Hybrid power ship energy management optimization method and system

Abstract

The invention relates to a hybrid power ship energy management optimization method and system. The method comprises the following steps: constructing a logic threshold rule according to a power combination of a target hybrid power ship; establishing an ECMS optimization model of the target hybrid power ship; solving the ECMS optimization model according to a logic threshold rule to obtain an energy management optimization model of the target hybrid power ship under the logic threshold rule; and solving the energy management optimization model of the target hybrid power ship under the logic threshold rule to obtain an optimized energy management strategy of the target hybrid power ship. Compared with the prior art, the energy utilization rate of the ship is improved, the operation cost is reduced, the economic benefit of the ship is improved, and a more efficient and reliable solution is provided for energy management of the hybrid power ship.

Description

Technical Field

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

[0002] In recent years, driven by the push for sustainable development and green shipping, hybrid ships have become a key development direction. Combining the advantages of internal combustion engines and electric motors, hybrid ships offer low noise, reduced emissions, energy conservation, environmental protection, and strong economic efficiency. However, traditional ship energy management systems often suffer from irrational energy allocation when faced with complex navigation conditions, leading to energy waste or insufficient power, impacting ship performance. Therefore, ship energy management strategies, particularly mode switching decisions, are crucial for optimizing energy allocation, improving economic efficiency, and overall performance.

[0003] The ECMS algorithm is an efficient real-time energy management algorithm. Its core idea is to achieve instantaneous optimal control of the system by converting electrical energy consumption into equivalent fuel consumption, thereby improving fuel economy. Compared with traditional global optimization algorithms such as dynamic programming (DP), this algorithm has a series of advantages such as real-time, high efficiency and flexibility. However, considering the complexity of ship operating conditions and the service life of ship equipment, the use of traditional ECMS algorithms will face the risk of rapid decline in battery health or even damage due to frequent overcharging and discharging. The motor and engine may also be damaged due to rapid conversion between high and low power, or the fuel stack may be in a high-load state due to unreasonable distribution, resulting in equipment life degradation. For example, patent application CN118636752A discloses a multi-stack fuel cell ship energy management method based on model prediction. This method solves the allocated power for the fuel cell system and the energy storage system by establishing a constraint on minimum total hydrogen consumption. It then allocates power to the fuel cell system and the energy storage system based on the allocated power. The voltage of each fuel cell at rated current is then calculated and sorted in ascending order to create an activation sequence for the multiple fuel cell stacks. However, this approach fails to fully consider factors such as the actual degradation level, temperature characteristics, and internal resistance differences of each fuel cell stack. When the performance of each stack varies significantly, evenly allocating power may result in some stacks being subjected to prolonged high loads, accelerating their degradation and, consequently, impacting the lifespan and stability of the entire system. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a hybrid ship energy management optimization method and system, which improves the energy utilization rate of the ship, reduces operating costs, improves the economic benefits of the ship, and provides a more efficient and reliable solution for the energy management of hybrid ships.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A hybrid ship energy management optimization method comprises the following steps:

[0007] Constructing logic threshold rules according to the power combination of the target hybrid ship;

[0008] Establish an ECMS optimization model for the target hybrid ship;

[0009] Solving the ECMS optimization model according to the logic threshold rule to obtain an energy management optimization model of the target hybrid ship under the logic threshold rule;

[0010] The energy management optimization model of the target hybrid ship under the logic threshold rule is solved to obtain the optimized energy management strategy of the target hybrid ship.

[0011] Furthermore, the power combination of the hybrid ship includes a fuel cell, a lithium battery and a supercapacitor.

[0012] Furthermore, the logic threshold rule is specifically as follows:

[0013] When the lithium battery is in high SOC mode, if the maximum power output of the lithium battery can meet the current load of the ship, only the lithium battery will be used for power supply, avoiding the operation of the fuel cell;

[0014] When the lithium battery is in the medium SOC mode, the hybrid mode is adopted to coordinate the power output of the lithium battery and fuel cell;

[0015] When the lithium battery is in low SOC mode, the lithium battery is charged first, the fuel cell bears the load, and at the same time the charge level of the lithium battery is increased to prevent over-discharge.

[0016] Furthermore, when the lithium battery is in a high SOC mode, the output power rule of the fuel cell is:

[0017] When the minimum output power of the fuel cell is greater than the load demand power, the reference power of the fuel cell is set to its minimum output power;

[0018] When the load demand power is less than the sum of the minimum output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to its minimum output power;

[0019] When the load demand power is less than the sum of the maximum output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to the difference between the load demand power and the discharge power of the lithium battery;

[0020] When the load demand power is greater than the sum of the maximum output power of the fuel cell and the discharge efficiency of the lithium battery, the reference power of the fuel cell is set to its maximum output power.

[0021] Furthermore, when the lithium battery is in the medium SOC mode, the output power rule of the fuel cell is:

[0022] When the load demand power is less than the minimum output power of the fuel cell, the reference power of the fuel cell is set to its minimum output power;

[0023] When the load demand power is less than the difference between the optimal output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to the load demand power;

[0024] When the load demand power is less than the sum of the fuel cell's optimal output power and the lithium battery's discharge power, the fuel cell's reference power is set to its optimal output power;

[0025] When the load demand power is less than the maximum output power of the fuel cell, the reference power of the fuel cell is set to the load demand power;

[0026] When the load demand power is greater than the maximum output power of the fuel cell, the reference power of the fuel cell is set to its maximum output power.

[0027] Furthermore, when the lithium battery is in a low SOC mode, the output power rule of the fuel cell is:

[0028] When the load demand power is less than the difference between the maximum output power of the fuel cell and the charging power of the lithium battery, the fuel cell reference power is set to the sum of the load demand power and the charging power of the lithium battery;

[0029] When the load demand power is greater than the difference between the maximum output power of the fuel cell and the charging power of the lithium battery, the fuel cell reference power is set to its maximum output power.

[0030] Furthermore, the equivalent hydrogen optimization problem of the ECMS optimization model of the target hybrid ship is:

[0031] minC=min(C fc +kC bat )

[0032] C fc =aP fc +b

[0033]

[0034] Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, C fcis the instantaneous hydrogen fuel consumption of the fuel cell, k is the SOC correction coefficient of the lithium battery, C bat is the instantaneous hydrogen fuel consumption of the lithium battery, a is the instantaneous hydrogen fuel consumption of the fuel cell, P fc is the fuel cell output power, b is the equivalent instantaneous hydrogen fuel consumption of the lithium battery, P bat is the output power of the lithium battery, η dis is the discharge efficiency of the lithium battery, η chg The charging efficiency of lithium batteries.

[0035] Furthermore, the energy management optimization model of the target hybrid ship under the logic threshold rule is:

[0036]

[0037] Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, k is the lithium battery SOC correction coefficient, P bat is the output power of the lithium battery, k is the SOC correction coefficient of the lithium battery, η dis is the discharge efficiency of the lithium battery, η chg The charging efficiency of lithium batteries.

[0038] Furthermore, the optimized energy management strategy is the optimal output power of the fuel cell and the lithium battery.

[0039] According to another aspect of the present invention, a hybrid ship energy management optimization system is provided, comprising:

[0040] Constructing a logic threshold rule construction module, which is used to construct a logic threshold rule according to the power combination of the target hybrid ship;

[0041] ECMS optimization model building module, used to build the ECMS optimization model of the target hybrid ship;

[0042] an energy management optimization model construction module, configured to solve the ECMS optimization model according to the logic threshold rule to obtain an energy management optimization model of a target hybrid ship under the logic threshold rule;

[0043] The energy management strategy acquisition module is used to solve the energy management optimization model of the target hybrid ship under the logic threshold rule to obtain the optimized energy management strategy of the target hybrid ship.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention constructs a logical threshold rule according to the power combination of the target hybrid ship, quickly responds to changes in the ship's navigation state through the logical threshold rule, and establishes an ECMS optimization model of the target hybrid ship. The ECMS optimization model is solved according to the logical threshold rule to obtain an energy management optimization model of the target hybrid ship under the logical threshold rule, and the energy management strategy of the optimized target hybrid ship is solved, thereby optimizing the coordinated work of the fuel cell and the lithium battery, thereby improving the energy utilization rate of the ship. At the same time, under the specification of the logical threshold, the various components of the ship's power system avoid excessively rapid or drastic power changes, reduce the wear rate of parts in the power system, extend the life of the power system, reduce operating costs, and improve the economic benefits of the ship.

[0046] 2. The logic threshold rules of the present invention are simple and efficient, and can make decisions quickly. Combined with the real-time optimization capability of the ECMS algorithm, the ECMS algorithm makes up for the problems of inaccurate and difficult calculations of traditional global algorithms such as DP. The rigid rules of the logic gate valve are used to make up for the shortcomings of the real-time algorithm ECMS, which has a high equipment loss rate due to its too rapid response, thereby ensuring the stability and reliability of the system in complex environments and providing a more efficient and reliable solution for the energy management of hybrid ships. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a hybrid ship energy management optimization method proposed by the present invention;

[0048] Figure 2 Schematic diagram of the control mode of supercapacitor. DETAILED DESCRIPTION

[0049] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0050] English abbreviations involved:

[0051] Equivalent Consumption Minimization Strategy: Equivalent Consumption Minimization Strategy, ECMS

[0052] State of Charge: State of Charge, SOC

[0053] Example 1

[0054] This embodiment provides a hybrid ship energy management optimization method, such as Figure 1 As shown, the following steps are included:

[0055] S1. Constructing a logic threshold rule according to the power combination of the target hybrid ship.

[0056] This example uses a hybrid passenger ship powered by fuel cells as the target hybrid vessel. The ship is equipped with fuel cells, lithium batteries, and supercapacitors. The fuel cell is a proton exchange membrane fuel cell, model ProtonMotor "PM Basic A 50" maritime, with a rated power of 80kW, an output voltage of 140-260V, and an output current of 280-520A. The lithium battery pack has a capacity of 180Ah, an output voltage of 430-660V, an energy output of 94.5kWh, and a maximum discharge current of 360A. The supercapacitor has a rated capacity of 130F, a rated voltage of 56V, a maximum continuous discharge current of 30A, a maximum peak current of 1800A, a maximum series voltage of 750V, a single cell energy storage of 3Wh, and 23 cells.

[0057] The following rules are set for the target hybrid ship: 1) Add a first-order low-pass filter to divide the load power into different frequencies. The supercapacitor is responsible for the high-frequency component of the load power. The control mode of the supercapacitor is as follows: Figure 2 As shown, the supercapacitor SOC is limited to the range of [0.1-0.95]. When the high-frequency component of the load is less than 0 and the supercapacitor SOC is greater than 0.95, the supercapacitor does not operate to prevent overcharging. When the high-frequency component of the load is greater than 0 and the supercapacitor SOC is less than 0.1, the supercapacitor does not operate to prevent over-discharge. When the supercapacitor SOC is within the normal range, it bears the high-frequency component of the load. 2) Three fuel cell operation modes are designed, with the minimum output power of the fuel cell being 10kW, at which the fuel cell efficiency is approximately 60%, the optimal output power being 46kW, and the maximum output power being set at 80kW. The battery charging and discharging power are set at 30% of the battery capacity, with the optimal discharge power being 20% ​​of the battery capacity. Three modes are divided according to the battery SOC range: Mode 1 is the high SOC mode, with the SOC in the high range (SOC>80%); Mode 2 is the medium SOC mode, with the SOC in the normal range (50%≤SOC≤80%); and Mode 3 is the low SOC mode, with the SOC in the low range (SOC<50%).

[0058] The logic threshold rules are as follows:

[0059] When the lithium battery is in high SOC mode (SOC>80%), if the maximum power output of the lithium battery can meet the current load of the ship, only the lithium battery will be used for power supply, avoiding the operation of the fuel cell. When the lithium battery state of charge is high, in order to avoid overcharging of the lithium battery and affecting the safety of the power system, the lithium battery state of charge value is reduced to a reasonable range. In this stage, the lithium battery provides energy to the power system, and the fuel cell outputs as little power as possible. The output power rules of the fuel cell are:

[0060] Sub-mode 1: When the minimum output power of the fuel cell is greater than the load demand power, the reference power of the fuel cell is set to its minimum output power;

[0061] Sub-mode 2: When the load demand power is less than the sum of the fuel cell's minimum output power and the lithium battery's discharge power, the fuel cell's reference power is set to its minimum output power;

[0062] Sub-mode 3: When the load demand power is less than the sum of the maximum output power of the fuel cell and the discharge power of the lithium battery, the fuel cell reference power is set to the difference between the load demand power and the discharge power of the lithium battery;

[0063] Sub-mode 4: When the load demand power is greater than the sum of the maximum output power of the fuel cell and the discharge efficiency of the lithium battery, the reference power of the fuel cell is set to its maximum output power.

[0064] When the lithium battery is in the medium SOC mode (50% ≤ SOC ≤ 80%), the hybrid mode is used to coordinate the power output of the lithium battery and fuel cell. At this time, the lithium battery charge and discharge efficiency is near the optimal efficiency point. In order to extend the service life of the fuel cell, the fuel cell should also operate near the optimal efficiency point as much as possible. The output power rule of the fuel cell is:

[0065] Sub-mode 5: When the load demand power is less than the minimum output power of the fuel cell, the reference power of the fuel cell is set to its minimum output power;

[0066] Sub-mode 6: When the load demand power is less than the difference between the fuel cell's optimal output power and the lithium battery's discharge power, the fuel cell's reference power is set to the load demand power;

[0067] Sub-mode 7: When the load demand power is less than the sum of the fuel cell's optimal output power and the lithium battery's discharge power, the fuel cell's reference power is set to its optimal output power;

[0068] Sub-mode 8: When the load demand power is less than the maximum output power of the fuel cell, the fuel cell reference power is set to the load demand power;

[0069] Sub-mode 9: When the load demand power is greater than the maximum output power of the fuel cell, the reference power of the fuel cell is set to its maximum output power.

[0070] When the lithium battery is in low SOC mode (SOC < 50%), the lithium battery is charged first, and the fuel cell bears the load, while increasing the lithium battery charge level to prevent over-discharge. When the lithium battery state of charge is low, its charge and discharge efficiency will also decrease. Therefore, when the lithium battery is in a low state of charge, the fuel cell, in addition to providing the load power demand, tries to keep the lithium battery SOC within the normal working range to improve the overall efficiency of the system and avoid safety issues caused by over-discharge of the lithium battery. The output power rules of the fuel cell are as follows:

[0071] Submode 10: When the load demand power is less than the difference between the fuel cell maximum output power and the lithium battery charging power, the fuel cell reference power is set to the sum of the load demand power and the lithium battery charging power;

[0072] Submode 11: When the load demand power is greater than the difference between the maximum output power of the fuel cell and the charging power of the lithium battery, the fuel cell reference power is set to its maximum output power.

[0073] S2. Establish an ECMS optimization model for the target hybrid ship.

[0074] The ECMS optimization model for the target hybrid ship is a real-time optimized energy management model that rationally distributes the energy flow between the fuel cell and the lithium battery to minimize fuel consumption while maintaining the lithium battery SOC in a high efficiency range. The equivalent hydrogen optimization problem of the ECMS optimization model for the target hybrid ship is:

[0075] minC=min(C fc +kC bat )

[0076] C fc =aP fc +b

[0077]

[0078] Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, C fc is the instantaneous hydrogen fuel consumption of the fuel cell, k is the SOC correction coefficient of the lithium battery, C bat is the instantaneous hydrogen fuel consumption of the lithium battery, a is the instantaneous hydrogen fuel consumption of the fuel cell, P fc is the fuel cell output power, b is the equivalent instantaneous hydrogen fuel consumption of the lithium battery, P bat is the output power of the lithium battery, η dis is the discharge efficiency of the lithium battery, ηchg The charging efficiency of lithium batteries.

[0079] The constraints of the ECMS optimization model for the target hybrid ship are:

[0080] k=1-2μ[S-0.5SOC max +SOC min ] / (SOC max -SOC min )

[0081] SOC min <SOC<SOC max

[0082] P fc,min <P fc <P fc,max

[0083] P bat,min <P bat <P bat,max

[0084] In the formula, k is the SOC correction coefficient of the lithium battery, μ is the SOC balance correction coefficient of the lithium battery, S is the real-time state of charge value of the lithium battery, SOC min is the minimum SOC value of the lithium battery, SOC is the SOC value of the lithium battery, SOC max is the maximum SOC value of the lithium battery, P fc,min is the minimum output power of the fuel cell, P fc is the fuel cell output power, P fc,max is the maximum output power of the fuel cell, P bat,min is the minimum output power of the lithium battery, P bat is the output power of the lithium battery, P bat,max The maximum output power of the lithium battery.

[0085] S3. Solve the ECMS optimization model according to the logical threshold rule to obtain the energy management optimization model of the target hybrid ship under the logical threshold rule.

[0086] The power combination of hybrid ships satisfies the supply and demand balance to maintain the stability and safety of the system. Its power balance equation is:

[0087] P fc ·η fcDC +P bat ·η batDC =P load

[0088] Where, P fc is the fuel cell output power, η fcDC is the fuel cell DC-DC converter efficiency, Pbat is the output power of the lithium battery, η batDC is the efficiency of the lithium battery DC-DC converter, P load is the total load power requirement of the ship.

[0089] Combining the logical threshold rule with the equivalent hydrogen optimization problem of the ECMS optimization model of the target hybrid ship and its power balance equation, the energy management optimization model of the target hybrid ship under the logical threshold rule can be obtained as follows:

[0090]

[0091] Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, k is the lithium battery SOC correction coefficient, P bat is the output power of the lithium battery, k is the SOC correction coefficient of the lithium battery, η dis is the discharge efficiency of the lithium battery, η chg The charging efficiency of lithium batteries.

[0092] The charge and discharge efficiency of lithium batteries is shown in the following formula:

[0093]

[0094] Where η dis is the discharge efficiency of the lithium battery, R dis is the internal resistance of lithium battery discharge, P bat is the output power of the lithium battery, η chg is the charging efficiency of the lithium battery, R chg is the internal resistance of lithium battery charging, U ocv is the open circuit voltage of the lithium battery.

[0095] S4. Solve the energy management optimization model of the target hybrid ship under the logic threshold rule to obtain the optimized energy management strategy of the target hybrid ship.

[0096] The optimized energy management strategy of the target hybrid ship is the optimal output power of lithium batteries and fuel cells.

[0097] The optimal reference power of lithium batteries is shown in the following formula:

[0098]

[0099] K2=a·k·η dis

[0100]

[0101] Where x is the key state parameter in the equivalent hydrogen optimization model, R is the internal resistance of the lithium battery, a is the instantaneous hydrogen fuel consumption of the fuel cell, K1 is the charge state correction coefficient in the equivalent hydrogen optimization, K2 is the discharge state correction coefficient in the equivalent hydrogen optimization, and ... dis is the lithium battery discharge state parameter, R dis is the internal resistance of lithium battery discharge, U ocv is the open circuit voltage of the lithium battery, x chg is the lithium battery charging state parameter, R chg is the internal resistance of lithium battery charging, x min is the minimum allowable parameter of lithium battery under discharge state, U batmin is the minimum allowable voltage of lithium battery, x max It is the maximum allowable parameter of lithium battery under charging state, U batmax The maximum allowable voltage of lithium battery.

[0102] The calculation of the optimal reference power of the fuel cell is shown as follows:

[0103]

[0104] Where, P fcref is the optimal reference power of the fuel cell, P load is the total load power demand of the ship, P batref is the optimal reference power of lithium battery, η batDC is the efficiency of the lithium battery DC-DC converter, η fcDC is the fuel cell DC-DC converter efficiency.

[0105] Example 2

[0106] This embodiment provides a hybrid ship energy management optimization system, including:

[0107] Constructing a logic threshold rule construction module, which is used to construct a logic threshold rule according to the power combination of the target hybrid ship;

[0108] ECMS optimization model building module, used to build the ECMS optimization model of the target hybrid ship;

[0109] The energy management optimization model construction module is used to solve the ECMS optimization model according to the logical threshold rule and obtain the energy management optimization model of the target hybrid ship under the logical threshold rule;

[0110] The energy management strategy acquisition module is used to solve the energy management optimization model of the target hybrid ship under the logic threshold rule to obtain the optimized energy management strategy of the target hybrid ship.

[0111] The rest is the same as Example 1.

[0112] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A hybrid ship energy management optimization method, characterized in that: The following steps are involved: Constructing logic threshold rules according to the power combination of the target hybrid ship; Establish an ECMS optimization model for the target hybrid ship; Solving the ECMS optimization model according to the logic threshold rule to obtain an energy management optimization model of the target hybrid ship under the logic threshold rule; The energy management optimization model of the target hybrid ship under the logic threshold rule is solved to obtain the optimized energy management strategy of the target hybrid ship.

2. The hybrid ship energy management optimization method according to claim 1, characterized in that: The power combination of the hybrid ship includes fuel cells, lithium batteries and super capacitors.

3. The hybrid ship energy management optimization method according to claim 1, characterized in that: The logic threshold rules are specifically: When the lithium battery is in high SOC mode, if the maximum power output of the lithium battery can meet the current load of the ship, only the lithium battery will be used for power supply, avoiding the operation of the fuel cell; When the lithium battery is in the medium SOC mode, the hybrid mode is adopted to coordinate the power output of the lithium battery and fuel cell; When the lithium battery is in low SOC mode, the lithium battery is charged first, the fuel cell bears the load, and at the same time the charge level of the lithium battery is increased to prevent over-discharge.

4. The hybrid ship energy management optimization method according to claim 3, characterized in that: When the lithium battery is in high SOC mode, the output power rule of the fuel cell is: When the minimum output power of the fuel cell is greater than the load demand power, the reference power of the fuel cell is set to its minimum output power; When the load demand power is less than the sum of the minimum output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to its minimum output power; When the load demand power is less than the sum of the maximum output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to the difference between the load demand power and the discharge power of the lithium battery; When the load demand power is greater than the sum of the maximum output power of the fuel cell and the discharge efficiency of the lithium battery, the reference power of the fuel cell is set to its maximum output power.

5. The hybrid ship energy management optimization method according to claim 3, characterized in that: When the lithium battery is in the medium SOC mode, the output power rule of the fuel cell is: When the load demand power is less than the minimum output power of the fuel cell, the reference power of the fuel cell is set to its minimum output power; When the load demand power is less than the difference between the optimal output power of the fuel cell and the discharge power of the lithium battery, the reference power of the fuel cell is set to the load demand power; When the load demand power is less than the sum of the fuel cell's optimal output power and the lithium battery's discharge power, the fuel cell's reference power is set to its optimal output power; When the load demand power is less than the maximum output power of the fuel cell, the reference power of the fuel cell is set to the load demand power; When the load demand power is greater than the maximum output power of the fuel cell, the reference power of the fuel cell is set to its maximum output power.

6. The hybrid ship energy management optimization method according to claim 3, characterized in that: When the lithium battery is in low SOC mode, the output power rule of the fuel cell is: When the load demand power is less than the difference between the maximum output power of the fuel cell and the charging power of the lithium battery, the fuel cell reference power is set to the sum of the load demand power and the charging power of the lithium battery; When the load demand power is greater than the difference between the maximum output power of the fuel cell and the charging power of the lithium battery, the fuel cell reference power is set to its maximum output power.

7. The hybrid ship energy management optimization method according to claim 1, characterized in that: The equivalent hydrogen optimization problem of the ECMS optimization model of the target hybrid ship is: minC=min(aP fc +b+kC bat ) C fc =aP fc +b Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, C fc is the instantaneous hydrogen fuel consumption of the fuel cell, k is the SOC correction coefficient of the lithium battery, C bat is the instantaneous hydrogen fuel consumption of the lithium battery, a is the instantaneous hydrogen fuel consumption of the fuel cell, P fc is the fuel cell output power, b is the equivalent instantaneous hydrogen fuel consumption of the lithium battery, P bat is the output power of the lithium battery, η dis is the discharge efficiency of the lithium battery, η chg The charging efficiency of lithium batteries.

8. The hybrid ship energy management optimization method according to claim 1, characterized in that: The energy management optimization model of the target hybrid ship under the logic threshold rule is: Where C is the equivalent hydrogen cost function of lithium battery charging and discharging, k is the lithium battery SOC correction coefficient, P bat is the output power of the lithium battery, k is the SOC correction coefficient of the lithium battery, η dis is the discharge efficiency of the lithium battery, η chg The charging efficiency of lithium batteries.

9. The hybrid ship energy management optimization method according to claim 1, characterized in that: The optimized energy management strategy is the optimal output power of the fuel cell and the lithium battery.

10. A hybrid ship energy management optimization system, characterized in that: include: Constructing a logic threshold rule construction module, which is used to construct a logic threshold rule according to the power combination of the target hybrid ship; ECMS optimization model building module, used to build the ECMS optimization model of the target hybrid ship; an energy management optimization model construction module, configured to solve the ECMS optimization model according to the logic threshold rule to obtain an energy management optimization model of a target hybrid ship under the logic threshold rule; The energy management strategy acquisition module is used to solve the energy management optimization model of the target hybrid ship under the logic threshold rule to obtain the optimized energy management strategy of the target hybrid ship.

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