Energy power freight ship propulsion system and propulsion method

Abstract

The invention provides an energy power freight ship propulsion system and method, and the system comprises a main propulsion module, an energy supply module, an auxiliary energy module and an intelligent energy efficiency management center module, and an intelligent energy efficiency management center builds an energy consumption prediction model through a digital twin unit. The strategy generation unit generates a multi-time scale energy distribution strategy based on a multi-objective optimization model, the adaptive learning unit optimizes parameters, and the method realizes low-carbon efficient navigation through real-time data acquisition and dynamic optimization decision, and relates to the technical field of ship power systems. The invention solves the problems of high emission of traditional ships and the contradiction between cruising and response of new energy ships, is suitable for various freight ships, and has remarkable environmental protection and economic values.

Description

Technical Field

[0001] This invention relates to the field of marine power system technology, and in particular to a propulsion system and propulsion method for an energy-powered cargo ship. Background Technology

[0002] Traditional cargo ships mainly rely on heavy oil or diesel engines for power, which has the following significant drawbacks:

[0003] Severe pollution from emissions: Burning heavy oil produces large amounts of carbon dioxide, nitrogen oxides, sulfur oxides and particulate matter. Among them, nitrogen oxide emissions account for more than 15% of global mobile source emissions, and there is a significant gap in standards.

[0004] Low energy efficiency: The thermal efficiency of diesel engine propulsion systems is only 35%-40%, and the efficiency drops sharply under partial load conditions, making it difficult to meet the comprehensive energy efficiency index (EEXI) during ocean voyages;

[0005] Energy diversification risk: Reliance on fossil fuels makes us highly susceptible to oil price fluctuations and makes us unable to adapt to the trend of green and low-carbon development.

[0006] Low level of intelligence: Energy allocation relies on human experience and cannot be dynamically optimized according to the sailing environment (such as wind, waves, ocean currents) and load changes, resulting in additional energy loss of 10%-15%.

[0007] Existing new energy ship solutions (such as pure battery power and single fuel cell power) have limitations: pure battery power is limited by battery energy density, and the range is less than 500 nautical miles, which cannot meet the needs of ocean voyages; single fuel cell power has a slow dynamic response (response time ≥ 10 seconds), which is difficult to cope with transient conditions such as ship acceleration and change of direction, and the system has no redundancy backup, resulting in insufficient reliability.

[0008] Therefore, developing a multi-energy coordinated, intelligent, low-carbon, and efficient cargo ship propulsion system has become the key to breaking through the emission reduction bottleneck in the shipping industry. Summary of the Invention

[0009] To overcome existing problems, this application provides an energy-powered cargo ship propulsion system and method, aiming to overcome the shortcomings of traditional cargo ships such as high emissions, low energy efficiency, single energy source, and short range and slow response of existing new energy solutions. The system and method provide an energy-powered cargo ship propulsion system and method that achieves low carbon emissions, long range, and high reliability of ships through the coordinated supply of multiple clean energy sources, intelligent energy efficiency management, and dynamic optimization control.

[0010] The technical solution adopted by the embodiments of this application to solve its technical problem is:

[0011] A propulsion system and method for an energy-powered cargo ship includes a main propulsion module, an energy supply module, an auxiliary energy module, and an intelligent energy efficiency management central module.

[0012] The main propulsion module includes at least one electric propulsion unit powered by clean energy;

[0013] The energy supply module includes at least one high-energy-density power battery pack, one fuel cell system and an energy management controller. The power battery pack and the fuel cell system are connected in parallel through the energy management controller to jointly power the main propulsion module.

[0014] The auxiliary energy module includes a flexible photovoltaic power generation component laid on the ship's deck. The flexible photovoltaic power generation component is connected to the energy supply module. The flexible photovoltaic power generation component uses a rollable thin-film solar panel, and its edges are provided with a fast drainage and anti-slip structure.

[0015] The signal input terminal of the intelligent energy efficiency management central module is connected to the navigation environment perception unit and the ship load monitoring unit. The navigation environment perception unit includes a meteorological routing module, a wind speed and direction sensor, and a wave sensor. The ship load monitoring unit includes a cargo weight distribution sensor and a ship draft sensor. The control output terminal is connected to the energy management controller and the main propulsion module, and includes a digital twin unit, a strategy generation unit, an adaptive learning unit, and a task-energy collaborative optimization unit.

[0016] Preferably, the digital twin unit establishes a high-fidelity ship motion model and energy system model based on the ship's physical parameters. The energy system model calculates the instantaneous total energy consumption using a formula, as follows:

[0017] = + ,

[0018] = ,

[0019] = ,

[0020] = ,

[0021] In the formula, For total resistance, For seawater density, For ship speed, For square coefficient, For the captain, For the width of the boat, For draft, For wind speed, The speed of the ocean current; For the instantaneous energy consumption of ships, To promote energy consumption, To aid in the system's energy consumption, To improve overall system efficiency, This refers to the total energy consumption of ship navigation equipment (GPS), radar, communication equipment, lighting systems, and cabin air conditioning.

[0022] Preferably, the strategy generation unit is configured to generate an energy allocation strategy based on a multi-objective optimization model, wherein the objective function of the multi-objective optimization model is as follows:

[0023] ,

[0024] ,

[0025] ,

[0026] ,

[0027] In the formula, Total energy consumption; Constraints on clean energy emissions; For operating costs; The mass of hydrogen consumed by the fuel cell; 0.93 is the carbon emission coefficient of hydrogen over its entire life cycle; 0.08 represents the power generation of the photovoltaic module; 0.08 represents the carbon emission allocation coefficient for the entire life cycle of the photovoltaic module. For hydrogen procurement costs; For power system maintenance costs; Labor and site costs for port energy supply; , , Weighting coefficients (satisfying) + + =1), and the constraints include:

[0028] ,

[0029] In the formula, For fuel cell output power, This refers to the output power of the power battery. This indicates the battery's state of charge.

[0030] Preferably, the digital twin unit establishes a high-fidelity ship motion model and energy system model based on the ship's physical parameters, which is used to simulate and predict the ship's energy consumption status in different environments in real time.

[0031] The strategy generation unit, which is connected to the digital twin unit, is configured to generate multi-timescale energy allocation strategies for a future period of time by continuously optimizing the prediction results.

[0032] The adaptive learning unit is used to record historical navigation data, energy consumption data and operator intervention records, and continuously optimizes the parameters of the digital twin unit and the optimization algorithm of the strategy generation unit through machine learning algorithms.

[0033] The task-energy co-optimization unit is configured to: when a new navigation task is received, use the task duration, economic requirements, and carbon emission limits as boundary conditions, and perform optimization calculations together with the state of the energy system to output the recommended optimal departure time, speed curve, and energy replenishment plan.

[0034] Preferably, the intelligent energy efficiency management central module is configured to: dynamically optimize the power output ratio of the power battery pack and fuel cell system based on real-time collected navigation environment data, ship load data and preset navigation plans, and generate the optimal propulsion power command.

[0035] Preferably, the energy allocation strategy includes a multi-timescale sequence: a range-level strategy plans the start-up and shutdown of fuel cells and the average power and target state of charge range of batteries for each flight segment; a condition-level strategy dynamically adjusts the power allocation ratio for the next tens of minutes to several hours; and a second-level strategy prioritizes the use of power batteries to provide peak power.

[0036] A propulsion method for an energy-powered cargo ship includes the following steps:

[0037] S1: Before sailing, input the sailing plan into the intelligent energy efficiency management central module, and the system performs a self-check and assesses the remaining power of the energy supply module;

[0038] S2: During navigation, the navigation environment perception unit and the ship load monitoring unit continuously collect real-time data and send it to the intelligent energy efficiency management central module;

[0039] S3: The intelligent energy efficiency management central module is based on real-time data and preset algorithms. The preset algorithm is a multi-objective optimization algorithm. The optimization objectives include the lowest energy consumption throughout the journey, zero carbon emissions, and optimal ship operation economy. It also uses model predictive control methods to solve for the optimal energy allocation sequence in the future finite time domain and calculates the optimal energy allocation strategy and propulsion power requirements under the current voyage.

[0040] S3.1: The digital twin unit receives real-time data and performs advanced simulation and prediction of the ship's future speed, resistance and energy consumption;

[0041] S3.2: The strategy generation unit takes the minimum net energy consumption and optimal operating cost as the comprehensive objective function, and uses the model predictive control method to solve the optimal energy allocation sequence in the future finite time domain in a rolling manner.

[0042] S3.3: The adaptive learning unit performs online correction of the model and strategy based on the deviation between the actual navigation results and the predicted results;

[0043] S4: The intelligent energy efficiency management central module sends instructions to the energy management controller according to the strategy to control the start-up and shutdown of the power battery pack and fuel cell system and the power output ratio, and at the same time sends propulsion power instructions to the main propulsion module.

[0044] S5: The auxiliary energy module works continuously under sunlight conditions, and the generated electrical energy is used to supplement the energy supply module;

[0045] S6: Repeat steps S2 to S5 until the end of the voyage, achieving low-carbon emission propulsion throughout the entire journey.

[0046] The advantages of the embodiments of this application are:

[0047] The intelligent energy efficiency management center, through multi-source information fusion and real-time optimization, ensures that the energy system always operates in its most efficient range and makes full use of free solar energy, significantly improving overall energy efficiency and reducing operating costs. The multi-energy parallel architecture provides redundancy and backup, improving system reliability. At the same time, with the decline in the cost of hydrogen energy and photovoltaics, the system's total life cycle cost will be highly competitive. The power battery compensates for the slow dynamic response of fuel cells, while the fuel cell solves the bottleneck of short battery range. The two work together to meet the needs of ocean voyages while ensuring the flexibility of ship maneuvering.

[0048] The intelligent energy efficiency management central module is a decision-making system that performs real-time dynamic optimization based on multi-source data such as weather, load, and navigation plan. Its multi-objective optimization algorithm aims to simultaneously optimize multiple objectives such as energy efficiency and economy. Photovoltaics, as an auxiliary energy source, subtly extend the range, batteries, as a power buffer, ensure the ship's power performance, and fuel cells, as the energy foundation, ensure the range. The three work together to solve the range and power contradiction that cannot be solved by a single technology. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the propulsion system for an energy-powered cargo ship according to the present invention;

[0050] Figure 2 This is a schematic diagram of the propulsion method for energy-powered cargo ships according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. In addition, for the sake of convenience, the terms "upper," "lower," "left," and "right" are equivalent to the upper, lower, left, and right directions of the accompanying drawings themselves, and the terms "first," "second," etc., are used for descriptive purposes and have no other special meaning.

[0052] This application provides a propulsion system and method for energy-powered cargo ships, addressing problems in the prior art. The intelligent energy efficiency management center, through multi-source information fusion and real-time optimization, ensures the energy system always operates in its most efficient range, fully utilizing free solar energy to significantly improve overall energy efficiency and reduce operating costs. The multi-energy parallel architecture provides redundancy and backup, enhancing system reliability. Furthermore, with the decreasing costs of hydrogen and photovoltaic energy, the system's total lifecycle cost will be highly competitive. The power battery compensates for the slow dynamic response of fuel cells, while the fuel cell solves the bottleneck of short battery range. The two work together to meet the needs of ocean voyages while ensuring the flexibility of ship maneuvering.

[0053] The intelligent energy efficiency management central module is a decision-making system that performs real-time dynamic optimization based on multi-source data such as weather, load, and navigation plan. Its multi-objective optimization algorithm aims to simultaneously optimize multiple objectives such as energy efficiency and economy. Photovoltaics, as an auxiliary energy source, subtly extend the range, batteries, as a power buffer, ensure the ship's power performance, and fuel cells, as the energy foundation, ensure the range. The three work together to solve the range and power contradiction that cannot be solved by a single technology.

[0054] The technical solution in this application is to solve the above problems, and the overall approach is as follows:

[0055] Example 1

[0056] This embodiment provides a propulsion system for an energy-powered cargo ship, such as... Figure 1 As shown, it includes a main propulsion module, an energy supply module, an auxiliary energy module, and an intelligent energy efficiency management central module;

[0057] The main propulsion module includes at least one electric propulsion unit powered by clean energy;

[0058] Furthermore, the main propulsion module is used to drive the ship's navigation, including at least one electric propulsion main unit, which is a permanent magnet synchronous motor with a rated power of 5000-20000kW and an efficiency of ≥96%. It is connected to the propeller through a reduction gearbox, receives power commands from the intelligent energy efficiency management central module, and outputs propulsion torque.

[0059] The energy supply module includes at least one high-energy-density power battery pack, one fuel cell system and an energy management controller. The power battery pack and the fuel cell system are connected in parallel through the energy management controller to jointly power the main propulsion module.

[0060] Furthermore, the energy supply module provides energy to the system, including: a high-energy-density power battery pack: using lithium iron phosphate batteries with an energy density ≥200Wh / kg and a total capacity of 5000-20000kWh, supporting fast charging and deep discharging, used to provide transient peak power;

[0061] Fuel cell system: It adopts proton exchange membrane fuel cell with rated power of 3000-15000kW and hydrogen fuel cell efficiency ≥60%, serving as the main energy source for driving range;

[0062] Energy management controller: Electrically connected to the power battery pack and fuel cell system, it achieves voltage matching through a DC / DC converter and controls the power output ratio of the two.

[0063] The auxiliary energy module includes flexible photovoltaic power generation components laid on the ship's deck. The flexible photovoltaic power generation components are connected to the energy supply module. The flexible photovoltaic power generation components use rollable thin-film solar panels, and their edges are equipped with rapid drainage and anti-slip structures.

[0064] Furthermore, the auxiliary energy module includes flexible photovoltaic power generation components laid on the ship's deck, using cadmium telluride thin-film batteries with a conversion efficiency of ≥18%, a weight of ≤2kg / m², and a total installed capacity of 500-2000kW. It is connected to the energy supply module through an MPPT controller to supplement and charge the power battery pack.

[0065] The signal input terminal of the intelligent energy efficiency management central module is connected to the navigation environment perception unit and the ship load monitoring unit. The navigation environment perception unit includes a weather routing module, wind speed and direction sensors, and wave sensors. The ship load monitoring unit includes a cargo weight distribution sensor and a ship draft sensor. The control output terminal is connected to the energy management controller and the main propulsion module, respectively, and includes a digital twin unit, a strategy generation unit, an adaptive learning unit, and a task-energy collaborative optimization unit.

[0066] Furthermore, the signal input end connects to the navigation environment perception unit (including wind speed sensor, ocean current speed meter, GPS positioning, and weather radar) and the ship load monitoring unit (including main engine current sensor, battery SOC sensor, and fuel cell hydrogen consumption sensor).

[0067] Control output: Electric propulsion main unit that connects the energy management controller and the main propulsion module.

[0068] Digital twin units, based on the ship's physical parameters, establish high-fidelity ship motion models and energy system models to simulate and predict the ship's energy consumption status in different environments in real time.

[0069] Furthermore, the digital twin unit establishes a high-fidelity ship motion model and energy system model based on physical parameters such as ship draft, displacement, and hull form, and simulates energy consumption status in real time.

[0070] The digital twin unit establishes a high-fidelity ship motion model and energy system model based on the ship's physical parameters. The energy system model calculates the instantaneous total energy consumption using the following formula:

[0071] = + ,

[0072] = ,

[0073] = ,

[0074] = ,

[0075] In the formula, For total resistance, For seawater density, For ship speed, For square coefficient, For the captain, For the width of the boat, For draft, For wind speed, The speed of the ocean current; For the instantaneous energy consumption of ships, To promote energy consumption, To aid in the system's energy consumption, To improve overall system efficiency, This refers to the total energy consumption of ship navigation equipment (GPS), radar, communication equipment, lighting systems, and cabin air conditioning.

[0076] Furthermore, the parameters for calculating total resistance are: seawater density. A seawater density of 1025 kg / m³ was obtained from nearshore waters at normal temperature. In cold, open ocean waters, a density of 1027 kg / m³ can be obtained; the square coefficient is... The value is determined based on the ship type: 0.82-0.88 for bulk carriers and 0.65-0.72 for container ships; ship length... , ship width Draft Based on the actual dimensions indicated on the ship's design drawings, in meters; wind speed The average wind speed over a 10-minute period, measured in m / s, is collected in real time by the navigation environment sensing unit; ocean current speed is also measured. The surface ocean current velocity is collected in real time and the unit is m / s.

[0077] Auxiliary system energy consumption definition and calculation:

[0078] This refers to the total energy consumption of the ship's navigation equipment (GPS, radar, communication equipment, lighting system, and cabin air conditioning), with the power of each device taken as its rated power, such as 5kW for navigation equipment and 3kW for communication equipment.

[0079] The strategy generation unit, which is connected to the digital twin unit, is configured to generate multi-timescale energy allocation strategies for a future period of time based on the prediction results.

[0080] Furthermore, the strategy generation unit generates multi-timescale energy allocation strategies based on the prediction results of the digital twin unit;

[0081] The strategy generation unit is configured to generate energy allocation strategies based on a multi-objective optimization model, the objective function of which is as follows:

[0082] ,

[0083] ,

[0084] ,

[0085] ,

[0086] In the formula, Total energy consumption; Constraints on clean energy emissions; For operating costs; The mass of hydrogen consumed by the fuel cell; 0.93 is the carbon emission coefficient of hydrogen over its entire life cycle; 0.08 represents the power generation of the photovoltaic module; 0.08 represents the carbon emission allocation coefficient for the entire life cycle of the photovoltaic module. For hydrogen procurement costs; For power system maintenance costs; Labor and site costs for port energy supply; , , Weighting coefficients (satisfying) + + =1), and the constraints include:

[0087] ,

[0088] In the formula, For fuel cell output power, This refers to the output power of the power battery. This indicates the battery's state of charge.

[0089] Furthermore, the parameters of the objective function are defined as follows:

[0090] in, The mass of hydrogen consumed by the fuel cell, in kg; 0.93 is the carbon emission coefficient of hydrogen over its entire life cycle. The value represents the power generation of the photovoltaic module, in kWh, and 0.08 is the carbon emission allocation factor for the entire life cycle of the photovoltaic module.

[0091] in, The cost of hydrogen procurement is calculated based on a unit price of 80 yuan / kg, multiplied by the mass consumed. ; The maintenance cost of the power system is calculated based on a fixed cost of 5,000 yuan per voyage; The labor and site costs for port energy replenishment are calculated based on a fixed cost of 3,000 yuan per voyage.

[0092] Weighting coefficient selection principles and adaptive mechanism:

[0093] Value selection principle: For total energy consumption weight, For emission constraint weights, The weighting is based on operating costs, with a total value of 1. The specific value is determined according to the requirements of the navigation mission: for environmentally priority scenarios, the weighting is... =0.3、 =0.5、 =0.2; Economic priority scenario =0.3、 =0.2、 =0.5; Equilibrium scenario takes =0.4、 =0.3、 =0.3.

[0094] Adaptive Mechanism: The intelligent energy efficiency management central module can automatically adjust the weighting coefficients based on real-time received navigation commands. The adjustment trigger condition is: when the "environmental protection enhancement" command is received, Automatically increased to 0.6. and Reduced to 0.2 and 0.2 respectively; when a "cost control" instruction is received, Automatically increased to 0.6. and They decreased to 0.2 and 0.2 respectively.

[0095] An adaptive learning unit is used to record historical navigation data, energy consumption data, and operator intervention records, and continuously optimizes the parameters of the digital twin unit and the optimization algorithm of the strategy generation unit through machine learning algorithms.

[0096] Furthermore, the adaptive learning unit optimizes model parameters and algorithms through machine learning;

[0097] The mission-energy co-optimization unit is configured to: when a new navigation mission is received, use mission duration, economic requirements, and carbon emission limits as boundary conditions, and perform optimization calculations together with the state of the energy system to output recommended optimal departure time, speed curve, and energy replenishment plan.

[0098] Furthermore, the mission-energy co-optimization unit combines the navigation mission with the energy status to output the optimal navigation plan.

[0099] The intelligent energy efficiency management central module is configured to dynamically optimize the power output ratio of the power battery pack and fuel cell system based on real-time collected navigation environment data, ship load data and preset navigation plans, and generate the optimal propulsion power command.

[0100] Energy allocation strategies include multiple time scale sequences: range-level strategies plan the start-up and shutdown of fuel cells and average power and the target state of charge range of batteries for each flight segment; operating condition-level strategies dynamically adjust the power allocation ratio for the next tens of minutes to several hours; and second-level strategies prioritize the use of power batteries to provide peak power.

[0101] Example 2

[0102] This embodiment provides a propulsion method for an energy-powered cargo ship, such as... Figure 2 As shown, a propulsion method for an energy-powered cargo ship includes the following steps:

[0103] S1: Before sailing, input the sailing plan into the intelligent energy efficiency management central module, and the system will perform a self-check and assess the remaining power of the energy supply module;

[0104] Furthermore, by inputting the flight plan, route, expected speed, cargo weight, and the self-check system status of the intelligent energy efficiency management central module, the remaining power of the energy supply module, the initial value of the battery SOC, and the hydrogen storage of the fuel cell are evaluated through digital twin unit simulation, and suggestions on whether refueling is needed are output.

[0105] S2: During navigation, the navigation environment perception unit and the ship load monitoring unit continuously collect real-time data and send it to the intelligent energy efficiency management central module.

[0106] Furthermore, during navigation, the navigation environment perception unit collects data such as wind speed, ocean current, and wave height every 10 seconds; the ship load monitoring unit collects parameters such as electric propulsion main engine power, battery SOC, and fuel cell hydrogen consumption in real time, all of which are sent to the intelligent energy efficiency management central module.

[0107] S3: The intelligent energy efficiency management central module is based on real-time data and preset algorithms. The preset algorithm is a multi-objective optimization algorithm. The optimization objectives include the lowest energy consumption throughout the journey, zero carbon emissions, and the best ship operation economy. It also uses model predictive control methods to solve for the optimal energy allocation sequence in the future finite time domain and calculates the optimal energy allocation strategy and propulsion power requirements under the current voyage.

[0108] S3.1: The digital twin unit receives real-time data and performs advanced simulation and prediction of the ship's future speed, resistance and energy consumption;

[0109] S3.2: The strategy generation unit takes the minimum net energy consumption and optimal operating cost as the comprehensive objective function, and uses the model predictive control method to solve the optimal energy allocation sequence in the future finite time domain in a rolling manner.

[0110] S3.3: The adaptive learning unit performs online correction of the model and strategy based on the deviation between the actual navigation results and the predicted results;

[0111] Furthermore, the digital twin unit receives real-time data, inputs it into the energy consumption prediction model, and simulates and predicts the trends of ship speed, resistance and energy consumption changes 5-30 minutes in advance.

[0112] The strategy generation unit is based on a multi-objective optimization model and uses the model predictive control (MPC) method to solve the optimal energy allocation sequence for the next 30 minutes to 2 hours in a rolling manner, including range-level, operational-level, and second-level strategies.

[0113] The adaptive learning unit compares the actual energy consumption with the predicted energy consumption, calculates the deviation, and updates the parameters of the digital twin unit and the optimization algorithm of the strategy generation unit through the parameter correction model.

[0114] S4: The intelligent energy efficiency management central module sends instructions to the energy management controller according to the strategy to control the start-up and shutdown of the power battery pack and fuel cell system and the power output ratio, while sending propulsion power instructions to the main propulsion module.

[0115] Furthermore, the intelligent energy efficiency management central module sends instructions to the energy management controller to control the power output ratio of the power battery pack and fuel cell system. For example, during transient acceleration, the battery provides 80% of the peak power; during constant speed navigation, the fuel cell provides 90% of the base load power; at the same time, it sends the optimal propulsion power command to the electric propulsion main unit.

[0116] S5: The auxiliary energy module continues to work under sunlight conditions, replenishing the energy supply module with the generated electricity;

[0117] Furthermore, when the flexible photovoltaic power generation module operates under illumination conditions with a light intensity ≥200W / m², the power will be preferentially supplied to the power battery pack through the MPPT controller, thereby extending the driving range.

[0118] S6: Repeat steps S2 to S5 until the end of the voyage, achieving low-carbon emission propulsion throughout the entire journey.

[0119] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A propulsion system for an energy-powered cargo ship, characterized in that, It includes a main propulsion module, an energy supply module, an auxiliary energy module, and an intelligent energy efficiency management central module; The main propulsion module includes at least one electric propulsion unit powered by clean energy; The energy supply module includes at least one high-energy-density power battery pack, one fuel cell system and an energy management controller. The power battery pack and the fuel cell system are connected in parallel through the energy management controller to jointly power the main propulsion module. The auxiliary energy module includes flexible photovoltaic power generation components laid on the ship's deck, and the flexible photovoltaic power generation components are connected to the energy supply module; The signal input terminal of the intelligent energy efficiency management central module is connected to the navigation environment perception unit and the ship load monitoring unit, and the control output terminal is connected to the energy management controller and the main propulsion module respectively. It also includes a digital twin unit, a strategy generation unit, an adaptive learning unit, and a task-energy collaborative optimization unit.

2. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The digital twin unit establishes a high-fidelity ship motion model and energy system model based on the ship's physical parameters. The energy system model calculates the instantaneous total energy consumption using the following formula: = + ， = ， = ， = ， In the formula, For total resistance, For seawater density, For ship speed, For square coefficient, For the captain, For the width of the boat, For draft, For wind speed, The speed of the ocean current; For the instantaneous energy consumption of ships, To promote energy consumption, To aid in the system's energy consumption, To improve overall system efficiency, This refers to the total energy consumption of ship navigation equipment (GPS), radar, communication equipment, lighting systems, and cabin air conditioning.

3. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The strategy generation unit is configured to generate an energy allocation strategy based on a multi-objective optimization model, the objective function of which is as follows: ， ， ， ， In the formula, Total energy consumption; Constraints on clean energy emissions; For operating costs; The mass of hydrogen consumed by the fuel cell; 0.93 is the carbon emission coefficient of hydrogen over its entire life cycle; 0.08 represents the power generation of the photovoltaic module; 0.08 represents the carbon emission allocation coefficient for the entire life cycle of the photovoltaic module. For hydrogen procurement costs; For power system maintenance costs; Labor and site costs for port energy supply; , , Weighting coefficients (satisfying) + + =1), and the constraints include: ， In the formula, For fuel cell output power, This refers to the output power of the power battery. This indicates the battery's state of charge.

4. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The digital twin unit establishes a high-fidelity ship motion model and energy system model based on the ship's physical parameters, which is used to simulate and predict the ship's energy consumption status in different environments in real time. The strategy generation unit, which is connected to the digital twin unit, is configured to generate multi-timescale energy allocation strategies for a future period of time by continuously optimizing the prediction results. The adaptive learning unit is used to record historical navigation data, energy consumption data and operator intervention records, and continuously optimizes the parameters of the digital twin unit and the optimization algorithm of the strategy generation unit through machine learning algorithms. The task-energy co-optimization unit is configured to: when a new navigation task is received, use the task duration, economic requirements, and carbon emission limits as boundary conditions, and perform optimization calculations together with the state of the energy system to output the recommended optimal departure time, speed curve, and energy replenishment plan.

5. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The intelligent energy efficiency management central module is configured to dynamically optimize the power output ratio of the power battery pack and fuel cell system based on real-time collected navigation environment data, ship load data and preset navigation plans, and generate the optimal propulsion power command.

6. The propulsion system for an energy-powered cargo ship according to claim 4, characterized in that, The energy allocation strategy includes a multi-timescale sequence: a range-level strategy plan for fuel cell start-up and shutdown, average power, and target state of charge range for each flight segment; The operating condition-level strategy dynamically adjusts the power allocation ratio for the next tens of minutes to several hours; the second-level strategy prioritizes the use of the power battery to provide peak power.

7. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The flexible photovoltaic power generation module uses a rollable thin-film solar panel with a fast drainage and anti-slip structure on its edges.

8. The propulsion system for an energy-powered cargo ship according to claim 1, characterized in that, The navigation environment perception unit includes a weather routing module, a wind speed and direction sensor, and a wave sensor; the ship load monitoring unit includes a cargo weight distribution sensor and a ship draft sensor.

9. A propulsion method for an energy-powered cargo ship based on the system described in any one of claims 1-8, characterized in that, Includes the following steps: S1: Before sailing, input the sailing plan into the intelligent energy efficiency management central module, and the system performs a self-check and assesses the remaining power of the energy supply module; S2: During navigation, the navigation environment perception unit and the ship load monitoring unit continuously collect real-time data and send it to the intelligent energy efficiency management central module; S3: The intelligent energy efficiency management central module calculates the optimal energy allocation strategy and propulsion power requirements for the current flight segment based on real-time data and preset algorithms; S3.1: The digital twin unit receives real-time data and performs advanced simulation and prediction of the ship's future speed, resistance and energy consumption; S3.2: The strategy generation unit takes the minimum net energy consumption and optimal operating cost as the comprehensive objective function, and uses the model predictive control method to solve the optimal energy allocation sequence in the future finite time domain in a rolling manner. S3.3: The adaptive learning unit performs online correction of the model and strategy based on the deviation between the actual navigation results and the predicted results; S4: The intelligent energy efficiency management central module sends instructions to the energy management controller according to the strategy to control the start-up and shutdown of the power battery pack and fuel cell system and the power output ratio, and at the same time sends propulsion power instructions to the main propulsion module. S5: The auxiliary energy module works continuously under sunlight conditions, and the generated electrical energy is used to supplement the energy supply module; S6: Repeat steps S2 to S5 until the end of the voyage, achieving low-carbon emission propulsion throughout the entire journey.

10. A propulsion method for an energy-powered cargo ship according to claim 9, characterized in that, The preset algorithm in step S3 is a multi-objective optimization algorithm. The optimization objectives include minimizing the total energy consumption, zero carbon emissions, and optimal ship operation economy. The optimal energy allocation sequence in the future finite time domain is solved by model predictive control method.

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