A green hydrogen energy storage system for ship navigation and a dynamic energy efficiency optimization control method

Abstract

The present application relates to the field of shipping technology, and discloses a green hydrogen energy storage system for ship navigation and a dynamic energy efficiency optimization control method. The method establishes a dynamic coupling model of environmental parameters and new energy parameters. A dynamic adjustment factor is generated based on the comparison result of water flow rate and the set threshold value; the super capacitor response weight is determined in combination with wave height to realize load mutation suppression. The hydrogen production priority coefficient is determined by the wind power / photovoltaic power generation power and the electrolytic tank efficiency, and the hydrogen fuel cell output power is smoothly adjusted by using the hyperbolic tangent function in combination with the hydrogen storage tank pressure. Based on the wave height influence factor, the fuel cell power range is limited, the output power of the lithium battery is calculated according to the lithium battery power distribution coefficient, and the remaining load is dynamically distributed to the super capacitor. Through the cooperative optimization of environmental parameters and equipment state, the present application solves the problem of hydrogen-electric hybrid energy collaborative control hysteresis, and improves the energy distribution efficiency and system stability under complex sea conditions.

Description

Technical Field

[0001] This invention relates to the field of shipping technology, and in particular to a green hydrogen energy storage system and dynamic energy efficiency optimization control method for ship navigation. Background Technology

[0002] Traditional ship energy management methods have significant shortcomings when dealing with complex navigation environments: First, they lack quantitative modeling of the dynamic coupling effects of multi-dimensional environmental parameters such as water flow velocity, wave height, and wind direction, resulting in propulsion power demand prediction deviations often exceeding 15%. Second, the coordinated control of hydrogen fuel cells, lithium batteries, and supercapacitors often adopts a fixed weight allocation strategy, which cannot adapt to sudden energy changes under extreme conditions such as typhoons and rapid currents. Third, existing technologies lack real-time optimization of the closed-loop relationship between hydrogen storage tank pressure, electrolysis efficiency, and fuel cell power, resulting in hydrogen energy utilization rates generally below 60%. Furthermore, in the instantaneous power gap caused by sudden changes in heading angle (>5° / s), the response delay of supercapacitors often leads to transient instability of the propulsion system. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a green hydrogen energy storage system for ship navigation and a dynamic energy efficiency optimization control method.

[0004] In a first aspect, the present invention provides a dynamic energy efficiency optimization control method for a green hydrogen energy storage system used in ship navigation, comprising:

[0005] S1. Real-time acquisition of environmental and new energy parameters; the environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity threshold, wave height, wave height influencing factor, and wave height as a design benchmark for wave resistance; the new energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, hydrogen storage tank minimum safe pressure, hydrogen fuel cell maximum allowable power, and lithium battery power allocation coefficient.

[0006] S2. Based on the comparison results of the water flow velocity and the positive / negative flow velocity thresholds, a dynamic adjustment factor for the water flow velocity is calculated using an adjustment coefficient: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor for the water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor for the water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height.

[0007] S3. Determine the hydrogen production priority coefficient by the dynamic adjustment factor of water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power, and combine the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure to determine the adjusted hydrogen fuel cell output power using a hyperbolic tangent function.

[0008] S4. Determine the final output power of the hydrogen fuel cell based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell, and the maximum allowable power of the hydrogen fuel cell. Determine the output power of the lithium battery based on the lithium battery power allocation coefficient, the motor power demand, and the final output power of the hydrogen fuel cell. Determine the output power of the supercapacitor based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power, and the wave height response weight of the supercapacitor.

[0009] Optionally, the environmental parameters also include the heading angle change rate, S2 further includes heading angle abrupt change pre-compensation power, and the calculation method for the supercapacitor output power in S4 is as follows:

[0010] The pre-compensation power for the abrupt change in heading angle is superimposed on the supercapacitor output power to form the compensated supercapacitor output power.

[0011] The heading angle change pre-compensation power is calculated by multiplying the heading angle change rate by the motor power requirement.

[0012] Optionally, S3 further includes:

[0013] When the pressure of the hydrogen storage tank is lower than the preset warning threshold, the hydrogen production priority coefficient is adjusted according to the hydrogen storage tank pressure and the preset warning threshold to obtain the adjusted hydrogen production priority coefficient.

[0014] Optionally, the calculation of the dynamic adjustment factor for water flow velocity in step S2 further includes a calibration process for the adjustment coefficient:

[0015] When the water flow velocity exceeds the forward flow velocity threshold, the first adjustment coefficient decreases inversely based on the ratio of the real-time draft to the reference draft.

[0016] When the water flow velocity is lower than the negative flow velocity threshold, the second adjustment coefficient is dynamically increased based on the square root relationship between the ship's real-time speed and the design reference speed.

[0017] The first adjustment coefficient and the second adjustment coefficient are used to calculate the weight allocation of the positive velocity difference and the negative velocity difference, respectively.

[0018] Optionally, the new energy parameters also include the lithium battery state of charge, and the lithium battery output power calculation in S4 further includes a lithium battery protection strategy:

[0019] When the state of charge of the lithium battery is lower than the preset threshold, power compensation demand is generated based on the difference between the state of charge of the lithium battery and the preset threshold.

[0020] The final output power of the hydrogen fuel cell is increased by the power compensation requirement.

[0021] Optionally, the environmental parameters also include the wind turbine blade angle of attack and the wind direction angle; the new energy parameters also include the maximum power point voltage, the maximum power point current, and the inverter efficiency; and the wind / photovoltaic power generation in S1 is calculated as follows:

[0022] ;

[0023] in, Indicates wind / solar power generation capacity. This represents the voltage at the maximum power point. This represents the maximum power point current. This represents the blade angle-of-attack compensation coefficient. Indicates inverter efficiency. Indicates the angle of attack of the wind turbine blade. Indicates the wind direction angle.

[0024] Optionally, the environmental parameters also include the calm sea state reference wave height and the angle between the surge propagation direction and the current heading. The wave height influence factor in S4 is calculated as follows:

[0025] The wave height influence factor is dynamically generated based on the ratio of wave height to the baseline wave height for calm sea states, combined with the trigonometric function relationship between the surge propagation direction and the current heading.

[0026] Optionally, the new energy parameters also include proton exchange membrane temperature, optimal operating temperature, electrolyzer current density, and optimal current density. The calculation method for the electrolyzer efficiency in S3 is as follows:

[0027] Linear efficiency compensation is performed based on the difference between the proton exchange membrane temperature and the optimal operating temperature, and a secondary efficiency correction is added based on the difference between the electrolyzer current density and the optimal current density.

[0028] Optionally, the new energy parameters also include the cumulative operating time of the hydrogen fuel cell and the number of cycles of the lithium battery, and S4 further includes a lifespan equalization control strategy:

[0029] Based on the cumulative operating time of the hydrogen fuel cell and the number of cycles of the lithium battery, the weighting coefficients of the hydrogen fuel cell and the lithium battery are dynamically generated.

[0030] The weighting coefficient of the hydrogen fuel cell decreases in an inverse relationship with the increase of the cumulative operating time, and the weighting coefficient of the lithium battery decreases in an inverse relationship with the increase of the number of cycles.

[0031] The weighting coefficients for hydrogen fuel cells and lithium batteries are used to adjust the power distribution ratio between hydrogen fuel cells and lithium batteries.

[0032] Secondly, the present invention also provides a green hydrogen energy storage system for marine applications, applicable to the method described in any of the first aspects, comprising:

[0033] The data acquisition module is used to collect environmental and new energy parameters in real time. The environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity thresholds, wave height, wave height influence factor, and wave height as a design benchmark for wave resistance. The new energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, minimum safe pressure of hydrogen storage tank, maximum allowable power of hydrogen fuel cell, and lithium battery power allocation coefficient.

[0034] The calculation module is used to calculate a dynamic adjustment factor for water flow velocity based on the comparison result between the water flow velocity and the positive / negative flow velocity thresholds, using an adjustment coefficient: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor for water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor for water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height.

[0035] The first determining module is used to determine the hydrogen production priority coefficient by the dynamic adjustment factor of water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power, and to determine the adjusted hydrogen fuel cell output power by combining the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure using a hyperbolic tangent function.

[0036] The second determining module is used to determine the final output power of the hydrogen fuel cell based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell and the maximum allowable power of the hydrogen fuel cell; to determine the output power of the lithium battery based on the lithium battery power allocation coefficient, the motor power demand and the final output power of the hydrogen fuel cell; and to determine the output power of the supercapacitor based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power and the supercapacitor wave height response weight.

[0037] The present invention has the following technical effects:

[0038] This invention constructs a dynamic optimization system for a hydrogen-electric hybrid energy system through the coordinated control of environmental parameters and equipment status. A dynamic adjustment factor for water flow rate triggered by a water flow rate threshold optimizes the environmental adaptability of the multi-objective algorithm, matching propulsion power requirements under conditions such as downstream acceleration and upstream deceleration, avoiding the global search bias of traditional fixed-weight strategies. By using the nonlinear response function of wave height and the wave height as a design benchmark for wave resistance, hierarchical control of the supercapacitor compensation weight is achieved, effectively mitigating load surges caused by wave impacts. Combining dual closed-loop control of new energy generation capacity and hydrogen storage pressure, a hyperbolic tangent function is used to dynamically smooth the fuel cell power curve, ensuring continuous hydrogen supply while preventing the risk of excessive pressure in the hydrogen storage system. A power allocation strategy based on wave height influence factors, through the synergistic effect of the lithium battery power coefficient and the supercapacitor response weight, forms a multi-scale compensation mechanism, balancing the steady-state efficiency and transient stability of the propulsion system. Building upon this foundation, a heading angle abrupt change pre-compensation mechanism is introduced. By calibrating steering dynamics parameters, the energy storage output is corrected in advance, resolving the power oscillation problem caused by rapid heading adjustments. A hydrogen storage pressure gradient monitoring and electrolysis efficiency optimization model is implemented, achieving an adaptive balance between hydrogen production priority and fuel cell output through dynamic gain correction of pressure differences. A lifespan balancing strategy driven by equipment aging parameters incorporates fuel cell operating time and lithium battery cycle count into a weighted decay function, automatically avoiding equipment operating restrictions under complex conditions and extending the service life of key components. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of a dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation, provided by an embodiment of the present invention.

[0041] Figure 2 A schematic diagram of wind direction angle provided for an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram showing the angle between the surge direction and the heading, provided for an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0044] First, a brief description of the structure of the ship energy system corresponding to the green hydrogen energy storage system and dynamic energy efficiency optimization control method for ship navigation provided in the embodiments of the present invention will be given.

[0045] The ship's energy system consists of multiple functional modules, which interact and coordinate control through a power network and data communication links. The energy supply module includes a wind turbine and a photovoltaic array, connected to the DC bus via a pitch control system and a maximum power point tracker, respectively. The generated electricity is preferentially supplied to a proton exchange membrane electrolyzer for water electrolysis to produce hydrogen. The resulting hydrogen is purified, compressed, and stored in a hydrogen storage tank. Excess electrical energy is stored in a lithium-ion battery pack via a bidirectional DC / DC converter. The hydrogen fuel cell stack is connected to the hydrogen storage tank via a pressure reducing valve assembly, converting the chemical energy of hydrogen into electrical energy to power the propulsion system. The energy storage buffer module includes lithium-ion battery packs and supercapacitors. The lithium-ion battery packs are distributed along both sides of the hull, handling basic load frequency regulation and short-to-medium-term energy storage. The supercapacitors are installed near the propulsion motor drive cabinet and directly connected to the motor's DC bus via a fast-response circuit, specifically for compensating for instantaneous power surges.

[0046] The propulsion module consists of a permanent magnet synchronous propulsion motor and a steering servo. The propulsion motor is connected to the DC bus via a multi-level inverter, driving the propeller to generate propulsion. Its controller has a built-in torque observation algorithm to calculate the required power in real time and feed it back to the central control system. The steering servo is equipped with an independent servo drive unit, and the power demand signal is synchronously uploaded to the supercapacitor control module. The environmental perception module integrates fluid dynamics sensors and attitude monitoring units. A three-dimensional lidar mounted on the bow scans the wave height and water velocity ahead, a Doppler current meter on the bottom measures the longitudinal and transverse velocity components, and a side pressure sensor array detects the wave impact force. The attitude monitoring unit outputs the ship's roll angle, pitch angle, and heading angle change rate in real time through fiber optic gyroscopes and MEMS accelerometers. All sensor data is transmitted to the central control module via a CAN bus.

[0047] The central control module comprises a multi-objective optimization controller and hierarchical actuators. The optimization controller uses a dynamic weighted particle swarm optimization algorithm to generate a hydrogen-electric hybrid power supply strategy by integrating environmental parameters, equipment status, and navigation requirements. The hierarchical actuators translate strategy commands into equipment-level control signals, including a hydrogen fuel cell power regulator, a lithium battery charge / discharge manager, and a supercapacitor pre-charge controller. The auxiliary protection module includes a hydrogen production efficiency monitoring unit and a safety interlock device. The former collects the electrolyzer membrane electrode operating parameters in real time using a temperature-current density composite sensor, while the latter includes a hydrogen storage tank pressure gradient monitor and a lithium battery pack temperature difference equalizer, triggering tiered protection commands under abnormal operating conditions.

[0048] The interconnection of each module is achieved through dual power and data channels: environmental sensing data is transmitted to the central controller via a CAN bus, and optimization commands are sent to the actuators via the Modbus protocol; energy storage devices and propulsion motors interact via a DC bus, and the supercapacitor bank achieves sub-millisecond response by being directly connected to the drive cabinet via hardwiring; the electrolyzer and fuel cell form a closed loop through a hydrogen pipeline, and pressure sensors and mass flow meters provide closed-loop feedback for the hydrogen energy link. This system architecture covers the entire energy production, storage, distribution, and consumption chain, supporting the physical implementation of the method provided in this embodiment of the invention.

[0049] Figure 1 This invention provides a schematic flowchart of a dynamic energy efficiency optimization control method for a green hydrogen energy storage system used in ship navigation, comprising:

[0050] S1. Real-time acquisition of environmental and new energy parameters; environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity threshold, wave height, wave height influencing factor, and wave height as a design benchmark for wave resistance; new energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, minimum safe pressure of hydrogen storage tank, maximum allowable power of hydrogen fuel cell, and lithium battery power allocation coefficient.

[0051] During ship navigation, the power demand of the propulsion motor reflects the instantaneous load of the propeller. Water flow velocity data is acquired through Doppler sensors on the hull, and the positive and negative flow velocity thresholds are dynamically calibrated based on the ship's draft. Wave height parameters are generated by scanning with a bow lidar, and the impact intensity of waves on the hull is quantified by combining the wave height with the wave resistance design benchmark. The wave height influence factor is a quantification coefficient characterizing the additional load of wind and waves on the propulsion system, and it can be calculated based on wave height. Among the new energy parameters, wind power generation is calculated based on the wind turbine speed and blade angle of attack, and the output curve is corrected by introducing maximum power point tracking voltage / current parameters; photovoltaic power generation is obtained after inverter efficiency calibration, and the wind / photovoltaic power generation described in this embodiment can represent the sum of the two. The hydrogen storage tank pressure sensor monitors the hydrogen energy reserve in real time, and the electrolyzer efficiency is dynamically calibrated based on the proton exchange membrane temperature and rated power parameters. The lithium battery power allocation coefficient is calculated based on the state of charge and cycle life parameters. The setting of positive / negative flow velocity thresholds is simultaneously linked to the ship's real-time speed and design reference speed to ensure the adaptability of the dynamic adjustment factor calculation to the operating conditions.

[0052] S2. Based on the comparison results of water flow velocity and positive / negative flow velocity thresholds, a dynamic adjustment factor for water flow velocity is calculated using an adjustment coefficient: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor for water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor for water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height.

[0053] Specifically, this step essentially generates dynamic adjustment factors based on environmental parameters:

[0054] ;

[0055] in, This represents the dynamic adjustment factor for water flow velocity. , , This represents the adjustment coefficient. Indicates water flow velocity. Indicates the forward flow velocity threshold. Indicates the negative flow velocity threshold. Indicates wave height. Indicates the design reference wave height for wave resistance. This indicates the high response weight of the supercapacitor.

[0056] The water flow velocity threshold divides the ship's navigation conditions into downstream acceleration, upstream deceleration, and steady state. When the downstream velocity exceeds the positive threshold, the inertia weight adjustment factor increases linearly with the velocity difference, enhancing the global search capability of the multi-objective algorithm in downstream conditions and preventing local optima from deviating from actual requirements. In upstream conditions, the adjustment factor suppresses the weight coefficient in the opposite direction, reducing the algorithm's convergence speed to adapt to complex resistance environments. The wave height response weight is dynamically adjusted using an S-shaped function. When the real-time wave height approaches the ship's wave resistance design benchmark, the weight value rapidly jumps from a low range to a high level, ensuring that the supercapacitor preferentially responds to sudden power changes under large wave impacts. The dynamic coupling relationship of environmental parameters is encoded as adjustment factors, forming the boundary constraints for multi-objective optimization.

[0057] S3. The hydrogen production priority coefficient is determined by the dynamic adjustment factor of water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power. Combined with the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure, the hyperbolic tangent function is used to determine the adjusted hydrogen fuel cell output power.

[0058] Specifically, this step essentially involves optimizing hydrogen energy utilization based on new energy parameters:

[0059] ;

[0060] ;

[0061] in, This represents the hydrogen production priority coefficient. Indicates wind / solar power generation capacity. Indicates the efficiency of the electrolytic cell. Indicates the rated power of the electrolytic cell. This indicates the adjusted output power of the hydrogen fuel cell. Indicates the basic output power of the hydrogen fuel cell. Indicates the pressure of the hydrogen storage tank. Indicates the minimum safe pressure of the hydrogen storage tank. It is the hyperbolic tangent function.

[0062] The hydrogen production priority coefficient is determined based on the ratio of renewable energy power generation to the electrolyzer's rated capacity, combined with a dynamic adjustment factor for water flow rate. When wind or solar power generation reaches a preset proportion of the electrolyzer's rated power, the water electrolysis hydrogen production process is forcibly initiated to avoid wind and solar power curtailment. When the water flow rate exceeds a positive threshold, the dynamic adjustment factor for water flow rate increases the hydrogen production priority coefficient, prompting the electrolyzer to prioritize the use of surplus renewable energy power for hydrogen production under downstream operating conditions, thereby increasing the pressure reserve of the hydrogen storage tank. When the water flow rate is below a negative threshold, the dynamic adjustment factor for water flow rate decreases the hydrogen production priority coefficient to reduce hydrogen production energy consumption and ensure that fuel cell power generation can withstand the reverse flow resistance.

[0063] The base power of the hydrogen fuel cell is dynamically corrected based on the hydrogen storage tank pressure. A hyperbolic tangent function is used to smooth the output curve. When the hydrogen storage pressure approaches the safety lower limit, the function output value rapidly decays to limit the fuel cell power and prevent excessive hydrogen consumption in the storage system. Lithium-ion battery state of charge and supercapacitor voltage data are used in the correction calculation to ensure that the energy storage device remains within a safe charge / discharge range during optimization. The coordinated processing of new energy parameters and environmental parameters achieves a dynamic balance between hydrogen production and consumption.

[0064] S4. The final output power of the hydrogen fuel cell is determined based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell, and the maximum allowable power of the hydrogen fuel cell. The output power of the lithium battery is determined based on the lithium battery power allocation coefficient, the motor power demand, and the final output power of the hydrogen fuel cell. The output power of the supercapacitor is determined based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power, and the wave height response weight of the supercapacitor.

[0065] Specifically, this step essentially involves performing a multi-level energy distribution:

[0066] ;

[0067] in, This indicates the final output power of the hydrogen fuel cell. Indicates the wave height influence factor. Indicates the maximum permissible power of the hydrogen fuel cell. Indicates the output power of the lithium battery. Indicates the required power of the motor. Indicates the power distribution factor of the lithium battery. This indicates the output power of the supercapacitor.

[0068] The power allocation of hydrogen fuel cells is adjusted by wave height influencing factors. When the surge direction forms a lateral angle with the ship's course, the output ratio of fuel cells is increased to compensate for the propulsion power loss caused by wind and wave drag. The lithium battery power allocation coefficient is set according to the current propulsion load classification. Under heavy load conditions, the coefficient value is increased to take advantage of the high power density characteristics of lithium batteries, while under light load conditions, the coefficient value is decreased to extend battery cycle life. The supercapacitor power is allocated in real time based on wave height response weights and a pre-compensation mechanism for the rate of change of course is introduced: when it is detected that the ship is about to make a large-angle turn, the charging and discharging state of the supercapacitor is adjusted in advance to offset the power surge at the moment of steering motor start-up. The output commands of each energy device are issued after dynamic weighting and fusion, forming a hybrid energy supply scheme that takes into account both stability and economy.

[0069] In some implementations, the environmental parameters also include the rate of change of heading angle, S2 further includes heading angle abrupt change pre-compensation power, and the calculation method for the supercapacitor output power in S4 is as follows:

[0070] The pre-compensation power for the sudden change in heading angle is superimposed on the output power of the supercapacitor to form the compensated output power of the supercapacitor.

[0071] The pre-compensation power for abrupt changes in heading angle is calculated by multiplying the rate of change of heading angle by the power required by the motor.

[0072] Specifically, the heading angle abrupt change compensation strategy in S2 is as follows:

[0073] ;

[0074] in, This indicates the pre-compensation power for sudden changes in the heading angle of the supercapacitor. Indicates the rate of change of heading angle. This represents the steering compensation coefficient.

[0075] The rate of change of a ship's heading angle characterizes the severity of a turning maneuver, and its value can be measured in real time by a gyro-stabilized platform. When a ship performs emergency collision avoidance or turns in narrow channels, the rate of change of heading angle rises rapidly, requiring the propulsion system to generate additional power to maintain rudder effectiveness and heading control. The core of the heading angle abrupt change compensation strategy lies in anticipating the surge in power demand caused by the turning maneuver and preemptively releasing energy reserves from energy storage devices. The turning compensation coefficient reflects the correlation between the ship's turning inertia and propulsion load. The coefficient value is related to the ship's hydrodynamic characteristics and propeller layout; streamlined hulls typically use lower compensation coefficients to reduce energy redundancy. The calculation method for the compensated supercapacitor output power can be... and The sum of these terms will not be elaborated further using formulas here.

[0076] The calculation of the heading angle change pre-compensation power is based on the product of the heading angle change rate and the propulsion power demand. When the heading angle change rate exceeds a preset threshold, the compensation mechanism is automatically activated, and the value of the heading angle change pre-compensation power increases linearly with the rate of change. There is a time lag between the generation of the supercapacitor pre-charge command and the steering operation. The pre-charge cycle needs to match the mechanical response delay of the steering servo to ensure that the peak capacitor discharge is synchronized with the propulsion power demand. During the steering process, the heading angle change pre-compensation power is preferentially released from the supercapacitor to avoid lithium battery life degradation due to high current impact.

[0077] In practical applications, the execution of the pre-compensation strategy relies on multi-sensor data fusion. The inertial measurement unit provides real-time feedback of the ship's angular acceleration, which, combined with channel curvature data provided by the electronic chart, predicts the steering power demand in the next few seconds. When continuous steering commands or abnormal contraction of the track width are detected, the compensation coefficient is dynamically adjusted upwards to cope with the combined steering load. After the steering maneuver is completed, the pre-compensation power for the sudden change in heading angle decays exponentially, smoothly transitioning to a steady-state power distribution mode, avoiding power oscillations that cause propulsion motor speed fluctuations.

[0078] This strategy proves highly effective in sharp bends and obstacle avoidance scenarios. In the initial stage of the turn, the supercapacitor instantly releases its pre-stored energy to compensate for the power shortfall caused by the response delay of traditional energy equipment, maintaining the dynamic stability of the propulsion system. In the mid-to-late stages of the turn, the hydrogen fuel cell and lithium battery take over the load in an optimized ratio, forming a tiered power supply structure. The synergistic effect of the pre-compensation mechanism and real-time power allocation ensures a balance between course control accuracy and energy efficiency under complex maneuvering conditions.

[0079] In some implementations, S3 further includes:

[0080] When the pressure of the hydrogen storage tank is lower than the preset warning threshold, the hydrogen production priority coefficient is adjusted according to the hydrogen storage tank pressure and the preset warning threshold to obtain the adjusted hydrogen production priority coefficient.

[0081] Specifically, the low hydrogen storage pressure compensation strategy in S3 is as follows:

[0082] ;

[0083] in, This represents the corrected hydrogen production priority coefficient. This indicates the pressure warning threshold of the hydrogen storage tank. This represents the pressure compensation gain coefficient.

[0084] Hydrogen storage tank pressure monitoring is a core component of the safe operation of the hydrogen energy supply chain. Pressure values ​​are collected in real time through a distributed sensor array, reflecting the dynamic changes in hydrogen reserves. Low hydrogen storage pressure indicates an imbalance between hydrogen production and consumption, necessitating priority to ensure the continuity of fuel cell power supply. The pressure warning threshold is set based on historical ship navigation data and the characteristics of hydrogen energy equipment. The threshold level is negatively correlated with the hydrogen storage tank volume and the rated power of the fuel cell. Ships with larger volumes or lower fuel cell power typically have higher warning thresholds to extend the buffer time.

[0085] The pressure compensation gain coefficient is used to adjust the correction magnitude of the hydrogen production priority coefficient, and its value is related to the ship's current speed and the intensity of environmental disturbances. Under countercurrent or high-wave conditions, the gain coefficient increases linearly with decreasing speed, enhancing the compensation response under low hydrogen storage pressure conditions. During the hydrogen production priority correction process, the incremental allocation of electrolyzer input power follows a stepped adjustment rule, prioritizing the use of renewable energy generation margins, followed by activating lithium battery reserve power, to avoid additional load impact on the main propulsion system.

[0086] In actual operation, when the hydrogen storage pressure remains below the warning threshold and the hydrogen production priority coefficient reaches its upper limit, the system automatically triggers multi-level interlocking protection. The first level of protection limits the rate of decrease in fuel cell output power to prevent a sudden drop in hydrogen storage pressure from causing a hydrogen supply interruption; the second level of protection increases the operating priority of the electrolyzer, forcing more renewable energy power to be allocated for hydrogen production; the third level of protection activates the emergency hydrogen supply module, calling upon backup hydrogen storage units to maintain minimum power output. The synergistic effect of the multi-level protection mechanism ensures the functional continuity of the hydrogen energy supply chain under extreme operating conditions.

[0087] This strategy proves highly effective during prolonged headwind navigation or in scenarios involving hydrogen energy equipment failure. The pressure compensation mechanism dynamically adjusts hydrogen production power based on the rate of hydrogen storage pressure decay, slowing the pressure drop while preventing energy allocation disorder. The corrected hydrogen production priority coefficient complements the multi-level interlocking protection, gradually restoring the hydrogen storage system to equilibrium while ensuring the basic power output of the fuel cell. A closed-loop feedback mechanism between pressure data and compensation parameters enables adaptive adjustment of the hydrogen production-storage-consumption chain.

[0088] In some implementations, the calculation of the dynamic adjustment factor for water flow velocity in S2 further includes a calibration process for the adjustment coefficient:

[0089] When the water flow velocity exceeds the forward flow velocity threshold, the first adjustment coefficient decreases inversely based on the ratio of the real-time draft to the reference draft.

[0090] When the water flow velocity is lower than the negative flow velocity threshold, the second adjustment coefficient is dynamically increased based on the square root relationship between the ship's real-time speed and the design reference speed.

[0091] The first adjustment coefficient and the second adjustment coefficient are used to calculate the weight allocation of the positive velocity difference and the negative velocity difference, respectively.

[0092] Specifically, the water flow velocity regulation coefficient in S2 satisfies:

[0093] ;

[0094] Where k1 represents the first adjustment coefficient and k2 represents the second adjustment coefficient, which are the same parameters as k1 and k2 mentioned above. Indicates real-time draft. Indicates the reference draft. This represents the speed compensation coefficient. Indicates the ship's real-time speed. Indicates the design reference speed.

[0095] A ship's draft directly affects the contact area between the hull and the water flow, thus altering the effect of the water flow velocity on propulsion resistance. The reference draft, a core parameter in ship design, reflects the standard draft conditions under full load, and its value is closely related to the hull form and cargo distribution. The calculation of the water flow velocity adjustment coefficient incorporates a draft ratio term. When the real-time draft deviates from the reference value, the adjustment coefficient dynamically decreases under downstream conditions, compensating for nonlinear changes in hull friction resistance. The design reference speed represents the typical value of the ship within its economic speed range, corresponding to the peak point of the main engine propulsion efficiency curve, and is used to standardize the calculation benchmark for the adjustment coefficient at different speeds.

[0096] In downstream operating conditions, the calculation of the adjustment coefficient focuses on the hull resistance characteristics. Increased draft leads to a larger wetted surface area of ​​the hull, increasing the proportion of frictional resistance from the water flow. Therefore, the adjustment coefficient gain is reduced to prevent the algorithm from over-responding to downstream advantages. As the draft decreases, wave-making resistance becomes the dominant factor, and the adjustment coefficient increases with the decrease in the draft ratio, enhancing the algorithm's global search capability in shallow waterways. The baseline draft, used as the denominator, normalizes parameters for different hull types, ensuring the lateral comparability of the adjustment coefficient as hull dimensions change.

[0097] Speed-related terms play a crucial role in counter-current operation. A design reference speed is used as the denominator in the square root calculation, mapping the real-time speed to a standardized proportional range. When the speed is below the reference value, the gain of the adjustment coefficient increases sublinearly with decreasing speed, adapting to the nonlinear load characteristics of the propulsion system at low speeds. As the speed increases above the reference value, the rate of increase in the gain slows down, preventing the algorithm from falling into oscillating convergence under high-speed counter-current conditions. The reference speed setting must be matched to the characteristics of the ship's main engine to ensure optimal sensitivity of the adjustment coefficient within the typical operating range.

[0098] In practical applications, the dynamic calculation of the water flow velocity adjustment coefficient is carried out throughout the entire navigation process. Under downstream conditions, a Doppler current meter monitors the longitudinal water flow component in real time, and generates adjustment coefficient commands based on draft data. These commands are then fed back to the multi-objective optimization algorithm via a weight update module. Under upstream conditions, the lateral flow velocity component is acquired through a side pressure sensor array, and the adjustment coefficient is calculated based on the ship's speed data, dynamically adjusting the algorithm's convergence threshold. The coupling effect between draft and speed is transformed into algorithm parameters through the coefficient calculation formula, forming an adaptive optimization mechanism for the water flow environment.

[0099] This strategy performs exceptionally well in shallow waters and under variable load conditions. When draft fluctuates with cargo load, the adjustment coefficient automatically compensates for changes in hull resistance characteristics, maintaining the convergence stability of the algorithm under different load conditions. When speed fluctuates due to wind and current, the adjustment coefficient dynamically balances the relationship between propulsion load and energy distribution, suppressing power oscillations caused by sudden changes in countercurrent resistance. The coordinated processing of baseline parameters and real-time data enables the energy management system to adapt to changes in the ship's hydrodynamic environment, improving control robustness under complex hydrological conditions.

[0100] In some implementations, the new energy parameters also include the lithium battery state of charge, and the lithium battery output power calculation in S4 further includes lithium battery protection strategies:

[0101] When the state of charge of the lithium battery is lower than the preset threshold, power compensation demand is generated based on the difference between the state of charge of the lithium battery and the preset threshold.

[0102] The final output power of hydrogen fuel cells can be increased by compensating for power requirements.

[0103] Specifically, the lithium battery protection strategy in S3 is as follows:

[0104] ;

[0105] in, This indicates the power compensation amount of the lithium battery. This indicates the corrected output power of the fuel cell. This represents the hydrogen energy compensation coefficient. This indicates the critical value for the state of charge of a lithium battery. This indicates the state of charge of the lithium battery.

[0106] The state of charge (SOC) of a lithium battery reflects the real-time available energy of an energy storage device. Setting its critical value requires comprehensive consideration of battery chemical characteristics and ship navigation conditions. The critical value is determined based on the battery cycle life curve and safe depth of discharge. Different lithium battery types have different thresholds; lithium iron phosphate batteries, due to their high stability, can have lower critical values, while ternary lithium batteries require conservative settings to avoid over-discharge risks. The calculation of the SOC difference reflects the degree of deviation between the current energy storage level and the safety boundary. Non-negative constraints ensure that the compensation mechanism is triggered only when actual energy storage is insufficient.

[0107] The hydrogen energy compensation coefficient is related to the proportion of fuel cell reserve power that can be deployed, and its value is dynamically linked to the ship's navigation phase and the intensity of environmental disturbances. A lower compensation coefficient is used during stable transoceanic navigation to prioritize hydrogen fuel economy, while the coefficient is increased during port maneuvers or typhoon avoidance to ensure system reliability. The generation of compensation power follows a gradual adjustment rule: initially, fuel cell output is increased at a fixed slope, switching to an exponential growth mode as the state-of-charge difference continues to widen, balancing response speed and equipment impact.

[0108] In actual operation, the lithium battery protection strategy and multi-level energy distribution form a coordinated control system. When the state of charge difference triggers the compensation mechanism, the fuel cell power command is superimposed with a compensation component, while the upper limit of the lithium battery discharge power is reduced, forming dual protection. The allocation of compensation power prioritizes the short-term overload capacity of the supercapacitor, and secondly achieves load transfer through the smooth increase of the fuel cell's base power. When the state of charge recovers to above the critical value, the compensation power gradually withdraws according to the preset decay rate to avoid propulsion system oscillation caused by sudden power command changes.

[0109] This strategy proves highly effective in scenarios with prolonged low wind speeds or insufficient solar power output. As the state of charge (SOC) of the lithium-ion batteries continuously approaches its critical value, the hydrogen energy compensation mechanism gradually takes over the load demand, alleviating the pressure on energy storage equipment. During the compensation process, the flexible adjustment of fuel cell power matches the chemical relaxation characteristics of lithium-ion batteries, avoiding electrode material degradation caused by high current surges. The dynamic binding of the SOC difference calculation and the compensation coefficient achieves a balance between energy storage safety and hydrogen energy economy, extending the cycle life of lithium-ion batteries while maintaining the continuity of energy supply for the entire ship.

[0110] In some implementations, environmental parameters also include the wind turbine blade angle of attack and the wind direction angle, while renewable energy parameters include the maximum power point voltage, maximum power point current, and inverter efficiency. The wind / photovoltaic power generation in S1 is calculated as follows:

[0111] ;

[0112] in, Indicates wind / solar power generation capacity. Indicates inverter efficiency. This represents the voltage at the maximum power point. This represents the maximum power point current. This represents the blade angle-of-attack compensation coefficient. Indicates the angle of attack of the wind turbine blade. Indicates the wind direction angle.

[0113] Figure 2 This is a schematic diagram of a wind direction angle provided in an embodiment of the present invention. The wind direction angle is measured by an ultrasonic anemometer at the top of the mast, and the data is used to correct the effective wind energy coefficient in the calculation of wind power generation. When the wind direction forms an angle with the main shaft of the wind turbine, the actual captured wind energy will attenuate according to a cosine law.

[0114] In power generation calculations, the wind direction angle term is used to characterize the influence of the angle between the wind direction and the wind turbine's main shaft. When the wind direction is directly opposite the blade's plane of rotation, the wind direction angle is 0, and the wind energy utilization rate reaches its peak. As the angle increases, the effective wind energy decreases according to a cosine law, avoiding power estimation errors under crosswind conditions.

[0115] Real-time calculation of wind / solar power generation is a core input parameter for ship hybrid energy systems. Inverter efficiency characterizes the energy loss in the power conversion process; its value is dynamically calibrated through an online efficiency curve, and the curve data is stored in the non-volatile memory of the inverter control unit. Maximum power point tracking (MPPT) voltage and current reflect the optimal output of the photovoltaic array or wind turbine under specific environmental conditions. The tracking algorithm adjusts the operating point in real time based on parameters such as irradiance and wind speed to ensure that the power generation equipment always operates within its high-efficiency range.

[0116] The angle of attack of a wind turbine blade is defined as the angle between the blade chord and the actual incoming flow direction. Its sine function term is used to quantify the energy capture efficiency degradation under crosswind conditions. When the wind direction forms a non-perpendicular angle with the blade's plane of rotation, the sine value of the angle of attack increases, leading to a decrease in theoretical power generation. The angle of attack parameter is obtained through data fusion from a Doppler wind-measuring radar and a mechanical wind vane, and dynamic compensation is performed using feedback signals from the wind turbine's yaw system to eliminate interference from ship roll motion on the angle of attack measurement.

[0117] The adjustment coefficient in the power generation calculation formula is used to balance the contribution weights of different power generation units. The photovoltaic power generation component is directly calculated by multiplying the maximum power point tracking voltage and current, while the wind power generation component is corrected by introducing an angle-of-attack term. The adjustment coefficient is set differently according to the type of power generation equipment; photovoltaic systems focus on temperature compensation effects, while wind power systems focus on turbulence intensity suppression. Dynamic coupling of multi-source data ensures that the formula output value truly reflects the actual power generation capacity, avoiding the cumulative deviation between theoretical calculations and measured power.

[0118] In practical applications, the power generation calculation module integrates multiple anomaly handling mechanisms. When a jump in sensor data or communication delay is detected, it automatically switches to historical data prediction mode, using the trend extrapolation of previous sampling points to generate a temporary power estimate. Instantaneous attitude changes caused by ship maneuvers trigger data smoothing filtering, and a sliding time window algorithm suppresses the impact of high-frequency oscillation components on the calculation results. The power generation output value is transmitted to the energy management main control unit after multi-level verification, serving as the basic input for hydrogen production priority decision-making and energy storage scheduling.

[0119] This computational strategy demonstrates robustness under complex sea conditions and equipment aging scenarios. When the surface roughness of wind turbine blades increases due to salt spray corrosion, the angle-of-attack correction term automatically compensates for power generation losses caused by aerodynamic performance degradation. Output power fluctuations caused by decreased photovoltaic panel cleanliness are partially offset by dynamic adjustments to the maximum power point tracking parameters. The synergistic effect of the multi-parameter coupled computational model and the anomaly handling mechanism ensures the reliability of new energy power generation data, providing accurate input for the optimal allocation of ship energy systems.

[0120] In some implementations, environmental parameters also include the calm sea state reference wave height and the angle between the surge propagation direction and the current heading. The wave height influence factor in S4 is calculated as follows:

[0121] Based on the ratio of wave height to the baseline wave height for calm sea states, and combined with the trigonometric function relationship between the surge propagation direction and the current heading, a wave height influence factor is dynamically generated.

[0122] Specifically, the wave height influence factor in S4 is:

[0123] ;

[0124] in, Indicates the baseline wave height for calm sea states. This indicates the angle between the surge direction and the heading. Figure 3 This is a schematic diagram showing the angle between the surge direction and the heading, provided for an embodiment of the present invention.

[0125] The calm sea state reference wave height serves as a normalized benchmark for assessing surge intensity, and its value is determined statistically based on historical meteorological data from the ship's navigation area. This parameter eliminates dimensional differences in the absolute values ​​of wave height across different sea areas, mapping real-time wave heights to a relative proportional range. The measurement of the angle between the surge direction and the heading relies on the coordinated positioning of the bow wave height radar array and the electronic compass. The radar scan acquires the surge propagation direction vector, and the electronic compass provides the ship's heading reference; the difference between these vectors yields the real-time angle. The introduction of this angle reflects the nonlinear effect of lateral surges on the ship's roll resistance; the lateral thrust reaches its peak when the surge direction is perpendicular to the heading.

[0126] The wave height impact factor calculation integrates the dual effects of surge intensity and direction. The ratio of wave height to the reference wave height characterizes the relative disturbance level of the current sea state; a larger ratio indicates a more significant additional load on the propulsion system from the surge. The sine function maps the included angle to the interval between zero and unit. When the surge direction is exactly the same as or opposite to the heading, the sine value is zero, at which point the surge only causes hull pitching motion, with a small impact on propulsion power demand. When the surge direction is perpendicular to the heading, the sine value reaches its maximum, and the lateral impact significantly increases the steering sustaining power. The product of these two factors ensures that the factor adjustment simultaneously responds to the combined effects of the absolute value of wave height and the relative angle of direction.

[0127] In actual operation, the wave height influence factor serves as a feedforward correction term for fuel cell power allocation. When the factor value exceeds a preset threshold, a dynamic compensation mechanism for fuel cell power is triggered, with the compensation amount increasing non-linearly with the factor value. The hull roll resistance caused by lateral surges is converted into additional output of the propulsion system through power compensation, offsetting the speed loss caused by the surge. The periodic scan data from the wave height radar undergoes moving average filtering to eliminate the interference of short-term wave height fluctuations on factor calculations, ensuring the stability of the correction commands. The heading differential processing of the electronic compass data can distinguish between autonomous ship steering and surge-forced steering, avoiding miscompensation during autonomous steering.

[0128] This correction strategy is highly effective in crosswind and cross-wave navigation scenarios. When continuous lateral surges cause periodic rolling of the hull, the wave height influence factor remains high, and the fuel cell output power increases synchronously, matching the additional energy required to maintain rudder efficiency. As the surge direction dynamically changes with the navigation position, the sine function term automatically adjusts the factor weights, ensuring that the power compensation precisely matches the real-time disturbance. The regional adaptive characteristics of the reference wave height ensure the universality of the correction strategy when the ship is navigating in different waters, without requiring manual parameter resetting.

[0129] In some implementations, the new energy parameters also include proton exchange membrane temperature, optimal operating temperature, electrolyzer current density, and optimal current density. The calculation method for the electrolyzer efficiency in S3 is as follows:

[0130] Linear efficiency compensation is performed based on the difference between the proton exchange membrane temperature and the optimal operating temperature, and a secondary efficiency correction is added based on the difference between the electrolyzer current density and the optimal current density.

[0131] Specifically, the real-time calculation of the electrolytic cell efficiency in S3 is as follows:

[0132] ;

[0133] in, This indicates the basic efficiency of the electrolytic cell. Indicates the temperature compensation coefficient. Indicates the temperature of the proton exchange membrane. Indicates the optimal operating temperature. This represents the current density compensation coefficient. This indicates the current density in the electrolytic cell. This indicates the optimal current density.

[0134] Proton exchange membrane temperature monitoring is achieved using an embedded thermocouple array. Sensors are distributed in key areas of the bipolar plate flow channel in the electrolyzer to capture real-time temperature gradient changes at the membrane electrode during the reaction. Temperature data is processed by an anti-electromagnetic interference filter circuit to eliminate interference from the high-voltage electrolysis current and ensure accurate temperature sampling. The electrolyzer current density is calculated as the ratio of the total electrolysis current to the effective reaction area. The effective area is dynamically corrected based on the membrane electrode activation state to compensate for the loss of active area due to catalyst aging or impurity adsorption.

[0135] The dynamic correction model for electrolyzer efficiency comprehensively considers the nonlinear coupling effect of temperature and current density. When the proton exchange membrane temperature deviates from the optimal operating range, the temperature compensation term linearly increases the efficiency loss coefficient. Under high-temperature conditions, proton conduction is accelerated but the risk of membrane dehydration is exacerbated, while under low-temperature conditions, the decrease in proton mobility leads to an increase in ohmic losses. The current density compensation term adopts a quadratic function form to reflect the combined effects of bubble shielding and concentration polarization during high current density operation. When the current density approaches the design upper limit, the coefficient of the quadratic term increases significantly, strengthening the suppression of the efficiency decay trend.

[0136] In actual operation, the coordinated control of temperature and current density is achieved through a multi-loop controller. The temperature control loop adjusts the coolant flow rate based on real-time efficiency loss, while the current density control loop dynamically limits the fluctuation range of the electrolysis current. The output commands of the two loops are weighted and fused to generate an electrolysis voltage adjustment signal, ensuring the smoothness of the efficiency correction process. Membrane electrode health status data participates in the calculation of the correction coefficient. When a decrease in catalyst activity is detected, the optimal temperature setpoint is automatically reduced to adapt to changes in material performance.

[0137] This correction strategy is highly effective under conditions of fluctuating renewable energy input and extreme environmental temperatures. When a sudden drop in photovoltaic power leads to a decrease in electrolysis current density, the secondary compensation term automatically reduces the efficiency correction magnitude to maintain the stability of hydrogen production efficiency. When navigating in high-temperature and high-humidity sea areas, the temperature compensation term prioritizes limiting the rate of increase in electrolysis current density to avoid overheating damage to the membrane electrode. The synergistic effect of the multi-parameter closed-loop correction mechanism and equipment aging adaptation extends the service life of core components of the electrolyzer and ensures the efficiency balance of the hydrogen production system throughout its entire life cycle.

[0138] In some implementations, the new energy parameters also include the cumulative operating time of the hydrogen fuel cell and the number of lithium battery cycles, and S4 further includes a lifespan equalization control strategy:

[0139] Based on the cumulative operating time of the hydrogen fuel cell and the number of cycles of the lithium battery, the weighting coefficients of the hydrogen fuel cell and the lithium battery are dynamically generated.

[0140] The weighting coefficient of hydrogen fuel cells decreases in an inverse relationship with the increase of cumulative operating time, while the weighting coefficient of lithium batteries decreases in an inverse relationship with the increase of the number of cycles.

[0141] The weighting coefficients for hydrogen fuel cells and lithium batteries are used to adjust the power distribution ratio between hydrogen fuel cells and lithium batteries.

[0142] Specifically, lifetime balancing control in S4 is as follows:

[0143] ;

[0144] in, This represents the weighting coefficient for fuel cells. This represents the weighting coefficient for lithium batteries. This represents the SOC adjustment coefficient. Indicates the cumulative operating time of the hydrogen fuel cell. Indicates the temperature regulation coefficient. This indicates the number of cycles a lithium battery can withstand.

[0145] The cumulative operating time of hydrogen fuel cells reflects the aging degree of the stack components, and its data is continuously recorded and categorized for storage through the operation log module. The time-weighted decay function adopts a hyperbolic function form. In the initial stage, the weight decay rate is slow, ensuring sufficient break-in for newly installed fuel cells. As the operating time approaches the design life, the decay rate accelerates nonlinearly, guiding the system to gradually reduce its output priority. The lithium battery cycle count is based on the charge-discharge depth integration method. Shallow and deep cycles are converted into standard cycle counts using equivalent coefficients, accurately quantifying the battery life degradation process.

[0146] The weight allocation mechanism for lifetime equalization control is deeply coupled with a multi-objective optimization algorithm. The time weight of the hydrogen fuel cell serves as an upper limit constraint on power allocation, restricting the high-load operating time of aging stacks; the cycle weight of the lithium battery serves as a penalty term in the cost function, inhibiting frequent use of high-cycle-count batteries. The weight coefficients are dynamically adjusted based on equipment health assessment results. When the stack voltage consistency deviation widens or the battery internal resistance abnormally increases, the corresponding weight coefficients decay more rapidly, forming a feedforward control link for equipment protection.

[0147] In actual operation, the weighting coefficients form a dynamic mapping relationship with real-time operating conditions. During continuous transoceanic voyages, the fuel cell time-weight decay rate increases with rising ambient temperature, and the duration of a single continuous operation is actively limited during voyages in high-temperature sea areas. Under frequent start-stop conditions in ports, the lithium battery cycle weight introduces a charge / discharge rate correction factor, with high-rate charge / discharge operations being converted into an additional increment of equivalent cycle counts. The update cycle of the weighting parameters is synchronized with the equipment health diagnosis cycle, ensuring the timeliness of aging data and control strategies.

[0148] This balancing strategy is highly effective in the later stages of equipment performance degradation. When the activated area of ​​the fuel cell stack decreases due to long-term operation, the time-weighted decay function reduces its baseline power ratio, transferring load increments to backup stacks with higher health. When the capacity dispersion of individual cells within a lithium battery pack increases, the cyclic weight penalty term suppresses the scheduling priority of that battery pack, guiding the system to prioritize the use of battery modules with similar health. The closed-loop feedback mechanism between the weighting coefficients and health parameters enables synchronized control of the aging process of multiple energy devices, avoiding systemic risks caused by excessive wear and tear of a single device.

[0149] The historical data learning module of the ship's energy management system continuously optimizes the weight function parameters. By analyzing the lifespan degradation patterns of similar equipment under similar operating conditions, the curvature coefficient of the hyperbolic function is dynamically adjusted to ensure that the weight degradation trend matches the measured aging curve. The lithium battery equivalent cycle model calibrates the conversion factor based on historical charge-discharge records, improving the accuracy of cycle count statistics. The synergistic effect of the self-learning mechanism and the preset weight function enhances the adaptability of lifespan balancing control to complex application scenarios and extends the overall service life of key equipment groups.

[0150] The following provides an exemplary description of the method provided in the embodiments of the present invention.

[0151] During a ship's voyage, the energy management system achieves autonomous and coordinated control of the hydrogen-electric hybrid energy system based on multi-source sensing data and dynamic optimization algorithms. After the ship starts, the environmental sensing module continuously collects parameters such as water flow velocity, wave height, and rate of change of heading angle, while the new energy supply module monitors wind / photovoltaic power generation, hydrogen storage tank pressure, and energy storage equipment status in real time. The central control module inputs the above data into a multi-objective optimization algorithm to generate an energy supply strategy that balances energy efficiency, safety, and equipment lifespan, and dynamically adjusts the output ratio of each energy device through hierarchical actuators.

[0152] During normal navigation, the system prioritizes using renewable energy to power the electrolyzer for hydrogen production, with surplus energy stored in the lithium-ion battery pack. When the water flow velocity falls below a negative threshold, the algorithm automatically increases the output weight of the hydrogen fuel cell to compensate for the additional propulsion load under countercurrent conditions. When wave height is within a moderate range, the supercapacitor remains in standby mode, while the lithium-ion battery handles the basic load frequency regulation. The output power of the hydrogen fuel cell is dynamically and smoothly adjusted according to the hydrogen storage pressure, maintaining a stable hydrogen supply chain while avoiding equipment protection actions triggered by drastic pressure fluctuations.

[0153] When encountering sudden wind, waves, or rapids, the 3D lidar detects a sharp increase in wave height, and the side pressure sensor captures high-frequency wave impact signals. The optimized algorithm immediately responds to environmental disturbances, activating a wave height nonlinear compensation mechanism: increasing fuel cell power output to offset lateral surge drag, while simultaneously activating the supercapacitor's millisecond-level response function to fill the instantaneous power gap between the steering gear and propulsion motor. If the surge duration exceeds a preset threshold, the system automatically enters anti-wave mode, limiting the lithium battery's discharge power limit and gradually transferring the load increment to the fuel cell to prevent overloading of the energy storage device. When the heading angle change rate sensor detects an emergency collision avoidance maneuver, the pre-compensation mechanism injects buffer energy into the supercapacitor in advance, ensuring the continuity of propulsion power during the turn.

[0154] In complex hydrological environments (such as narrow waterways), Doppler current meters detect an abnormal increase in the lateral velocity component, and the ship's attitude monitoring unit identifies a compound steering load. The algorithm dynamically adjusts the inertia weight coefficient based on velocity thresholds and heading trends, enhancing the global search capability of the particle swarm optimization algorithm under complex constraints. At this time, the hydrogen supply module switches to a high redundancy mode, with the electrolyzer prioritizing the consumption of surplus renewable energy to increase the hydrogen production rate. The hydrogen storage tank pressure gradient monitor adjusts the fuel cell output in real time, forming a dynamic balance between hydrogen production and power generation. When the lithium battery's state of charge approaches its safe lower limit, the hydrogen compensation mechanism is activated, gradually taking over the propulsion load and creating a charging recovery window for the energy storage equipment.

[0155] In scenarios involving equipment aging after long-term voyages, the system identifies performance degradation trends through accumulated operational data: when the voltage consistency of the fuel cell stack decreases, the lifespan balancing model automatically reduces its output priority; when the internal resistance of the lithium battery increases due to the increase in the number of cycles, the charge / discharge manager limits its power fluctuation amplitude. The central controller synchronously optimizes the electrolyzer operating parameters, adjusting the hydrogen production efficiency compensation coefficient based on the membrane electrode temperature and current density to offset the energy efficiency loss caused by material aging. The self-learning module combines historical voyage data to predict future operating conditions and pre-generates weighted coefficient adjustment schemes, ensuring that the energy allocation strategy continuously adapts to the evolution of equipment performance.

[0156] This system achieves autonomous energy management under all operating conditions through a closed-loop architecture of environmental perception, dynamic optimization, and hierarchical execution. It maximizes renewable energy utilization during normal navigation, ensures millisecond-level power compensation under sudden load changes, and maintains power supply stability during equipment aging. The output ratio, response timing, and protection thresholds of each energy device are dynamically adjusted based on real-time data, forming an intelligent energy network with environmental adaptability and equipment fault tolerance, significantly improving navigation safety and energy economy under complex sea conditions.

[0157] This invention also provides a green hydrogen energy storage system for ship navigation, applicable to any of the methods described in the above embodiments, including:

[0158] The data acquisition module is used to collect environmental and renewable energy parameters in real time. Environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity thresholds, wave height, wave height influencing factor, and wave height as a design benchmark for wave resistance. Renewable energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, minimum safe pressure of hydrogen storage tank, maximum allowable power of hydrogen fuel cell, and lithium battery power allocation coefficient.

[0159] The calculation module is used to calculate the dynamic adjustment factor of water flow velocity based on the comparison results of water flow velocity and positive / negative flow velocity thresholds, through adjustment coefficients: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor of water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor of water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height.

[0160] The first determining module is used to determine the hydrogen production priority coefficient by dynamically adjusting the water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power, and combined with the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure, the hyperbolic tangent function is used to determine the adjusted hydrogen fuel cell output power.

[0161] The second determining module is used to determine the final output power of the hydrogen fuel cell based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell, and the maximum allowable power of the hydrogen fuel cell; to determine the output power of the lithium battery based on the lithium battery power allocation coefficient, the motor power demand, and the final output power of the hydrogen fuel cell; and to determine the output power of the supercapacitor based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power, and the supercapacitor wave height response weight.

[0162] The system provided in this embodiment of the invention has the same technical features as the method embodiment described above, and therefore can achieve the same technical effects, which will not be repeated here.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic energy efficiency optimization control method for a green hydrogen energy storage system used in ship navigation, characterized in that, include: S1. Real-time acquisition of environmental and new energy parameters; the environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity threshold, wave height, wave height influencing factor, and wave height as a design benchmark for wave resistance; the new energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, hydrogen storage tank minimum safe pressure, hydrogen fuel cell maximum allowable power, and lithium battery power allocation coefficient. S2. Based on the comparison results of the water flow velocity and the positive / negative flow velocity thresholds, a dynamic adjustment factor for the water flow velocity is calculated using an adjustment coefficient: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor for the water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor for the water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height. S3. Determine the hydrogen production priority coefficient by the dynamic adjustment factor of water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power, and combine the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure to determine the adjusted hydrogen fuel cell output power using a hyperbolic tangent function. S4. Determine the final output power of the hydrogen fuel cell based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell, and the maximum allowable power of the hydrogen fuel cell. Determine the output power of the lithium battery based on the lithium battery power allocation coefficient, the motor power demand, and the final output power of the hydrogen fuel cell. Determine the output power of the supercapacitor based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power, and the wave height response weight of the supercapacitor.

2. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The environmental parameters also include the rate of change of heading angle, S2 further includes the heading angle abrupt change pre-compensation power, and the calculation method for the supercapacitor output power in S4 is as follows: The pre-compensation power for the abrupt change in heading angle is superimposed on the supercapacitor output power to form the compensated supercapacitor output power. The heading angle change pre-compensation power is calculated by multiplying the heading angle change rate by the motor power requirement.

3. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, S3 further includes: When the pressure of the hydrogen storage tank is lower than the preset warning threshold, the hydrogen production priority coefficient is adjusted according to the hydrogen storage tank pressure and the preset warning threshold to obtain the adjusted hydrogen production priority coefficient.

4. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The calculation of the dynamic adjustment factor for water flow velocity in step S2 further includes the calibration process of the adjustment coefficient: When the water flow velocity exceeds the forward flow velocity threshold, the first adjustment coefficient decreases inversely based on the ratio of the real-time draft to the reference draft. When the water flow velocity is lower than the negative flow velocity threshold, the second adjustment coefficient is dynamically increased based on the square root relationship between the ship's real-time speed and the design reference speed. The first adjustment coefficient and the second adjustment coefficient are used to calculate the weight allocation of the positive velocity difference and the negative velocity difference, respectively.

5. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The new energy parameters also include the state of charge of the lithium battery, and the lithium battery output power calculation in S4 further includes lithium battery protection strategies: When the state of charge of the lithium battery is lower than the preset threshold, power compensation demand is generated based on the difference between the state of charge of the lithium battery and the preset threshold. The final output power of the hydrogen fuel cell is increased by the power compensation requirement.

6. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The environmental parameters also include the wind turbine blade angle of attack and the wind direction angle. The new energy parameters also include the maximum power point voltage, maximum power point current, and inverter efficiency. The wind / photovoltaic power generation in S1 is calculated as follows: ； in, Indicates wind / solar power generation capacity. This represents the voltage at the maximum power point. This represents the maximum power point current. This represents the blade angle-of-attack compensation coefficient. Indicates inverter efficiency. Indicates the angle of attack of the wind turbine blade. Indicates the wind direction angle.

7. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The environmental parameters also include the baseline wave height for calm sea states and the angle between the wave propagation direction and the current heading. The wave height influence factor in S4 is calculated as follows: The wave height influence factor is dynamically generated based on the ratio of wave height to the baseline wave height for calm sea states, combined with the trigonometric function relationship between the surge propagation direction and the current heading.

8. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system for ship navigation according to claim 1, characterized in that, The new energy parameters also include proton exchange membrane temperature, optimal operating temperature, electrolyzer current density, and optimal current density. The calculation method for the electrolyzer efficiency in S3 is as follows: Linear efficiency compensation is performed based on the difference between the proton exchange membrane temperature and the optimal operating temperature, and a secondary efficiency correction is added based on the difference between the electrolyzer current density and the optimal current density.

9. The dynamic energy efficiency optimization control method for a green hydrogen energy storage system used in ship navigation according to claim 1, characterized in that, The new energy parameters also include the cumulative operating time of the hydrogen fuel cell and the number of cycles of the lithium battery. S4 further includes a lifespan equalization control strategy: Based on the cumulative operating time of the hydrogen fuel cell and the number of cycles of the lithium battery, the weighting coefficients of the hydrogen fuel cell and the lithium battery are dynamically generated. The weighting coefficient of the hydrogen fuel cell decreases in an inverse relationship with the increase of the cumulative operating time, and the weighting coefficient of the lithium battery decreases in an inverse relationship with the increase of the number of cycles. The weighting coefficients for hydrogen fuel cells and lithium batteries are used to adjust the power distribution ratio between hydrogen fuel cells and lithium batteries.

10. A green hydrogen energy storage system for ship navigation, characterized in that, The method applicable to any one of claims 1-9, comprising: The data acquisition module is used to collect environmental and new energy parameters in real time. The environmental parameters include motor power demand, water flow velocity, positive / negative flow velocity thresholds, wave height, wave height influence factor, and wave height as a design benchmark for wave resistance. The new energy parameters include wind / photovoltaic power generation, electrolyzer efficiency, electrolyzer rated power, hydrogen storage tank pressure, minimum safe pressure of hydrogen storage tank, maximum allowable power of hydrogen fuel cell, and lithium battery power allocation coefficient. The calculation module is used to calculate a dynamic adjustment factor for water flow velocity based on the comparison result between the water flow velocity and the positive / negative flow velocity thresholds, using an adjustment coefficient: when the water flow velocity exceeds the positive flow velocity threshold, the dynamic adjustment factor for water flow velocity increases linearly by the difference; when the water flow velocity is lower than the negative flow velocity threshold, the dynamic adjustment factor for water flow velocity decreases linearly by the difference; when the water flow velocity is higher than the negative flow velocity threshold but does not exceed the positive flow velocity threshold, the baseline value is maintained; and the wave height response weight of the supercapacitor is determined according to the wave height and the wave resistance design baseline wave height. The first determining module is used to determine the hydrogen production priority coefficient by the dynamic adjustment factor of water flow rate, wind / photovoltaic power generation, electrolyzer efficiency and electrolyzer rated power, and to determine the adjusted hydrogen fuel cell output power by combining the hydrogen production priority coefficient, hydrogen storage tank pressure and hydrogen storage tank minimum safe pressure using a hyperbolic tangent function. The second determining module is used to determine the final output power of the hydrogen fuel cell based on the wave height influence factor, the adjusted output power of the hydrogen fuel cell and the maximum allowable power of the hydrogen fuel cell; to determine the output power of the lithium battery based on the lithium battery power allocation coefficient, the motor power demand and the final output power of the hydrogen fuel cell; and to determine the output power of the supercapacitor based on the motor power demand, the final output power of the hydrogen fuel cell, the lithium battery output power and the supercapacitor wave height response weight.

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