Optimization method of new energy ship energy management system

Abstract

The invention discloses an optimization method for a new energy ship energy management system, and the method comprises the steps: S1, obtaining a priority assumption condition related to the energy use of a new energy ship according to a pre-created new energy ship system; s2, constructing a ship energy management mathematical model about a new energy ship system based on priority hypothesis conditions; s3, according to the constructed ship energy management mathematical model, obtaining an objective function model and optimization constraint conditions of ship energy for quantitatively evaluating advantages and disadvantages of different energy use schemes; s4, ship operation energy consumption and historical navigation data are collected and obtained; s5, according to the historical data, obtaining a neural network prediction model for pre-estimating the photovoltaic generating capacity and the ship navigation power demand based on weather prediction data; and S6, according to the neural network prediction model, obtaining estimated data of the photovoltaic power generation amount and the ship navigation power demand, and based on the target function model of the ship energy and the optimization constraint condition, obtaining an optimal energy use scheme of a corresponding new energy ship energy management system according to an optimization algorithm. The problems that an existing ship energy management system and a calculation method thereof have remarkable defects in the aspects of energy efficiency optimization, multi-energy coordination, dynamic response, operation simplicity and convenience, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capacity, and complex requirements and environmental protection requirements of modern shipping are difficult to meet are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy ship energy management, and in particular to an optimization method for a new energy ship energy management system. Background Art

[0002] In today's globalized context, the shipping industry, as an important pillar of global trade, faces tremendous environmental pressure and energy challenges. Traditional ship power systems mainly rely on fossil fuels, such as heavy oil and diesel, which lead to the emission of large amounts of greenhouse gases and harmful substances, seriously affecting the marine and atmospheric environment. As organizations such as the International Maritime Organization (IMO) gradually strengthen the control of ship emissions, governments and scientific research institutions around the world are actively exploring the application of clean energy on ships to achieve sustainable development of the shipping industry.

[0003] New energy ships, especially those using clean energy such as hydrogen, fuel cells, wind, and solar energy, are gradually becoming an important development direction for future shipping. However, the energy management of new energy ships faces complex challenges. First, there are differences in conversion efficiency and storage capacity between different energy sources. How to maximize energy utilization efficiency without affecting navigation safety becomes a key issue. Secondly, the power systems of new energy ships are more diversified, and effective optimization methods are needed to coordinate the energy distribution of various subsystems to achieve the best operating state. In addition, the energy consumption patterns of new energy ships under different navigation conditions are also significantly different, and dynamic optimization strategies are needed to adapt to the changing navigation environment. The existing energy management systems and calculation methods of new energy ships have the following deficiencies:

[0004] 1) Insufficient energy efficiency optimization: The existing ship energy management system has limitations in energy efficiency optimization, making it difficult to adjust energy distribution in real time under different navigation conditions, thus failing to maximize the use of available energy resources.

[0005] 2) Poor coordination of multi-energy systems: In the integration and management of multi-energy systems, existing methods lack an effective coordination mechanism, resulting in low conversion efficiency between different energy sources and failure to fully utilize the advantages of various energy sources.

[0006] 3) Limited response capability: Existing computing methods lack the ability to respond quickly to dynamic navigation environments and are unable to adjust energy in a timely manner according to changing navigation needs, affecting the overall energy efficiency and navigation safety of the system.

[0007] 4) Lack of intelligent management: Current energy management systems mostly rely on preset fixed parameters and empirical values, lack intelligent learning and adaptability, and are difficult to optimize and adjust according to actual operating conditions.

[0008] 5) Poor compatibility with new energy sources: Existing ship energy management systems are mostly designed based on traditional fossil fuel power, and show poor compatibility in integrating and managing new energy sources (such as hydrogen energy, fuel cells, wind energy, etc.).

[0009] 6) Lack of comprehensive optimization strategy: Existing calculation methods usually only optimize a single goal (such as energy saving or emission reduction), and lack a comprehensive, multi-objective optimization strategy for the entire ship system.

[0010] 7) Difficulty in maintenance and upgrading: Existing energy management systems have difficulties in maintenance and system upgrades, especially when system requirements change or new technologies are introduced, it is difficult to flexibly adapt and update.

[0011] In summary, the existing ship energy management systems and their calculation methods have significant defects in energy efficiency optimization, multi-energy coordination, dynamic response, ease of operation, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capabilities. They are difficult to meet the complex needs and environmental protection requirements of modern shipping, and a more advanced optimization method is urgently needed to improve the overall performance. Summary of the invention

[0012] The present invention provides a calculation optimization method for a new energy ship energy management system to overcome the above technical problems.

[0013] In order to achieve the above object, the technical solution of the present invention is:

[0014] A method for optimizing a new energy ship energy management system comprises the following steps:

[0015] S1: Obtain priority assumptions about energy use of new energy ships based on the pre-created new energy ship system;

[0016] S2: Based on the priority assumptions, a ship energy management mathematical model for the new energy ship system is constructed, and the ship energy management mathematical model includes a photovoltaic panel model, a hydrogen production electrolyzer model, a fuel cell model, and an energy storage lithium battery model;

[0017] S3: Based on the constructed ship energy management mathematical model, obtain the objective function model and optimization constraints of ship energy for quantitatively evaluating the advantages and disadvantages of different energy use schemes;

[0018] S4: Collect and obtain historical data on ship operation energy consumption and navigation;

[0019] The historical data includes heading weather forecast data, and power consumption data and photovoltaic power generation under the corresponding heading weather data;

[0020] The heading weather forecast data includes temperature, humidity, wind speed, sunshine duration and cloud cover;

[0021] S5: According to the historical data, a pre-established deep neural network model is trained to obtain a neural network prediction model for estimating photovoltaic power generation and ship navigation power demand based on weather forecast data;

[0022] S6: Obtain estimated data on photovoltaic power generation and ship navigation power demand based on the neural network prediction model, and obtain the optimal energy usage plan for the corresponding new energy ship energy management system based on the objective function model and optimization constraints of the ship energy according to the optimization algorithm.

[0023] Furthermore, the new energy ship system described in S1 includes a photovoltaic module, an energy storage lithium battery module, an electrolyzer module, a hydrogen storage tank module, a fuel cell module and an energy management system;

[0024] The photovoltaic module is used to absorb solar radiation when the ship is working to generate electrical energy, which is used to provide electrical energy for the ship's power system or transmit it to the energy storage lithium battery module and the electrolyzer module;

[0025] The energy storage lithium battery module is used to store the electric energy transmitted by the photovoltaic module for power consumption of the ship's power load;

[0026] The electrolyzer module is used to use the output electric energy of the photovoltaic module to perform hydrogen production operation, and transmit the produced hydrogen to the hydrogen storage tank module for storage;

[0027] The fuel cell module is used to use the hydrogen stored in the hydrogen storage tank module to perform an electrochemical reaction to consume the hydrogen and generate electrical energy for power consumption of the ship's power load;

[0028] The energy management system is used to manage the power generation of the photovoltaic module, the power storage of the energy storage lithium battery module, the power generation of the fuel cell module and the power consumption status of the ship.

[0029] Furthermore, the priority assumptions for energy use of new energy ships described in S1 are as follows:

[0030] S11: Obtain and compare the output power P of the photovoltaic module 光 The current power demand of the ship P 船 size;

[0031] If the output power of the photovoltaic module P 光 ≥Ship's current power demand P 船 , then the priority of energy use of new energy ships is to use photovoltaic modules to generate electricity;

[0032] S12: Obtain the difference between the output power of the photovoltaic module and the power required by the ship S 余1 ,

[0033] If the difference residual S 余1 Meet 50%P 船 ≥S 余1 ≥0, and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has not been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 Cache to energy storage lithium battery;

[0034] Otherwise, the difference residual S 余1 >50%P 船 , and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 The electrolyzer module is used to perform hydrogen production operations and store the obtained hydrogen in a hydrogen storage tank;

[0035] S13: If the output power of the photovoltaic module P 光 <Ship’s current power demand P 船 , then the priority of energy use in new energy ships is to use energy storage lithium battery modules and fuel cell modules to generate electricity.

[0036] Furthermore, the photovoltaic panel model in S2 is used to obtain key parameters of the objective function model for constructing ship energy, and includes the output current I pν With the output voltage V pv , whose expression is

[0037]

[0038] Where: I pv Represents the output current of the photovoltaic panel; N p With N s Indicates the number of photovoltaic panels connected in series and in parallel; I ph with I sd Indicates the current of the serial and parallel ports; V pv Represents the output voltage of photovoltaic; R sd With R sh It represents the equivalent resistance between nodes in parallel and series circuits; K represents the photovoltaic comprehensive efficiency coefficient; T represents the photovoltaic temperature;

[0039] The energy storage lithium battery model is used to obtain key parameters of the objective function model for constructing ship energy, including the output battery power of the energy storage lithium battery With input battery power Its expression is

[0040]

[0041] Where: E batt (t) represents the energy stored in the lithium battery at time t; η batt Indicates the charge and discharge efficiency; and Respectively represent the output and input battery power of the energy storage lithium battery; E batterymax Indicates the energy capacity of lithium batteries; SOC min With SOC max Indicates the minimum and maximum battery SOC capacity;

[0042] The hydrogen production electrolyzer model is used to obtain key parameters for constructing the objective function model of ship energy, including at least the input power P of the electrolyzer within a given time period. e,El (t), which is expressed as

[0043] P El (t) = η EL P e,El (t)

[0044]

[0045] Where: P e,EL (t) η EL They represent the input electrical energy to the electrolyzer, the hydrogen produced by the electrolyzer, and the efficiency of the electrolyzer in a given period of time, respectively; and represents the boundaries of the electrolyzer operating condition input; and Indicates the upper and lower limit slopes of the electrolytic cell;

[0046] The fuel cell model is used to obtain key parameters of the objective function model for constructing ship energy, including the output voltage V fc and the fuel cell current I fc , whose expression is

[0047] V out =NV fc

[0048] V fc =E nernst -v act -v conc -v ohmic

[0049]

[0050] Where: V out Indicates the stack voltage; V fc Indicates the output voltage, Enernst is the thermodynamic electromotive force, T fc Indicates temperature; and Represent the pressure of oxygen and hydrogen respectively; v act Expressed by the empirical coefficient δ 1,2,3,4 and Activation loss of composition; v ohm Represents ohmic voltage; R M With R c Represent electronic resistance and proton resistance respectively; v con represents the concentration overvoltage, j,j max , and β represent the maximum current, maximum current density, and concentration loss, respectively; I fc represents the fuel cell current.

[0051] Furthermore, the objective function model for obtaining ship energy in S3 is expressed as

[0052] P PV (t)+P F (t)+P LIB (t)+P g (t) = P D (t)+P e,EL (t)

[0053] sumP ePV (t)-sumP E (t) = 0

[0054] S SOC (t+1)=S SOC (t)-P LIB

[0055] S MH (t+1)=S MH (t)-(P E (t)×τ)×η EL -(P FC (t)×τ)×η F

[0056] P PV (t) = I PV *V PV

[0057] P E (t) = I fc *V fc

[0058]

[0059] Where: t represents the total running time, P g(t) represents the load power, P D represents the energy demand of the ship, P PV Represents photovoltaic power generation, P FC represents the fuel cell power, P LIB Indicates the power of the energy storage lithium battery, P E Indicates the power consumed by the hydrogen storage electrolyzer, P ePV represents excess photovoltaic power, S SOC Indicates the charging status of the lithium battery, S MH Indicates hydrogen storage capacity; η EL represents the efficiency of the electrolytic cell; N represents the overall operating time term; P g (t) i represents the load power of the ship at a certain time i; represents the average load power of the ship during the entire operation cycle; τ represents the power relaxation factor; η F represents the fuel cell efficiency; F represents the mean square error of the load power;

[0060] The optimization constraints include lithium battery constraints, fuel cell constraints, energy storage constraints, and ship stable operation constraints;

[0061] The lithium battery constraints:

[0062] 0≤P LIBc (t)≤Temp cha (t)

[0063] 0≤P LIBd (t)≤Temp dis (t)

[0064] Where: P LIBc (t) and P LIBd (t) represents the charging and discharging power of lithium battery, Temp cha (t) and Temp dis (t) represent the charge and discharge levels at time t, respectively;

[0065] The fuel cell constraints:

[0066] 0≤P E (t)≤Temp E (t)

[0067] 0≤P FC (t)≤Temp FC (t)

[0068] Where: Temp E (t) and Temp FC (t) represent the amount of hydrogen and electricity generated by the fuel cell, respectively;

[0069] The energy storage constraints:

[0070] SOC(t+1)=SOC(t)+P LIB (t)-P LIBd (t),0≤SOC(t)≤1

[0071] HSR(t+1)=HSR(t)+P E (t)-P FC (t),0≤HSR(t)≤1

[0072] Where: SOC(t) represents the amount of electricity stored in the lithium battery at time t, HSR(t) represents the amount of hydrogen stored at time t; HSR(t+1) represents the amount of hydrogen stored at time t+1; SOC(t+1) represents the amount of electricity stored in the lithium battery at time t+1;

[0073] The ship stable operation constraints:

[0074]

[0075] Furthermore, the S6 specifically includes the following steps:

[0076] S61: obtaining estimated data of photovoltaic power generation and ship navigation power demand according to the neural network prediction model, taking the objective function model as the individual fitness function in the optimization algorithm, and taking the allocation and use scheme of the photovoltaic panel model, hydrogen production electrolyzer model, fuel cell model and energy storage lithium battery model corresponding to the estimated data as a solution of the optimization algorithm;

[0077] S62: Select an optimization algorithm and set algorithm configuration parameters;

[0078] The optimization algorithm includes at least one of linear programming, nonlinear programming, genetic algorithm and particle swarm algorithm;

[0079] The algorithm configuration parameters include at least the population size and the maximum number of iterations;

[0080] S63: using the ship's new energy consumption data, navigation mission data, and performance data of each module as input data of the optimization algorithm, and according to the estimated data of photovoltaic power generation and ship navigation power demand, obtaining an energy use plan that satisfies the optimization constraints and makes the objective function model reach the optimal state through iterative calculation of the optimization algorithm;

[0081] Said navigation mission data includes at least ship type, ship tonnage, navigation distance, navigation time and operation time;

[0082] The performance data of each module at least includes

[0083] Photovoltaic performance data: the number of photovoltaic panel cells in series and parallel connection N p With N s ;

[0084] Electrolyzer performance data: Boundaries for electrolyzer operating condition inputs and

[0085] Lithium battery performance data: Energy capacity E of lithium battery batterymax ;

[0086] Fuel cell performance data: The amount of hydrogen produced by the electrolyzer in a given period of time

[0087] Beneficial effects: The present invention provides an optimization method for a new energy ship energy management system. By constructing a ship energy management mathematical model for a new energy ship system, an objective function model and optimization constraints for ship energy used to quantitatively evaluate the advantages and disadvantages of different energy usage plans are obtained. Through multi-energy coordinated optimization, efficient coordination and management of multiple energy systems are achieved, and the advantages of various new energy sources (such as hydrogen energy, photovoltaics, electric energy, etc.) are fully utilized to improve the overall system efficiency. By obtaining a neural network prediction model based on weather forecast data to estimate photovoltaic power generation and ship navigation power demand, it is possible to quickly respond to changing conditions during navigation and ensure the stability and efficient operation of the system in a dynamic environment. Based on the objective function model and optimization constraints of ship energy, the optimal energy usage plan corresponding to the new energy ship energy management system is obtained according to the optimization algorithm, which can improve the energy utilization efficiency of the ship under different navigation conditions by optimizing energy distribution in real time, significantly reduce fuel consumption and operating costs, significantly improve the overall performance of the ship energy management system, and promote the intelligent, green and efficient development of ship energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0089] Figure 1 It is a flow chart of the optimization method of the new energy ship energy management system of the present invention;

[0090] Figure 2 This is a schematic diagram of a new energy ship system pre-created in this embodiment;

[0091] Figure 3This is the core block diagram of the optimization method of the new energy ship energy management system in this embodiment;

[0092] Figure 4 This is a diagram showing the energy flow analysis results of the whole ship for excess photovoltaic hydrogen production in this embodiment;

[0093] Figure 5 This is a diagram showing the energy flow analysis results of the entire ship with constant power hydrogen production in this embodiment. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] This embodiment provides a method for optimizing a new energy ship energy management system. Figure 1 As shown, the following steps are included:

[0096] S1: Obtain priority assumptions about energy use of new energy ships based on the pre-created new energy ship system;

[0097] Specifically, Figure 2 As shown, the new energy ship system includes a photovoltaic module, an energy storage lithium battery module, an electrolyzer module, a hydrogen storage tank module, a fuel cell module and an energy management system;

[0098] The photovoltaic module is used to absorb solar radiation when the ship is working to generate electrical energy, which is used to provide electrical energy for the ship's power system or transmit it to the energy storage lithium battery module and the electrolyzer module;

[0099] The energy storage lithium battery module is used to store the electric energy transmitted by the photovoltaic module for power consumption of the ship's power load;

[0100] The electrolyzer module is used to use the output electric energy of the photovoltaic module to perform hydrogen production operation, and transmit the produced hydrogen to the hydrogen storage tank module for storage;

[0101] The fuel cell module is used to use the hydrogen stored in the hydrogen storage tank module to perform an electrochemical reaction to consume the hydrogen and generate electrical energy for power consumption of the ship's power load;

[0102] The energy management system is used to manage the power generation of photovoltaic modules, the power storage of energy storage lithium battery modules, the power generation of fuel cell modules, the power consumption of ships and the power consumption of electrolyzers;

[0103] In a specific embodiment, the priority assumptions regarding the use of energy for new energy ships are as follows:

[0104] S11: Obtain and compare the output power P of the photovoltaic module 光 The current power demand of the ship P 船 size;

[0105] If the output power of the photovoltaic module P 光 ≥Ship's current power demand P 船 , then the priority of energy use of new energy ships is to use photovoltaic modules to generate electricity;

[0106] Specifically, when the electricity generated by the photovoltaic system can meet or exceed the current power demand of the ship, the system should give priority to using photovoltaic power generation;

[0107] The judgment is mainly based on the real-time output power P of the photovoltaic system. 光 The current power demand of the ship P 船 If the PV output power is greater than or equal to the demand (P 光 ≥P 船 ), it is determined that photovoltaic power generation is used first;

[0108] S12: Obtain the difference between the output power of the photovoltaic module and the power required by the ship S 余1 ,

[0109] If the difference residual S 余1 Meet 50%P 船 ≥S 余1 ≥0, and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has not been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 Cache to energy storage lithium battery;

[0110] Otherwise, the difference residual S 余1 >50%P 船 , and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 The electrolyzer module is used to perform hydrogen production operations and store the obtained hydrogen in a hydrogen storage tank, where the electrolyzer is in an available state by default and the hydrogen storage tank has enough space to store the newly generated hydrogen;

[0111] S13: If the output power of the photovoltaic module P 光 <Ship’s current power demand P 船 , then the priority of energy use in new energy ships is to use energy storage lithium battery modules and fuel cell modules to generate electricity.

[0112] Specifically, during periods of low photovoltaic power generation, such as when sailing in rainy weather or at night, electricity is provided by energy storage batteries and fuel cells;

[0113] Judgment condition: The power generated by the photovoltaic system P 光 Not enough to meet the ship's power P 船 Demand, such as when the photovoltaic output power is extremely low, less than 20% of the normal power generation or zero due to rainy weather or no light at night, and the energy storage battery has sufficient power available for use, or the fuel cell has sufficient fuel for power generation.

[0114] The judgment basis is that the output power of the photovoltaic system is less than 20% of the normal power generation. 光 Far below demand, and the current state (charge or fuel level) of storage batteries and fuel cells;

[0115] S2: Based on the priority assumptions, a ship energy management mathematical model for the new energy ship system is constructed, and the ship energy management mathematical model includes a photovoltaic panel model, a hydrogen production electrolyzer model, a fuel cell model, and an energy storage lithium battery model;

[0116] Specifically, the photovoltaic panel model is used to obtain key parameters for constructing an objective function model of ship energy, and includes the output current I of the photovoltaic panel pv With the output voltage V pv , whose expression is

[0117]

[0118] Where: I pv Represents the output current of the photovoltaic panel; N p With N s Indicates the number of photovoltaic panels connected in series and in parallel; I ph with I sd Indicates the current of the serial and parallel ports; V pv Represents the output voltage of photovoltaic; R sd With R sh It represents the equivalent resistance between nodes in parallel and series circuits; K represents the photovoltaic comprehensive efficiency coefficient; T represents the photovoltaic temperature;

[0119] The energy storage lithium battery model is used to obtain key parameters of the objective function model for constructing ship energy, including the output battery power of the energy storage lithium battery With input battery power Its expression is

[0120]

[0121] Where: E batt (t) represents the energy stored in the lithium battery at time t; ηbatt Indicates the charge and discharge efficiency; and Respectively represent the output and input battery power of the energy storage lithium battery; E batterymax Indicates the energy capacity of lithium batteries; SOC min With SOC max Indicates the minimum and maximum battery SOC capacity;

[0122] The hydrogen production electrolyzer model is used to obtain key parameters for constructing the objective function model of ship energy, including at least the input power P of the electrolyzer within a given time period. e,El (t), which is expressed as

[0123] P El (t) = η EL P e,El (t)

[0124]

[0125] Where: P e,EL (t) η EL They represent the input electrical energy to the electrolyzer, the hydrogen produced by the electrolyzer, and the efficiency of the electrolyzer in a given period of time, respectively; and represents the boundaries of the electrolyzer operating condition input; and Indicates the upper and lower limit slopes of the electrolytic cell;

[0126] The fuel cell model is used to obtain key parameters of the objective function model for constructing ship energy, including the output voltage V fc and the fuel cell current I fc , whose expression is

[0127] V out =NV fc

[0128] V fc =E nernst -v act -v conc -v ohmic

[0129]

[0130]

[0131] v ohm =I FC (R M +R C )

[0132]

[0133] Where: V out Indicates the stack voltage; V fc Indicates the output voltage, E nernst is the thermodynamic electromotive force, T fc Indicates temperature; and Represent the pressure of oxygen and hydrogen respectively; v act Expressed by the empirical coefficient δ 1,2,3,4 and Activation loss of composition; v ohm Represents ohmic voltage; R M With R c Represent electronic resistance and proton resistance respectively; v con represents the concentration overvoltage, j,j max , and β represent the maximum current, maximum current density, and concentration loss, respectively; I fc represents the fuel cell current;

[0134] S3: Based on the constructed ship energy management mathematical model, obtain the objective function model and optimization constraints of ship energy for quantitatively evaluating the advantages and disadvantages of different energy use schemes;

[0135] The objective function model for obtaining ship energy is expressed as follows:

[0136] P PV (t)+P F (t)+P LIB (t)+P g (t) = P D (t)+P e,EL (t)

[0137] sumP ePV (t)-sumP E (t) = 0

[0138] S SOC (t+1)=S SOC (t)-P LIB

[0139] S MH (t+1)=S MH (t)-(P E (t)×τ)×η EL -(P FC (t)×τ)×η F

[0140] P PV (t) = I PV *V PV

[0141] P F (t) = I fc *V fc

[0142]

[0143] Where: t represents the total running time, P g (t) represents the load power, P D represents the energy demand of the ship, P PV Represents photovoltaic power generation, P FC represents the fuel cell power, P LIB Indicates the power of the energy storage lithium battery, P E Indicates the power consumed by the hydrogen storage electrolyzer, P ePV represents excess photovoltaic power, S SOC Indicates the charging status of the lithium battery, S MH Indicates hydrogen storage capacity; η EL represents the efficiency of the electrolytic cell; N represents the overall operating time term; P g (t) i represents the load power of the ship at a certain time i; represents the average load power of the ship during the entire operation cycle; τ represents the power relaxation factor; η F represents the fuel cell efficiency; F represents the mean square error of the load power;

[0144] The optimization constraints include lithium battery constraints, fuel cell constraints, energy storage constraints and ship stable operation constraints;

[0145] The lithium battery constraints:

[0146] 0≤P LIBc (t)≤Temp cha (g)

[0147] 0≤P LIBd (t)≤Temp dis (t)

[0148] Where: P LIBc (t) and P LIBd (t) represents the charging and discharging power of lithium battery, Temp cha (t) and Temp dis (t) represent the charge and discharge levels at time t, respectively;

[0149] The fuel cell constraints:

[0150] 0≤P E (t)≤Temp E (t)

[0151] 0≤P FC (t)≤Temp FC (t)

[0152] Where: Temp E (t) and Temp FC (t) represent the amount of hydrogen and electricity generated by the fuel cell, respectively;

[0153] The energy storage constraints:

[0154] SOC(t+1)=SOC(t)+P LIB (t)-P LIBd (t),0≤SOC(t)≤1

[0155] HSR(t+1)=HSR(t)+P E (t)-P FC (t),0≤HSR(t)≤1

[0156] Where: SOC(t) represents the amount of electricity stored in the lithium battery at time t, HSR(t) represents the amount of hydrogen stored at time t; HSR(t+1) represents the amount of hydrogen stored at time t+1; SOC(t+1) represents the amount of electricity stored in the lithium battery at time t+1;

[0157] The ship stable operation constraints:

[0158] Temp cha (t)+Temp dis (t) = 1

[0159] 0≤Temp E (t)+Temp FC (t)≤1

[0160] 0≤Temp cha (t)+Temp FC (t)≤1

[0161] 0≤Temp dis (t)+Temp E (t)≤1

[0162] S4: Collect and obtain historical data on ship operation energy consumption and navigation;

[0163] The historical data includes heading weather forecast data, and power consumption data and photovoltaic power generation under the corresponding heading weather data;

[0164] S5: Based on the historical data, a pre-established deep neural network model is trained to obtain a neural network prediction model for estimating photovoltaic power generation and ship navigation power demand based on weather forecast data, specifically including the following steps:

[0165] S51: Dataset acquisition:

[0166] Collect and collect historical data on ship operation energy consumption and navigation, including:

[0167] Weather forecast data for the route: such as temperature, humidity, wind speed, sunshine duration, cloud cover, etc. These data can be obtained from the meteorological department;

[0168] Ship power power consumption data: Collect historical power consumption data from the historical operation data of ship power equipment;

[0169] Photovoltaic power generation data: records the actual power generated by the photovoltaic system within a preset time period. This data can be obtained from the monitoring platform of the photovoltaic power generation system;

[0170] A complete data set is obtained based on the heading weather forecast data, historical power consumption data, and photovoltaic power generation data, the heading weather forecast data is used as feature data, the power consumption data and photovoltaic power generation data are used as label data, and data standardization or normalization processing is performed on them;

[0171] S52: Model selection and training

[0172] Use the commonly used prediction neural network models (including but not limited to deep convolutional neural network CNN and BP neural network, etc.), divide the processed data set into training set, validation set and test set, and the general preset ratio is 70:20:10. Use the training set to train the selected model, and use the test set to evaluate the prediction performance of the model, and use the mean square error (MSE), mean absolute error (MAE) and other indicators to measure the prediction performance of the model;

[0173] S53: Prediction and Application

[0174] By inputting future weather forecast data into the trained model, the future photovoltaic power generation and ship navigation power demand can be predicted;

[0175] S6: Obtain estimated data on photovoltaic power generation and ship navigation power demand based on the neural network prediction model, and obtain the optimal energy usage plan for the corresponding new energy ship energy management system based on the objective function model and optimization constraints of the ship energy according to the optimization algorithm

[0176] The specific steps include:

[0177] S61: obtaining estimated data of photovoltaic power generation and ship navigation power demand according to the neural network prediction model, taking the objective function model as the individual fitness function in the optimization algorithm, and taking the allocation and use scheme of the photovoltaic panel model, hydrogen production electrolyzer model, fuel cell model and energy storage lithium battery model corresponding to the estimated data as a solution of the optimization algorithm;

[0178] S62: Select an optimization algorithm and set algorithm configuration parameters;

[0179] The optimization algorithm includes at least one of linear programming, nonlinear programming, genetic algorithm and particle swarm algorithm; the algorithm configuration includes at least population size and maximum number of iterations;

[0180] Specifically, according to the characteristics and complexity of the mathematical model, select the appropriate optimization algorithm to solve it. Common optimization algorithms include linear programming, nonlinear programming, genetic algorithm, particle swarm algorithm, etc.

[0181] Algorithm configuration: Configure algorithm parameters, such as population size, number of iterations, crossover probability, mutation probability, etc., to ensure the effectiveness and efficiency of the algorithm.

[0182] S63: The ship energy usage data, navigation mission data and equipment performance data are used as input data of the optimization algorithm, and based on the estimated data of photovoltaic power generation and ship navigation power demand, the optimization algorithm is iteratively calculated to obtain an energy usage plan that meets the optimization constraints and makes the objective function model reach the optimal state.

[0183] In this embodiment, the energy management system plays a vital role in ships or similar multi-energy systems, especially in the management of complex energy conversion and storage processes such as photovoltaic power generation, lithium battery storage, fuel cell power generation, ship power consumption and electrolyzer power consumption. The calculation of the energy balance strategy is aimed at ensuring that the energy flow between all energy components reaches the optimal state to meet the ship's power needs and maximize energy utilization efficiency.

[0184] The calculation of energy balance strategy mainly includes the following aspects:

[0185] 1) Demand forecasting: It is necessary to predict the power demand of the ship in the future. It is usually based on factors such as the ship's sailing plan, current sailing status (such as speed, heading), load conditions (such as lighting, power system, etc.) and historical data.

[0186] 2) Energy supply assessment, which evaluates the availability and output capacity of various energy supply sources (photovoltaic power generation, fuel cells, lithium batteries, etc.). This includes considering factors such as weather conditions, fuel cell fuel inventory, lithium battery remaining power and charge and discharge rates.

[0187] 3) Application of optimization algorithms: Use optimization algorithms to calculate the best energy allocation strategy to find a solution that meets electricity demand while minimizing energy usage.

[0188] In the "energy allocation strategy" stage of ship operation energy management, such as Figure 3 As shown, it is mainly through a series of mathematical methods, algorithms or tools to fine-tune and optimize the energy use of ships to achieve the predetermined optimization goals (such as minimum energy cost, minimum main energy use, minimum greenhouse gas emissions, etc.). The following are the specific steps of the energy allocation strategy:

[0189] A. Determine the optimization objectives and optimization constraints

[0190] Optimization objectives: clarify the optimization objectives of ship operation energy management to improve energy-efficient operation, etc.

[0191] Constraints: Analyze and determine various constraints during ship operation, including navigation mission requirements, equipment performance limitations, etc.

[0192] B. Build a mathematical model

[0193] Objective function: According to the optimization goal, a corresponding objective function is established to quantitatively evaluate the advantages and disadvantages of different energy use schemes.

[0194] Constraint equations: Convert constraints into mathematical equations or inequalities to ensure that the optimization results are consistent with the actual situation.

[0195] C. Select optimization algorithm

[0196] Optimization algorithm: According to the characteristics and complexity of the mathematical model, select the appropriate optimization algorithm to solve it. Common optimization algorithms include linear programming, nonlinear programming, genetic algorithm, particle swarm algorithm, etc.

[0197] Algorithm configuration: Configure algorithm parameters, such as population size, number of iterations, crossover probability, mutation probability, etc., to ensure the effectiveness and efficiency of the algorithm.

[0198] D. Run the optimization algorithm

[0199] Input data: Input the ship’s new energy usage data, navigation mission data, equipment performance data, etc. into the optimization algorithm;

[0200] Said navigation mission data includes ship type: such as cargo ship, passenger ship, tanker, container ship, etc., ship tonnage, sailing distance, sailing time and operating time, etc.;

[0201] The device performance data includes device performance data:

[0202] Photovoltaic performance data: the number of photovoltaic panels connected in series and in parallel (N p With N s );

[0203] Electrolyzer performance data: Boundaries for electrolyzer operating conditions input ( and );

[0204] Lithium battery performance data: Energy capacity E of lithium battery batterymax ;

[0205] Fuel cell performance data: The amount of hydrogen produced by the electrolyzer in a given period of time

[0206] Optimize upper and lower limits of constraints, etc.

[0207] E. Algorithm execution: Run the optimization algorithm to find the energy usage plan that meets the constraints and makes the objective function reach the optimal state through iterative calculation, that is, the use of the "photovoltaic panel model, hydrogen electrolyzer model, fuel cell model and energy storage lithium battery model" corresponding to the "photovoltaic power generation and ship electricity demand estimation".

[0208] Beneficial effects: The present invention effectively overcomes the shortcomings of the existing ship energy management system by introducing advanced optimization methods, and has the following beneficial effects:

[0209] 1) By optimizing energy distribution in real time (i.e., realizing the use distribution plan of photovoltaic panel model, hydrogen electrolyzer model, fuel cell model and energy storage lithium battery model for ship load power supply according to different photovoltaic power generation and ship power demand), the energy utilization efficiency of ships under different navigation conditions is improved, and fuel consumption and operating costs are significantly reduced.

[0210] 2) Multi-energy coordination and optimization realizes efficient coordination and management of multiple energy systems, gives full play to the advantages of various new energy sources (such as hydrogen energy, photovoltaics, electric energy, etc.), and improves the overall system efficiency.

[0211] 3) The present invention can quickly respond to changing conditions during navigation, ensuring the stability and efficient operation of the system in a dynamic environment. That is, during navigation, the changing conditions mainly refer to those external and internal factors that affect the navigation status and energy system performance, which may include but are not limited to:

[0212] Natural environmental conditions: such as wind direction, wind speed, water flow speed, wave size, sea condition changes, etc. These factors directly affect the ship's navigation resistance and stability, and thus affect the load demand of the energy system.

[0213] Changes in navigation status: such as adjustments in ship speed, changes in course, obstacles or restricted areas on the route, etc. These changes require the energy system to be able to quickly adjust output to meet navigation needs.

[0214] Internal state of the energy system: such as the operating state of various new energy equipment (such as fuel cells, photovoltaics, hydrogen production, etc.), the charging and discharging state of the energy storage system, changes in energy conversion efficiency, etc. These changes in internal states also require the system to be able to respond quickly and make adjustments;

[0215] The basis for ensuring the stability and efficient operation of the system in a dynamic environment is:

[0216] A. Use sensors to monitor navigation conditions and energy system status in real time, providing data support for rapid response.

[0217] B. Based on real-time monitoring data, adjust the output and distribution of the energy system in real time to meet navigation needs.

[0218] C. Make full use of the complementarity and substitutability between different energy sources, and achieve optimal combination and complementary utilization of multiple energy sources through energy management. For example, increase the output of fuel cells when photovoltaic power is insufficient, or increase the direct output of electrolytic hydrogen production when the energy storage system has sufficient power.

[0219] D. Simple process The design is concise and intuitive, which reduces the complexity of the system and makes it easier for crew members to master and apply it flexibly in actual operations.

[0220] E. Compatible with multiple new energy sources: The present invention has good compatibility and can flexibly integrate and manage multiple new energy sources, providing strong support for the development of future new energy ships.

[0221] F. Using different optimization schemes can achieve a balance in energy saving, emission reduction, cost control and other aspects, and improve the overall benefits of the system.

[0222] Through the above beneficial effects, this embodiment significantly improves the overall performance of the ship energy management system and promotes the intelligent, green and efficient development of ship energy management.

[0223] In addition, this embodiment performs example calculations for the following two schemes: 1) Excessive photovoltaic hydrogen production ship operation scheme; 2) Constant power hydrogen production ship operation scheme. First, the input data collection phase collects relevant data on photovoltaic power generation, lithium batteries, proton exchange membrane fuel cells, and electrolyzers. Different energy sources such as system photovoltaic power generation, fuel cell power generation, lithium battery charging and discharging, and electrolyzer power consumption and hydrogen production are integrated into the yacht's energy system to ensure that they can work in coordination to meet the ship's energy needs.

[0224] The different energy sources are integrated into the yacht's energy system, that is, these different energy sources are integrated into the ship's power system through an energy management system, which monitors and dispatches the operating status of each energy source in real time to ensure that the energy can effectively match the ship's needs under different navigation conditions.

[0225] Photovoltaic power generation: The photovoltaic system collects solar energy and converts it into direct current. This electricity can be directly used for the daily power needs of the ship, or converted into alternating current for the ship through an inverter. Excess electricity can be stored in lithium batteries for future use.

[0226] Fuel cell power generation: Fuel cells use hydrogen and oxygen stored in hydrogen tanks to perform electrochemical reactions to produce electricity and water. Fuel cells can be used as a primary or backup power source to provide power when photovoltaic power generation is insufficient.

[0227] Lithium battery charging and discharging: Lithium batteries can not only store excess electricity from photovoltaic power generation, but also release the stored electricity during peak electricity demand periods to balance the supply and demand of the system. The charging and discharging control of lithium batteries is regulated by the energy management system to achieve optimal energy utilization.

[0228] Electrolyzers consume electricity to produce hydrogen: When photovoltaic power generation is sufficient or there is excess power from the grid, electricity can be used to electrolyze water to produce hydrogen, which is stored in hydrogen tanks. This process helps convert excess electrical energy into chemical energy storage so that it can be used to generate electricity through fuel cells when needed.

[0229] The method of this embodiment is used to optimize the scheduling of the above energy sources to ensure that the ship can operate efficiently and stably in different operating modes.

[0230] Next, energy supply and demand analysis is conducted based on the energy supply and demand of the ship's historical navigation (energy supply and demand analysis specifically includes data collection and collation of historical navigation data: collecting historical energy supply and demand data of the ship under different navigation conditions, including navigation speed, route, weather conditions, load conditions, etc. Energy supply data: collecting actual output data of different energy sources such as photovoltaic power generation, fuel cell power generation, lithium battery charging and discharging, and electrolyzer hydrogen production; energy demand data: recording the power demand of the ship at different stages of navigation, including the power consumption of the propulsion system, power system, and living facilities.) to ensure energy balance under various operating conditions. By setting the objective function model to minimize total energy consumption and maximize energy operation efficiency, the power balance calculation results of the above two schemes are finally used to guide the effective operation of the ship.

[0231] like Figure 4The result of excess photovoltaic hydrogen production (Scheme 1) is shown. Since the ship needs to work all day, a small amount of fixed power electricity will be produced to power the ship's load. From 0 to 5 o'clock, the photovoltaic panels cannot work because the sun has not risen yet. At this time, the power generated by the ship itself is not enough to support the load demand. Therefore, during this period of time, the battery and hydrogen fuel cell must work together to provide the necessary power for the ship's load. The battery provides part of the power by discharging, while the hydrogen fuel cell provides another part of the power by burning hydrogen. This combination ensures that the ship can operate stably even without photovoltaic power generation. From 7 to 16 o'clock, the photovoltaic panels start to work, providing power for the ship's load and generating excess photovoltaic power. During this period of time, the photovoltaic panels can not only meet the power demand of the ship, but also generate excess photovoltaic power. This part of power is all used to generate hydrogen. At this time, the power generated by the ship itself is all stored in the battery. In this way, during the day when photovoltaic power generation is sufficient, it can not only meet the current power demand, but also reserve energy for the night and the period without photovoltaic power generation. From 17 to 24 o'clock, as the sun sets, the photovoltaic panels gradually stop working. At this time, the battery discharges first to provide power for the ship's load. When the battery is low, the hydrogen fuel cell takes over and continues to supply power to the ship's load. This solution fully utilizes the power generation capacity of photovoltaic panels during the day, and at the same time provides a stable power supply at night and during periods when photovoltaic panels cannot work through energy storage equipment (batteries and hydrogen tanks). This not only improves the efficiency of energy utilization, but also ensures the stability and reliability of the ship's all-weather operation.

[0232] like Figure 5 The result of constant power hydrogen production (Scheme 2) is shown in the figure. As with Scheme 1, at 0-5, the working mode of the battery and the fuel cell is basically the same. However, at 7, 8, and 16, since hydrogen is produced constantly, when the photovoltaic panels can provide a certain amount of power but cannot provide it completely, the ship itself needs to provide some additional power to produce hydrogen, so the power of the ship's power grid increases. At 17-24, the photovoltaic panels gradually stop working. At this time, the hydrogen fuel cell first works to provide electricity for the ship's load. When the hydrogen reserves are insufficient, the battery takes over and continues to supply power to the ship's load.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing a new energy ship energy management system, characterized in that: The following steps are involved: S1: Obtain priority assumptions about energy use of new energy ships based on the pre-created new energy ship system; S2: Based on the priority assumptions, a ship energy management mathematical model for the new energy ship system is constructed, and the ship energy management mathematical model includes a photovoltaic panel model, a hydrogen production electrolyzer model, a fuel cell model, and an energy storage lithium battery model; S3: Based on the constructed ship energy management mathematical model, obtain the objective function model and optimization constraints of ship energy for quantitatively evaluating the advantages and disadvantages of different energy use schemes; S4: Collect and obtain historical data on ship operation energy consumption and navigation; The historical data includes heading weather forecast data, and power consumption data and photovoltaic power generation under the corresponding heading weather data; The heading weather forecast data includes temperature, humidity, wind speed, sunshine duration and cloud cover; S5: According to the historical data, a pre-established deep neural network model is trained to obtain a neural network prediction model for estimating photovoltaic power generation and ship navigation power demand based on weather forecast data; S6: Obtain estimated data on photovoltaic power generation and ship navigation power demand based on the neural network prediction model, and obtain the optimal energy usage plan for the corresponding new energy ship energy management system based on the objective function model and optimization constraints of the ship energy according to the optimization algorithm.

2. The optimization method of a new energy ship energy management system according to claim 1 is characterized in that: The new energy ship system described in S1 includes a photovoltaic module, an energy storage lithium battery module, an electrolyzer module, a hydrogen storage tank module, a fuel cell module and an energy management system; The photovoltaic module is used to absorb solar radiation when the ship is working to generate electrical energy, which is used to provide electrical energy for the ship's power system or transmit it to the energy storage lithium battery module and the electrolyzer module; The energy storage lithium battery module is used to store the electric energy transmitted by the photovoltaic module for power consumption of the ship's power load; The electrolyzer module is used to use the output electric energy of the photovoltaic module to perform hydrogen production operation, and transmit the produced hydrogen to the hydrogen storage tank module for storage; The fuel cell module is used to use the hydrogen stored in the hydrogen storage tank module to perform an electrochemical reaction to consume the hydrogen and generate electrical energy for power consumption of the ship's power load; The energy management system is used to manage the power generation of the photovoltaic module, the power storage of the energy storage lithium battery module, the power generation of the fuel cell module and the power consumption status of the ship.

3. The optimization method of a new energy ship energy management system according to claim 2 is characterized in that: The priority assumptions for energy use of new energy ships described in S1 are as follows: S11: Obtain and compare the output power P of the photovoltaic module 光 The current power demand of the ship P 船 size; If the output power of the photovoltaic module P 光 ≥Ship's current power demand P 船 , then the priority of energy use for new energy ships is to use photovoltaic modules to generate electricity; S12: Obtain the difference between the output power of the photovoltaic module and the power required by the ship S 余1 , If the difference residual S 余1 Meet 50%P 船 ≥S 余1 ≥0, and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has not been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 Cache to energy storage lithium battery; Otherwise, if the difference residual S 余1 >50%P 船 , and the preset maximum capacity of the energy storage lithium battery or the preset charging limit has been reached, the priority of energy use of new energy ships is to convert the difference remaining amount S 余1 The electrolyzer module is used to perform hydrogen production operations and store the obtained hydrogen in a hydrogen storage tank; S13: If the output power of the photovoltaic module P 光 <Ship’s current power demand P 船 , then the priority of energy use in new energy ships is to use energy storage lithium battery modules and fuel cell modules to generate electricity.

4. The optimization method of a new energy ship energy management system according to claim 1 is characterized in that: The photovoltaic panel model in S2 is used to obtain the key parameters of the objective function model for constructing the ship energy, and includes the output current I pv With output voltage V pv , whose expression is Where: I pv Represents the output current of the photovoltaic panel; N p With N s Indicates the number of photovoltaic panels connected in series and in parallel; I ph with I sd Indicates the current of the serial and parallel ports; V pv Represents the output voltage of photovoltaic; R sd With R sh It represents the equivalent resistance between nodes in parallel and series circuits; K represents the photovoltaic comprehensive efficiency coefficient; T represents the photovoltaic temperature; The energy storage lithium battery model is used to obtain key parameters of the objective function model for constructing ship energy, including the output battery power of the energy storage lithium battery With input battery power Its expression is Where: E batt (t) represents the energy stored in the lithium battery at time t; η batt Indicates the charge and discharge efficiency; and Respectively represent the output and input battery power of the energy storage lithium battery; E batterymax Indicates the energy capacity of lithium batteries; SOC min With SOC max Indicates the minimum and maximum battery SOC capacity; The hydrogen production electrolyzer model is used to obtain key parameters for constructing the objective function model of ship energy, including at least the input power P of the electrolyzer within a given time period. e，El (t), which is expressed as P El (t)=η EL P e，El (t) Where: P e，EL (t) η EL They represent the input electrical energy to the electrolyzer, the hydrogen produced by the electrolyzer, and the efficiency of the electrolyzer in a given period of time, respectively; and represents the boundaries of the electrolyzer operating condition input; and Indicates the upper and lower limit slopes of the electrolytic cell; The fuel cell model is used to obtain key parameters of the objective function model for constructing ship energy, including the output voltage V fc and the fuel cell current I fc , whose expression is V out =NV fc V fc =E nernst -v act -v conc -v ohmic v ohm =I FC (R M +R C ) Where: V out Indicates the stack voltage; V fc Indicates the output voltage, E nernst represents the thermodynamic electromotive force, T fc Indicates temperature; and Represent the pressure of oxygen and hydrogen respectively; v act Expressed by the empirical coefficient δ 1，2，3，4 and Activation loss of the composition; v ohm Represents ohmic voltage; R M With R c Represent electronic resistance and proton resistance respectively; v con represents the concentration overvoltage, j, j max , and β represent the maximum current, maximum current density, and concentration loss, respectively; I fc represents the fuel cell current.

5. The optimization method of a new energy ship energy management system according to claim 1 is characterized in that: The objective function model for obtaining ship energy in S3 is expressed as P PV (t)+P F (t)+P LIB (t)+P g (t) = P D (t)+P e，EL (t) sumP ePV (t)-sumP E (t)=0 S SOC (t+1)=S SOC (t)-P LIB S MH (t+1)=S MH (t)-(P E (t)×τ)×η EL -(P FC (t)×τ)×η F P PV (t)=I PV *V PV P F (t)=I fc *V fc Where: t represents the total running time, P g (t) represents the load power, P D represents the energy demand of the ship, P PV Represents photovoltaic power generation, P FC represents the fuel cell power, P LIB Indicates the power of the energy storage lithium battery, P E Indicates the power consumed by the hydrogen storage electrolyzer, P ePV represents excess photovoltaic power, S SOC Indicates the charging status of the lithium battery, S MH Indicates hydrogen storage capacity; η EL represents the efficiency of the electrolytic cell; N represents the overall operating time term; P g (t) i represents the load power of the ship at a certain time i; represents the average load power of the ship during the entire operation period; τ represents the power relaxation factor; η F represents the fuel cell efficiency; F represents the mean square error of the load power; The optimization constraints include lithium battery constraints, fuel cell constraints, energy storage constraints and ship stable operation constraints; The lithium battery constraints: 0≤P LIBc (t)≤Temp cha (t) 0≤P LIBd (t)≤Temp dis (t) Where: P LIBc (t) and P LIBd (t) represents the charging and discharging power of lithium battery, Temp cha (t) and Temp dis (t) represent the charge and discharge levels at time t, respectively; The fuel cell constraints: 0≤P E (t)≤Temp E (t) 0≤P FC (t)≤Temp FC (t) Where: Temp E (t) and Temp FC (t) represent the amount of hydrogen and electricity generated by the fuel cell, respectively; The energy storage constraints: SOC(t+1)=SOC(t)+P LIB (t)-P LIBd (t),0≤SOC(t)≤1 HSR(t+1)=HSR(t)+P E (t)-P FC (t),0≤HSR(t)≤1 Where: SOC(t) represents the amount of electricity stored in the lithium battery at time t, HSR(t) represents the amount of hydrogen stored at time t; HSR(t+1) represents the amount of hydrogen stored at time t+1; SOC(t+1) represents the amount of electricity stored in the lithium battery at time t+1; The ship stable operation constraints:

6. The optimization method of a new energy ship energy management system according to claim 1 is characterized in that: The S6 specifically comprises the following steps: S61: obtaining estimated data of photovoltaic power generation and ship navigation power demand according to the neural network prediction model, taking the objective function model as the individual fitness function in the optimization algorithm, and taking the allocation and use scheme of the photovoltaic panel model, hydrogen production electrolyzer model, fuel cell model and energy storage lithium battery model corresponding to the estimated data as a solution of the optimization algorithm; S62: Select an optimization algorithm and set algorithm configuration parameters; The optimization algorithm includes at least one of linear programming, nonlinear programming, genetic algorithm and particle swarm algorithm; The algorithm configuration parameters include at least the population size and the maximum number of iterations; S63: using the ship's new energy consumption data, navigation mission data, and performance data of each module as input data of the optimization algorithm, and according to the estimated data of photovoltaic power generation and ship navigation power demand, obtaining an energy use plan that satisfies the optimization constraints and makes the objective function model reach the optimal state through iterative calculation of the optimization algorithm; Said navigation mission data includes at least ship type, ship tonnage, navigation distance, navigation time and operation time; The performance data of each module at least includes Photovoltaic performance data: the number of photovoltaic panel cells in series and parallel connection N p With N s ; Electrolyzer performance data: Boundaries for electrolyzer operating condition inputs and Lithium battery performance data: Energy capacity E of lithium battery batterymax ; Fuel cell performance data: The amount of hydrogen produced by the electrolyzer in a given period of time

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