A system and method for emission optimization control of a hybrid wind-sail assisted fuel alternative vessel

Abstract

The embodiment of the application discloses a kind of alternative fuel sail navigation hybrid power ship emission optimization control system and method, its through emission characteristic analysis unit forms CO2 emission space-time distribution characteristic mapping table, through emission optimization decision unit, NSGA-III optimization algorithm is used to solve the CO2 emission optimization model constructed, obtains the optimization result corresponding to the lowest CO2 emission of alternative fuel sail navigation hybrid power ship;Through emission optimization evaluation unit, the CO2 emission level corresponding to the operation scheme of the alternative fuel sail navigation hybrid power ship after optimization is evaluated and analyzed;Meanwhile, through CO2 emission optimization control unit, the collaborative optimization control of alternative fuel sail navigation hybrid power ship is realized, and then the optimization control of alternative fuel sail navigation hybrid power ship with the lowest CO2 emission is realized.

Description

Technical Field

[0001] This invention relates to the field of alternative fuel sail-assisted hybrid power ship technology, and in particular to an emission optimization control system and method for alternative fuel sail-assisted hybrid power ships. Background Technology

[0002] Low-carbon development of ships is an inevitable trend for the shipping industry to cope with climate change and achieve a green and low-carbon transformation. To address the increasingly prominent drawbacks of traditional fossil fuels, reduce greenhouse gas emissions from ships, and promote the transformation and upgrading of the shipping industry's energy structure, achieving energy conservation and emission reduction in ships, particularly addressing pollution problems such as CO2 emissions from fossil fuel combustion, has become an urgent need for the shipping industry. The limitations of traditional fossil fuels make the search for alternative fuels inevitable. However, alternative fuels generally suffer from low energy density, resulting in significant deficiencies in ship power supply and range. Therefore, simply relying on alternative fuels cannot completely solve the energy and emission problems of ships.

[0003] Introducing wind power, a renewable energy source, can effectively compensate for the energy density limitations of alternative fuels and further improve the energy efficiency of ships. However, although the application of wind power alleviates the problem of low energy density of alternative fuels to some extent, hybrid-powered ships using alternative fuels and sails still face the challenge of optimizing CO2 emissions control in practice. CO2 emission control in hybrid-powered ships using alternative fuels and sails is influenced by many factors and is quite complex. Therefore, under complex ship operating conditions, the inability to effectively coordinate the relationship between alternative fuels and wind power to ensure continuous and optimized CO2 emission control remains a pressing technical challenge that needs to be addressed. Summary of the Invention

[0004] Based on this, in order to address the shortcomings of the aforementioned technical problems, a method and control system for optimizing emissions from alternative fuel-powered sail-assisted hybrid ships are proposed.

[0005] An emission optimization control system for alternative fuel-powered sail-assisted hybrid ships includes: a data acquisition and analysis unit, an emission characteristic analysis unit, an emission optimization decision unit, an emission optimization evaluation unit, and a CO2 emission optimization control unit;

[0006] The data acquisition and analysis unit is used to acquire various types of ship operation data, including but not limited to sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, fuel injection timing, wave height, wave direction, wind direction and wind speed data.

[0007] The emission characteristic analysis unit is used to obtain the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, so as to form a CO2 emission spatiotemporal distribution characteristic mapping table.

[0008] The emission optimization decision unit is used to solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing for the lowest CO2 emissions of the alternative fuel sail-assisted hybrid power ship. The CO2 emission optimization model combines the energy consumption of the alternative fuel sail-assisted hybrid power ship with the fuel carbon emission conversion coefficient to characterize the mapping relationship between the energy consumption and CO2 emissions of the alternative fuel sail-assisted hybrid power ship, and calculates the CO2 emission optimization results of the alternative fuel sail-assisted hybrid power ship.

[0009] The emission optimization assessment unit is used to evaluate and analyze the CO2 emission optimization results corresponding to the optimized operation scheme of the alternative fuel sail-assisted hybrid power ship based on the optimization results, and output the CO2 emission level of the optimized alternative fuel sail-assisted hybrid power ship.

[0010] The CO2 emission optimization control unit is used to achieve coordinated optimization control of alternative fuel sail-assisted hybrid power vessels based on the optimization results, so as to achieve the lowest CO2 emission optimization control of alternative fuel sail-assisted hybrid power vessels.

[0011] Optionally, in one embodiment, the different spatiotemporal distribution characteristics refer to the changes in CO2 emissions with the ship's sailing time and space.

[0012] Optionally, in one embodiment, the energy consumption of the alternative fuel sail-assisted hybrid power vessel is obtained through a pre-built energy consumption model of the alternative fuel sail-assisted hybrid power vessel, and the specific calculation formula is as follows:

[0013]

[0014] In the formula, q represents the energy consumption per unit distance of the ship; R represents the total resistance experienced by the hybrid power ship with alternative fuel and sail assistance; F T η represents the sail thrust; ω represents the wake coefficient; η represents the wake coefficient. S Indicates the transmission efficiency of the shaft system; η G Indicates transmission efficiency; η R J represents the relative rotational efficiency of the propeller; K represents the propulsion coefficient; Q The torque coefficient is represented by t; the thrust reduction factor is represented by g. main This indicates the host's power consumption rate.

[0015] Optionally, in one embodiment, the objective function and constraints of the CO2 emission optimization model are as follows:

[0016]

[0017] θ min ≤θ≤θ max (3)

[0018] v min ≤v≤v max (4)

[0019] α min ≤α≤α max (5)

[0020] P min ≤P≤P max (6)

[0021]

[0022] In the formula, The figure represents the CO2 emissions of a hybrid power vessel with alternative fuels and sail-assisted propulsion; k represents the fuel carbon emission conversion coefficient; θ represents the sail angle of attack; v represents the ship speed; α represents the fuel mixture ratio; and P represents the injection pressure. Indicating the jet timing, equation (3) defines the sail angle of attack constraint condition, θ min and θ max Representing the minimum and maximum sail angles of attack, respectively; Equation (4) defines the speed constraints for hybrid power vessels using alternative fuels and sails for navigation, v min and v max Equation (5) defines the minimum and maximum speeds of the hybrid power vessel with alternative fuel and sail-assisted propulsion, respectively; Equation (5) defines the fuel mixing ratio constraint condition, α min and α max These represent the minimum and maximum fuel mixture ratios for alternative fuel-powered sail-assisted hybrid vessels, respectively; Equation (6) defines the fuel injection pressure constraint condition, P min and P max Let represent the minimum fuel injection pressure and the maximum fuel injection pressure, respectively; Equation (7) defines the fuel injection timing constraint condition. and These represent the minimum and maximum fuel injection timing, respectively.

[0023] Optionally, in one embodiment, the step of solving the constructed CO2 emission optimization model using the NSGA-III optimization algorithm includes:

[0024] S1. Initialize and encode the parameters to be optimized; wherein, the parameters to be optimized are the sail angle of attack, ship speed, alternative fuel mixture ratio, fuel injection pressure, and fuel injection timing parameters;

[0025] S2. Based on the encoded parameters to be optimized, randomly generate the initial population P. t Each individual represents a possible operating configuration of a hybrid electric vessel with alternative fuel and sail-assisted navigation; the possible operating configuration specifically corresponds to each data combination form corresponding to the parameter to be optimized in the CO2 emission optimization model, wherein the individual fitness is the CO2 emission of the hybrid electric vessel with alternative fuel and sail-assisted navigation, and its specific individual fitness value is obtained through the optimization objective function of the CO2 emission optimization model;

[0026] S3. Based on individual fitness, the initial population P t Perform non-dominant treatment and generate offspring population Q t That is, for the initial population P t Individuals are sorted non-dominated by their corresponding CO2 emission fitness values ​​from smallest to largest. The n individuals with the lowest CO2 emissions are selected as the next generation, and crossover and mutation operations are performed to obtain the offspring population Q. t ;

[0027] S4. Initial population P t With offspring population Q t Combined, a new generation of population R is generated. t ;

[0028] S5, Calculate the population R t The individual fitness is determined, and individuals are sorted from smallest to largest based on the new individual fitness. The n individuals corresponding to low CO2 emissions are selected as the next generation to form a new parent population P. t+1 And perform crossover and mutation operations to obtain a new offspring population Q. t+1 ;

[0029] S6, Population P t+1 With Q t+1 Combined, a new population R is generated. t+1 The optimization is performed iteratively based on the S5 method until the preset termination condition is met and the optimization result is output. The termination condition can be the set number of iterations.

[0030] S7. The optimization results are transmitted to the emission optimization assessment unit and the CO2 emission optimization control unit to realize the assessment and control of CO2 emissions from alternative fuel sail-assisted hybrid ships.

[0031] Optionally, in one embodiment, the formula corresponding to the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel calculated by the emission optimization assessment unit is:

[0032]

[0033] In the formula, The CO2 emission index represents the alternative fuel sail-assisted hybrid power vessel, used to analyze the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel; M represents the load capacity of the alternative fuel sail-assisted hybrid power vessel; and D represents the sailing distance of the alternative fuel sail-assisted hybrid power vessel.

[0034] Optionally, in one embodiment, the control process of the collaborative optimization control includes: optimizing the angle of attack of the sail through the sail control system of the alternative fuel sail-assisted hybrid power ship, optimizing the ship speed through the ship control system, and controlling the mixing ratio of the alternative fuel, the fuel injection pressure and the fuel injection timing through the electronic control system of the alternative fuel engine according to the control parameters corresponding to the optimization results, so that the alternative fuel sail-assisted hybrid power ship achieves the best CO2 emission control effect during navigation.

[0035] Furthermore, to address the shortcomings of traditional technologies, an emission optimization control method based on the aforementioned alternative fuel sail-assisted hybrid power ship emission optimization control system is proposed, the corresponding steps of which include:

[0036] S81. Obtain various types of ship operation data through the data acquisition and analysis unit;

[0037] S82. By obtaining the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, a CO2 emission spatiotemporal distribution characteristic mapping table is formed.

[0038] S83. Solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing corresponding to the lowest CO2 emission of the alternative fuel sail-assisted hybrid power ship, so as to achieve the optimization control of CO2 emission of the alternative fuel sail-assisted hybrid power ship.

[0039] S84. Based on the optimization results, the decision control unit is optimized to achieve coordinated optimization control of key parameters such as sail angle of attack, ship speed, alternative fuel mixture ratio, fuel injection pressure, and fuel injection timing of the alternative fuel sail-assisted hybrid power vessel. This enables the alternative fuel sail-assisted hybrid power vessel to achieve the best CO2 emission control effect during navigation. At the same time, based on the optimization results, the CO2 emission index of the alternative fuel sail-assisted hybrid power vessel is calculated, thereby realizing the analysis and evaluation of the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel.

[0040] Furthermore, the present invention also proposes a computer-readable storage medium including computer instructions that, when executed on a computer, cause the computer to perform the method described thereon.

[0041] Implementing the embodiments of the present invention will have the following beneficial effects:

[0042] This invention addresses the challenges of CO2 emission control in hybrid-powered ships aided by alternative fuels and sails. By optimizing the synergistic use of wind energy and alternative fuels, it enhances the ship's emission control capabilities under various operating conditions, thereby achieving dual optimization of ship energy efficiency and emissions. This technological breakthrough not only contributes to the green transformation of the shipbuilding industry but also provides new technical support for the low-carbon development of the global shipping industry. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] in:

[0045] Figure 1 This is a framework diagram of the emission optimization control system for alternative fuel sail-assisted hybrid power ships described in this invention.

[0046] Figure 2 This is a diagram illustrating the solution process of the CO2 emission optimization model for alternative fuel sail-assisted hybrid ships based on the NSGA-III algorithm described in this invention.

[0047] Figure 3 This is a diagram illustrating the process of optimizing CO2 emission control for alternative fuel-powered sail-assisted hybrid ships as described in this invention.

[0048] Figure 4 This is a flowchart of the CO2 emission optimization control method for alternative fuel-powered sail-assisted hybrid ships according to the present invention. Detailed Implementation

[0049] 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.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. It is understood that the terms “first,” “second,” etc., as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element without departing from the scope of this application, and similarly, a second element may be referred to as a first element. Both the first element and the second element are elements, but they are not the same element.

[0051] In this embodiment, as Figure 1-4 As shown, an emission optimization control system for alternative fuel-powered sail-assisted hybrid ships is proposed, comprising: a data acquisition and analysis unit, an emission characteristic analysis unit, an emission optimization decision unit, an emission optimization evaluation unit, and a CO2 emission optimization control unit;

[0052] In some specific embodiments, the data acquisition and analysis unit integrates multiple sensor technologies to collect and acquire various types of ship operation data in real time. This ship operation data includes, but is not limited to, key parameter data such as sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, fuel injection timing, wave height, wave direction, wind direction, and wind speed. Specifically, the sensors involved include: angle sensors or dedicated angle-of-attack sensors to acquire sail angle of attack; marine speedometers, such as Doppler sonar speedometers or turbine speed sensors, to acquire ship speed; GPS to measure speed relative to the ground; fuel-air ratio sensors, such as broadband oxygen sensors or AFR sensors, to acquire fuel mixture ratio; pressure sensors installed on the fuel supply line to acquire fuel injection pressure; fuel injection timing managed by the engine control unit using multiple sensors; wave height acquired using buoy-type wave height meters, lidar, ultrasonic sensors, or accelerometers; wave direction acquired using direction sensors or buoy-type wave sensors; wind direction acquired using wind vanes; and wind speed acquired using anemometers, providing more accurate navigation information and performance parameters for subsequent units.

[0053] In some specific embodiments, the emission characteristic analysis unit is used to obtain the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, so as to form a CO2 emission spatiotemporal distribution characteristic mapping table. This table reflects the relationship between ship operation data and CO2 emissions under different time and space conditions (i.e., navigation conditions and navigation environment corresponding to different times and navigation positions). In this way, it guides ship operators to analyze and optimize the ship's carbon emission performance.

[0054] The different spatiotemporal distribution characteristics refer to the changes in CO2 emissions over time and space. Specifically, this includes different operating conditions (such as different geographical locations, different loads at different times, ship speed, sail angle of attack, fuel mixture ratio, etc.) and different navigation environment conditions (such as different geographical locations, different wind speeds, wind directions, wave heights, wave directions, etc.). For example, when a ship operates under heavy load and light load, high speed and low speed, different sail angle of attack settings, and different fuel mixture ratios, its CO2 emissions will exhibit different characteristics. Similarly, when a ship sails in different sea areas, CO2 emissions may vary significantly due to changes in wind force and ocean currents. Likewise, the CO2 emission characteristics of ships will also differ under different seasons or climatic conditions. By integrating multiple sensor technologies, real-time data on CO2 emission patterns under different operating conditions and spatiotemporal distribution patterns of CO2 emissions under different navigation positions and navigation environment conditions can be collected. This allows the decision-making model of the emission optimization decision unit to use these data tables to calculate carbon emissions under different parameter combinations through a fitness function and to find the optimal solution through the NSGA-III optimization algorithm. Ultimately, the optimized parameters output by the CO2 emission optimization model, including the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing, are applied in actual operation to achieve the lowest CO2 emissions for sail-assisted hybrid ships.

[0055] In some specific embodiments, the emission optimization decision unit is used to solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing for the lowest CO2 emissions of the alternative fuel sail-assisted hybrid power ship. The CO2 emission optimization model combines the energy consumption of the alternative fuel sail-assisted hybrid power ship with the fuel carbon emission conversion coefficient to characterize the mapping relationship between the energy consumption and CO2 emissions of the alternative fuel sail-assisted hybrid power ship, and calculates the CO2 emission optimization results of the alternative fuel sail-assisted hybrid power ship.

[0056] The energy consumption of alternative fuel sail-assisted hybrid power vessels is obtained through a pre-constructed energy consumption model. This model comprehensively considers various factors such as total ship resistance, sail thrust, shaft transmission efficiency, propeller relative rotation efficiency, and main engine energy consumption rate under different navigation environment conditions. It can realize the analysis and calculation of the energy consumption of alternative fuel sail-assisted hybrid power vessels. The specific calculation formula is as follows:

[0057]

[0058] In the formula, q represents the energy consumption per unit distance of the ship; R represents the total resistance experienced by the hybrid power ship with alternative fuel and sail assistance; F T η represents the sail thrust; ω represents the wake coefficient; η represents the wake coefficient. S Indicates the transmission efficiency of the shaft system; η G Indicates transmission efficiency; η R J represents the relative rotational efficiency of the propeller; K represents the propulsion coefficient; Q The torque coefficient is represented by t; the thrust reduction factor is represented by g. main Indicates the host power consumption rate;

[0059] The CO2 emission optimization model is used to calculate the CO2 emissions of the alternative fuel sail-assisted hybrid power vessel by combining the energy consumption with the fuel carbon emission conversion coefficient. This allows the model to determine optimized control schemes for reducing CO2 emissions from the alternative fuel sail-assisted hybrid power vessel based on different optimization parameters such as sail angle of attack, vessel speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing. Specifically, the objective function and constraints of the CO2 emission optimization model are as follows:

[0060]

[0061] θ min ≤θ≤θ max (3)

[0062] v min ≤v≤v max (4)

[0063] α min ≤α≤α max (5)

[0064] P min ≤P≤P max (6)

[0065]

[0066] In the formula, The figure represents the CO2 emissions of a hybrid power vessel with alternative fuels and sail-assisted propulsion; k represents the fuel carbon emission conversion coefficient; θ represents the sail angle of attack; v represents the ship speed; α represents the fuel mixture ratio; and P represents the injection pressure. Indicating the jet timing, equation (3) defines the sail angle of attack constraint condition, θ min and θ max Representing the minimum and maximum sail angles of attack, respectively; Equation (4) defines the speed constraints for hybrid power vessels using alternative fuels and sails for navigation, v min and v max Equation (5) defines the minimum and maximum speeds of the hybrid power vessel with alternative fuel and sail-assisted propulsion, respectively; Equation (5) defines the fuel mixing ratio constraint condition, α min and α max These represent the minimum and maximum fuel mixture ratios for alternative fuel-powered sail-assisted hybrid vessels, respectively; Equation (6) defines the fuel injection pressure constraint condition, P min and P max Let represent the minimum fuel injection pressure and the maximum fuel injection pressure, respectively; Equation (7) defines the fuel injection timing constraint condition. and These represent the minimum and maximum fuel injection timing, respectively.

[0067] The carbon emission conversion factor k depends on the characteristics of the specific alternative fuel. For example, the carbon emission conversion factor for heavy oil is 3.114. This factor is determined by comprehensively considering factors such as the fuel's chemical composition and combustion efficiency. This conversion factor allows the conversion of fuel consumption into corresponding carbon emissions.

[0068] The process involves defining the optimization objective as CO2 emission optimization, specifically minimizing CO2 emissions. The NSGA-III optimization algorithm is then used to solve the constructed CO2 emission optimization model, yielding the optimal solution. This optimal solution represents the lowest CO2 emission level for hybrid-powered ships using alternative fuels and sails, providing a basis for decision-making and assisting users in constructing specific CO2 emission optimization control schemes for their application scenarios. The specific steps include:

[0069] S1. Initialize and encode the parameters to be optimized; wherein, the parameters to be optimized are the sail angle of attack, speed, alternative fuel mixture ratio, fuel injection pressure, fuel injection timing, etc., and the parameters to be optimized are converted into a format that can be processed by the NSGA-III optimization algorithm through encoding, usually binary strings or other types of encoding forms;

[0070] S2. Based on the encoded parameters to be optimized, randomly generate the initial population P.t Each individual represents a possible operational configuration for a hybrid electric vessel with alternative fuel and sail-assisted propulsion. These possible operational configurations specifically correspond to the parameters to be optimized in the CO2 emission optimization model, such as sail angle of attack, ship speed, alternative fuel mix ratio, fuel injection pressure, and fuel injection timing, for each data combination. The individual fitness is the CO2 emission of the hybrid electric vessel with alternative fuel and sail-assisted propulsion, and its specific individual fitness value is obtained through the optimization objective function of the CO2 emission optimization model.

[0071] S3. Based on individual fitness, the initial population P t Perform non-dominant treatment and generate offspring population Q t That is, for the initial population P t Individuals are sorted non-dominated by their corresponding CO2 emission fitness values ​​from smallest to largest. The n individuals with the lowest CO2 emissions are selected as the next generation, and crossover and mutation operations are performed to obtain the offspring population Q. t ;

[0072] S4. Initial population P t With offspring population Q t Combined, a new generation of population R is generated. t ;

[0073] S5, Calculate the population R t The individual fitness is determined, and individuals are sorted from smallest to largest based on the new individual fitness. The n individuals corresponding to low CO2 emissions are selected as the next generation to form a new parent population P. t+1 And perform crossover and mutation operations to obtain a new offspring population Q. t+1 ;

[0074] S6, Population P t+1 With Q t+1 Combined, a new population R is generated. t+1 The optimization is performed iteratively based on the S5 method until the preset termination condition is met and the optimization result is output. The termination condition can be the set number of iterations.

[0075] S7. The optimization results are transmitted to the emission optimization assessment unit and the CO2 emission optimization control unit to realize the assessment and control of CO2 emission characteristics of alternative fuel sail-assisted hybrid ships.

[0076] In summary, solving the CO2 emission optimization model using the NSGA-III optimization algorithm can find the optimal solution under complex and variable environmental conditions, helping to reduce CO2 emissions and improve operational efficiency, and facilitating timely adjustments to ship operation strategies.

[0077] In some specific embodiments, the emission optimization assessment unit is used to evaluate and analyze the CO2 emission optimization results corresponding to the optimized operating scheme of the alternative fuel sail-assisted hybrid power vessel based on the optimization results, and output the optimized CO2 emission level of the alternative fuel sail-assisted hybrid power vessel. The evaluation and analysis method is shown in the following formula:

[0078]

[0079] In the formula, The CO2 emission index represents the alternative fuel sail-assisted hybrid power vessel, used to analyze the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel; M represents the load capacity of the alternative fuel sail-assisted hybrid power vessel; and D represents the sailing distance of the alternative fuel sail-assisted hybrid power vessel.

[0080] In some specific embodiments, a CO2 emission optimization control unit is used to achieve coordinated optimization control of the alternative fuel sail-assisted hybrid power vessel based on the optimization results, so as to achieve the lowest CO2 emission optimization control of the alternative fuel sail-assisted hybrid power vessel. The control process of the coordinated optimization control includes: optimizing the sail angle of attack through the sail control system of the alternative fuel sail-assisted hybrid power vessel, optimizing the ship speed through the ship control system, and controlling the mixing ratio of the alternative fuel, the fuel injection pressure, and the fuel injection timing through the alternative fuel engine electronic control system according to the control parameters corresponding to the optimization results, so that the alternative fuel sail-assisted hybrid power vessel achieves the best emission control effect during navigation.

[0081] The specific implementation steps of the emission optimization control system for alternative fuel-powered sail-assisted hybrid ships are as follows:

[0082] S71. Receive optimization results;

[0083] S72. Based on the optimization results, the sail angle of attack is adjusted through the sail control system to ensure that the sail can provide the maximum propulsion for the alternative fuel sail-assisted hybrid power vessel under the current environmental conditions such as wind direction and wind speed.

[0084] S73. Based on the optimization results, the ship speed is adjusted through the ship control system to achieve efficient navigation of hybrid power ships with alternative fuel and sail assistance;

[0085] S74. Based on the optimization results, the mixing ratio of alternative fuels, the fuel injection pressure, and the fuel injection timing are controlled by the electronic control system of the alternative fuel engine to ensure that the fuel can burn more completely and reduce carbon emission pollution.

[0086] By implementing synergistic optimization control of the aforementioned alternative fuel-powered sail-assisted hybrid power vessels, and based on the analysis of the CO2 emission reduction effect of alternative fuel-powered sail-assisted hybrid power vessels, the best CO2 emission control effect can be achieved.

[0087] Based on the same inventive concept, this invention also proposes an emission optimization control method for hybrid power ships using alternative fuels and sail-assisted navigation, the corresponding steps of which include:

[0088] S81. The data acquisition and analysis unit acquires the ship operation data required by the alternative fuel sail-assisted hybrid ship emission optimization control system for ship emission optimization decision-making and control, including but not limited to sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, fuel injection timing, wave height, wave direction, wind direction and wind speed data.

[0089] S82. By obtaining the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, a CO2 emission spatiotemporal distribution characteristic mapping table is formed. Then, the emission characteristics of alternative fuel sail-assisted hybrid ships are analyzed through the CO2 emission spatiotemporal distribution characteristic mapping table, including the CO2 emission characteristics of alternative fuel sail-assisted hybrid ships under different operating conditions, as well as the CO2 emission spatiotemporal distribution characteristics under different navigation positions and navigation environment conditions.

[0090] S83. Solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing corresponding to the lowest CO2 emission of the alternative fuel sail-assisted hybrid power ship, so as to achieve the optimization control of CO2 emission of the alternative fuel sail-assisted hybrid power ship.

[0091] S84. Based on the optimization results, the CO2 emission optimization control unit achieves coordinated optimization control of key parameters such as sail angle of attack, ship speed, alternative fuel mixture ratio, fuel injection pressure, and fuel injection timing of the alternative fuel sail-assisted hybrid power vessel. This enables the alternative fuel sail-assisted hybrid power vessel to achieve the best CO2 emission control effect during navigation. At the same time, based on the optimization results, the CO2 emission index of the alternative fuel sail-assisted hybrid power vessel is calculated, thereby realizing the analysis and evaluation of the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel.

[0092] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium including computer instructions that, when executed on a computer, cause the computer to perform the method described thereon.

[0093] Implementing the embodiments of the present invention will have the following beneficial effects:

[0094] This invention addresses the challenges of CO2 emission control in hybrid-powered ships aided by alternative fuels and sails. By optimizing the synergistic control mechanism of wind power and alternative fuels, it enhances the ship's CO2 emission control capabilities under different operating conditions, thereby optimizing both ship energy efficiency and CO2 emissions. This technological breakthrough not only contributes to the green transformation of the shipbuilding industry but also provides new technical support for the low-carbon development of the global shipping industry.

[0095] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An emission optimization control system for alternative fuel-powered sail-assisted hybrid power ships, comprising: The system includes a data acquisition and analysis unit, an emission characteristic analysis unit, an emission optimization decision-making unit, an emission optimization evaluation unit, and a CO2 emission optimization control unit. The data acquisition and analysis unit is used to acquire various types of ship operation data, including but not limited to sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, fuel injection timing, wave height, wave direction, wind direction and wind speed data. The emission characteristic analysis unit is used to obtain the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, so as to form a CO2 emission spatiotemporal distribution characteristic mapping table. The emission optimization decision unit is used to solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing for the lowest CO2 emissions of the alternative fuel sail-assisted hybrid power ship. The CO2 emission optimization model combines the energy consumption of the alternative fuel sail-assisted hybrid power ship with the fuel carbon emission conversion coefficient to characterize the mapping relationship between the energy consumption and CO2 emissions of the alternative fuel sail-assisted hybrid power ship, and calculates the CO2 emission optimization results of the alternative fuel sail-assisted hybrid power ship. The emission optimization assessment unit is used to evaluate and analyze the CO2 emission optimization results corresponding to the optimized operation scheme of the alternative fuel sail-assisted hybrid power ship based on the optimization results, and output the CO2 emission level of the optimized alternative fuel sail-assisted hybrid power ship. The CO2 emission optimization control unit is used to achieve coordinated optimization control of alternative fuel sail-assisted hybrid power vessels based on the optimization results, so as to achieve the lowest CO2 emission optimization control of alternative fuel sail-assisted hybrid power vessels.

2. The emission optimization control system for alternative fuel-powered sail-assisted hybrid power ships according to claim 1, characterized in that, The energy consumption of the alternative fuel sail-assisted hybrid power vessel is obtained through a pre-built energy consumption model, and the specific calculation formula is as follows: （1） In the formula, q Energy consumption per unit distance; R This indicates the total resistance experienced by a hybrid-powered vessel aided by alternative fuels and sails. Indicates the thrust of the sail; Indicates the wake coefficient; Indicates the transmission efficiency of the shaft system; Indicates transmission efficiency; Indicates the relative rotational efficiency of the propeller; Indicates the propulsion coefficient; Indicates the torque coefficient; Indicates the thrust reduction factor; This indicates the host's power consumption rate.

3. The alternative fuel-powered sail-assisted hybrid power ship emission optimization control system according to claim 2, characterized in that, The objective function and constraints of the CO2 emission optimization model are as follows: （2） （3） （4） （5） （6） （7） In the formula, This indicates the CO2 emissions of hybrid-powered ships equipped with alternative fuels and sails. Indicates the fuel carbon emission conversion factor; Indicates the angle of attack of the sail; Indicates the ship's speed; Indicates the fuel mixture ratio; Indicates injection pressure; Indicating the jet timing, equation (3) defines the sail angle of attack constraint condition. and Let represent the minimum and maximum sail angles of attack, respectively; Equation (4) defines the speed constraints for hybrid power ships with alternative fuel and sail-assisted navigation. and Let represent the minimum and maximum speeds of the hybrid power vessel with alternative fuel and sail-assisted propulsion, respectively; Equation (5) defines the fuel mixing ratio constraint condition. and These represent the minimum and maximum fuel mixture ratios for alternative fuel-powered sail-assisted hybrid ships, respectively; Equation (6) defines the fuel injection pressure constraint condition. and Let represent the minimum fuel injection pressure and the maximum fuel injection pressure, respectively; Equation (7) defines the fuel injection timing constraint condition. and These represent the minimum and maximum fuel injection timing, respectively.

4. The emission optimization control system for alternative fuel-powered sail-assisted hybrid power ships according to claim 1, characterized in that, The steps for solving the constructed CO2 emission optimization model using the NSGA-III optimization algorithm include: S1. Initialize and encode the parameters to be optimized; wherein, the parameters to be optimized are the sail angle of attack, ship speed, alternative fuel mixture ratio, fuel injection pressure, and fuel injection timing parameters; S2. Based on the encoded parameters to be optimized, randomly generate an initial population. P t Each individual represents a possible operating configuration of a hybrid electric vessel with alternative fuel and sail-assisted propulsion; the possible operating configuration specifically corresponds to each data combination form of the parameter to be optimized, and the individual fitness is the CO2 emission of the hybrid electric vessel with alternative fuel and sail-assisted propulsion, and its specific individual fitness value is obtained through the optimization objective function of the CO2 emission optimization model; S3. Initial population based on individual fitness P t Perform non-dominant treatment and generate offspring populations Q t That is, for the initial population P t The individuals in the dataset are non-dominated and ranked in ascending order of their CO2 emission fitness values. Those with low CO2 emissions are then selected. n Each individual is used as the next generation individual, and crossover and mutation operations are performed to obtain the offspring population. Q t ; S4. Initial population P t With offspring population Q t Combined, a new generation of population is generated. R t ; S5, Calculate the population R t The individual fitness was determined, and individuals were sorted from smallest to largest based on the new individual fitness to select those corresponding to low CO2 emissions. n Individuals, as the next generation, form a new parental population. P t+1 And perform crossover and mutation operations to obtain new offspring populations. Q t+1 ; S6, Population P t+1 and Q t+1 Combine to generate a new population. R t+1 The optimization is performed iteratively based on the S5 method until the preset termination condition is met and the optimization result is output. The termination condition is the set number of iterations. S7. The optimization results are transmitted to the emission optimization assessment unit and the CO2 emission optimization control unit to realize the assessment and control of CO2 emissions from alternative fuel sail-assisted hybrid ships.

5. The alternative fuel-powered sail-assisted hybrid power ship emission optimization control system according to claim 1, characterized in that, The formula corresponding to the CO2 emission level of alternative fuel sail-assisted hybrid ships calculated by the emission optimization assessment unit is as follows: （8） In the formula, This indicates the CO2 emissions of hybrid-powered ships equipped with alternative fuels and sails. The CO2 emission index represents the CO2 emission level of alternative fuel sail-assisted hybrid ships, and is used to analyze the CO2 emission level of alternative fuel sail-assisted hybrid ships. Indicates the payload of a hybrid-powered vessel equipped with alternative fuels and sails for navigation; This indicates the sailing distance of a hybrid-powered vessel aided by alternative fuels and sails.

6. The emission optimization control system for alternative fuel-powered sail-assisted hybrid power ships according to claim 1, characterized in that, The aforementioned collaborative optimization control process includes: optimizing the angle of attack of the sail through the sail control system of the alternative fuel sail-assisted hybrid power ship; optimizing the ship speed through the ship control system; and controlling the mixing ratio of the alternative fuel, the fuel injection pressure, and the fuel injection timing through the electronic control system of the alternative fuel engine according to the control parameters corresponding to the optimization results, so that the alternative fuel sail-assisted hybrid power ship can achieve the best CO2 emission control effect during navigation.

7. An emission optimization control method based on the emission optimization control system for alternative fuel sail-assisted hybrid power ships as described in claim 1, characterized in that, The corresponding steps include: S81. Obtain various types of ship operation data through the data acquisition and analysis unit; S82. By obtaining the mapping relationship between ship operation data and CO2 emission data under different spatiotemporal distribution characteristics, a CO2 emission spatiotemporal distribution characteristic mapping table is formed. S83. Solve the constructed CO2 emission optimization model using the NSGA-III optimization algorithm to obtain the optimal sail angle of attack, ship speed, fuel mixture ratio, fuel injection pressure, and fuel injection timing corresponding to the lowest CO2 emission of the alternative fuel sail-assisted hybrid power ship, so as to achieve the optimization control of CO2 emission of the alternative fuel sail-assisted hybrid power ship. S84. Based on the optimization results, the key parameters of the alternative fuel sail-assisted hybrid power vessel are synergistically optimized and controlled by optimizing the decision control unit. The key parameters include sail angle of attack, ship speed, alternative fuel mixture ratio, fuel injection pressure, and fuel injection timing, so that the alternative fuel sail-assisted hybrid power vessel achieves the best CO2 emission control effect during navigation. At the same time, based on the optimization results, the CO2 emission index of the alternative fuel sail-assisted hybrid power vessel is calculated, thereby realizing the analysis and evaluation of the CO2 emission level of the alternative fuel sail-assisted hybrid power vessel.

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