components of each order into low-frequency power components and high-frequency power components. S3. High-frequency power component reconstruction generates high-frequency reconstruction components of each order by superimposing the EMD decomposition results. Low-frequency power component reconstruction generates low-frequency reconstructed components of various orders by superimposing the EMD decomposition results. If the filter order k=n, the division starts from IMFn, and the IMF components before IMFn and the IMFn components are superimposed and reconstructed into high-frequency power components. The components following the IMFn component are superimposed and reconstructed into low-frequency power components. ; S4. Treat the high-frequency power component as a high-frequency power signal. The low-frequency power component is allocated to the energy storage battery pack as a low-frequency power signal. Distribute power to the fuel cell and determine the low-frequency power at each moment. Is it within the high-efficiency operating range of the fuel cell, i.e., the minimum output power? With maximum output power This allows the fuel cell to operate in a stable and efficient range.

[0008] Preferably, in step S4, the low-frequency power signal is processed using a logic threshold method. The process of allocating resources to the fuel cell, enabling the fuel cell system to operate in a high-efficiency range, includes the following steps: S4-1, if Less than Then P fc =0.3*P0, P bat =P r -0.3*P0; S4-2, if Greater than Then P fc =P0,P bat =P r -P0; S4-3, if Greater than and less than Then P fc = P bat = ; in This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. l (t) represents the low-frequency power at each moment, P l For low-frequency power signals, P fc P0 is the rated power of the fuel cell, Pbat is the output power of the energy storage battery, Pr is the power required for ship operation, and Ph is the high-frequency power signal.

[0009] Preferably, a mathematical model of a marine fuel cell-energy storage battery hybrid power system is established, including the hydrogen consumption of the fuel cell system and a mathematical model of the energy storage battery module based on performance parameters such as current, power and state of charge. Hydrogen consumption of fuel cell systems:

[0010] In the formula, Input power to the system; Hydrogen has a low calorific value;

[0011] in, This refers to the output power of the fuel cell; For fuel cell system efficiency;

[0012] Rated power of the fuel cell; This refers to the output power of the fuel cell; , , , and These are the fitting coefficients; A mathematical model for an energy storage battery module is constructed based on performance parameters such as current, power, and state of charge. Its expression is as follows:

[0013]

[0014]

[0015] In the formula, For the output power of the energy storage battery, To output current to the energy storage battery, This is the output voltage of the fuel cell. To store energy for energy storage batteries, This refers to the rated capacity of the energy storage battery. This indicates the remaining capacity of the energy storage battery at different times.

[0016] The expansion of energy storage by battery is achieved through the series and parallel connection of individual cells, as expressed in the following formula:

[0017] In the formula and This represents the number of series and parallel connections of individual battery cells; , , , and These are the rated capacity, voltage, current, output power, and stored energy of the energy storage battery pack.

[0018] Preferably, the economic objective function of the aforementioned marine fuel cell hybrid power system is based on the investment cost of the power system equipment. To represent the economy of the system, its expression is:

[0019] In the formula, and These are the purchase costs of fuel cells and the purchase costs of individual energy storage battery cells, respectively. Rated power of the fuel cell system; and These represent the number of energy storage battery cells connected in series and the number of energy storage battery cells connected in parallel, respectively.

[0020] Preferably, the objective function for output stability of the fuel cell system is expressed as follows:

[0021] In the formula, For the output power fluctuation rate of the fuel cell system, The output power fluctuation of the fuel cell system under the entire ship's operating conditions is denoted by t, where t is time.

[0022] Preferably, the objective function for hydrogen consumption in a marine fuel cell hybrid power system is expressed as follows:

[0023] In the formula, The hydrogen consumption rate of the fuel cell system is given by t, where t is time. Hydrogen consumption of the fuel cell system under all ship operating conditions.

[0024] Preferably, the various constraints include fuel cell output power constraints, energy storage battery pack output power constraints, energy storage battery SOC constraints, and power balance constraints, the expressions of which are as follows:

[0025] In the formula, This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. fc Pbat represents the output power of the fuel cell, Pbat represents the output power of the energy storage battery, Pbat-min represents the minimum output power of the energy storage battery, Pbat-max represents the maximum output power of the energy storage battery, and the energy storage battery's SOC constraint is also mentioned. Power required for ship operating conditions.

[0026] Preferably, based on the economic objective function, the fuel cell output stability objective function, the hydrogen consumption objective function, and the constraints, the capacity configuration of the ship's fuel cell power system is optimized using a multi-objective optimization particle swarm optimization algorithm. The specific process includes the following steps: Step 1: Set the number of iterations for the multi-objective optimization particle swarm optimization algorithm. Under the constraints, initialize the particle population N to obtain the initial system capacity configuration scheme module parameters: The system capacity configuration scheme module parameters include the rated power of the fuel cell system. and ; Then, combined with the power requirements of the ship's operating conditions. Parameters of the system capacity configuration scheme module After energy allocation is completed through the energy management strategy of the marine fuel cell power system, the corresponding economic objective function value, output stability objective function value and system hydrogen consumption objective function value of this configuration module are calculated by combining the mathematical model of the marine fuel cell-storage battery hybrid power system. The initial particle optimal fitness value Pbest and non-dominated set rep are updated accordingly. Step 2: Update the particle position and velocity through the multi-objective particle swarm algorithm to obtain the updated system capacity configuration scheme module parameters. After completing the energy allocation through the energy management strategy of the ship fuel cell power system, combine the mathematical model of the ship fuel cell-storage battery hybrid power system to calculate the three objective function values ​​under the new configuration scheme module, and mutate and update the optimal fitness value Pbest and the non-dominated set rep, and iterate in this way. Step 3: Repeat step 2 above for iteration until the set number of iterations is reached, and obtain the optimal solution set of the non-dominated set rep. That is, the optimal configuration scheme of the ship fuel cell power system is solved based on the multi-objective particle swarm optimization algorithm.

[0027] Preferably, in step 2, the principle for updating the optimal fitness value Pbest and the non-dominated set rep under the three objective function values ​​of the new configuration scheme module is as follows: when comparing the three objective function values ​​of the configuration scheme module in the non-dominated set rep with the three objective function values ​​of the new configuration scheme module, if all three objective function values ​​of a configuration scheme module are lower than those of any other configuration scheme module, then the configuration scheme module is excluded from the non-dominated set rep; otherwise, the configuration scheme module is retained in the non-dominated set rep, thereby updating the optimal fitness value Pbest and the non-dominated set rep.

[0028] Preferably, the energy storage battery includes a storage battery and / or a lithium battery.

[0029] The beneficial effects of this invention are: 1. This invention constructs an economic objective function, a fuel cell system output stability objective function, a hydrogen consumption objective function, and various constraints for a marine fuel cell hybrid power system by using an energy management strategy for the marine fuel cell power system and a mathematical model of the marine fuel cell-storage battery hybrid power system. Based on this, a multi-objective optimization particle swarm optimization algorithm is used to optimize the capacity configuration of the marine fuel cell power system. Under various objective function values, various relatively optimized configuration schemes are obtained to adapt to different operating conditions, meet the requirements of various ship loads and operating conditions, reduce the economic cost and hydrogen consumption of the system, and improve the service life of the fuel cell system.

[0030] 2. The energy management strategy for marine fuel cell power systems based on EMD-logic thresholds established in this invention can keep the fuel cell in a stable and efficient operating range, and the energy storage battery can effectively perform peak shaving and valley filling to reduce fuel cell output fluctuations and meet the needs of various ship loads and operating conditions. The invention also solves for an optimal configuration scheme for the fuel cell hybrid power system that can adapt to various operating conditions, thereby reducing the economic cost and hydrogen consumption of the system and improving the service life of the fuel cell system. Attached Figure Description

[0031] Figure 1 This is a flowchart of the method for optimizing the capacity configuration of a ship fuel cell power system in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the ship fuel cell-energy storage battery power system in an embodiment of the present invention; Figure 3 This is a flowchart of the energy management strategy for the ship fuel cell-storage battery power system in an embodiment of the present invention; Figure 4 This is a flowchart of the capacity configuration optimization method for marine fuel cell power systems based on particle swarm optimization in this embodiment of the invention. Detailed Implementation

[0032] 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.

[0033] In the description of this invention, it should be understood that if terms such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, they are only for the convenience of describing the invention and 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, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0034] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" 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 communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0035] Example 1 A method for optimizing the capacity configuration of a marine fuel cell power system, such as Figures 1-4 As shown, it includes the following steps: Establish an energy management strategy for marine fuel cell power systems, and through this strategy, transform the original ship power demand signal... (t) distributes energy between the fuel cell and energy storage battery in the marine fuel cell power system; the marine fuel cell power system includes the fuel cell and energy storage battery. Establish a mathematical model for a marine fuel cell-energy storage battery hybrid power system; Based on the energy management strategy of the marine fuel cell power system and the mathematical model of the marine fuel cell-storage battery hybrid power system, we construct the economic objective function, the output stability objective function, the hydrogen consumption objective function, and various constraints of the marine fuel cell hybrid power system. Based on economic objective functions, fuel cell output stability objective functions, hydrogen consumption objective functions, and constraints, a particle swarm optimization algorithm for multi-objective optimization is used to optimize the capacity configuration of a ship's fuel cell power system. Various optimized configuration results are obtained under different objective function values, adaptable to various operating conditions. These optimized configuration results can include multiple different biased capacity configuration schemes for the ship's fuel cell power system, such as comprehensive biased configuration schemes or biased configuration schemes targeting a specific objective function value. Therefore, different configuration schemes can be selected for different operating environments.

[0036] Example 2 The energy storage battery includes a storage battery and / or a lithium battery. Based on Example 1, in Example 2, the energy storage battery is a lithium battery.

[0037] The energy management strategy for the marine fuel cell power system includes the following steps: S1, The original ship power demand signal (t), decomposed into several IMFs using the EMD algorithm. i component h i (t) and residual components res; S2. Based on MSAM, select the filter coefficient k and reconstruct the IMF components of each order into low-frequency power components and high-frequency power components. S3. High-frequency power component reconstruction generates high-frequency reconstruction components of each order by superimposing the EMD decomposition results. Low-frequency power component reconstruction generates low-frequency reconstructed components of various orders by superimposing the EMD decomposition results. If the filter order k=n, the division starts from IMFn, and the IMF components before IMFn and the IMFn components are superimposed and reconstructed into high-frequency power components. The components following the IMFn component are superimposed and reconstructed into low-frequency power components. This leads to the formation of an energy management strategy for the ship's fuel cell power system, which facilitates the subsequent acquisition of indicators such as the economy and hydrogen consumption of the final power system through energy allocation among various power sources. S4. Treat the high-frequency power component as a high-frequency power signal. The low-frequency power component is allocated to the lithium battery pack as a low-frequency power signal. Distribute power to the fuel cell and determine the low-frequency power at each moment. Is it within the high-efficiency operating range of the fuel cell, i.e., the minimum output power? With maximum output power This allows the fuel cell to operate in a stable and efficient range.

[0038] The EMD-logic threshold energy management model established in this invention can keep the fuel cell in a stable and efficient operating range, and the energy storage battery can effectively perform peak shaving and valley filling to reduce the output fluctuation of the fuel cell and meet the load requirements of the ship. It can also solve for the optimal configuration scheme of the fuel cell hybrid power system to reduce the economic cost and hydrogen consumption of the system and improve the service life of the fuel cell system.

[0039] Further, in step S1, the original ship power demand signal (t), decomposed into several IMFs i component h i The expression for (t) and the residual component res is:

[0040] In the formula, For the first i The first-order intrinsic mode function components, and res are the residual components.

[0041] In step S2, based on MSAM, filter coefficients k are selected to reconstruct the IMF components of each order into low-frequency power components and high-frequency power components, the expression of which is:

[0042] In the formula, n is the order of the IMF being calculated, 1≤n≤p, p is the total number of IMFs, and mean() is the average value operator; when When the value deviates significantly from zero, it is assumed that the trend of EMD changes starting from the nth order, and k=n is set.

[0043] MSAM stands for the mean of standardized accumulated modes, and EMD stands for classical mode decomposition.

[0044] In step S3, the high-frequency power component reconstruction generates high-frequency reconstruction components of each order by superimposing them from top to bottom based on the EMD decomposition results. The expression for this is:

[0045] In the formula, f2c(p+1) is the p+1 order high-frequency reconstruction component, and IMFp is the p-th order IMF; In step S4, the low-frequency power component reconstruction generates low-frequency reconstruction components of various orders by superimposing them from bottom to top based on the EMD decomposition results. The expression for this is:

[0046] In the formula, c2f(p+1) is the p+1 order low-frequency reconstruction component; If the filter order k=n, then the division starts from IMFn. The IMF components before IMFn, including the IMFn components, are superimposed and reconstructed into high-frequency power components. The components following the IMFn component are superimposed and reconstructed into low-frequency power components. .

[0047] Furthermore, in step S4, the low-frequency power signal is processed using a logic threshold method. The process of allocating resources to the fuel cell, enabling the fuel cell system to operate in a high-efficiency range, includes the following steps: S4-1, if Less than Then P fc =0.3*P0, P bat =P r -0.3*P0; S4-2, if Greater than Then P fc =P0,P bat =P r -P0; S4-3, if Greater than and less than Then P fc = P bat = ; in This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. l (t) represents the low-frequency power at each moment, P l For a low-frequency power signal using low-frequency power components, P fc P0 is the output power of the fuel cell, and P0 is the rated power of the fuel cell. Pbat represents the power required for ship operation, Pbat represents the output power of the lithium battery, and Ph represents the high-frequency power signal represented by high-frequency power components.

[0048] Furthermore, a mathematical model of a marine fuel cell-energy storage battery hybrid power system is established, including the hydrogen consumption of the fuel cell system and a mathematical model of the lithium battery energy storage module based on performance parameters such as current, power and state of charge. Hydrogen consumption of fuel cell systems:

[0049] In the formula, Input power to the system; The lower heating value of hydrogen is taken as 120 MJ / kg;

[0050] in, This refers to the output power of the fuel cell; For fuel cell system efficiency;

[0051] Rated power of the fuel cell; This refers to the output power of the fuel cell; , , , and These are the fitting coefficients; A mathematical model for a lithium battery energy storage module is constructed based on performance parameters such as current, power, and state of charge. Its expression is as follows:

[0052]

[0053]

[0054] In the formula, For lithium battery output power, For lithium battery output current, This is the output voltage of the fuel cell. To store energy for lithium batteries The rated capacity of the lithium battery, This indicates the remaining capacity of the lithium battery at different times.

[0055] The expansion of lithium battery energy storage is achieved through the series and parallel connection of individual cells, as expressed in the following formula:

[0056] In the formula and This represents the number of series and parallel connections of individual battery cells; , , , and These are the rated capacity, voltage, current, output power, and stored energy of the lithium battery pack.

[0057] Furthermore, the economic objective function of the aforementioned marine fuel cell hybrid power system is based on the investment cost of the power system equipment. To represent the economy of the system, its expression is:

[0058] In the formula, and The figures are the purchase cost of fuel cells and the purchase cost of individual energy storage batteries, respectively, in units of ¥ / kW and ¥ / unit; Rated power of the fuel cell system; and These represent the number of lithium battery cells connected in series and the number of battery cells connected in parallel, respectively.

[0059] Furthermore, the objective function for the output stability of the fuel cell system is expressed as follows:

[0060] In the formula, For the output power fluctuation rate of the fuel cell system, The output power fluctuation of the fuel cell system under the entire ship's operating conditions is given by t, where t is time. This indicates the stability of the fuel cell system's output, in order to extend the lifespan of the fuel cell system.

[0061] Furthermore, the objective function for hydrogen consumption in a marine fuel cell hybrid power system is expressed as follows:

[0062] In the formula, The hydrogen consumption rate of the fuel cell system is given by t, where t is time. The hydrogen consumption of the fuel cell system under all ship operating conditions represents the system's fuel economy.

[0063] Furthermore, various constraints include fuel cell output power constraints, lithium battery pack output power constraints, lithium battery SOC constraints, and power balance constraints, the expressions of which are:

[0064] In the formula, This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. fc Pbat represents the output power of the fuel cell, Pbat represents the output power of the lithium battery, Pbat-min represents the minimum output power of the lithium battery, Pbat-max represents the maximum output power of the lithium battery, and the lithium battery SOC constraint is also mentioned. Power required for ship operating conditions.

[0065] Furthermore, in order to ensure that the fuel cell operates in a high-efficiency range to reduce hydrogen consumption, a setting is made... The operating range is determined; considering battery safety and lifespan, its effective SOC range is set to 20%–80%; to meet the power performance requirements of the ship, a power balance must be maintained, therefore the lithium battery output power is set accordingly. With fuel cell output power Then equals the power demand of the ship under operating conditions. .

[0066] Furthermore, based on the economic objective function, the fuel cell output stability objective function, the hydrogen consumption objective function, and the constraints, the capacity configuration of the ship's fuel cell power system is optimized using a multi-objective optimization particle swarm optimization algorithm. The specific process includes the following steps: Step 1: Set the number of iterations for the multi-objective optimization particle swarm optimization algorithm. Under the constraints, initialize the particle population N to obtain the initial system capacity configuration scheme module parameters: The system capacity configuration scheme module parameters include the rated power of the fuel cell system. and ; Then, combined with the power requirements of the ship's operating conditions. Parameters of the system capacity configuration scheme module After energy allocation is completed through the energy management strategy of the marine fuel cell power system, the corresponding economic objective function value, output stability objective function value and system hydrogen consumption objective function value of this configuration scheme module are calculated by combining the mathematical model of the marine fuel cell-storage battery hybrid power system. Finally, these three function values ​​are fed back to the configuration optimization model to update the initial particle optimal fitness value Pbest and the non-dominated set rep. Step 2: Update the particle positions and velocities using the multi-objective particle swarm optimization algorithm to obtain the updated system capacity configuration module parameters. Then, input these parameters into the ship fuel cell power system model as described above. After energy allocation is completed through the ship fuel cell power system energy management strategy, combine the ship fuel cell-storage battery hybrid power system mathematical model to calculate the three objective function values ​​under the new configuration module. Feed these values ​​back to the configuration optimization model to mutate and update the optimal fitness value Pbest and the non-dominated set rep, and iterate in this way. Step 3: Repeat step 2 above for iteration until the set number of iterations is reached, and obtain the optimal solution set of the non-dominated set rep. That is, the optimal configuration scheme of the ship fuel cell power system is solved based on the multi-objective particle swarm optimization algorithm.

[0067] Furthermore, the required power output under operating conditions of the target vessel can be determined using actual operating data. .

[0068] Furthermore, a model of a ship fuel cell power system is constructed by combining the energy management strategy of the ship fuel cell power system with the mathematical model of the ship fuel cell-storage battery hybrid power system.

[0069] In some embodiments, the number of iterations in step 3 may be set to at least 500.

[0070] Furthermore, in step 2, the principle for updating the optimal fitness value Pbest and the non-dominated set rep under the three objective function values ​​of the new configuration scheme module is as follows: when comparing the three objective function values ​​of all configuration scheme modules in the non-dominated set rep with the three objective function values ​​of the new configuration scheme module, if the three objective function values ​​of a configuration scheme module (here, the configuration scheme module is taken as both the existing configuration scheme modules and the new configuration scheme module in the non-dominated set rep) are lower than the three objective function values ​​of any other configuration scheme module (here, the configuration scheme module is taken as both the existing configuration scheme modules and the new configuration scheme module in the non-dominated set rep), then the configuration scheme module is excluded from the non-dominated set rep; otherwise, the configuration scheme module is retained in the non-dominated set rep, thereby updating the optimal fitness value Pbest and the non-dominated set rep.

[0071] Example 3 The structure of the marine fuel cell-energy storage battery power system targeted by this invention is as follows: Figure 1 As shown, the system mainly includes modules such as fuel cells, lithium battery packs, DC buses, DC / DC choppers, DC / AC inverters, and propulsion motors. Correspondingly, this invention also proposes an energy management strategy for the aforementioned marine fuel cell-storage battery power system. The specific energy management strategy process is as follows: Figure 2 As shown, an energy management strategy for a marine fuel cell power system based on EMD-logic gate constraints is used to decompose the ship's power demand into high- and low-frequency power components, and then the low-frequency power is determined at each moment. Is it within the high-efficiency operating range of the fuel cell, i.e., the minimum output power? With maximum output power This allows the fuel cell to operate in a stable and efficient range, while the energy storage battery effectively performs peak shaving and valley filling.

[0072] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0073] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for optimizing the capacity configuration of a marine fuel cell power system, characterized in that: Includes the following steps: Establish an energy management strategy for marine fuel cell power systems, which will be used to allocate energy between fuel cells and energy storage batteries in the marine fuel cell power system according to the power requirements of the ship. Establish a mathematical model for a marine fuel cell-energy storage battery hybrid power system; Based on the energy management strategy of the marine fuel cell power system and the mathematical model of the marine fuel cell-storage battery hybrid power system, we construct the economic objective function, the output stability objective function, the hydrogen consumption objective function, and various constraints of the marine fuel cell hybrid power system. Based on the economic objective function, the fuel cell output stability objective function, the hydrogen consumption objective function, and the constraints, the capacity configuration of the ship's fuel cell power system is optimized and solved using a particle swarm optimization algorithm for multi-objective optimization. Establish a mathematical model of a marine fuel cell-energy storage battery hybrid power system, including the hydrogen consumption of the fuel cell system and the mathematical model of the energy storage module of the energy storage battery based on performance parameters of current, power and state of charge. The energy management strategy for the marine fuel cell power system includes the following steps: S1, The original ship power demand signal (t), decomposed into several IMFs using the EMD algorithm. i component h i (t) and residual components res; S2. Based on MSAM, select the filter coefficient k and reconstruct the IMF components of each order into low-frequency power components and high-frequency power components. S3. High-frequency power component reconstruction generates high-frequency reconstruction components of each order by superimposing the EMD decomposition results. Low-frequency power component reconstruction generates low-frequency reconstructed components of various orders by superimposing the EMD decomposition results. If the filter order k=n, the division starts from IMFn, and the IMF components before IMFn and the IMFn components are superimposed and reconstructed into high-frequency power components. The components following the IMFn component are superimposed and reconstructed into low-frequency power components. ; S4. Treat the high-frequency power component as a high-frequency power signal. The low-frequency power component is allocated to the energy storage battery pack as a low-frequency power signal. Distribute power to the fuel cell and determine the low-frequency power at each moment. Is it within the high-efficiency operating range of the fuel cell, i.e., the minimum output power? With maximum output power This allows the fuel cell to operate in a stable and efficient range.

2. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: In step S4, the low-frequency power signal is processed using a logic threshold method. The process of allocating resources to the fuel cell, enabling the fuel cell system to operate in a high-efficiency range, includes the following steps: S4-1, if Less than Then P fc =0.3 P0, P bat =P r -0.3 P0; S4-2, if Greater than Then P fc =P0,P bat =P r -P0; S4-3, if Greater than and less than Then P fc = P bat = ; in This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. l (t) represents the low-frequency power at each moment, P l For low-frequency power signals, P fc P0 is the rated power of the fuel cell, Pbat is the output power of the energy storage battery, Pr is the power required for ship operation, and Ph is the high-frequency power signal.

3. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: Hydrogen consumption of fuel cell systems: In the formula, Input power to the system; Hydrogen has a low calorific value; in, This refers to the output power of the fuel cell; For fuel cell system efficiency; Rated power of the fuel cell; This refers to the output power of the fuel cell; , , , and These are the fitting coefficients; A mathematical model for an energy storage battery module is constructed based on performance parameters such as current, power, and state of charge. Its expression is as follows: In the formula, For the output power of the energy storage battery, To output current to the energy storage battery, This is the output voltage of the fuel cell. To store energy for energy storage batteries, This refers to the rated capacity of the energy storage battery. This indicates the remaining capacity of the energy storage battery at different times; The expansion of energy storage by battery is achieved through the series and parallel connection of individual cells, as expressed in the following formula: In the formula and This represents the number of series and parallel connections of individual battery cells; , , , and These are the rated capacity, voltage, current, output power, and stored energy of the energy storage battery pack.

4. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: The economic objective function of the aforementioned marine fuel cell hybrid power system is based on the investment cost of the power system equipment. To represent the economy of the system, its expression is: In the formula, and These are the purchase costs of fuel cells and the purchase costs of individual energy storage battery cells, respectively. Rated power of the fuel cell system; and These represent the number of energy storage battery cells connected in series and the number of energy storage battery cells connected in parallel, respectively.

5. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: The objective function for output stability of the fuel cell system is expressed as follows: In the formula, For the output power fluctuation rate of the fuel cell system, The output power fluctuation of the fuel cell system under the entire ship's operating conditions is denoted by t, where t is time.

6. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: The objective function for hydrogen consumption in a marine fuel cell hybrid power system is expressed as follows: In the formula, The hydrogen consumption rate of the fuel cell system is given by t, where t is time. Hydrogen consumption of the fuel cell system under all ship operating conditions.

7. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 1, characterized in that: The various constraints include fuel cell output power constraints, energy storage battery pack output power constraints, energy storage battery remaining capacity (SOC) constraints, and power balance constraints, the expressions of which are: In the formula, This represents the minimum output power of the fuel cell system. P represents the maximum output power of the fuel cell system. fc Pbat represents the output power of the fuel cell, Pbat-min represents the minimum output power of the energy storage battery, Pbat-max represents the maximum output power of the energy storage battery, and SOC represents the remaining capacity constraint of the energy storage battery. Power required for ship operating conditions.

8. The method for optimizing the capacity configuration of a marine fuel cell power system as described in any one of claims 1-7, characterized in that: Based on the economic objective function, the fuel cell output stability objective function, the hydrogen consumption objective function, and the constraints, the capacity configuration of the ship's fuel cell power system is optimized using a multi-objective optimization particle swarm optimization algorithm. The specific process includes the following steps: Step 1: Set the number of iterations for the multi-objective optimization particle swarm optimization algorithm. Under the constraints, initialize the particle population N to obtain the initial system capacity configuration scheme module parameters: The system capacity configuration scheme module parameters include the rated power of the fuel cell system. and ; Then, combined with the power requirements of the ship's operating conditions. After the energy allocation is completed through the energy management strategy of the ship fuel cell power system, the parameters of the system capacity configuration scheme module are combined with the mathematical model of the ship fuel cell-storage battery hybrid power system to calculate the corresponding economic objective function value, output stability objective function value and system hydrogen consumption objective function value of this configuration scheme module, so as to update the initial particle optimal fitness value Pbest and non-dominated set rep; Step 2: Update the particle position and velocity through the multi-objective particle swarm algorithm to obtain the updated system capacity configuration scheme module parameters. After completing the energy allocation through the energy management strategy of the ship fuel cell power system, combine the mathematical model of the ship fuel cell-storage battery hybrid power system to calculate the three objective function values ​​under the new configuration scheme module, and mutate and update the optimal fitness value Pbest and the non-dominated set rep, and iterate in this way. Step 3: Repeat step 2 above for iteration until the set number of iterations is reached, and obtain the optimal solution set of the non-dominated set rep. That is, the optimal configuration scheme of the ship fuel cell power system is solved based on the multi-objective particle swarm optimization algorithm.

9. The method for optimizing the capacity configuration of a marine fuel cell power system as described in claim 8, characterized in that: In step 2, the principle for updating the optimal fitness value Pbest and the non-dominated set rep under the three objective function values ​​of the new configuration scheme module is as follows: when comparing the three objective function values ​​of the configuration scheme module in the non-dominated set rep with the three objective function values ​​of the new configuration scheme module, if all three objective function values ​​of a configuration scheme module are lower than those of any other configuration scheme module, then the configuration scheme module is excluded from the non-dominated set rep; otherwise, the configuration scheme module is retained in the non-dominated set rep, thereby updating the optimal fitness value Pbest and the non-dominated set rep.

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