A series-parallel diesel-electric hybrid ship mode switching dynamic coordination control strategy

Abstract

This invention discloses a dynamic coordinated control strategy for mode switching in a series-parallel diesel-electric hybrid ship, belonging to the field of marine power control technology. The strategy includes the following steps: S1, connecting the shaft-driven motor and the main engine in parallel via a gearbox, and connecting the generator set and the energy storage battery pack to the DC bus to obtain a series-parallel power system; S2, constructing a mathematical model system; S3, obtaining the optimal operating mode discrimination result by constructing fuzzy membership functions; S4, formulating target torque distribution rules and constructing a state-space model using model predictive control theory; S5, obtaining control commands by optimizing the target torque distribution result in the rolling time domain; S6, obtaining the mode switching process by controlling the clutch state, the shaft-driven motor operating mode, and the speed and torque matching of each power source. This control strategy solves the problems of difficult multi-power source coordination, large switching impact, severe torque fluctuations, and inaccurate mode discrimination during mode switching in series-parallel diesel-electric hybrid ships.

Description

Technical Field

[0001] This invention relates to the field of marine power control technology, and in particular to a dynamic coordinated control strategy for mode switching of hybrid diesel-electric hybrid ships applicable to medium-sized ships with a deadweight of 1,000 to 5,000 tons. Background Technology

[0002] The green and low-carbon transformation of the shipping industry has become a global consensus. Coupled diesel-electric hybrid vessels, which combine environmental protection and endurance, precisely meet the IMO's new carbon intensity regulations and the energy conservation and emission reduction policies of various countries. They have become the core development direction for the power systems of medium-sized coastal and inland waterway vessels of 1,000 to 5,000 tons. These vessels integrate multiple power sources such as main engines, shaft motors, generator sets, and energy storage battery packs. Through the coupled topology, they can switch between four operating modes: mechanical propulsion, electric propulsion, charging while sailing, and hybrid propulsion, and can flexibly adapt to different speed and load conditions.

[0003] However, existing hybrid ship mode switching control strategies have many key technical shortcomings. Mode discrimination relies on fixed logic thresholds or simple operating condition parameter matching, resulting in a high misjudgment rate, frequent switching, and difficulty in accurately identifying the optimal operating mode. Under the hybrid topology, the dynamic response characteristics of the main engine and shaft-driven motors differ significantly, clutch torque transmission exhibits nonlinear slippage characteristics, and there is a lack of a systematic speed-torque matching mechanism. This makes shaft impact, torque fluctuations, and even brief power interruptions easy to occur during mode switching, which may lead to safety hazards such as shaft breakage in extreme cases. The power distribution rules of multiple power sources lack dynamic optimization and do not fully coordinate the dynamic changes in the output characteristics of each power source and the state of charge of the battery pack. This results in excessive clutch slippage work, equipment overheating and aging, and difficulty in smoothing power fluctuations, affecting power supply stability. At the same time, the control strategy is not fully adapted to the characteristics of the hybrid composite topology, resulting in a mismatch between control logic and structural characteristics, which cannot meet the core requirements of stable, efficient, and safe operation of ships under multiple operating conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic coordinated control strategy for mode switching in a hybrid diesel-electric ship, thereby solving the aforementioned technical problems.

[0005] To achieve the above objectives, this invention provides a dynamic coordinated control strategy for mode switching in a hybrid diesel-electric ship, comprising the following steps: S1. By connecting the shaft-driven motor and the main unit in parallel via a gearbox, and connecting the generator set and the energy storage battery pack to the DC bus, a hybrid power system with four operating modes is obtained, including mechanical propulsion, electric propulsion, navigation charging and hybrid propulsion. S2. Based on the topology structure of step S1, a mathematical model system reflecting the actual working characteristics of the system is obtained by modularly constructing the host output characteristic model, shaft motor efficiency model, battery pack equivalent circuit model, clutch torque transmission model and propulsion system coupling model. S3. Based on the mathematical model system established in step S2, the optimal parameters are solved by constructing a fuzzy membership function that integrates the health status (PHM) of the equipment and using an improved genetic algorithm. Combined with the power demand of the propulsion system, the operating status of the equipment and PHM data, the optimal operating mode discrimination result is obtained. S4. Based on the mode discrimination results of step S3, the target torque distribution rule is formulated through model predictive control (MPC) theory, and a state-space model is constructed. S5. Based on the state-space model constructed in step S4, the target torque distribution result is optimized by rolling time domain optimization, and the control quantity is corrected online by combining the real-time collected equipment operation feedback data to obtain the dynamically adjusted control command. S6. Based on the control commands output in step S5, the clutch engagement and disengagement states, shaft motor operating modes, and speed and torque matching of each power source are controlled through a preset typical mode switching process to obtain a smooth and efficient mode switching process.

[0006] Preferably, in step S1, the gearbox is configured with a transmission ratio on the main engine side and a transmission ratio on the shaft-driven motor side, and speed matching is achieved through differentiated transmission ratio design; the energy storage battery pack adopts a grid-connected hot standby redundancy design, which, when the system demand power fluctuates, works in conjunction with the generator set to supply power, smooth out power fluctuations and ensure power supply stability, providing power support for the dual-mode switching of the shaft-driven motor and the stable operation of each device; the shaft-driven motor has the ability to switch between power generation and drive modes. When the output power of the main engine is greater than the propeller propulsion demand power, the shaft-driven motor switches to power generation mode, absorbs the excess power of the main engine and converts it into electrical energy; when the propulsion load is at a low operating condition, the shaft-driven motor switches to drive mode and drives the propeller to run independently.

[0007] Preferably, the joint power supply rule between the energy storage battery and the engine in step S1 is as follows: under steady-state conditions, the generator set undertakes 70%-80% of the power, and the battery set undertakes 20%-30%; under dynamic conditions, the battery set responds first, and the generator set responds according to... ≤10kW / s output adjustment; charging stops when battery SOC≥90%, discharging stops when SOC≤20% and triggers shaft-driven motor to generate electricity for replenishment.

[0008] Preferably, the four operating modes in step S1 are as follows: Electric propulsion mode: The shaft-driven motor bears all the propulsion torque, and the main unit stops running; Navigation charging mode: The main unit operates in the optimal torque range, and the shaft motor absorbs the excess power of the main unit and converts it into electrical energy. Mechanical propulsion mode: The main unit bears all the propulsion torque, and the shaft-driven motor stops running; Hybrid propulsion mode: The main unit operates within the rated torque range, while the shaft-driven motor supplements the remaining required torque.

[0009] Preferably, in step S2, the modular models are constructed in the following ways: Main unit output characteristic model: Based on the external characteristic curve fitted by experimental data, a black-box modeling method using experimental data fitting is adopted to construct the mapping relationship between the main unit output power and fuel consumption rate. The formula is as follows: ; ; ; in, This refers to the power of the main unit; This refers to the main engine torque; This refers to the main unit's rotational speed; For the main engine fuel consumption rate; The fuel consumption function is fitted by the engine speed and torque; This refers to the fuel consumption of the main engine. Shaft-driven motor efficiency model: Motor efficiency depends on motor speed and torque. A mathematical model of the shaft-driven motor is fitted using experimental data, and the formula is as follows: ; in, This refers to the motor torque; This refers to the motor speed; Motor power; The efficiency of the shaft-driven motor when it is used as a generator; The efficiency of the shaft-driven motor when it is used as an electric motor; Battery pack equivalent circuit model: The Rint equivalent circuit model is adopted, and the formula is as follows: ; ; ; in, This refers to the remaining state of charge of the battery. This refers to the battery current. Battery capacity; This is the battery open-circuit voltage; This refers to the battery's internal resistance. This refers to the battery output voltage. Battery power; Clutch torque transmission model: Based on the Coulomb friction model, the clutch operating states are divided into three types: disengagement, slippage, and engagement. In the disengagement state... Under slipping conditions, the gearbox working oil pressure gradually increases, and the driving and driven plates engage while sliding. The formula for calculating the torque transmitted by the clutch is: ; in, It is a symbolic function; This refers to the number of friction plates; Effective working area; For working hydraulic pressure; The dynamic friction coefficient; Let be the equivalent radius of the friction plate, and , , These are the inner and outer radii of the friction plate, respectively; In the locked state, the driving and driven discs rotate at the same speed. At this time, the clutch torque is generated by static friction, as shown in the formula: ; in, The moment of inertia is the rotational inertia of the main unit. The moment of inertia of the propeller side; For the host at time ; output torque; For shaft-driven motor at time ; output torque; For the shaft system at time The frictional resistance torque; For the propeller at time The load torque; The propulsion system coupling model includes a ship resistance model, a longitudinal dynamics model, and a propeller model. The ship resistance model is obtained by fitting cyclic data from ship propulsion characteristic test conditions, and the formula is as follows: ; in, The total resistance of the ship; This refers to the ship's actual speed. , , These are the resistance fitting coefficients; The calculation formula for the longitudinal dynamic model is as follows: ; in, This is the thrust reduction factor; For propeller thrust; For ship resistance; For ship quality; For ship speed; Propeller Model: The propeller converts mechanical energy into propulsive kinetic energy through interaction with hydrodynamics. The calculation formula is as follows: ; in, This is the propeller torque coefficient; This refers to the propeller torque; The diameter of the propeller; The density of seawater; The propeller speed is, and , This represents the propeller thrust coefficient.

[0010] The above models are coupled and integrated according to the topology of step S1 to obtain a mathematical model system that reflects the actual working characteristics of the entire hybrid power system.

[0011] Preferably, step S3 includes the following specific steps: S31. Based on the propulsion system demand power and equipment operating status parameters output by the mathematical model system constructed in step S2, the input and output variables of fuzzy discrimination are obtained by screening key influencing factors. The input variables are system demand power, battery SOC, shaft motor temperature, main unit efficiency and generator start-stop status, and the output variables are four operating modes. S32. Based on the determined range of fuzzy variable values, triangular, trapezoidal and Gaussian membership functions are selected to obtain the membership function expressions of each variable corresponding to different operating modes and the membership degree mapping relationship of each variable to each mode. S33. Based on the fuzzy membership function constructed in step S32, the fuzzy entropy of the fuzzy set corresponding to the four operating modes is obtained through the fuzzy entropy calculation formula, so as to quantify the uncertainty of the fuzzy set. S34. Based on the fuzzy entropy of each mode obtained in step S33, an optimization objective function is constructed with the sum of the fuzzy entropy of the four modes as the objective by using a summation minimization strategy. The formula is as follows: ; in, The fuzzy entropy of the mechanical propulsion mode; For the fuzzy entropy of the hybrid propulsion mode; For the fuzzy entropy of electric propulsion mode; For the fuzzy entropy of the navigation propulsion mode; S35. Based on the optimization objective function established in step S34, the optimal parameter combination of the membership function is obtained through the initialization, selection, crossover, and mutation iteration operations of the genetic algorithm. S36. Based on the membership function parameters optimized in step S35 and the equipment operation data fed back in real time in step S2, the optimal operating mode under the current working conditions is obtained according to the principle of maximum membership degree and in combination with PHM data.

[0012] Preferably, the specific steps of step S4 are as follows: S41. Based on the optimal operating mode output in step S36 and the equipment characteristic parameters of each modular model in step S2, the power source output logic is divided according to the four operating modes in step S1 to obtain the torque distribution scheme of the host, shaft motor and clutch in the corresponding mode, so as to clarify the output range and coordination relationship of each power source. S42. Based on the core parameters and torque distribution rules in the model of step S2, the state variable vector, control variable vector, and external disturbance vector are obtained by screening key system states, control and disturbance factors, among which the shaft motor speed... Main unit speed and the speed difference between the shaft-driven motor and the main unit. As a state variable; select shaft motor torque Main engine torque Clutch transmits torque As a control variable; ship resistance moment External disturbances; S43. Based on the variable system of step S42 and the dynamic characteristics of the propulsion system coupling model and clutch torque transmission model of step S2, establish the differential coupling relationship between variables to obtain the state-space equation in the continuous time domain, the formula is: ; in, The dynamic rate of change of the system state; Let be a vector of state variables, and ; The system matrix is, and , The damping coefficient on the main engine side. This is the propeller side damping coefficient; To control the input matrix, and ; To control the variable vector, and ; Let be the perturbation input matrix, and ; External disturbance vector, and ; Output variable vector; The output matrix is, and .

[0013] Preferably, the specific steps of step S5 are as follows: S51. Based on the continuous state-space equations output in step S43, a discretization transformation is performed using the forward Euler method to obtain a discretized state-space model adapted for computer numerical solution, as shown in the formula: ; in, For the discretized system matrix, and , It is a 3×3 identity matrix. The sampling time of the system; The control input matrix is ​​discretized, and ; Let be the discretized perturbation input matrix, and ; The output matrix is ​​discretized, and ; S52. Based on the discretized state-space model and the optimal operating mode control objective of step S36, by integrating the output tracking error and the control increment smoothing requirement, the cost function for rolling time-domain optimization is obtained, and the formula is: ; in, The cost function value; For prediction in the time domain; To control the time domain; for Predict the output vector at each time step; for Reference output vector at any time; This is the error weight matrix; To control the incremental weight matrix; for Control the increment vector at all times, and ; S53. Based on the physical limit parameters of the equipment in each modular model in step S2, and by sorting out the operating boundaries of the power source and energy storage equipment, the set of constraints for the optimization problem is obtained, and the formula is: ; in, This refers to the main engine torque; This refers to the torque of the shaft-driven motor. To transmit torque to the clutch; This refers to the main unit's rotational speed; This refers to the rotational speed of the shaft-driven motor. The battery is in its state of charge. S54. Based on the cost function constructed in step S52 and the set of constraints in step S53, the optimal solution in the rolling time domain is solved by numerical optimization algorithm to obtain the control quantity sequence of each discrete time step, so as to optimize the target torque distribution result and ensure that the control quantity meets both the optimization target and the equipment safety operation requirements. S55. Based on the real-time collected equipment operation feedback data and the optimized prediction value in step S54, the control parameters are adjusted through an adaptive PI correction mechanism to obtain the corrected precise control quantity, so as to compensate for the influence of model error and external disturbance on control accuracy. S56. Based on the control quantity sequence corrected in step S55, dynamic control commands are obtained through logical integration and format standardization.

[0014] Preferably, step S6 includes the following specific steps: S61. Based on the optimal operating mode output in step S36 and the dynamic control command output in step S56, the current operating mode, the target operating mode and the control parameters of each device are sorted out through information integration to obtain the prerequisite for mode switching. S62. Based on the topology and device connection logic of step S1, the standardized process schemes for the corresponding switching directions are obtained by decomposing six typical switching directions, so as to clarify the action sequence of the clutch, shaft motor and host. S63. Based on the clutch model and propulsion system coupling model core parameters from step S2, construct a quantitative evaluation system for switching effects, including: Mode switching time: The time it takes for a hybrid power system to transition from the current operating mode to the target mode, expressed by the formula: ; in, This refers to the mode switching time. This refers to the clutch disengagement time; This refers to the clutch slippage time; This refers to the clutch lock-up time; Torque settling time; Shaft shock: Used to quantify the degree of impact during mode switching, the formula is: ; in, Impact level; Angular acceleration; The angular velocity at the clutch output end; The time from the start to the end of clutch slippage, and ; Clutch slippage work: This measures the energy loss caused by friction during the engagement of a wet clutch. The formula is: ; in, This is the slippage work of the clutch; This refers to the clutch friction torque; The angular velocity of the main drive disk on the host side; The angular velocity of the propeller-side drive disk; S64. Based on the determined switching process scheme and dynamic control instructions, the switching process of the coordinated action of each device is obtained by controlling the switching of clutch engagement and disengagement states, the switching of shaft motor working mode, and the adjustment of host speed and torque. The device operation data during the switching process is collected in real time. Preferably, the six typical switching directions in step S62 are: mechanical propulsion → hybrid propulsion, hybrid propulsion → mechanical propulsion, navigation charging → mechanical propulsion, mechanical propulsion → navigation charging, hybrid propulsion → electric propulsion, and electric propulsion → hybrid propulsion.

[0015] Therefore, the present invention employs the above-mentioned dynamic coordination control strategy for mode switching in a hybrid diesel-electric ship, which has the following beneficial effects: 1. Through the dual-mode design of shaft-driven motors for both power generation and drive, the grid-connected hot standby redundancy layout of energy storage battery packs, the differentiated transmission ratio configuration of gearboxes, and the precise adaptation to four operating modes, this topology fully leverages the synergistic advantages of the hybrid topology's "high efficiency of mechanical propulsion + environmental friendliness of electric propulsion." Furthermore, the dynamic power distribution rules between the generator set and battery packs mitigate power fluctuations, aligning with energy conservation and emission reduction subsidy policies. This topology can directly meet the retrofitting needs of existing 1000-5000 ton medium-sized vessels, eliminating the need for significant equipment layout adjustments by shipyards. After retrofitting a single vessel, low-speed fuel consumption is reduced by 30%-40%, and the main engine's low-load operating time is reduced by more than 50%, significantly shortening the investment recovery period and enhancing market acceptance and commercialization potential.

[0016] 2. By modularly constructing a coupled model of the host, shaft-driven motor, battery pack, clutch, and propulsion system, and forming a complete mathematical model system, the nonlinear characteristics of multiple power sources and the coupling relationship of the system are accurately characterized. This solves the problems of large control errors and poor adaptability caused by the lack of accurate model support in existing control strategies. It enables rapid calibration and dynamic updating of equipment parameters, providing a reliable theoretical basis for subsequent mode discrimination and torque distribution. This improves the matching degree between control commands and actual system operating conditions by more than 40%, significantly reduces the uncertainty in the mode switching process, and enhances the scientificity and reliability of the technical solution.

[0017] 3. By adopting a pattern discrimination scheme that integrates fuzzy membership functions based on PHM (Property Health Management) data and an improved genetic algorithm, and by optimizing membership function parameters and minimizing fuzzy entropy, the scheme effectively addresses the shortcomings of existing strategies that rely on fixed thresholds, such as high misjudgment rates and frequent switching. It can respond in real time to dynamic factors such as propulsion power fluctuations and equipment condition degradation, improving pattern discrimination accuracy by over 20%. This avoids shaft shocks and energy losses caused by ineffective switching, enabling ships to accurately match the optimal operating mode under different speeds and load conditions. The main engine operates within its optimal torque range for 60% of the time, further reducing fuel consumption and emissions, strengthening the scheme's environmental advantages, and aligning with the core needs of the shipping industry's green and low-carbon transformation.

[0018] 4. By constructing a state-space model using Model Predictive Control (MPC) theory and combining rolling time-domain optimization with an adaptive PI correction mechanism, dynamic allocation of target torque and online correction of control quantities are achieved. This effectively addresses the problems of insufficient torque allocation coordination and poor switching smoothness in existing strategies. This control logic can compensate for model errors and external disturbances in real time, improving the speed-torque matching accuracy of the shaft-driven motor, main unit, and clutch by more than 30%, reducing shaft impact during mode switching by 50%, and controlling torque fluctuation within ±5%, completely avoiding the safety hazards of power interruption and shaft breakage. Simultaneously, through equipment physical limit constraints and dynamic SOC adaptation, clutch slippage work is reduced by 40%, and battery cycle life is extended by 30%, significantly reducing equipment maintenance costs, improving the reliability and economy of the solution, and enhancing its competitiveness for commercialization.

[0019] 5. By defining a standardized process for switching directions in six typical modes, and combining quantitative evaluation indicators such as switching time, shaft impact, and clutch slippage work, a closed-loop control system covering the entire process of judgment, allocation, execution, and evaluation is formed. This solves the shortcomings of existing solutions, such as ambiguous switching logic and lack of quantitative evidence for effects. This standardized process does not require complex on-site debugging and can be directly embedded into existing ship control systems, reducing the difficulty of shipyard implementation by 60% and significantly shortening the market education and promotion cycle. The quantitative evaluation system can monitor the switching effect in real time, providing data support for subsequent optimization and ensuring that the ship maintains stable and efficient operation throughout its entire life cycle.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 A flowchart of a dynamic coordination control strategy for mode switching in a series-parallel diesel-electric hybrid ship is provided for this invention. Figure 2 This is a block diagram of the topology of a diesel-electric hybrid power system provided by the present invention; Figure 3 A diagram showing the division of the operating modes of the hybrid power system provided by the present invention on the external characteristic curve of the host unit; Figure 4 A schematic diagram of the hybrid power system mode switching path provided by the present invention; Figure 5 The flowchart for dynamic coordination control of mode switching provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0023] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] In the context of the green and low-carbon transformation of the shipping industry, hybrid diesel-electric hybrid ships have become the core development direction of power systems for medium-sized ships. However, existing mode switching control strategies have key technical shortcomings: mode discrimination relies on fixed logic thresholds or simple operating condition matching, without considering dynamic factors such as power fluctuations and equipment health status under complex navigation scenarios, resulting in high misjudgment rates and frequent switching; under the hybrid topology, the dynamic response of the main engine and shaft-driven motors differs significantly, the clutch has nonlinear torque transmission characteristics, and there is a lack of a systematic speed-torque matching mechanism, which can easily cause shaft impact, torque fluctuations, or even power interruption during the switching process, posing safety hazards; the power distribution of multiple power sources lacks dynamic optimization, and the output characteristics of each device and changes in the state of charge of the battery pack are not fully coordinated, resulting in excessive clutch slippage work, equipment overheating and aging, and difficulty in smoothing power fluctuations; existing solutions are mostly adapted to single series or parallel topologies, lacking a dynamic coordination design covering the entire process of mode discrimination, torque distribution, switching execution, and effect evaluation, and cannot meet the needs of stable, efficient, and safe operation of hybrid ships under multiple operating conditions.

[0026] Based on the above analysis, this invention is designed, see appendix. Figure 1-5 A dynamic coordination control strategy for mode switching in a series-parallel diesel-electric hybrid ship includes the following steps: S1. A hybrid power system with four operating modes—mechanical propulsion, electric propulsion, navigation charging, and hybrid propulsion—is obtained by connecting the shaft-driven motor and the main engine in parallel via a gearbox, and the generator set and energy storage battery pack connected to the DC bus. (See [reference]). Figure 2 As shown; In step S1, the gearbox is configured with transmission ratios on both the main engine side and the shaft-driven motor side. This differentiated transmission ratio design achieves speed matching, providing a basis for subsequent torque coordination and distribution. The energy storage battery pack adopts a grid-connected hot standby redundancy design. When system power demand fluctuates, it works in conjunction with the generator set to smooth power fluctuations and ensure power supply stability, providing power support for the dual-mode switching of the shaft-driven motor and the stable operation of each device. The shaft-driven motor has the capability to switch between power generation and drive modes. When the main engine output power exceeds the propeller propulsion power requirement, the shaft-driven motor switches to power generation mode, absorbing excess power from the main engine and converting it into electrical energy. This avoids low-load operation of the main engine and charges the energy storage battery pack. When the propulsion load is at a lower operating condition, the shaft-driven motor switches to drive mode, driving the propeller independently, reducing fuel consumption, and supporting the battery pack charging and discharging and the shaft-driven motor operating mode control strategy.

[0027] The joint power supply rule for the energy storage battery and the engine in step S1 is as follows: Under steady-state conditions, the generator set undertakes 70%-80% of the power, and the battery set undertakes 20%-30%; under dynamic conditions, the battery set responds first, and the generator set responds according to... ≤10kW / s output adjustment; charging stops when battery SOC≥90%, discharging stops when SOC≤20% and triggers shaft-driven motor to generate electricity for replenishment.

[0028] See Figure 3 As shown, the four operating modes in step S1 are as follows: Electric propulsion mode: The shaft-driven motor bears all the propulsion torque, the main engine stops running, and it is suitable for low-speed conditions with a speed of ≤8kn. Navigation charging mode: The main unit operates in the optimal torque range, and the shaft motor absorbs the excess power of the main unit and converts it into electrical energy. Mechanical propulsion mode: The main engine bears all the propulsion torque, and the shaft motor stops running, which is suitable for high-speed conditions with a speed of ≥12kn; Hybrid propulsion mode: The main unit operates within the rated torque range, while the shaft-driven motor supplements the remaining required torque.

[0029] based on Figure 3 By determining the position of the required speed and required power of the propulsion system on the main engine MAP, the operating mode of the hybrid power system under the current operating conditions can be determined.

[0030] Specifically, a 1000-ton coastal bulk carrier was selected as the conversion platform. The main engine is a WP13G400E321 diesel engine with a rated power of 300kW. The shaft-driven motor is a YJ355-4 permanent magnet synchronous motor with a rated power of 150kW, supporting dual-mode switching for power generation and drive. The gearbox is an MB240 marine gearbox, with the main engine side transmission ratio set. , shaft-driven motor side transmission ratio The speed is precisely matched through differentiated transmission ratios; the generator set uses a QSL9-320 diesel generator with a rated power of 240kW, and the energy storage battery pack uses lithium iron phosphate power batteries with a capacity of 200kWh and a nominal voltage of 600V. It is connected to the DC bus through a DC / DC converter and adopts a grid-connected hot standby redundancy design to form a joint power supply system with the generator set.

[0031] S2. Based on the topology structure of step S1, a mathematical model system reflecting the actual working characteristics of the system is obtained by modularly constructing the host output characteristic model, shaft motor efficiency model, battery pack equivalent circuit model, clutch torque transmission model and propulsion system coupling model. In step S2, the modular models are constructed as follows: Main unit output characteristic model: Based on fitting external characteristic curves using experimental data, a black-box modeling method of fitting experimental data is adopted. Data fitting is performed using MATLAB / Simulink software to construct the mapping relationship between main unit output power and fuel consumption rate. The formula is as follows: ; ; ; in, This refers to the power of the main unit; This refers to the main engine torque; This refers to the main unit's rotational speed; For the main engine fuel consumption rate; The fuel consumption function is fitted by the engine speed and torque; This refers to the fuel consumption of the main engine. Shaft-driven motor efficiency model: Motor efficiency depends on motor speed and torque. Efficiency data were collected through motor bench tests in the speed range of 50~1500 r / min and the torque range of -1500~1500 N·m to determine the efficiency in power generation mode and drive mode. A mathematical model of the shaft-driven motor was then fitted, with the following formula: ; in, This refers to the motor torque; This refers to the motor speed; Motor power; Efficiency when used as a generator for a shaft-driven motor; Efficiency when used as an electric motor for a shaft-driven motor; Battery pack equivalent circuit model: The Rint equivalent circuit model is adopted, and the formula is as follows: ; ; ; in, This refers to the remaining state of charge of the battery. This refers to the battery current. Battery capacity; This is the battery open-circuit voltage; This refers to the battery's internal resistance. This refers to the battery output voltage. Battery power; Clutch torque transmission model: Based on the Coulomb friction model, the clutch operating states are divided into three types: disengagement, slippage, and engagement. In the disengagement state... Under slipping conditions, the gearbox working oil pressure gradually increases, and the driving and driven plates engage while sliding. The formula for calculating the torque transmitted by the clutch is: ; in, It is a symbolic function; This refers to the number of friction plates; Effective working area; For working hydraulic pressure; The dynamic friction coefficient; Let be the equivalent radius of the friction plate, and , , These are the inner and outer radii of the friction plate, respectively; In the locked state, the driving and driven discs rotate at the same speed. At this time, the clutch torque is generated by static friction, as shown in the formula: ; in, The moment of inertia is the rotational inertia of the main unit. The moment of inertia of the propeller side; For the host at time ; output torque; For shaft-driven motor at time ; output torque; For the shaft system at time The frictional resistance torque; For the propeller at time The load torque; The propulsion system coupling model includes a ship resistance model, a longitudinal dynamics model, and a propeller model. The ship resistance model is obtained by fitting cyclic data from ship propulsion characteristic test conditions, and the formula is as follows: ; in, The total resistance of the ship; This refers to the ship's actual speed. , , The resistance fitting coefficient is a constant obtained by using least squares and other fitting methods based on the cyclic data from ship propulsion characteristic test conditions. The calculation formula for the longitudinal dynamic model is as follows: ; in, This is the thrust reduction factor; For propeller thrust; For ship resistance; For ship quality; For ship speed; Propeller Model: The propeller converts mechanical energy into propulsive kinetic energy through interaction with hydrodynamics. The calculation formula is as follows: ; in, This is the propeller torque coefficient; This refers to the propeller torque; The diameter of the propeller; The density of seawater; The propeller speed is, and , This represents the propeller thrust coefficient.

[0032] The above models are coupled and integrated according to the topology of step S1 to obtain a mathematical model system that reflects the actual working characteristics of the entire hybrid power system.

[0033] S3. Based on the mathematical model system established in step S2, the optimal parameters are solved by constructing a fuzzy membership function that integrates the health status (PHM) of the equipment and using an improved genetic algorithm. Combined with the power demand of the propulsion system, the operating status of the equipment and PHM data, the optimal operating mode discrimination result is obtained, which is used to reduce the probability of mode misjudgment and frequent switching. based on Figure 5 As shown, the specific steps of step S3 include: S31. Based on the mathematical model system constructed in step S2, the propulsion system demand power and equipment operating status parameters are output. By screening key influencing factors, the input and output variables of fuzzy discrimination are obtained. The input variables are system demand power of 0~5000kW, battery SOC of 0~100%, shaft motor temperature of 0~85℃, main unit efficiency of 30%~55%, and generator set start-stop status. The output variables are four operating modes. S32. Based on the determined range of fuzzy variable values, triangular, trapezoidal and Gaussian membership functions are selected to obtain the membership function expressions of each variable corresponding to different operating modes and the membership degree mapping relationship of each variable to each mode. S33. Based on the fuzzy membership function constructed in step S32, the fuzzy entropy of the fuzzy set corresponding to the four operating modes is obtained through the fuzzy entropy calculation formula, so as to quantify the uncertainty of the fuzzy set and provide an evaluation basis for the optimization of the membership function. S34. Based on the fuzzy entropy of each mode obtained in step S33, an optimization objective function is constructed with the sum of the fuzzy entropy of the four modes as the objective by using a summation minimization strategy. The formula is as follows: ; in, The fuzzy entropy of the mechanical propulsion mode; For the fuzzy entropy of the hybrid propulsion mode; For the fuzzy entropy of electric propulsion mode; For the fuzzy entropy of the navigation propulsion mode; S35. Based on the optimization objective function established in step S34, the optimal parameter combination of the membership function is obtained by using a genetic algorithm with an initial population size of 50, a selection and crossover probability of 0.7, a mutation probability of 0.05, and an iteration count of 100, in order to reduce the uncertainty of fuzzy sets. S36. Based on the membership function parameters optimized in step S35 and the equipment operation data fed back in real time in step S2, the optimal operating mode under the current working conditions is obtained according to the principle of maximum membership degree and in combination with PHM data.

[0034] S4. Based on the mode discrimination results of step S3, the target torque distribution rules are formulated through model predictive control (MPC) theory, and a state-space model is constructed to lay the foundation for dynamic coordinated control. The specific steps of step S4 are as follows: S41. Based on the optimal operating mode output in step S36 and the equipment characteristic parameters of each modular model in step S2, the power source output logic is divided according to the four operating modes in step S1 to obtain the torque distribution scheme of the host, shaft motor and clutch in the corresponding mode, so as to clarify the output range and coordination relationship of each power source. S42. Based on the core parameters and torque distribution rules in the model of step S2, the state variable vector, control variable vector, and external disturbance vector are obtained by screening key system states, control and disturbance factors, among which the shaft motor speed... Main unit speed and the speed difference between the shaft-driven motor and the main unit. As a state variable; select shaft motor torque Main engine torque Clutch transmits torque As a control variable; ship resistance moment External disturbances; S43. Based on the variable system of step S42 and the dynamic characteristics of the propulsion system coupling model and clutch torque transmission model of step S2, establish the differential coupling relationship between variables to obtain the state-space equation in the continuous time domain, the formula is: ; in, The dynamic rate of change of the system state; Let be a vector of state variables, and ; The system matrix is, and , The damping coefficient on the main engine side. This is the propeller side damping coefficient; To control the input matrix, and ; To control the variable vector, and ; Let be the perturbation input matrix, and ; External disturbance vector, and ; Output variable vector; The output matrix is, and .

[0035] S5. Based on the state-space model constructed in step S4, the target torque distribution result is optimized through rolling time domain optimization, and the control quantity is corrected online by combining the real-time collected equipment operation feedback data to obtain the dynamically adjusted control command, which is used to ensure the accuracy and smoothness of mode switching. The specific steps of step S5 are as follows: S51. Based on the continuous state-space equations output in step S43, a discretization transformation is performed using the forward Euler method to obtain a discretized state-space model adapted for computer numerical solution, as shown in the formula: ; in, For the discretized system matrix, and , It is a 3×3 identity matrix. The sampling time of the system; The control input matrix is ​​discretized, and ; Let be the discretized perturbation input matrix, and ; The output matrix is ​​discretized, and ; S52. Based on the discretized state-space model and the optimal operating mode control objective of step S36, by integrating the output tracking error and the control increment smoothing requirement, the cost function for rolling time-domain optimization is obtained, and the formula is: ; in, The cost function value; For prediction in the time domain; To control the time domain; for Predict the output vector at each time step; for Reference output vector at any time; Assign output tracking accuracy weights to the error weight matrix; To control the incremental weight matrix, the smoothness of the control quantity is constrained; for Control the increment vector at all times, and ; S53. Based on the physical limit parameters of the equipment in each modular model in step S2, and by sorting out the operating boundaries of the power source and energy storage equipment, the set of constraints for the optimization problem is obtained, and the formula is: ; in, This refers to the main engine torque; This refers to the torque of the shaft-driven motor. To transmit torque to the clutch; This refers to the main unit's rotational speed; This refers to the rotational speed of the shaft-driven motor. The battery is in its state of charge. S54. Based on the cost function constructed in step S52 and the set of constraints in step S53, the optimal solution in the rolling time domain is solved by numerical optimization algorithm to obtain the control quantity sequence of each discrete time step, so as to optimize the target torque distribution result and ensure that the control quantity meets both the optimization target and the equipment safety operation requirements. S55. Based on the real-time collected equipment operation feedback data and the optimized prediction value in step S54, the control parameters are adjusted through an adaptive PI correction mechanism to obtain the corrected precise control quantity, so as to compensate for the influence of model error and external disturbance on control accuracy and improve control stability. S56. Based on the control quantity sequence corrected in step S55, dynamic control commands are obtained through logical integration and format standardization, providing a precise control basis for clutch state switching, shaft motor working mode conversion, and speed and torque matching of each power source.

[0036] Step S5 also includes fault tolerance steps, specifically: Battery failure (SOC jump ≥10% / s): Immediately disconnect the battery circuit, switch to mechanical propulsion mode, and the generator set provides full power; Clutch jamming (slippage time > 1.5s): Limit the main unit torque to ≤ 50% of the rated value, prohibit mode switching and trigger audible and visual alarms; Shaft motor failure (no speed response): Stop the motor and switch to mechanical propulsion mode, with the main unit undertaking all propulsion needs.

[0037] S6. Based on the control commands output in step S5, the clutch engagement and disengagement states, shaft motor operating modes, and speed and torque matching of each power source are controlled through a preset typical mode switching process to obtain a smooth and efficient mode switching process, which is used to clarify the switching steps and equipment coordination requirements. The specific steps of step S6 include: S61. Based on the optimal operating mode output in step S36 and the dynamic control command output in step S56, the current operating mode, the target operating mode and the control parameters of each device are sorted out through information integration to obtain the prerequisite for mode switching. S62. Based on the topology and device connection logic of step S1, the standardized process schemes for the corresponding switching directions are obtained by decomposing six typical switching directions, so as to clarify the action sequence of the clutch, shaft motor and host. See Figure 4 As shown, the six typical switching directions are: mechanical propulsion → hybrid propulsion, hybrid propulsion → mechanical propulsion, navigation charging → mechanical propulsion, mechanical propulsion → navigation charging, hybrid propulsion → electric propulsion, and electric propulsion → hybrid propulsion.

[0038] The typical switching process includes: mechanical propulsion → electric propulsion, electric propulsion → navigation charging → hybrid propulsion, navigation charging → electric propulsion → mechanical propulsion, and hybrid propulsion → navigation charging.

[0039] Taking mechanical propulsion → hybrid propulsion as an example, the process is as follows: shaft motor start-up (0~0.3s) → speed synchronization adjustment (0.3~1.5s) → clutch pre-charging (1.5~1.8s) → torque smooth transition (1.8~2.5s) → mode switching completed (2.5s); The typical switching process is completed in one step, adapting to the daily high-frequency navigation conditions of ships. The equipment coordination is simple, the switching time is short, averaging 2.3~2.8s, the shafting impact is small and the stability is high. In contrast, the general switching process is a low-frequency special condition switching that requires 2~3 typical switching combinations. The steps are cumbersome, the equipment coordination is complex, the switching time is long and the stability depends on the control accuracy of the typical switching.

[0040] S63. Based on the clutch model and propulsion system coupling model core parameters from step S2, construct a quantitative evaluation system for switching effects, including: Mode switching time: The time it takes for a hybrid power system to transition from the current operating mode to the target mode, expressed by the formula: The target is controlled within a preset switching time threshold of 3 seconds to ensure navigation safety; in, This refers to the mode switching time. This refers to the clutch disengagement time; This refers to the clutch slippage time; This refers to the clutch lock-up time; Torque settling time; Shaft shock: Used to quantify the degree of impact during mode switching, the formula is: ; in, Impact level; Angular acceleration; The angular velocity at the clutch output end; The time from the start to the end of clutch slippage, and The impact intensity is controlled within a preset impact intensity threshold of 5 rad / s. 2 To avoid shaft impact damage; Clutch slippage work: This measures the energy loss caused by friction during the engagement of a wet clutch. The formula is: ; in, This is the slippage work of the clutch; This refers to the clutch friction torque; The angular velocity of the main drive disk on the host side; The angular velocity of the propeller-side driven disc is set; the clutch slippage work is controlled within the threshold of 8kJ to reduce clutch wear and extend service life.

[0041] S64. Based on the determined switching process scheme and dynamic control commands, the switching process of the coordinated action of each device is obtained by controlling the clutch engagement and disengagement state switching, shaft motor working mode conversion, and host speed and torque adjustment, and the device operation data during the switching process is collected in real time.

[0042] Specifically, one embodiment of the present invention is as follows: a 3,000-ton coastal bulk carrier with a main engine model of MAN6S35ME-B9.3 and a rated power of 3,800kW; a shaft-driven motor model of YZS-1250 and a rated power of 1,200kW; a 500kWh lithium iron phosphate battery pack; and a gearbox transmission ratio of 1:1.2 on the main engine side and 1:1.5 on the shaft-driven motor side. The navigation route covers a distance of 350 nautical miles and includes four typical operating conditions: port shifting (speed 4~6 knots), coastal cruising (speed 10~12 knots), sailing under varying wind, wave, and weather conditions (speed 8~10 knots), and rapid catching up (speed 14~15 knots).

[0043] After implementing the control strategy of this invention, through modular model calibration, fuzzy membership function optimization fused with PHM data, MPC rolling time-domain optimization, and standardized switching process execution, the following results were achieved: During berthing in harbor, automatic switching to electric propulsion mode reduced fuel consumption by 38% compared to the traditional mode; during coastal cruising, switching to navigation charging mode maintained the main engine efficiency at 49.2% within the optimal torque range, with the shaft-driven motor absorbing 650kW of excess power to charge the battery, and the SOC stabilizing at 60%~75%; during wind, wave, and load variations during navigation, switching to hybrid propulsion mode reduced shaft impact to 3.8 rad / s. 2 Torque fluctuation was ±4.2%, with no power interruption; when switching to mechanical propulsion mode for rapid catching up, the power output was stable; the average switching time for the six typical modes was 2.3s, and the clutch slippage work was 6.5kJ, all of which were lower than the target threshold. The mode misjudgment rate was only 4.2%, which is 68% lower than the existing strategy.

[0044] In summary, this specific embodiment fully verifies the practicality and superiority of the present invention: First, through precise mode discrimination and dynamic torque distribution, it solves the pain points of poor adaptability to multiple operating conditions and frequent switching of traditional strategies, significantly reducing shaft impact and torque fluctuation, completely avoiding the risk of shaft breakage, and ensuring the safety of ship navigation; Second, it significantly reduces fuel consumption in low-speed operating conditions and reduces the low-load operating time of the main engine by 55%, which not only reduces the shipowner's operating costs but also meets the IMO's new carbon intensity regulations, helping shipowners obtain energy-saving and emission-reduction subsidies of 25% of the retrofit cost and enhance market competitiveness; Third, all models are based on bench tests and calibration of actual ship data, and the control strategy can be directly integrated into the existing ship control system. The standardized switching process does not require complex debugging, shortening the shipyard implementation cycle by 60%, adapting to the needs of retrofitting existing medium-sized ships of 1,000 to 5,000 tons and building new ships, and has high commercial feasibility; Fourth, the discrimination method integrating PHM data and MPC dynamic coordination control provides reliable technical support for the green and low-carbon transformation of the shipping industry.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A mode switching dynamic coordination control strategy for a series-parallel diesel-electric hybrid ship, characterized in that: Includes the following steps: S1. By connecting the shaft-driven motor and the main unit in parallel via a gearbox, and connecting the generator set and the energy storage battery pack to the DC bus, a hybrid power system with four operating modes is obtained, including mechanical propulsion, electric propulsion, navigation charging and hybrid propulsion. S2. Based on the topology structure of step S1, a mathematical model system reflecting the actual working characteristics of the system is obtained by modularly constructing the host output characteristic model, shaft motor efficiency model, battery pack equivalent circuit model, clutch torque transmission model and propulsion system coupling model. S3. Based on the mathematical model system established in step S2, the optimal parameters are solved by constructing a fuzzy membership function that integrates the health status (PHM) of the equipment and using an improved genetic algorithm. Combined with the power demand of the propulsion system, the operating status of the equipment and PHM data, the optimal operating mode discrimination result is obtained. S4. Based on the mode discrimination results of step S3, the target torque distribution rule is formulated through model predictive control (MPC) theory, and a state-space model is constructed. S5. Based on the state-space model constructed in step S4, the target torque distribution result is optimized by rolling time domain optimization, and the control quantity is corrected online by combining the real-time collected equipment operation feedback data to obtain the dynamically adjusted control command. S6. Based on the control commands output in step S5, the clutch engagement and disengagement states, shaft motor operating modes, and speed and torque matching of each power source are controlled through a preset typical mode switching process to obtain a smooth and efficient mode switching process.

2. The mode switching dynamic coordination control strategy for a series-parallel diesel-electric hybrid ship according to claim 1, characterized in that: In step S1, the gearbox is configured with transmission ratios on both the main engine side and the shaft-driven motor side, achieving speed matching through differentiated transmission ratio design. The energy storage battery pack adopts a grid-connected hot standby redundancy design, which, when the system's power demand fluctuates, works in conjunction with the generator set to supply power, smoothing out power fluctuations and ensuring power supply stability, providing power support for the dual-mode switching of the shaft-driven motor and the stable operation of each device. The shaft-driven motor has the capability to switch between power generation and drive modes. When the main engine output power is greater than the propeller propulsion power demand, the shaft-driven motor switches to power generation mode, absorbing the excess power of the main engine and converting it into electrical energy. When the propulsion load is at a low operating condition, the shaft-driven motor switches to drive mode, driving the propeller independently.

3. The dynamic coordination control strategy for mode switching of a hybrid diesel-electric ship according to claim 2, characterized in that: The combined power supply rule of the energy storage battery in step S1 and the engine is: in a steady state condition, the generator set bears 70%-80% power, and the battery set bears 20%-30%; in a dynamic condition, the battery set is preferentially responded, and the generator set adjusts the output according to ≤10kW / s; when the battery SOC is greater than or equal to 90%, the charging is stopped, and when the battery SOC is less than or equal to 20%, the discharging is stopped and the shaft generator is triggered to generate power.

4. The dynamic coordinated control strategy for mode switching of a hybrid diesel-electric ship according to claim 3, characterized in that: The four operating modes in step S1 are as follows: Electric propulsion mode: The shaft-driven motor bears all the propulsion torque, and the main unit stops running; Navigation charging mode: The main unit operates in the optimal torque range, and the shaft motor absorbs the excess power of the main unit and converts it into electrical energy. Mechanical propulsion mode: The main unit bears all the propulsion torque, and the shaft-driven motor stops running; Hybrid propulsion mode: The main unit operates within the rated torque range, while the shaft-driven motor supplements the remaining required torque.

5. The dynamic coordinated control strategy for mode switching of a hybrid diesel-electric ship according to claim 4, characterized in that: In step S2, the modular models are constructed as follows: Main unit output characteristic model: Based on the external characteristic curve fitted by experimental data, a black-box modeling method using experimental data fitting is adopted to construct the mapping relationship between the main unit output power and fuel consumption rate. The formula is as follows: ; ; ; in, This refers to the power of the main unit; This refers to the main engine torque; This refers to the main unit's rotational speed; For the main engine fuel consumption rate; The fuel consumption function is fitted by the engine speed and torque; This refers to the fuel consumption of the main engine. Shaft-driven motor efficiency model: Motor efficiency depends on motor speed and torque. A mathematical model of the shaft-driven motor is fitted using experimental data, and the formula is as follows: ； in, This refers to the motor torque; This refers to the motor speed; Motor power; The efficiency of the shaft-driven motor when it is used as a generator; The efficiency of the shaft-driven motor when it is used as an electric motor; Battery pack equivalent circuit model: The Rint equivalent circuit model is adopted, and the formula is as follows: ； ； ； in, This refers to the remaining state of charge of the battery. This refers to the battery current. Battery capacity; This is the battery open-circuit voltage; This refers to the battery's internal resistance. This refers to the battery output voltage. Battery power; Clutch torque transmission model: Based on the Coulomb friction model, the clutch operating states are divided into three types: disengagement, slippage, and engagement. In the disengagement state... Under slipping conditions, the gearbox working oil pressure gradually increases, and the driving and driven plates engage while sliding. The formula for calculating the torque transmitted by the clutch is: ; in, It is a symbolic function; This refers to the number of friction plates; Effective working area; For working hydraulic pressure; The dynamic friction coefficient; Let be the equivalent radius of the friction plate, and , , These are the inner and outer radii of the friction plate, respectively; In the locked state, the driving and driven discs rotate at the same speed. At this time, the clutch torque is generated by static friction, as shown in the formula: ; in, The moment of inertia is the rotational inertia of the main unit. The moment of inertia of the propeller side; For the host at time ; output torque; For shaft-driven motor at time ; output torque; For the shaft system at time The frictional resistance torque; For the propeller at time The load torque; The propulsion system coupling model includes a ship resistance model, a longitudinal dynamics model, and a propeller model. The ship resistance model is obtained by fitting cyclic data from ship propulsion characteristic test conditions, and the formula is as follows: ; in, The total resistance of the ship; This refers to the ship's actual speed. , , These are the resistance fitting coefficients; The calculation formula for the longitudinal dynamic model is as follows: ; in, This is the thrust reduction factor; For propeller thrust; For ship resistance; For ship quality; For ship speed; Propeller Model: The propeller converts mechanical energy into propulsive kinetic energy through interaction with hydrodynamics. The calculation formula is as follows: ; in, This is the propeller torque coefficient; This refers to the propeller torque; The diameter of the propeller; The density of seawater; The propeller speed is, and , This represents the propeller thrust coefficient.

6. The dynamic coordinated control strategy for mode switching of a hybrid diesel-electric ship according to claim 5, characterized in that: The specific steps of step S3 include: S31. Based on the propulsion system demand power and equipment operating status parameters output by the mathematical model system constructed in step S2, the input and output variables of fuzzy discrimination are obtained by screening key influencing factors. The input variables are system demand power, battery SOC, shaft motor temperature, main unit efficiency and generator start-stop status, and the output variables are four operating modes. S32. Based on the determined range of fuzzy variable values, triangular, trapezoidal and Gaussian membership functions are selected to obtain the membership function expressions of each variable corresponding to different operating modes and the membership degree mapping relationship of each variable to each mode. S33. Based on the fuzzy membership function constructed in step S32, the fuzzy entropy of the fuzzy set corresponding to the four operating modes is obtained through the fuzzy entropy calculation formula, so as to quantify the uncertainty of the fuzzy set. S34. Based on the fuzzy entropy of each mode obtained in step S33, an optimization objective function is constructed with the sum of the fuzzy entropy of the four modes as the objective by using a summation minimization strategy. The formula is as follows: ； in, The fuzzy entropy of the mechanical propulsion mode; For the fuzzy entropy of the hybrid propulsion mode; For the fuzzy entropy of electric propulsion mode; For the fuzzy entropy of the navigation propulsion mode; S35. Based on the optimization objective function established in step S34, the optimal parameter combination of the membership function is obtained through the initialization, selection, crossover, and mutation iteration operations of the genetic algorithm. S36. Based on the membership function parameters optimized in step S35 and the equipment operation data fed back in real time in step S2, the optimal operating mode under the current working conditions is obtained according to the principle of maximum membership degree and in combination with PHM data.

7. The dynamic coordinated control strategy for mode switching of a hybrid diesel-electric ship according to claim 6, characterized in that: The specific steps of step S4 are as follows: S41. Based on the optimal operating mode output in step S36 and the equipment characteristic parameters of each modular model in step S2, the power source output logic is divided according to the four operating modes in step S1 to obtain the torque distribution scheme of the host, shaft motor and clutch in the corresponding mode, so as to clarify the output range and coordination relationship of each power source. S42. Based on the core parameters and torque distribution rules in the model of step S2, the state variable vector, control variable vector, and external disturbance vector are obtained by screening key system states, control and disturbance factors, among which the shaft motor speed... Main unit speed and the speed difference between the shaft-driven motor and the main unit. As a state variable; select shaft motor torque Main engine torque Clutch transmits torque As a control variable; ship resistance moment External disturbances; S43. Based on the variable system of step S42 and the dynamic characteristics of the propulsion system coupling model and clutch torque transmission model of step S2, establish the differential coupling relationship between variables to obtain the state-space equation in the continuous time domain, the formula is: ; in, The dynamic rate of change of the system state; Let be a vector of state variables, and ; The system matrix is, and , The damping coefficient on the main engine side. This is the propeller side damping coefficient; To control the input matrix, and ; To control the variable vector, and ; Let be the perturbation input matrix, and ; External disturbance vector, and ; Output variable vector; The output matrix is, and .

8. The dynamic coordination control strategy for mode switching of a hybrid diesel-electric ship according to claim 7, characterized in that: The specific steps of step S5 are as follows: S51. Based on the continuous state-space equations output in step S43, a discretization transformation is performed using the forward Euler method to obtain a discretized state-space model adapted for computer numerical solution, as shown in the formula: ; in, For the discretized system matrix, and , It is a 3×3 identity matrix. The sampling time of the system; The control input matrix is ​​discretized, and ; Let be the discretized perturbation input matrix, and ; The output matrix is ​​discretized, and ; S52. Based on the discretized state-space model and the optimal operating mode control objective of step S36, by integrating the output tracking error and the control increment smoothing requirement, the cost function for rolling time-domain optimization is obtained, and the formula is: ； in, The cost function value; For prediction in the time domain; To control the time domain; for Predict the output vector at each time step; for Reference output vector at any time; This is the error weight matrix; To control the incremental weight matrix; for Control the increment vector at all times, and ; S53. Based on the physical limit parameters of the equipment in each modular model in step S2, and by sorting out the operating boundaries of the power source and energy storage equipment, the set of constraints for the optimization problem is obtained, and the formula is: ； in, This refers to the main engine torque; This refers to the torque of the shaft-driven motor. To transmit torque to the clutch; This refers to the main unit's rotational speed; This refers to the rotational speed of the shaft-driven motor. The battery is in its state of charge. S54. Based on the cost function constructed in step S52 and the set of constraints in step S53, the optimal solution in the rolling time domain is solved by numerical optimization algorithm to obtain the control quantity sequence of each discrete time step, so as to optimize the target torque distribution result and ensure that the control quantity meets both the optimization target and the equipment safety operation requirements. S55. Based on the real-time collected equipment operation feedback data and the optimized prediction value in step S54, the control parameters are adjusted through an adaptive PI correction mechanism to obtain the corrected precise control quantity, so as to compensate for the influence of model error and external disturbance on control accuracy. S56. Based on the control quantity sequence corrected in step S55, dynamic control commands are obtained through logical integration and format standardization.

9. The dynamic coordination control strategy for mode switching of a hybrid diesel-electric ship according to claim 8, characterized in that: The specific steps of step S6 include: S61. Based on the optimal operating mode output in step S36 and the dynamic control command output in step S56, the current operating mode, the target operating mode and the control parameters of each device are sorted out through information integration to obtain the prerequisite for mode switching. S62. Based on the topology and device connection logic of step S1, the standardized process schemes for the corresponding switching directions are obtained by decomposing six typical switching directions, so as to clarify the action sequence of the clutch, shaft motor and host. S63. Based on the clutch model and propulsion system coupling model core parameters from step S2, construct a quantitative evaluation system for switching effects, including: Mode switching time: The time it takes for a hybrid power system to transition from the current operating mode to the target mode, expressed by the formula: ; in, This refers to the mode switching time. This refers to the clutch disengagement time; This refers to the clutch slippage time; This refers to the clutch lock-up time; Torque settling time; Shaft shock: Used to quantify the degree of impact during mode switching, the formula is: ； in, Impact level; Angular acceleration; The angular velocity at the clutch output end; The time from the start to the end of clutch slippage, and ; Clutch slippage work: This measures the energy loss caused by friction during the engagement of a wet clutch. The formula is: ; in, This is the slippage work of the clutch; This refers to the clutch friction torque; The angular velocity of the main drive disk on the host side; The angular velocity of the propeller-side drive disk; S64. Based on the determined switching process scheme and dynamic control commands, the switching process of the coordinated action of each device is obtained by controlling the clutch engagement and disengagement state switching, shaft motor working mode conversion, and host speed and torque adjustment, and the device operation data during the switching process is collected in real time.

10. The dynamic coordinated control strategy for mode switching of a hybrid diesel-electric ship according to claim 9, characterized in that: The six typical switching directions in step S62 are: mechanical propulsion → hybrid propulsion, hybrid propulsion → mechanical propulsion, navigation charging → mechanical propulsion, mechanical propulsion → navigation charging, hybrid propulsion → electric propulsion, and electric propulsion → hybrid propulsion.

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