Hybrid power ship energy optimization method based on event triggering mechanism

Abstract

The invention provides a hybrid power ship energy optimization method based on an event triggering mechanism, and relates to the technical field of energy management. The method comprises the following steps: establishing a diesel engine torque model, a motor torque model, a fuel consumption model and a carbon emission model as states of the hybrid power system; estimating the state of the current system through volume Kalman filtering, and reducing a state prediction error; establishing a fuel consumption and carbon emission tradeoff model; establishing an event triggering condition; and judging whether the event reaches a trigger level, and optimizing system parameters. According to the hybrid power ship model predictive control energy optimization method based on event triggering, almost the same energy management consumption can be realized with shorter calculation overhead, the ship energy can be reasonably distributed according to the change of a tradeoff coefficient, and the balance between fuel consumption and carbon emission is achieved; and the energy optimization control under the constraint condition is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a hybrid power ship energy optimization method based on an event triggering mechanism. Background Art

[0002] Currently, shipping is the main form of freight in international trade, accounting for more than 85% of the world's international trade volume. According to the report of the International Maritime Organization (IMO), the carbon emissions of the international shipping industry will exceed 1 billion tons, accounting for 2-3% of the world's total. Solving the carbon emissions problem in the shipping industry is urgent.

[0003] A hybrid-powered ship is a ship that uses two or more power sources as propulsion. Such ships can alleviate carbon emissions in the shipping industry. However, the structure of the hybrid power system is relatively complex, which makes ship energy management very complex and has many uncertainties. This requires a stable and robust controller to face these challenges. Model predictive control controllers are widely used to solve energy optimization problems under constraints.

[0004] The model predictive control method also has some disadvantages:

[0005] First, the accuracy of the system model is very high. If the model is inaccurate, the control effect will not meet expectations.

[0006] Secondly, the core of model predictive control is to establish an optimization problem and then use the optimization algorithm to solve the best control strategy. As a result, a large amount of computing resources are consumed in the prediction process, the calculation cost is high and the time is long, and there is no guarantee that the optimization problem can be solved in real time.

[0007] These two main shortcomings of model predictive control are also the main reasons restricting its application in practical engineering and systems.

[0008] Therefore, there is an urgent need for a hybrid ship energy optimization method based on an event-triggered mechanism to solve the above technical problems. Summary of the invention

[0009] The purpose of the present invention is to provide a hybrid ship energy optimization method based on an event trigger mechanism. The method is based on the closed-loop control of the hybrid ship energy, and adaptively adjusts the optimization process of the model predictive controller through event triggering technology to reduce the occupation of computing resources and complete the energy optimization control of the hybrid ship.

[0010] The various technical effects that can be produced by the preferred technical solutions among the various technical solutions provided by the present invention are described in detail below.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] The present invention provides a hybrid ship energy optimization method based on an event trigger mechanism, comprising:

[0013] Establish diesel engine torque model, motor torque model, fuel consumption model and carbon emission model as the state of hybrid power system;

[0014] The current system state is estimated through cubature Kalman filtering to reduce the state prediction error;

[0015] Establish a fuel consumption and carbon emission trade-off model;

[0016] Establish event trigger conditions;

[0017] Determine whether the event has reached the trigger level and optimize system parameters.

[0018] Preferably, the diesel engine and the motor are connected to the ship propulsion device via a mechanical shaft, wherein:

[0019] The axis dynamic characteristics are described as:

[0020] In the formula, ω dsl is the angular velocity of the diesel engine, which is related to the engine speed N dsl There is a conversion relationship T dsl and T em are the diesel engine output torque and the motor output torque respectively; J s is the system moment of inertia; T undis is the propeller load disturbance, which is an unknown quantity and is observed by the observer and fed back to the controller.

[0021] Preferably, the diesel engine torque model, the motor torque model, the fuel consumption model and the carbon emission model are established as the state of the hybrid power system, including:

[0022] Use diesel engine torque distribution ratio u dsl With speed N dsl The torque output of the diesel engine is T dsl and fuel consumption Polynomial modeling is performed as follows:

[0023]

[0024]

[0025] In the formula, α N , α f are the fitting coefficients of torque output and fuel consumption, ρf is the density of diesel;

[0026] The carbon emission model is established using the Sigmoid function according to the diesel engine operating point, as follows:

[0027]

[0028] In the formula, σ1, σ2, and are fitting coefficients.

[0029] Preferably, the establishment of a diesel engine torque model, a motor torque model, a fuel consumption model and a carbon emission model as the state of the hybrid power system further includes:

[0030] The output torque of the motor is defined as: T em =ψu em , where ψ and u em They are the unit conversion factor and the motor output torque distribution ratio;

[0031] The motor output torque is modeled by the Willan equation as follows:

[0032]

[0033] T em ·ω dsl =e·P m -P0(Motoring)

[0034] Where P m is the electric power; e and P0 are Willan constant coefficients;

[0035] The battery is modeled by an equivalent circuit, and the control equation of the battery current is:

[0036] Where P B is the energy consumed or generated by the battery, U OC is the battery open circuit voltage, which has a linear relationship with the battery state of charge (SOC) and is defined as U OC =m1SOC+m2; the battery SOC state is Calculation; where Q B It is the battery capacity, in Ah.

[0037] Preferably, the establishing of the fuel consumption and carbon emission trade-off model comprises:

[0038] The trade-off coefficient τ is introduced to provide feedback on the relationship between engine fuel consumption and carbon emissions;

[0039] The trade-off model is expressed as:

[0040] in,

[0041]

[0042] In the formula, λ e are the tuning parameters confirmed by numerical simulation.

[0043] Preferably, the establishing event triggering condition includes:

[0044] Monitor the error between the actual state of the diesel engine speed and the predicted value to establish an event trigger condition;

[0045] The error between the actual state of charge of the monitored battery and the predicted value establishes an event trigger condition.

[0046] Preferably, the error between the actual state of the monitoring diesel engine speed and the predicted value to establish an event triggering condition includes:

[0047] At each time step, the deviation ΔN between the predicted diesel engine speed and the current actual engine speed dsl,i Exceeding the set trigger level N dsl,etlv , the event is triggered, expressed as:

[0048]

[0049] Where i represents each control moment in the control time domain N, and Respectively represent the predicted engine speed and the actual engine speed at each control moment; N dsl,etlv is the maximum permissible error in the predicted engine speed.

[0050] Preferably, the error between the actual state of charge of the monitoring battery and the predicted value establishes an event triggering condition, including:

[0051] The difference between the predicted battery SOC value and the actual reference battery SOC value ΔSOC within the time step calculated by the system i The sum exceeds the set trigger level SOC etlv , this event is triggered, expressed as:

[0052]

[0053] In the formula, SOC predict,i and SOC ref,i Represent the predicted SOC value and the reference SOC value respectively; SOC all,etlv is the maximum deviation of the predicted battery SOC value.

[0054] Preferably, the determining whether the event reaches a trigger level and optimizing the system parameters comprises:

[0055] The engine speed, battery SOC state, trade-off equivalent consumption, and engine and motor torque distribution ratio are set as state variables, and the engine and motor torque distribution ratio change rate and slack variables are control variables. The state variables and control variables are expressed as:

[0056]

[0057] In the hybrid power system, the event-triggered model predictive controller is used with the volumetric Kalman filter to achieve the trade-off between fuel consumption and carbon emissions. While meeting the required power and system constraints, it has the following objective function:

[0058]

[0059] Where M i and M N are the stage cost matrix and the terminal cost matrix respectively; N dsl,i and N dsl,ref,i are the optimized engine speed and the engine speed reference under fixed working conditions, respectively, where i=1,2,3,…,N; (N dsl,i -N dsl,ref,i ) 2 Indicates that the engine speed should be consistent with the expected speed. By using the terminal cost function (N dsl,N -N dsl,ref,N ) 2 Enforce constraints on engine speed.

[0060] Preferably, the following constraints must be met during the entire optimization process:

[0061] SOC min ≤SOC≤SOC max

[0062] u em,min ≤u em ≤u em,max

[0063] 0≤u dsl ≤N dsl

[0064]

[0065]

[0066] N dsl,min,soft -ε≤N dsl ≤N dsl,max,soft

[0067] SOC dsl,min,soft -ε≤SOC dsl ≤SOC dsl,max,soft

[0068]

[0069] 0≤ε

[0070] The present invention provides a hybrid ship energy optimization method based on an event trigger mechanism. Through the event trigger mechanism, the optimization process of the hybrid power system by the model predictive controller is adaptively adjusted, so that the method of the present invention can reduce the occupation of computing resources and reduce computing costs while maintaining better energy optimization control performance; by introducing a trade-off coefficient and establishing a trade-off model, the optimal trade-off between fuel consumption and carbon emissions in the hybrid power system is found, and the balance between fuel consumption and carbon emissions is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 It is a flow chart of a hybrid ship energy optimization method based on an event trigger mechanism of the present invention;

[0073] Figure 2 It is a structural schematic diagram of the ship power system in the present invention;

[0074] Figure 3 It is a diesel engine carbon emission chart;

[0075] Figure 4 It is the equivalent internal resistance model diagram of the battery;

[0076] Figure 5 This is a block diagram of the energy optimization method of hybrid ship model predictive control based on event triggering. DETAILED DESCRIPTION

[0077] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0078] like Figure 1As shown, the present invention provides a hybrid ship energy optimization method based on an event trigger mechanism, comprising:

[0079] S101: Establishing a diesel engine torque model, a motor torque model, a fuel consumption model, and a carbon emission model as the state of the hybrid power system;

[0080] S102: Estimate the state of the current system through volumetric Kalman filtering to reduce the state prediction error;

[0081] S103: Establish a fuel consumption and carbon emission trade-off model;

[0082] S104: Establish event triggering conditions;

[0083] S105: Determine whether the event reaches the trigger level and optimize system parameters.

[0084] That is, first, by establishing a diesel engine model and a motor model for the hybrid system, introducing a trade-off coefficient, and establishing a fuel consumption and carbon emission trade-off model. Secondly, through event triggering technology, by setting up a prediction module to determine whether the event reaches the trigger level, a decision is provided for whether the system should perform energy optimization to reduce the consumption of computing resources; finally, the speed, torque, fuel consumption and carbon emission trade-off coefficient of the engine and motor are adjusted using the event-triggered model predictive control energy optimization method to achieve energy optimization control.

[0085] Specifically, a hybrid power system model is established in a data-driven manner, and state estimation is performed by coordinating with volumetric Kalman filtering to reduce state prediction errors. A trade-off coefficient is introduced to establish a fuel consumption and carbon emission trade-off model to find a balance between ship fuel consumption and carbon emissions. The diesel engine speed deviation and the battery state of charge deviation are set as trigger events to perform event-triggered hybrid ship model predictive control energy optimization control to reduce the occupation of computing resources.

[0086] The specific implementation process includes:

[0087] S201: Establish a hybrid power system model.

[0088] like Figure 2 As shown, in this embodiment, the hybrid power system is composed of a diesel engine, a battery and an electric motor, wherein the battery is directly connected to the battery through an inverter. The diesel engine and the electric motor are connected to the ship propulsion device via a mechanical shaft.

[0089] First, S2011: establish the shaft dynamics model. The dynamic characteristics of the shaft of the power unit are described by the following formula:

[0090]

[0091] In the formula, ω dsl is the angular velocity of the diesel engine, which is related to the engine speed N dsl There is a conversion relationship T dsl and T em are the output torque of the diesel engine and the output torque of the motor respectively. s is the system moment of inertia. undis is the propeller load disturbance, which is an unknown quantity and will be observed by the observer and fed back to the controller.

[0092] S2012: Build a diesel engine model.

[0093] The diesel engine is modeled using a data-driven approach. The diesel engine modeling is divided into three parts: diesel engine torque model, fuel consumption model, and carbon emission model. dsl With speed N dsl The torque output of the diesel engine is T dsl and fuel consumption Polynomial modeling is performed as follows:

[0094]

[0095]

[0096] In the formula, α N , α f are the fitting coefficients of torque output and fuel consumption, ρ f is the density of diesel.

[0097] The ship carbon emission model can be modeled using the Sigmoid function based on the diesel engine operating point. Figure 3 As shown, there are two mutation points in diesel engine carbon emissions:

[0098] First, when N dsl =1000rpm, carbon emissions show a significant downward trend, indicating that the diesel engine is already operating at the emission efficiency point;

[0099] Second, when N dsl =1700rpm, and u dsl =80%, carbon emissions show a downward trend again.

[0100] Therefore, these two points can be used as the threshold points of the Sigmoid function to calculate the carbon emissions of diesel engines. Modeling is performed as follows:

[0101]

[0102] In the formula, σ1, σ2, and are fitting coefficients.

[0103] S2013: Build the motor model.

[0104] according to Figure 2 In the hybrid ship topology described, the motor is only connected to the battery, considering any other auxiliary power source, and the converter is set to 99% in this embodiment. Since the generation time of the motor torque is much shorter than the set sampling time of the controller in the embodiment, it is assumed that the output torque of the motor is generated instantaneously. So the output torque T em is defined as:

[0105]

[0106] In the formula, ψ and u em They are the unit conversion factor and the motor output torque distribution ratio respectively.

[0107] The Willan equation can be further used to model the motor output torque, which is defined as follows:

[0108]

[0109] T em ·ω dsl =e·P m -P0(Motoring) (6)

[0110] Where P m is the electric power, which is assumed to be equal to the battery power in this embodiment. e and P0 are both Willan constant coefficients.

[0111] S2014: Build a battery model.

[0112] The battery is modeled by a widely used equivalent circuit, such as Figure 4 As shown. The control equation of the battery current is:

[0113]

[0114] Where P B is the energy consumed or generated by the battery. In this embodiment, it is assumed to be equivalent to P m R is the internal resistance of the battery, which is assumed to be constant. OC is the battery open circuit voltage, which has a linear relationship with the battery state of charge (SOC) and is defined as U OC =m1SOC+m2. Then the battery SOC state can be calculated by the following formula:

[0115]

[0116] In the formula, Q B It is the battery capacity, in Ah.

[0117] The event-triggered hybrid ship model predictive control energy optimization method in this embodiment specifically includes:

[0118] Traditional controllers are optimized in a fixed cycle and send control signals to the hybrid power system. The optimization method proposed in the present invention determines whether the current state is optimized based on actual settings and needs, and its optimization is non-periodic. The recursive feasibility and stability of setting event trigger conditions using state prediction errors have been proven. In the energy optimization control based on the time trigger mechanism implemented in this embodiment, whether an event is triggered determines when the system takes action to achieve optimization. Therefore, how to design event trigger conditions in the event trigger mechanism is very important.

[0119] The following two events are proposed for the hybrid system shown in the embodiment:

[0120] Speed ​​deviation event: The deviation ΔN between the predicted diesel engine speed and the current actual engine speed at each time step dsl,i Exceeding the set trigger level N dsl,etlv In order to limit the deviation to an acceptable range, the event can be expressed as:

[0121]

[0122] Where i represents each control moment in the control time domain N, and Respectively represent the predicted engine speed and the actual engine speed at each control moment. dsl,etlv is the maximum permissible error in the predicted engine speed.

[0123] Battery SOC deviation: The difference between the predicted battery SOC value and the actual reference battery SOC value ΔSOC within the time step calculated by the system i The sum exceeds the set trigger level SOC etlv , this event is triggered, which can be expressed as:

[0124]

[0125] In the formula, SOC predict,i and SOC ref,i Represent the predicted SOC value and the reference SOC value respectively. all,etlv is the maximum deviation of the predicted battery SOC value.

[0126] Figure 5 The prediction module is responsible for collecting information and determining whether the trigger level has been reached and whether an event should be triggered. Once the speed deviation event or battery SOC state deviation is met, the prediction information of the control system is immediately updated, and the event-triggered model predictive controller performs real-time torque control on the hybrid ship to achieve energy optimization management.

[0127] It should be noted that in both cases, N dsl,etlv and SOC all,etlv The size of the set value will have a significant impact on the system control performance: if the set value is too large, the energy distribution optimization effect will be very poor; if the set value is too small, the entire system will increase the consumption of computing resources, resulting in a longer optimization calculation time.

[0128] Therefore, in different control systems, the actual preference options should be considered. For those who consider the optimization effect, the setting value of the corresponding trigger level can be increased; for control systems that emphasize calculation efficiency, the setting value of the trigger level can be reduced accordingly.

[0129] The filtering problems in this embodiment are actually all about obtaining the posterior probability distribution of the corresponding system state. However, due to different specific conditions, the analytical solution of the posterior probability can be obtained by solving the recursive Bayesian formula, or the posterior probability can be obtained by the method of calculating the expectation through large number statistical averaging.

[0130] Specifically, a cubature Kalman filter is used as the state observer, which is very suitable for high-dimensional problems. The spherical cube rule and Gaussian quadrature rule are used to numerically solve the spherical mean integral and estimate the mean and associated covariance of the posterior probability density.

[0131] The fuel consumption and carbon emission trade-off model established in this embodiment specifically includes: Figure 2 As shown in the hybrid system structure, the battery and the motor are directly connected through an inverter. Here, the change of the inverter conversion efficiency is not considered and it is assumed to be constant at 99%.

[0132] Therefore, by using the equivalent consumption formula, there is also a penalty of an equivalent coefficient λ, which depends on the battery charge and discharge state and the electric energy consumption of the motor, and when divided by the calorific value Q of the engine fuel f , which can be expressed as the mass of equivalent fuel consumption.

[0133] In addition, the present invention introduces an additional trade-off coefficient τ to feedback the relationship between engine fuel consumption and carbon emissions. Therefore, the trade-off model is expressed as follows

[0134]

[0135] in,

[0136]

[0137] In the formula, λ e are the tuning parameters confirmed by numerical simulation.

[0138] Through the above model, trade-off energy management can be achieved by evaluating energy efficiency and battery energy saving.

[0139] In this embodiment, engine speed, battery SOC status, trade-off equivalent consumption, engine and motor torque distribution ratio are selected as state variables, and engine and motor torque distribution ratio change rate and slack variables are selected as control variables to facilitate the implementation of the trade-off strategy under the entire operating conditions.

[0140] The state variables and control variables are expressed as:

[0141]

[0142] The goal of this embodiment is to use an event-triggered model predictive controller with a volumetric Kalman filter in a hybrid system to achieve a trade-off between fuel consumption and carbon emissions. Therefore, while meeting the required power and system constraints, it has the following objective function:

[0143]

[0144] Where M i and M N are the stage cost matrix and the terminal cost matrix respectively. dsl,i and N dsl,ref,i are the optimized engine speed and the engine speed reference under fixed working conditions, respectively, where i = 1, 2, 3, ..., N. At the same time, (N dsl,i -N dsl,ref,i ) 2 Indicates that the engine speed should be consistent with the expected speed to ensure stability, and by using the terminal cost function (N dsl,N -N dsl,ref,N ) 2 The engine speed is constrained. In other terms, the engine torque distribution ratio is not constrained, and only the motor torque distribution ratio is constrained to avoid large-scale oscillation of the motor torque distribution.

[0145] In this embodiment, the sampling time in the optimization process is set to 1 second to ensure that energy management can be performed within a longer time range. In order to ensure the stable operation of the hybrid ship system, the following constraints need to be met during the entire optimization process:

[0146] SOC min ≤SOC≤SOC max

[0147] u em,min ≤u em ≤u em,max

[0148] 0≤u dsl ≤N dsl

[0149]

[0150]

[0151] N dsl,min,soft -ε≤N dsl ≤N dsl,max,soft

[0152] SOC dsl,min,soft -ε≤SOC dsl ≤SOC dsl,max,soft

[0153]

[0154] 0≤ε (15)

[0155] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A hybrid ship energy optimization method based on event triggering mechanism, characterized in that: include: Establish diesel engine torque model, motor torque model, fuel consumption model and carbon emission model as the state of hybrid power system; The current system state is estimated through cubature Kalman filtering to reduce the state prediction error; Establish a fuel consumption and carbon emission trade-off model; Establish event trigger conditions; Determine whether the event has reached the trigger level and optimize system parameters.

2. The hybrid ship energy optimization method based on event triggering mechanism according to claim 1 is characterized in that: The diesel engine and the electric motor are connected to a ship propulsion device via a mechanical shaft, wherein: The axis dynamic characteristics are described as: In the formula, ω dsl is the angular velocity of the diesel engine, which is related to the engine speed N dsl There is a conversion relationship T dsl and T em are the diesel engine output torque and the motor output torque respectively; J s is the system moment of inertia; T undis is the propeller load disturbance, which is an unknown quantity and is observed by the observer and fed back to the controller.

3. The hybrid ship energy optimization method based on event triggering mechanism according to claim 2 is characterized in that: The diesel engine torque model, the motor torque model, the fuel consumption model and the carbon emission model are established as the state of the hybrid power system, including: Use diesel engine torque distribution ratio u dsl With speed N dsl The torque output of the diesel engine is T dsl and fuel consumption Polynomial modeling is performed as follows: In the formula, α N , α f are the fitting coefficients of torque output and fuel consumption, ρ f is the density of diesel; The carbon emission model is established using the Sigmoid function according to the diesel engine operating point, as follows: In the formula, σ1, σ2, and are fitting coefficients.

4. The hybrid ship energy optimization method based on event triggering mechanism according to claim 3 is characterized in that: The diesel engine torque model, the motor torque model, the fuel consumption model and the carbon emission model are established as the state of the hybrid power system, and further includes: The output torque of the motor is defined as: T em =ψu em , where ψ and u em They are the unit conversion factor and the motor output torque distribution ratio; The motor output torque is modeled by the Willan equation as follows: T em ·ω dsl =e·P m -P0(Motoring) Where P m is the electric power; e and P0 are Willan constant coefficients; The battery is modeled by an equivalent circuit, and the control equation of the battery current is: Where P B is the energy consumed or generated by the battery, U OC is the battery open circuit voltage, which has a linear relationship with the battery state of charge (SOC) and is defined as U OC =m1SOC+m2; the battery SOC state is Calculation; where Q B It is the battery capacity, in Ah.

5. The hybrid ship energy optimization method based on event triggering mechanism according to claim 4 is characterized in that: The establishment of a fuel consumption and carbon emission trade-off model comprises: The trade-off coefficient τ is introduced to provide feedback on the relationship between engine fuel consumption and carbon emissions; The trade-off model is expressed as: in, In the formula, λ e are the tuning parameters confirmed by numerical simulation.

6. The hybrid ship energy optimization method based on event triggering mechanism according to any one of claims 1 to 5, characterized in that: The establishing event triggering condition includes: Monitor the error between the actual state of the diesel engine speed and the predicted value to establish an event trigger condition; The error between the actual state of charge of the monitored battery and the predicted value establishes an event trigger condition.

7. The hybrid ship energy optimization method based on event triggering mechanism according to claim 6 is characterized in that: The error between the actual state of the diesel engine speed and the predicted value is used to establish an event trigger condition, including: At each time step, the deviation ΔN between the predicted diesel engine speed and the current actual engine speed dsl,i Exceeding the set trigger level N dsl,etlv , the event is triggered, expressed as: Where i represents each control moment in the control time domain N, and Respectively represent the predicted engine speed and the actual engine speed at each control moment; N dsl,etlv is the maximum permissible error in the predicted engine speed.

8. The hybrid ship energy optimization method based on event triggering mechanism according to claim 7 is characterized in that: The error between the actual state of charge of the monitoring battery and the predicted value establishes an event triggering condition, including: The difference between the predicted battery SOC value and the actual reference battery SOC value ΔSOC within the time step calculated by the system i The sum exceeds the set trigger level SOC etlv , this event is triggered, expressed as: In the formula, SOC predict,i and SOC ref,i Represent the predicted SOC value and the reference SOC value respectively; SOC all,etlv is the maximum deviation of the predicted battery SOC value.

9. The hybrid ship energy optimization method based on event triggering mechanism according to any one of claims 1 to 4, characterized in that: The determining whether the event reaches the trigger level and optimizing the system parameters includes: The engine speed, battery SOC state, trade-off equivalent consumption, and engine and motor torque distribution ratio are set as state variables, and the engine and motor torque distribution ratio change rate and slack variables are control variables. The state variables and control variables are expressed as: In the hybrid power system, the event-triggered model predictive controller is used with the volumetric Kalman filter to achieve the trade-off between fuel consumption and carbon emissions. While meeting the required power and system constraints, it has the following objective function: Where M i and M N are the stage cost matrix and the terminal cost matrix respectively; N dsl,i and N dsl,ref,i are the optimized engine speed and the engine speed reference under fixed working conditions, respectively, where i=1,2,3,…,N; (N dsl,i -N dsl,ref,i ) 2 Indicates that the engine speed should be consistent with the expected speed. By using the terminal cost function (N dsl,N -N dsl,ref,N ) 2 Enforce constraints on engine speed.

10. The hybrid ship energy optimization method based on event triggering mechanism according to any one of claims 1 to 4, characterized in that: During the entire optimization process, the following constraints must be met: SOC min ≤SOC≤SOC max in em,min in em in em,max 0≤u dsl ≤N dsl N dsl,min,soft -ε≤N dsl ≤N dsl,max,soft SOC dsl,min,soft -ε≤SOC dsl ≤SOC dsl,max,soft 0≤ε.

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