Energy management method, system and equipment for hydrogen-electricity hybrid tugboat and medium

Abstract

The invention provides an energy management method, system, equipment and medium for a hydrogen-electricity hybrid tugboat, and relates to the technical field of hybrid tugboat energy management.The energy management method comprises the steps that based on historical operation data of the tugboat, the ship speed and the output power of a propulsion motor are extracted as characteristic parameters, standard working conditions are divided through clustering analysis, and the energy management efficiency is improved; the current navigation working condition of the tug is recognized; constructing a hybrid power system performance model; and according to the current navigation working condition, the tug required power is obtained in real time in combination with the hybrid power system performance model, the optimal fuel cell output power is calculated by using a PMP algorithm, and the tug required power is distributed to the hydrogen fuel cell and the lithium battery in combination with the energy flow relation of the hydrogen-electricity hybrid power system. According to the invention, two power sources can be reasonably and uniformly distributed, so that energy distribution closely fits an actual operation scene, the energy utilization rate is maximized, the overall energy consumption is reduced, the service life of equipment is prolonged, and the operation and maintenance cost of the tug in the whole life cycle is reduced.

Description

Background Art

[0002] Tugboat operations are an important part of port operations, with complex operating conditions, frequent speed changes, large load changes, and low loads for most of the time. As a result, traditional fuel tugboats are difficult to meet carbon emissions and energy efficiency requirements under low operating conditions. Hydrogen-electric hybrid tugboats are powered by hydrogen fuel cells and lithium batteries, combining the clean and efficient nature of hydrogen fuel cells with the flexible energy storage characteristics of lithium batteries. In theory, they can significantly reduce pollutant emissions and optimize the ecological environment around ports. Since hydrogen-electric hybrid systems are relatively complex and involve the coordination of multiple energy sources, how to effectively manage the energy management of hydrogen-electric hybrid tugboats is an important research topic.

[0003] In the existing technology, the energy management method of hydrogen-electric hybrid tugboats mainly adopts preset rule-based energy distribution. According to the common operating conditions of tugboats, such as mooring, low-speed navigation, high-speed towing, etc., the energy supply ratios of hydrogen fuel cells and lithium batteries are set in advance, and they operate according to the established rules under the corresponding operating conditions; some technologies use simple intelligent algorithms to collect basic data such as the real-time speed and load of the tugboat, and adjust the energy output after rough calculation.

[0004] However, the actual operating conditions of tugboats are complex and changeable, and the preset rule-based energy allocation lacks flexibility, making it difficult to accurately match actual needs, often resulting in excessive consumption or idleness of a certain energy source, resulting in serious energy waste. The introduction of simple intelligent algorithms is too simple to deeply analyze the characteristics of complex operating conditions. When faced with emergencies or rapid switching of operating conditions, it is difficult to quickly make accurate energy allocation decisions, resulting in the inability of hydrogen-electric hybrid tugboats to stably maintain an efficient operating state, unable to achieve ideal energy-saving and performance goals, and low energy utilization efficiency. Summary of the invention

[0005] In view of the problems that the preset rule-based energy distribution method of the existing hydrogen-electric hybrid tugboat lacks flexibility and is difficult to match actual needs, the method of introducing a simple intelligent algorithm cannot deeply analyze the characteristics of complex working conditions, and the energy efficiency is low, the present invention provides an energy management method, system, equipment and medium for a hydrogen-electric hybrid tugboat, which can reasonably and evenly distribute energy to two power sources, so that the energy distribution is closely matched to the actual operation scenario, maximizes the energy utilization rate, reduces the overall energy consumption, extends the service life of the equipment, and reduces the operation and maintenance cost of the tugboat throughout its life cycle.

[0006] In a first aspect, the present invention provides an energy management method for a hydrogen-electric hybrid tugboat, the steps comprising: S1. Based on the historical operation data of the tugboat, the ship speed and propulsion motor output power are extracted as characteristic parameters, the standard working conditions are divided through cluster analysis, and the current sailing conditions of the tugboat are identified; S2. Construct a hybrid power system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid power system, model the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery, and set constraint boundaries, including the hydrogen fuel cell output power change rate boundary and SOC boundary; S3. The tugboat power requirement is obtained in real time based on the current sailing conditions and the hybrid power system performance model. The optimal fuel cell output power is calculated using the PMP (Pontryagin's Minimum Principle) algorithm. The tugboat power requirement is allocated to the hydrogen fuel cell and lithium battery based on the energy flow relationship of the hydrogen-electric hybrid power system.

[0007] It should be further explained that step S1 uses the K-means algorithm to perform cluster analysis, and the steps include: S101. Selecting initial cluster centers based on roulette method; S102. Assigning each sample point in the historical operation data to the nearest cluster based on the characteristic parameters to minimize the sum of the Euclidean distances of all sample points to their corresponding initial cluster centers; S103. Calculate the average value of all samples in each cluster, and use the center point of the cluster as the new cluster center; S104. Steps S102-S103 are executed repeatedly until the cluster centers converge and clustering is completed; S105. Dividing the clustering results into various standard operating conditions, and defining the ship speed and propulsion motor output power range of each standard operating condition; S106. Determine the current navigation condition according to the current ship speed and propulsion motor output power of the tugboat.

[0008] It should be further explained that the step of selecting the initial cluster center in step S101 is: S1011. Randomly and uniformly select a sample point from the historical operation data as the first initial cluster center; S1012. Calculate the Euclidean distance between each sample point in the historical operation data and the initial cluster center based on the characteristic parameters and the probability of being selected as the next initial cluster center. Combined with the probability distribution, determine the next initial cluster center based on the roulette method. Repeat the process until all initial cluster centers are selected.

[0009] It should be further explained that in step S104, the convergence error of the cluster center is 10 -6 .

[0010] It should be further explained that in step S1, the standard operating conditions include berthing conditions, slow speed conditions, service speed conditions, general towing conditions for berthing and unberthing, dynamic positioning conditions for berthing and unberthing, full service speed conditions, maximum towing conditions for berthing and unberthing, or maximum speed navigation conditions.

[0011] It should be further explained that, in the hybrid power system performance model of step S2, the energy flow relationship expression of the hydrogen-electric hybrid power system is:

[0012] In the formula, The power required for the tugboat; Output power for the propulsion motor; The power consumption of the whole ship's life load and operating load; Output power for lithium battery; is the net output power of the hydrogen fuel cell system; The expression for hydrogen fuel cell efficiency is:

[0013] In the formula, The power of hydrogen consumed by the hydrogen fuel cell system; The high calorific value of hydrogen, =143 kJ / g; Hydrogen fuel cell hydrogen consumption rate The expression is:

[0014] Where: is the number of hydrogen fuel cell units; is the molar mass of hydrogen, =2 g / mol; is the number of electrons in a hydrogen molecule, =2; I is the output current of the hydrogen fuel cell; F is Faraday constant, F=96485C / mol; Real-time charging efficiency of lithium batteries and real-time discharge efficiency The expression is:

[0015]

[0016] Where R is the internal resistance of the lithium battery; U is the open circuit voltage; The expression for the constraint boundary is:

[0017] In the formula, SOC is the state of charge of the lithium battery; is the rate of change of the output power of the hydrogen fuel cell; The superscript max represents the upper limit of each variable, and the superscript min represents the lower limit of each variable.

[0018] It should be further explained that in step S2, the hydrogen-electric hybrid power system uses hydrogen fuel cells, lithium battery packs and shore charging equipment as energy supply ends, and propulsion motors, propellers, life loads and operating loads as load ends.

[0019] It should be further explained that the Hamiltonian function of the power allocation using the PMP algorithm in step S3 is:

[0020] In the formula, is the optimal co-state variable of the current navigation condition, which is obtained by solving the particle swarm algorithm (PSO); a is the control coefficient. When the SOC of the power battery is greater than the artificially set SOC setting value, a =0, when the SOC of the power battery is not greater than the SOC setting value, a =1; is the SOC penalty item; It is the idling penalty item; is the overload penalty item; Optimal fuel cell output power for: .

[0021] It should be further explained that the steps for solving the optimal co-state variables of the current navigation condition include: S301. Obtaining propulsion motor output power data from the historical operation data of the tugboat to form a historical data set; S302. Extracting data corresponding to driving segments of each standard working condition from the historical data set for cluster analysis to obtain cluster results corresponding to each standard working condition; S303. Taking the optimization of equivalent hydrogen consumption of the whole ship as the goal, the particle swarm algorithm (PSO) is used to solve the theoretical optimal co-state variables of each standard working condition; S304. Combined with the constraint boundary, calculate the actual optimal co-state variable that satisfies the boundary conditions for each standard working condition, and form an optimal co-state variable table for each standard working condition; S305. Select the optimal co-state variable corresponding to the current navigation condition from the optimal co-state variable table.

[0022] It should be further explained that the specific steps of using the particle swarm algorithm (PSO) to solve the theoretical optimal co-state variables of each standard working condition include: S3031. Establish a particle swarm of co-state variables, each particle corresponds to a candidate value of the co-state variable, and initialize the speed and position of the particle; S3032. Calculate the fitness of each particle according to the optimization objective function. The fitness evaluation index is the equivalent hydrogen consumption rate. The lower the equivalent hydrogen consumption rate, the better the fitness. S3033. Compare the fitness of each particle's current position with the fitness of its historical optimal position. If the fitness of the current position is better, the current position is used as the new historical optimal position. S3034. Compare the fitness of each particle's current position with the fitness of the particle group's optimal position. If the fitness of the current position is better, use the current position as the new optimal position of the particle group. S3035. Update the speed and position of each particle. The calculation formula is as follows

[0023] In the formula, is the velocity of the i-th particle at the k-th iteration; is the position of the i-th particle at the k-th iteration; is the historical optimal position of the i-th particle at the k-th iteration; is the optimal position of the particle group for all particles at the kth iteration; Represents a random number between 0 and 1; and is the learning factor; w is the inertia factor, which is linearly decreased as follows:

[0024] In the formula, represents the initial weight; Inertia weight representing the maximum number of iterations; K represents the maximum number of iterations set; S3036. Determine whether the iteration end condition is met. If so, end the optimization process. Otherwise, return to step S3032 to continue optimization. The iteration end condition is that the number of iterations reaches the maximum number of iterations, the fitness is stable, or the group aggregation is stable.

[0025] It should be further explained that in step S3032, the optimization objective function is:

[0026] Where, J is the equivalent hydrogen consumption rate of the tugboat; is the instantaneous fuel consumption rate of the lithium battery, expressed as:

[0027] The expression is:

[0028] is the average efficiency of hydrogen fuel cell system; is the average charging efficiency of lithium batteries; is the average discharge efficiency of lithium batteries.

[0029] It should be further explained that the SOC penalty item The expression is:

[0030] In the formula, b is the SOC maintenance coefficient, and its value is 0.2; Set the value for SOC.

[0031] It should be further explained that the idling penalty The expression is:

[0032] in, =0.04; Overload Penalty The expression is:

[0033] in, =0.05.

[0034] It should be further explained that the SOC setting value is 0.5.

[0035] In a second aspect, the present invention provides an energy management system for a hydrogen-electric hybrid tugboat, which is used to implement the energy management method for the hydrogen-electric hybrid tugboat, comprising: Data acquisition module, used to collect historical operation data of tugboats and real-time ship speed and propulsion motor output power; The data processing and working condition identification module is used to extract the ship speed and propulsion motor output power as characteristic parameters based on the tugboat's historical operation data, divide the standard working conditions through cluster analysis, and identify the tugboat's current sailing condition based on the tugboat's real-time ship speed and propulsion motor output power; The hybrid system performance model building module is used to build the hybrid system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid system, the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery are modeled respectively, and the constraint boundaries are set; The power calculation and allocation module is used to obtain the tugboat's required power in real time according to the current sailing conditions and the hybrid power system performance model, calculate the optimal fuel cell output power using the PMP algorithm, and allocate the tugboat's required power to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system.

[0036] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the energy management method for the above-mentioned hydrogen-electric hybrid tugboat when executing the computer program.

[0037] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the energy management method for the above-mentioned hydrogen-electric hybrid tugboat.

[0038] The beneficial effects of the present invention are: 1. The energy management method for a hydrogen-electric hybrid tugboat provided by the present invention identifies the operating conditions of the hydrogen-electric hybrid tugboat, constructs a hybrid system performance model, obtains the tugboat's required power in real time according to the current navigation conditions combined with the hybrid system performance model, uses an algorithm to calculate the optimal fuel cell output power, and allocates the tugboat's required power to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid system. The present invention can give full play to the complementary advantages of hydrogen fuel cells and batteries, effectively solve the problem of required power under different working conditions, and achieve reasonable and balanced distribution to two power sources, which can maximize energy utilization and reduce overall energy consumption.

[0039] 2. The present invention realizes the identification of different operating conditions through situational awareness, provides a basis for subsequent energy allocation steps, and can make energy allocation closely fit the actual operating scenario, prevent overload and underload operation of power system components, extend the service life of equipment, and reduce the operation and maintenance cost of the tugboat throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 It is a flow chart of an energy management method for a hydrogen-electric hybrid tugboat in one embodiment of the present invention.

[0042] Figure 2 Schematic diagram of a hydrogen-electric hybrid power system architecture in one embodiment of the present invention.

[0043] Figure 3 It is a power battery SOC diagram obtained by simulating the energy management strategy in one embodiment of the present invention.

[0044] Figure 4 It is a fuel cell input and output power diagram obtained by simulating the energy management strategy in one embodiment of the present invention.

[0045] Figure 5 It is a schematic block diagram of an energy management system of a hydrogen-electric hybrid tugboat in one embodiment of the present invention.

[0046] Figure 6 It is a schematic diagram of the hardware structure of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in this specific embodiment. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this patent.

[0048] The energy management method of the hydrogen-electric hybrid tugboat involved in this application is mainly aimed at the technical field of energy management of hybrid tugboats. This application identifies the operating conditions of the hydrogen-electric hybrid tugboat, constructs a hybrid system performance model, obtains the tugboat's required power in real time according to the current navigation conditions combined with the hybrid system performance model, uses an algorithm to calculate the optimal fuel cell output power, and distributes the tugboat's required power to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid system. The present invention can give full play to the complementary advantages of hydrogen fuel cells and batteries, effectively solve the problem of required power under different working conditions, and achieve reasonable and balanced distribution to two power sources, which can maximize energy utilization and reduce overall energy consumption; through situational awareness, it can realize the identification of different operating conditions, provide a basis for subsequent energy distribution steps, and make energy distribution closely fit the actual operating scene, prevent overload and underload operation of power system components, extend the service life of equipment, and reduce the operation and maintenance cost of the tugboat throughout its life cycle.

[0049] The energy management method for a hydrogen-electric hybrid tugboat involved in the present application is mainly aimed at the technical problems that the preset rule-based energy distribution method of the existing hydrogen-electric hybrid tugboat lacks flexibility and is difficult to match actual needs, and the method of introducing a simple intelligent algorithm cannot deeply analyze the characteristics of complex working conditions and has low energy efficiency.

[0050] The energy management method of the hydrogen-electric hybrid tugboat involved in the present application will be described in detail below. For the purpose of explanation rather than limitation, specific details such as specific system structures and technologies are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0051] In the energy management method of the hydrogen-electric hybrid tugboat involved in the present application, the term "include" used indicates the existence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. The terms "include", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0052] In order to clearly describe the technical solution of the present application, the words "first", "second" and the like are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the difference.

[0053] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The energy management method for the hydrogen-electric hybrid tugboat provided in the embodiment of the present invention is executed by a computer device, and accordingly, the energy management system for the hydrogen-electric hybrid tugboat runs in the computer device.

[0056] Figure 1 This is a flow chart of an energy management method for a hydrogen-electric hybrid tugboat according to an embodiment of the present invention. Figure 1 The execution subject may be an energy management system for a hydrogen-electric hybrid tugboat. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0057] like Figure 1 As shown, the energy management method of the hydrogen-electric hybrid tugboat includes: Step S1, based on the historical operation data of the tugboat, extract the ship speed and propulsion motor output power as characteristic parameters, divide the standard working conditions through cluster analysis, and identify the current navigation condition of the tugboat.

[0058] Based on the historical operation data of the tugboat, the ship speed and propulsion motor output power are extracted as characteristic parameters, the standard operating conditions are divided through cluster analysis, and the current navigation condition of the tugboat is identified. The historical operation data accumulated by the tugboat can be effectively utilized, and the navigation states with similar characteristics can be classified to form standard operating conditions. It is helpful to clearly sort out and classify the complex and diverse navigation conditions of the tugboat, and it can also quickly and accurately judge the current working condition of the tugboat when it is running in real time, providing an accurate basis for the subsequent targeted energy management strategy formulation, so that the energy management system can better adapt to the actual operating status of the tugboat and improve energy utilization efficiency.

[0059] In some specific embodiments, the K-means algorithm is used to perform cluster analysis, and the steps include: S101. Selecting initial cluster centers based on roulette method; S102. Assigning each sample point in the historical operation data to the nearest cluster based on the characteristic parameters to minimize the sum of the Euclidean distances of all sample points to their corresponding initial cluster centers; S103. Calculate the average value of all samples in each cluster, and use the center point of the cluster as the new cluster center; S104. Steps S102-S103 are executed repeatedly until the cluster centers converge and clustering is completed; S105. Dividing the clustering results into various standard operating conditions, and defining the ship speed and propulsion motor output power range of each standard operating condition; S106. Determine the current navigation condition according to the current ship speed and propulsion motor output power of the tugboat.

[0060] Using K-means algorithm for cluster analysis and roulette method to select initial cluster centers can avoid the cluster results being too affected by the initial values ​​and improve cluster accuracy; the algorithm for allocating sample points and updating cluster centers based on Euclidean distance is simple and easy to implement, which can effectively classify historical operation data, determine the standard operating range, and provide reliable support for navigation condition judgment. In some specific embodiments, the step of selecting the initial cluster center is: S1011. Randomly and uniformly select a sample point from the historical operation data as the first initial cluster center; S1012. Calculate the Euclidean distance between each sample point in the historical operation data and the initial cluster center based on the characteristic parameters and the probability of being selected as the next initial cluster center. Combined with the probability distribution, determine the next initial cluster center based on the roulette method. Repeat the process until all initial cluster centers are selected.

[0061] Randomly selecting a sample point first and then determining other centers based on the probability distribution can make the initial clustering center more representative and more evenly distributed, thereby improving the clustering effect and making the division of standard working conditions more accurate, which is conducive to the precise judgment of subsequent navigation conditions.

[0062] In some specific embodiments, the convergence error of the cluster center is 10 -6 .

[0063] The convergence error of the cluster center is specified to ensure the accuracy of the clustering results, make the divided standard working conditions more accurate, reduce error accumulation, and lay the foundation for the accurate identification of tugboat navigation conditions and the precise implementation of energy management strategies.

[0064] In some specific embodiments, the standard operating conditions include berthing conditions, slow speed conditions, service speed conditions, general towing conditions for berthing and unberthing, dynamic positioning conditions for berthing and unberthing, full service speed conditions, maximum towing conditions for berthing and unberthing, or maximum speed sailing conditions.

[0065] Clarify the types of navigation conditions, covering the common operating states of tugboats, so as to facilitate the formulation of differentiated energy management strategies for different conditions, realize the refined allocation of energy, and improve the pertinence and effectiveness of energy utilization.

[0066] Step S2, constructing a hybrid power system performance model, based on the energy flow relationship of the hydrogen-electric hybrid power system, modeling the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery, and setting constraint boundaries, including the hydrogen fuel cell output power change rate boundary and the SOC boundary.

[0067] By establishing a hybrid power system performance model to describe the key performance indicators of the hybrid power system and clarify the flow of energy in the system, a basis is provided for accurately calculating and predicting system performance. At the same time, the set constraint boundaries can ensure that hydrogen fuel cells and lithium batteries operate within a safe and efficient range, avoiding system failures or performance degradation caused by problems such as excessive charging and discharging or rapid power changes, thereby ensuring the stable and reliable operation of the entire hybrid power system.

[0068] In some specific embodiments, in the hybrid power system performance model, the energy flow relationship expression of the hydrogen-electric hybrid power system is:

[0069] In the formula, The power required for the tugboat; Output power for the propulsion motor; The power consumption of the whole ship's life load and operating load; Output power for lithium battery; is the net output power of the hydrogen fuel cell system; The expression for hydrogen fuel cell efficiency is:

[0070] In the formula, The power of hydrogen consumed by the hydrogen fuel cell system; The high calorific value of hydrogen, =143 kJ / g; Hydrogen fuel cell hydrogen consumption rate The expression is:

[0071] Where: is the number of hydrogen fuel cell units; is the molar mass of hydrogen, =2 g / mol; is the number of electrons in a hydrogen molecule, =2; I is the output current of the hydrogen fuel cell; F is Faraday constant, F=96485C / mol; Real-time charging efficiency of lithium batteries and real-time discharge efficiency The expression is:

[0072]

[0073] Where R is the internal resistance of the lithium battery; U is the open circuit voltage; The expression for the constraint boundary is:

[0074] In the formula, SOC is the state of charge of the lithium battery; is the rate of change of the output power of the hydrogen fuel cell; The superscript max represents the upper limit of each variable, and the superscript min represents the lower limit of each variable.

[0075] The energy flow relationship, expressions for the efficiency and consumption rate of each component, and constraint boundary expressions are given, and a complete hybrid power system performance model is constructed, which provides a theoretical basis for accurately calculating the required power and energy distribution of the tugboat, ensuring that the energy management process is scientific and reasonable.

[0076] In some specific embodiments, the hydrogen-electric hybrid power system uses hydrogen fuel cells, lithium battery packs and shore charging equipment as energy supply ends, and uses propulsion motors, propellers, life loads and operating loads as load ends.

[0077] Defining the energy supply and load ends of the hydrogen-electric hybrid system and clarifying the system composition structure will help understand the input and output relationship of energy and provide clear ideas for the optimal design of the system and the formulation of energy management strategies.

[0078] Step S3, according to the current sailing conditions and the hybrid power system performance model, the required power of the tugboat is obtained in real time, the optimal fuel cell output power is calculated using the PMP (Pontryagin's Minimum Principle) algorithm, and the required power of the tugboat is allocated to the hydrogen fuel cell and the lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system.

[0079] The power demand of the tugboat is calculated in real time based on the navigation conditions and the hybrid power system performance model, and the PMP algorithm is used to achieve the optimal power distribution. This can give full play to the respective advantages of hydrogen fuel cells and lithium batteries, flexibly adjust the output power of both according to different operating conditions, avoid energy waste, maximize energy utilization efficiency, reduce the operating cost of the tugboat, and improve the comprehensive performance of the tugboat under various navigation conditions.

[0080] In some specific embodiments, the Hamiltonian function for power allocation using the PMP algorithm is:

[0081] In the formula, is the optimal co-state variable of the current navigation condition, which is obtained by solving the particle swarm algorithm (PSO); a is the control coefficient. When the SOC of the power battery is greater than the artificially set SOC setting value, a =0, when the SOC of the power battery is not greater than the SOC setting value, a =1; is the SOC penalty item; It is the idling penalty item; is the overload penalty item; Optimal fuel cell output power for: .

[0082] The PMP algorithm is used for power distribution. Through the Hamiltonian function and the set control coefficient and penalty term, multiple factors are comprehensively considered to optimize power distribution, which can improve energy utilization efficiency, ensure the reasonable charge state of the lithium battery, and reduce energy loss during idling and heavy load.

[0083] In some specific embodiments, the step of solving the optimal co-state variables of the current navigation condition includes: S301. Obtaining propulsion motor output power data from the historical operation data of the tugboat to form a historical data set; S302. Extracting data corresponding to driving segments of each standard working condition from the historical data set for cluster analysis to obtain cluster results corresponding to each standard working condition; S303. Taking the optimization of equivalent hydrogen consumption of the whole ship as the goal, the particle swarm algorithm (PSO) is used to solve the theoretical optimal co-state variables of each standard working condition; S304. Combined with the constraint boundary, calculate the actual optimal co-state variable that satisfies the boundary conditions for each standard working condition, and form an optimal co-state variable table for each standard working condition; S305. Select the optimal co-state variable corresponding to the current navigation condition from the optimal co-state variable table.

[0084] By acquiring historical data, performing cluster analysis, solving theoretical optimal co-state variables, determining actual optimal co-state variables in combination with constraint boundaries and selecting corresponding values, accurate co-state variables are provided for the PMP algorithm, making power allocation more in line with actual working conditions and optimizing energy management effects.

[0085] In some specific embodiments, the specific steps of using the particle swarm algorithm to solve the theoretical optimal co-state variables of each standard working condition include: S3031. Establish a particle swarm of co-state variables, each particle corresponds to a candidate value of the co-state variable, and initialize the speed and position of the particle; S3032. Calculate the fitness of each particle according to the optimization objective function. The fitness evaluation index is the equivalent hydrogen consumption rate. The lower the equivalent hydrogen consumption rate, the better the fitness. S3033. Compare the fitness of each particle's current position with the fitness of its historical optimal position. If the fitness of the current position is better, the current position is used as the new historical optimal position. S3034. Compare the fitness of each particle's current position with the fitness of the particle group's optimal position. If the fitness of the current position is better, use the current position as the new optimal position of the particle group. S3035. Update the speed and position of each particle. The calculation formula is as follows

[0086] In the formula, is the velocity of the i-th particle at the k-th iteration; is the position of the i-th particle at the k-th iteration; is the historical optimal position of the i-th particle at the k-th iteration; is the optimal position of the particle group for all particles at the kth iteration; Represents a random number between 0 and 1; and is the learning factor; w is the inertia factor, which is linearly decreased as follows:

[0087] In the formula, represents the initial weight; Inertia weight representing the maximum number of iterations; K represents the maximum number of iterations set; S3036. Determine whether the iteration end condition is met. If so, end the optimization process. Otherwise, return to step S3032 to continue optimization. The iteration end condition is that the number of iterations reaches the maximum number of iterations, the fitness is stable, or the group aggregation is stable.

[0088] In some specific embodiments, in step S3032, the optimization objective function of the particle swarm algorithm is:

[0089] Where, J is the equivalent hydrogen consumption rate of the tugboat; is the instantaneous fuel consumption rate of the lithium battery, expressed as:

[0090] The expression is:

[0091] is the average efficiency of hydrogen fuel cell system; is the average charging efficiency of lithium batteries; is the average discharge efficiency of lithium batteries; The optimization objective function of the particle swarm algorithm is clarified, with the optimal equivalent hydrogen consumption as the goal, and factors such as the efficiency of lithium batteries and hydrogen fuel cells are comprehensively considered to guide the algorithm to find the best solution, achieve efficient use of energy, and reduce the operating costs of tugboats.

[0092] In some specific embodiments, the SOC penalty term The expression is:

[0093] In the formula, b is the SOC maintenance coefficient, and its value is 0.2; Set the value for SOC.

[0094] Providing an expression for the SOC penalty term can effectively maintain the SOC of the lithium battery within a reasonable range, avoid the SOC being too low or too high affecting the battery performance and life, ensure stable and reliable operation of the battery, and improve the overall performance of the energy management system.

[0095] In some specific embodiments, the idle penalty term The expression is:

[0096] in, =0.04; Overload Penalty The expression is:

[0097] in, =0.05.

[0098] The expressions of idle penalty term and overload penalty term are given, which can suppress the unreasonable energy consumption of tugboat under idle and overload conditions, optimize power distribution, improve energy utilization efficiency and reduce operating costs.

[0099] In some specific embodiments, the SOC setting value is 0.5.

[0100] The SOC set value is set to 0.5 to provide a clear SOC control target for the energy management system, ensure that the lithium battery operates at a suitable charge state, balance the battery's charge and discharge performance, and extend the battery life.

[0101] In a specific embodiment, the energy management method of a hydrogen-electric hybrid tugboat includes: Step S1, based on the historical operation data of the tugboat, extract the ship speed and propulsion motor output power as characteristic parameters, use the K-means algorithm to perform cluster analysis, divide the standard working conditions, and identify the current navigation condition of the tugboat, the steps include: S101. Selecting initial cluster centers based on roulette method; S102. Assigning each sample point in the historical operation data to the nearest cluster based on the characteristic parameters to minimize the sum of the Euclidean distances of all sample points to their corresponding initial cluster centers; S103. Calculate the average value of all samples in each cluster, and use the center point of the cluster as the new cluster center; S104. Loop through steps S102-S103 until the cluster centers converge, clustering ends, and the convergence error of the cluster centers is 10 -6 ; S105. Dividing the clustering results into various standard operating conditions, and defining the ship speed and propulsion motor output power range of each standard operating condition; S106. Determine the current navigation condition according to the current ship speed and propulsion motor output power of the tugboat; The steps for selecting the initial cluster center are: S1011. Randomly and uniformly select a sample point from the historical operation data as the first initial cluster center; S1012. Calculate the Euclidean distance between each sample point in the historical operation data and the initial cluster center based on the characteristic parameters and the probability of being selected as the next initial cluster center, determine the next initial cluster center based on the roulette method in combination with the probability distribution, and repeat the process until all the initial cluster centers are selected; In this embodiment, according to the speed and power clustering results of different operation driving segments of the 7000 horsepower hydrogen-electric hybrid tugboat, a corresponding relationship table of 7 standard working conditions and characteristic data is formed, as shown in Table 1; Table 1 Standard working conditions-characteristic data correspondence table

[0102] Step S2, constructing a hybrid power system performance model, based on the energy flow relationship of the hydrogen-electric hybrid power system, respectively modeling the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery, and setting constraint boundaries, including the hydrogen fuel cell output power change rate boundary and the SOC boundary; In the hybrid system performance model, the energy flow relationship of the hydrogen-electric hybrid system is expressed as:

[0103] In the formula, The power required for the tugboat; Output power for the propulsion motor; The power consumption of the whole ship's life load and operating load; Output power for lithium battery; is the net output power of the hydrogen fuel cell system; The expression for hydrogen fuel cell efficiency is:

[0104] In the formula, The power of hydrogen consumed by the hydrogen fuel cell system; The high calorific value of hydrogen, =143 kJ / g; Hydrogen fuel cell hydrogen consumption rate The expression is:

[0105] Where: is the number of hydrogen fuel cell units; is the molar mass of hydrogen, =2 g / mol; is the number of electrons in a hydrogen molecule, =2; I is the output current of the hydrogen fuel cell; F is Faraday constant, F=96485C / mol; Real-time charging efficiency of lithium batteries and real-time discharge efficiency The expression is:

[0106]

[0107] Where R is the internal resistance of the lithium battery; U is the open circuit voltage; The expression for the constraint boundary is:

[0108] In the formula, SOC is the state of charge of the lithium battery; is the rate of change of the output power of the hydrogen fuel cell; The superscript max indicates the upper limit of each variable, and the superscript min indicates the lower limit of each variable; The hydrogen-electric hybrid system uses hydrogen fuel cells, lithium battery packs and shore charging equipment as the energy supply end, and propulsion motors, propellers, life loads and operating loads as the load end; Step S3, obtaining the tugboat power requirement in real time according to the current sailing conditions and the hybrid power system performance model, using the PMP (Pontryagin's Minimum Principle) algorithm to calculate the optimal fuel cell output power, and allocating the tugboat power requirement to the hydrogen fuel cell and the lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system; The Hamiltonian function for power allocation using the PMP algorithm is:

[0109] In the formula, is the optimal co-state variable of the current navigation condition, which is obtained by solving the particle swarm algorithm (PSO); a is the control coefficient. When the SOC of the power battery is greater than the artificially set SOC setting value, a =0, when the SOC of the power battery is not greater than the SOC setting value, a =1; is the SOC penalty item; It is the idling penalty item; is the overload penalty item; Optimal fuel cell output power for:

[0110] Among them, the steps for solving the optimal co-state variables of the current navigation condition include: S301. Obtaining propulsion motor output power data from the historical operation data of the tugboat to form a historical data set; S302. Extracting data corresponding to driving segments of each standard working condition from the historical data set for cluster analysis to obtain cluster results corresponding to each standard working condition; S303. Taking the optimization of equivalent hydrogen consumption of the whole ship as the goal, the particle swarm algorithm (PSO) is used to solve the theoretical optimal co-state variables of each standard working condition. The specific steps include: S3031. Establish a particle swarm of co-state variables, set the particle dimension to 1, the number of particles to 150, each particle corresponds to a candidate value of the co-state variable, and initialize the particle speed and position; S3032. Calculate the fitness of each particle according to the optimization objective function. The fitness evaluation index is the equivalent hydrogen consumption rate. The lower the equivalent hydrogen consumption rate, the better the fitness. The optimization objective function is:

[0111] Where, J is the equivalent hydrogen consumption rate of the tugboat; is the instantaneous fuel consumption rate of the lithium battery, expressed as:

[0112] The expression is:

[0113] is the average efficiency of hydrogen fuel cell system; is the average charging efficiency of lithium batteries; is the average discharge efficiency of lithium batteries; S3033. Compare the fitness of each particle's current position with the fitness of its historical optimal position. If the fitness of the current position is better, the current position is used as the new historical optimal position. S3034. Compare the fitness of each particle's current position with the fitness of the particle group's optimal position. If the fitness of the current position is better, use the current position as the new optimal position of the particle group. S3035. Update the speed and position of each particle. The calculation formula is as follows

[0114] In the formula, is the velocity of the i-th particle at the k-th iteration; is the position of the i-th particle at the k-th iteration; is the historical optimal position of the i-th particle at the k-th iteration; is the optimal position of the particle group for all particles at the kth iteration; Represents a random number between 0 and 1; and is the learning factor, all assigned a value of 2; w is the inertia factor, which is linearly decreased as follows:

[0115] In the formula, represents the initial weight, which is set to 0.4; The inertia weight representing the maximum number of iterations is set to 0.8; K represents the maximum number of iterations, which is set to 100; S3036. Determine whether the current iteration end condition is met. If so, the optimization process ends. Otherwise, the optimization process returns to step S3032 to continue. The iteration end condition is achieved when the number of iterations reaches the maximum number of iterations, the fitness is stable, or the group aggregation is stable. Any one of the conditions stops the iteration; Among them, the criterion for determining fitness stability is that the relative change rate of fitness in five consecutive generations is less than 0.01% during the iteration process; The criterion for determining the stability of the group aggregation is that during the iteration process, the average absolute distance between the optimal solution of the group and all particle positions for eight consecutive generations is less than 0.5% of the search range width and the maximum distance is less than 1.5% of the search range width; S304. Combined with the constraint boundary, calculate the actual optimal co-state variable that satisfies the boundary conditions for each standard working condition, and form an optimal co-state variable table for each standard working condition; S305. Selecting the optimal co-state variable corresponding to the current navigation condition from the optimal co-state variable table; SOC Penalty Item The expression is:

[0116] In the formula, b is the SOC maintenance coefficient, and its value is 0.2; Set a value for SOC; Idle Penalty The expression is:

[0117] in, =0.04; Overload Penalty The expression is:

[0118] in, =0.05; SOC setting value is 0.5.

[0119] A Simulink model was built in the MATLAB environment, and an example calculation was performed. When the tugboat was moving from service speed to maximum towing speed or maximum speed, the initial SOC value was set to 0.8, the SOC setting value was set to 0.5, and the energy management strategy was simulated. The power battery SOC diagram was as follows: Figure 3 As shown, the fuel cell input and output power diagram is as follows Figure 4 As shown. Figure 3 It can be seen that when the tugboat is operating from the service speed to the maximum towing or maximum speed, the initial power battery SOC is high, and the tugboat hybrid system implements the energy management strategy with the optimal equivalent hydrogen consumption, and the SOC continues to decrease. When the SOC drops to the set value, it implements the energy management strategy of SOC maintenance, and the SOC is maintained near 0.5; Figure 4 It can be concluded that when the tugboat hybrid system implements the strategy of optimal equivalent hydrogen consumption, the output power of the fuel cell fluctuates around 5,000 horsepower, which is determined by the characteristics of the fuel cell itself; when the SOC maintenance strategy is implemented, the output power of the fuel cell increases, which is caused by the boundary conditions of SOC maintenance. By increasing the output power of the fuel cell, the purpose of maintaining the SOC of the power battery is achieved. Figure 3 and Figure 4From the comparison, it can be seen that the fuel cell is never in a heavy-load condition, and is only in an idling condition for a short time. Therefore, the durability of the fuel cell is improved to a certain extent. At the same time, the power battery always works in a high-efficiency range, thereby delaying the attenuation of the power battery.

[0120] The following is an embodiment of the energy management system of a hydrogen-electric hybrid tugboat provided in an embodiment of the present disclosure. The active load reduction optimization system and the energy management method for a hydrogen-electric hybrid tugboat in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the energy management system for a hydrogen-electric hybrid tugboat, reference can be made to the embodiment of the energy management method for a hydrogen-electric hybrid tugboat in the above-mentioned embodiments.

[0121] A mobile terminal implementing various embodiments of the present invention will now be described with reference to the accompanying drawings. In the subsequent description, suffixes such as "module", "component" or "unit" used to represent elements are used only to facilitate the description of the embodiments of the present invention and have no specific meaning in themselves. Therefore, "module" and "component" can be used interchangeably.

[0122] like Figure 5 As shown, the energy management system of the hydrogen-electric hybrid tugboat includes: Data acquisition module, used to collect historical operation data of tugboats and real-time ship speed and propulsion motor output power; The data processing and working condition identification module is used to extract the ship speed and propulsion motor output power as characteristic parameters based on the tugboat's historical operation data, divide the standard working conditions through cluster analysis, and identify the tugboat's current sailing condition based on the tugboat's real-time ship speed and propulsion motor output power; The hybrid system performance model building module is used to build the hybrid system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid system, the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery are modeled respectively, and the constraint boundaries are set; The power calculation and allocation module is used to obtain the tugboat's required power in real time according to the current sailing conditions and the hybrid power system performance model, calculate the optimal fuel cell output power using the PMP algorithm, and allocate the tugboat's required power to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system.

[0123] The energy management system of the hydrogen-electric hybrid tugboat is used to implement the energy management method of the hydrogen-electric hybrid tugboat, including: S1. Based on the historical operation data of the tugboat, the ship speed and propulsion motor output power are extracted as characteristic parameters, the standard working conditions are divided through cluster analysis, and the current sailing conditions of the tugboat are identified; S2. Construct a hybrid power system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid power system, model the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery, and set constraint boundaries, including the hydrogen fuel cell output power change rate boundary and SOC boundary; S3. The tugboat power requirement is obtained in real time based on the current sailing conditions and the hybrid power system performance model. The optimal fuel cell output power is calculated using the PMP (Pontryagin's Minimum Principle) algorithm. The tugboat power requirement is allocated to the hydrogen fuel cell and lithium battery based on the energy flow relationship of the hydrogen-electric hybrid power system.

[0124] The present application also provides an electronic device for implementing various embodiments of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0125] Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0126] Figure 6 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0127] The electronic device 500 includes, but is not limited to, components such as a processor 501, a network module 502, an audio output unit 503, an input unit 504, a display unit 506, a user input unit 507, an interface unit 508, and a memory 509. Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0128] In the embodiments of the present invention, electronic devices include but are not limited to laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0129] In the embodiment of the present application, the processor 501 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0130] The display unit 506 is used to display information input by the user or information provided to the user. The display unit 506 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc.

[0131] The user input unit 507 may include, but is not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0132] The interface unit 508 is an interface for connecting external devices to the electronic device 500. For example, the external devices may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, an earphone port, and the like.

[0133] In addition, the electronic device 500 includes some functional modules not shown, which will not be described in detail here.

[0134] Those skilled in the art will appreciate that the various aspects of the electronic device provided by the present application may be implemented as a system, method or program product. Therefore, the various aspects of the present disclosure may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which may be collectively referred to as a "circuit", "module" or "system" herein.

[0135] The present application also provides a storage medium, in which a program product capable of implementing the energy management method of a hydrogen-electric hybrid tugboat is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0136] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0137] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for energy management of a hydrogen-electric hybrid tugboat, characterized in that the steps include: S1. Based on the historical operation data of the tugboat, the ship speed and propulsion motor output power are extracted as characteristic parameters, the standard working conditions are divided through cluster analysis, and the current sailing condition of the tugboat is identified according to the real-time ship speed and propulsion motor output power of the tugboat; S2. Construct a hybrid power system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid power system, model the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery, and set constraint boundaries, including the hydrogen fuel cell output power change rate boundary and SOC boundary; S3. According to the current sailing conditions and the hybrid power system performance model, the required power of the tugboat is obtained in real time, the optimal fuel cell output power is calculated using the PMP algorithm, and the required power of the tugboat is allocated to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system.

2. The energy management method of a hydrogen-electric hybrid tugboat according to claim 1, characterized in that: Step S1 uses K-means algorithm to perform cluster analysis, and the steps include: S101. Selecting initial cluster centers based on roulette method; S102. Assigning each sample point in the historical operation data to the nearest cluster based on the characteristic parameters to minimize the sum of the Euclidean distances of all sample points to their corresponding initial cluster centers; S103. Calculate the average value of all samples in each cluster, and use the center point of the cluster as the new cluster center; S104. Steps S102-S103 are executed repeatedly until the cluster centers converge and clustering is completed; S105. Dividing the clustering results into various standard operating conditions, and defining the ship speed and propulsion motor output power range of each standard operating condition; S106. Determine the current navigation condition according to the current ship speed and propulsion motor output power of the tugboat.

3. The energy management method of a hydrogen-electric hybrid tugboat according to claim 1, characterized in that: In the hybrid power system performance model of step S2, the energy flow relationship of the hydrogen-electric hybrid power system is expressed as: In the formula, The power required for the tugboat; Output power for the propulsion motor; The power consumption of the whole ship's life load and operating load; Output power for lithium battery; is the net output power of the hydrogen fuel cell system; The expression for hydrogen fuel cell efficiency is: In the formula, The power of hydrogen consumed by the hydrogen fuel cell system; The high calorific value of hydrogen, =143 kJ / g; Hydrogen fuel cell hydrogen consumption rate The expression is: Where: is the number of hydrogen fuel cell units; is the molar mass of hydrogen, =2 g / mol; is the number of electrons in a hydrogen molecule, =2; I is the output current of the hydrogen fuel cell; F is Faraday constant, F=96485C / mol; Real-time charging efficiency of lithium batteries and real-time discharge efficiency The expression is: Where R is the internal resistance of the lithium battery; U is the open circuit voltage; The expression for the constraint boundary is: In the formula, SOC is the state of charge of the lithium battery; is the rate of change of the output power of the hydrogen fuel cell; The superscript max represents the upper limit of each variable, and the superscript min represents the lower limit of each variable.

4. The energy management method of a hydrogen-electric hybrid tugboat according to claim 1, characterized in that: The Hamiltonian function of power allocation using the PMP algorithm in step S3 is: In the formula, is the optimal co-state variable of the current navigation condition, which is obtained by solving the particle swarm algorithm (PSO); a is the control coefficient. When the SOC of the power battery is greater than the artificially set SOC setting value, a =0, when the SOC of the power battery is not greater than the SOC setting value, a =1; is the SOC penalty item; It is the idling penalty item; is the overload penalty item; Optimal fuel cell output power for: 。 5. The energy management method of a hydrogen-electric hybrid tugboat according to claim 4, characterized in that: The steps for solving the optimal co-state variables of the current navigation condition include: S301. Obtaining propulsion motor output power data from the historical operation data of the tugboat to form a historical data set; S302. Extracting data corresponding to driving segments of each standard working condition from the historical data set for cluster analysis to obtain cluster results corresponding to each standard working condition; S303. Taking the optimization of equivalent hydrogen consumption of the whole ship as the goal, the particle swarm algorithm is used to solve the theoretical optimal co-state variables of each standard working condition; S304. Combined with the constraint boundary, calculate the actual optimal co-state variable that satisfies the boundary conditions for each standard working condition, and form an optimal co-state variable table for each standard working condition; S305. Select the optimal co-state variable corresponding to the current navigation condition from the optimal co-state variable table.

6. The energy management method of a hydrogen-electric hybrid tugboat according to claim 4, characterized in that: SOC Penalty Item The expression is: In the formula, b is the SOC maintenance coefficient, and its value is 0.2; Set the value for SOC.

7. The energy management method of a hydrogen-electric hybrid tugboat according to claim 5, characterized in that: Idle Penalty The expression is: in, =0.04; Overload Penalty The expression is: in, =0.

05.

8. An energy management system for a hydrogen-electric hybrid tugboat, characterized in that: The energy management method for implementing the hydrogen-electric hybrid tugboat as claimed in any one of claims 1 to 7 comprises: Data acquisition module, used to collect historical operation data of tugboats and real-time ship speed and propulsion motor output power; The data processing and working condition identification module is used to extract the ship speed and propulsion motor output power as characteristic parameters based on the tugboat's historical operation data, divide the standard working conditions through cluster analysis, and identify the tugboat's current sailing condition based on the tugboat's real-time ship speed and propulsion motor output power; The hybrid system performance model building module is used to build the hybrid system performance model. Based on the energy flow relationship of the hydrogen-electric hybrid system, the hydrogen consumption rate of the hydrogen fuel cell and the charging and discharging efficiency of the lithium battery are modeled respectively, and the constraint boundaries are set; The power calculation and allocation module is used to obtain the tugboat's required power in real time according to the current sailing conditions and the hybrid power system performance model, calculate the optimal fuel cell output power using the PMP algorithm, and allocate the tugboat's required power to the hydrogen fuel cell and lithium battery in combination with the energy flow relationship of the hydrogen-electric hybrid power system.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the energy management method for a hydrogen-electric hybrid tugboat as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the energy management method of the hydrogen-electric hybrid tugboat as described in any one of claims 1-7 are implemented.

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