Energy management method and device for hybrid electric ship

Abstract

The present invention discloses an energy management method and device for an oil-electric hybrid power ship. The method comprises the following steps: obtaining a first power demand of the ship at a current moment, obtaining a fuel consumption function related to a control variable, using battery output power as a control variable, and determining an optimal control variable based on the battery output power; obtaining a second power demand of the ship at a previous moment; determining a torque demand of the ship based on the second power demand; and according to the torque demand, distributing a first torque corresponding to the engine and a second torque corresponding to the electric motor multiple times to obtain multiple torque combinations; then determining an optimal torque combination at a previous moment and co-morphic variables corresponding to the optimal torque combination, optimizing the co-morphic variables, and obtaining an optimal co-morphic variable at a current moment; and determining a minimum total fuel consumption of the ship at a current moment based on the optimal control variables and the optimal co-morphic variables, so as to perform energy management on the ship, thereby improving the fuel economy of the ship.

Description

Technical Field

[0001] The present invention belongs to the technical field of hybrid power ship energy control, and in particular relates to an energy management method and device for an oil-electric hybrid power ship. Background Art

[0002] Currently, global optimization control strategies that can achieve the optimal total fuel consumption have become a research hotspot. They mainly rely on Pontryagin's minimum principle (PMP) and Bellman dynamic programming (DP) theory to solve the dynamic optimization problem of system energy.

[0003] In reality, the DP algorithm is computationally intensive and time-consuming, making it difficult to apply in practice. Energy optimization control strategies based on the PMP algorithm, however, have become a research hotspot in dynamic global optimization theory in recent years due to their faster computational speed and lower computational complexity compared to the DP algorithm.

[0004] However, due to the nonlinear, multivariable, and time-varying factors in the power system of hybrid electric ships, if only the PMP algorithm is used to determine the optimal energy allocation strategy, the obtained energy allocation strategy is only the optimal under global consideration of the operating range, and cannot guarantee the optimal allocation throughout the entire operating range. Summary of the Invention

[0005] The present invention provides an energy management method and device for a hybrid electric ship, so as to solve the problem that it is difficult to ensure that the energy distribution in the entire operating range is kept in real-time optimal distribution when performing energy management on the hybrid electric ship.

[0006] According to one aspect of the present invention, a method for energy management of a hybrid electric ship is provided, the method comprising:

[0007] Obtaining a first power demand of the ship at a current moment, where the first power demand is satisfied by a first real-time power of the engine and / or a second real-time power of the electric motor, where the second real-time power is determined based on a battery output power, where the battery output power is the output power corresponding to a battery that provides electrical energy to the electric motor;

[0008] obtaining a fuel consumption function, wherein the fuel consumption function is related to the control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum;

[0009] using the battery output power as the control variable and determining the optimal control variable based on the battery output power;

[0010] obtaining a second power requirement of the ship at a previous moment, and determining a torque requirement of the ship based on the second power requirement;

[0011] According to the torque demand, a first torque corresponding to the engine and a second torque corresponding to the electric motor are distributed multiple times to obtain multiple torque combinations, wherein each distribution obtains a set of the torque combinations, and the sum of the first torque and the second torque in each set of the torque combinations is the torque demand;

[0012] Determining an optimal torque combination at a previous moment and a co-state variable corresponding to the optimal torque combination from the plurality of torque combinations;

[0013] Optimizing the co-state variables to obtain the optimal co-state variables at the current moment;

[0014] The minimum total fuel consumption of the ship at the current moment is determined according to the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

[0015] According to one aspect of the present invention, there is provided an energy management device for a hybrid electric ship, the device comprising:

[0016] a first power demand acquisition module, configured to acquire a first power demand of the vessel at a current moment, wherein the power demand is satisfied by a first real-time power of the engine and / or a second real-time power of the electric motor, wherein the second real-time power is determined according to a battery output power, wherein the battery output power is the output power corresponding to a battery that provides electrical energy to the electric motor;

[0017] a fuel consumption function acquisition module, configured to acquire a fuel consumption function, wherein the fuel consumption function is related to the control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum;

[0018] an optimal control variable determination module, configured to use the battery output power as the control variable and determine the optimal control variable based on the battery output power;

[0019] a torque requirement determination module, configured to obtain a second power requirement of the ship at a previous moment, and determine a torque requirement of the ship based on the second power requirement;

[0020] a distribution module, configured to distribute the first torque corresponding to the engine and the second torque corresponding to the electric motor multiple times according to the torque demand to obtain multiple torque combinations, wherein each distribution obtains a set of the torque combinations, and the sum of the first torque and the second torque in each set of the torque combinations is the torque demand;

[0021] a co-state variable determination module, configured to determine an optimal torque combination at a previous moment and a co-state variable corresponding to the optimal torque combination from the plurality of torque combinations;

[0022] An optimization module, configured to optimize the co-state variables to obtain the optimal co-state variables at the current moment;

[0023] An energy management module is used to determine the minimum total fuel consumption of the ship at a current moment based on the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

[0024] According to another aspect of the present invention, an electronic device is provided, comprising:

[0025] at least one processor; and

[0026] a memory communicatively connected to the at least one processor; wherein,

[0027] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy management method for a hybrid electric ship described in any embodiment of the present invention.

[0028] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy management method of a hybrid electric ship according to any embodiment of the present invention when executed.

[0029] The technical solution of the embodiment of the present invention provides an energy management method for a hybrid electric ship, the method comprising: obtaining a first power demand of the ship at the current moment, the first power demand being satisfied by the first real-time power of the engine and / or the second real-time power of the electric motor, the second real-time power being determined according to the battery output power, the battery output power being the output power corresponding to the battery that provides electrical energy to the electric motor; obtaining a fuel consumption function, wherein the fuel consumption function is related to a control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimized; using the battery output power as a control variable, and determining the optimal control variable based on the battery output power; obtaining the second power demand of the ship at the previous moment. , and based on the second power demand, determine the torque demand of the ship; according to the torque demand, distribute the first torque corresponding to the engine and the second torque corresponding to the electric motor multiple times to obtain multiple torque combinations, wherein each distribution obtains a set of torque combinations, and the sum of the first torque and the second torque in each torque combination is the torque demand; from the multiple torque combinations, determine the optimal torque combination at the previous moment and the co-state variables corresponding to the optimal torque combination; optimize the co-state variables to obtain the optimal co-state variables at the current moment; determine the minimum total fuel consumption of the ship at the current moment based on the optimal control variables and the optimal co-state variables, so as to manage the energy of the ship and improve the fuel economy of the hybrid ship.

[0030] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0032] Figure 1 This is a flow chart of an energy management method for a hybrid electric ship provided according to the first embodiment of the present invention;

[0033] Figure 2 This is a torque limitation schematic diagram provided according to the first embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of a closed-loop control provided according to the first embodiment of the present invention;

[0035] Figure 4This is a schematic structural diagram of an energy management device for a hybrid electric ship according to a second embodiment of the present invention;

[0036] Figure 5 The present invention is a schematic structural diagram of an electronic device for implementing an energy management method for a hybrid electric ship according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] Example 1

[0040] Figure 1 A flow chart of an energy management method for a hybrid electric ship is provided for the first embodiment of the present invention.

[0041] In current energy management for hybrid electric vessels, energy management can be achieved by determining an energy control strategy, and the minimum total fuel consumption at each moment can be used as the target of the energy control strategy for hybrid electric vessels. The hybrid ship energy control strategy based on the PMP algorithm transforms the dynamic global optimization problem into solving the co-state parameters of the Hamiltonian function. Through iterative calculations, the control variables are optimized to achieve the goal of energy allocation between power sources. However, the energy control strategy solved by the PMP algorithm model is based on optimization. It often uses a sampling time as the optimization interval, establishes an optimization objective function, and uses an optimization algorithm to solve it, ultimately obtaining the instantaneous optimal operating point. The goal of the instantaneous optimization strategy is to optimize the instantaneous control target, but it cannot guarantee the optimal target throughout the entire operating range, and the calculation is relatively complex. Examples of this type of energy management strategy include the Pontryagin minimum principle method and the equivalent fuel consumption minimum principle method.

[0042] Therefore, the embodiment of the present invention can be optimized based on the PMP algorithm model to obtain a power configuration between the electric motor and the engine that can be dynamically adjusted, and the minimum total fuel consumption corresponding to each moment in the entire operating range can be obtained, so as to ultimately obtain the optimal energy control strategy and perform energy management on the ship.

[0043] It should be noted that in the embodiments, expressions with black dots above them are in vector form.

[0044] In addition, hybrid ship refers to a ship that is powered by both diesel and electricity. In other words, hybrid ship is equivalent to diesel-electric hybrid ship. In the following description, diesel engine is equivalent to engine.

[0045] The method can be executed by an energy management device for a hybrid electric ship. The energy management device for the hybrid electric ship can be implemented in the form of hardware and / or software.

[0046] like Figure 1 As shown, the method includes the following steps:

[0047] S110. Obtain a first power demand of the ship at the current moment, where the first power demand is satisfied by a first real-time power of the engine and / or a second real-time power of the electric motor, and the second real-time power is determined according to a battery output power, where the battery output power is an output power corresponding to a battery that provides electrical energy to the electric motor.

[0048] In a ship that can be powered by a combination of an engine and an electric motor, the engine can be a diesel engine. The power of the ship requires the fuel consumption of the engine. Considering the complexity of the engine, a quasi-static model can be established for the engine, where the fuel rate m of the engine isf It can be expressed as:

[0049] Where, ω e and T e are the engine speed and torque respectively.

[0050] In addition, in the engine model, Where, P e Indicates the available power of the engine, H LHV Indicates the lower heating value of the fuel.

[0051] Part of the ship's power also needs to be provided by an electric motor. The electric motor can be a three-phase asynchronous motor, which can be used as a traction motor to provide torque or as a generator to store electricity for the battery. Therefore, the motor power can be expressed as:

[0052]

[0053] Where: ω m is the motor speed; η em is the motor driving efficiency; η ge is the efficiency of the motor as a generator, T m is the torque of the direct motor.

[0054] The electric motor can act as a generator to charge the battery. The battery can be modeled using a circular model, and the expressions for battery SOC and battery output power are as follows:

[0055]

[0056] Among them, SOC is the battery SOC change rate; U oc is the open circuit voltage of the battery; R b is the internal resistance of the battery; Qb is the maximum capacity of the battery; Pbat is the output power of the battery.

[0057] In the embodiment of the present invention, a dual reduction ratio can be used to transmit power, namely the main reduction ratio and the PTI reduction ratio (PTI, Power Take-in), namely:

[0058] Where T2 and n2 are the output torque and speed; T1 and n1 are the input torque and speed. a is the transmission ratio of the transmission system. The torque synthesis device can use a parallel gearbox. In this case, only the superposition of torques can be considered, and the modeling is as follows:

[0059] T out =T in1 +T in2

[0060] Where, Tin1 and T in2 is the input torque; T out is the output torque.

[0061] When the ship obtains power and drives the propeller to move, the motion equations of the ship and propeller can be as follows:

[0062]

[0063] Where, v s is the speed of the ship in m / s; R t is the resistance of the ship, in N; M p is the torque output by the engine, in N·m; M f is the resistance torque, in N·m; b is the reduction ratio of the gearbox.

[0064] Among them, the ship's resistance R t =f(v s ), where Where λ' is the ship resistance coefficient and θ is a constant.

[0065] In order to achieve the optimal hybrid power distribution at each moment, the first power demand of the ship at the current moment can be obtained according to the specific navigation situation of the current ship. The first power demand is satisfied by the first real-time power of the engine and / or the second real-time power of the motor. Let the system power demand at the current moment be P req (t), then P req (t) = P e (t)+P m (t), where P e (t) is the engine power, that is, the first real-time power; P m (t) is the motor power, also known as the second real-time power. In a specific implementation, either the first real-time power or the second real-time power can be 0. This means that the power provided to the ship can be in three situations: the first is when both the engine and the motor provide power simultaneously; the second is when only the engine provides power, with no motor providing power; and the third is when only the motor provides power, with no engine providing power.

[0066] The battery provides power to the motor, and the battery can be a battery pack. Motor power P m The calculation formula of (t) is as follows:

[0067]

[0068] Where η m (t) is the motor charging and discharging efficiency, which can be obtained from the motor efficiency diagram.

[0069] S120 . Obtain a fuel consumption function, wherein the fuel consumption function is related to a control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum.

[0070] When determining the energy control strategy of a hybrid electric ship, it can be converted into an optimal control problem. The fuel consumption function related to the control variable can be obtained. Then, during the process from the start to the end of the ship's operation, the control variable corresponding to the minimum function value of the fuel consumption function is found as the optimal control variable.

[0071] In one embodiment, the fuel consumption function is: Where J is the fuel consumption, t0-t f is the duration of the cycle condition, is the fuel consumption rate, and u(t) is the control variable.

[0072] Under the premise of ensuring sufficient power to complete the ship's navigation, the minimum total fuel consumption can be used as the target of the energy control strategy of the hybrid ship. Therefore, the fuel consumption function can be set as

[0073] S130 : Using the battery output power as a control variable, and determining an optimal control variable based on the battery output power.

[0074] During the navigation process of the ship, when the first power demand of the ship is determined, the controllable variables are only the power corresponding to the engine and the motor. If the second real-time power of the motor is determined, the first real-time power of the engine is also determined. The second real-time power of the motor is determined by the battery power corresponding to the battery that provides it with electrical energy. Therefore, the battery output power P bat (t) is used as the control vector, i.e. u(t) = P bat (t). In practical applications, there are numerical restrictions on the device parameters of various properties of electric motors, engines, and batteries. Based on these numerical restrictions, the minimum theorem and Hamiltonian function can be combined to determine the optimal control variables.

[0075] In one embodiment, S130 includes the following steps:

[0076] S130-1, respectively determine first constraint conditions for the engine's operating speed, engine torque, and engine torque change rate.

[0077] The first constraint is as follows:

[0078] ω e,min ≤ω e (t)≤ω e,max

[0079] T e,min ≤T e (t)≤T e,max

[0080]

[0081] Where: e (t) is the engine speed at the current moment, ω e,min and ω e,max are the engine idle speed and maximum speed, respectively, which are determined by the engine's own working characteristics; T e,min and T e,max The engine speed is ω e The friction torque and the maximum torque that can be output by the engine at (t) can be obtained from the engine universal characteristic diagram; is the engine torque change rate, and They are the limits of the fastest decrease and increase rates of engine torque respectively.

[0082] S130 - 2 , determining second constraints on the current state of charge of the battery, the battery charging power, and the battery output power respectively.

[0083] The second constraint is as follows:

[0084] SOC min ≤SOC(t)≤SOC max

[0085] P bat,min ≤P bat (t)≤P bat,max

[0086] Where: SOC(t) is the current state of charge of the battery, SOC min and SOC max The lowest and highest SOC allowed for the battery to operate; P bat,min is the battery charging power limit of the battery at the current moment, P bat,max is the battery output power limit of the battery at the current moment. The battery charging power limit and battery output power limit are functions of the internal resistance and open circuit voltage respectively.

[0087] S130-3, respectively determine third constraint conditions for the motor speed, motor torque, and motor torque change rate of the motor.

[0088] ω m,min ≤ω m (t)≤ω m,max

[0089] T m,min ≤Tm (t)≤T m,max

[0090]

[0091] Where: m (t) is the motor speed of the motor, ω m,min and ω m,max They are the minimum and maximum speeds of the motor, respectively, which are determined by the motor's own working characteristics. When the clutch is engaged, the speed is the same as the engine speed; T m (t) is the motor torque, T m,min and T m,max is the maximum input and output torque that the motor can achieve under the current speed and the battery's maximum charge and output power limits; m,min and T m,max They are the fastest decreasing and increasing rate limits of the motor torque respectively.

[0092] S130-4, obtain the state equation, the state equation is: x(t) = f(x(t), u(t), t), where the terminal state is x(t f )=x f , u(t) is the control variable.

[0093] When using the minimum theorem to solve the optimal control problem, the state equation can be set in advance. The state equation reflects the relationship between the input and the state. The state equation is: x(t) = f(x(t), u(t), t), x(t)∈R n , the initial transition state is x(t0)=x0, and the terminal state is x(t f )=x f The terminal state x(t f ) satisfies the terminal constraint equation: N(x(t f ),t f )=0, where (x(t f ),t f ) is an m-dimensional continuously differentiable scalar function, and m≤n. The control vector u(t) satisfies u(t)∈U∈R r .

[0094] S130-5: Under the constraints of the first constraint, the second constraint, and the third constraint, the fuel consumption function is updated. The updated fuel consumption function is: Among them, Φ(x(x f ),x f ) and L(x(t),u(t),t) are continuously differentiable scalar functions.

[0095] Under the constraints of the first, second, and third constraints, the control vector is constrained, and we can obtain g(x(t),u(t),t)≥0, where g(x(t),u(t),t) is an L-dimensional vector function, and L≤R. Then, the fuel consumption function at this time can be updated and expressed as At this time, the optimal control variable is the control variable corresponding to the undetermined terminal time.

[0096] S130-6, using the Hamiltonian function, determine the optimal control variable based on the updated fuel consumption function.

[0097] The core of the optimal control problem is to find the optimal allowable control variable that satisfies the conditions, thereby minimizing the fuel consumption function and minimizing fuel consumption. This can be done by introducing a Hamiltonian function and applying it to the updated fuel consumption function to determine the optimal control variable.

[0098] In one embodiment, S130-6 includes the following steps:

[0099] Based on the updated fuel consumption function and the Hamiltonian function, the first Hamiltonian function expression is obtained. The first Hamiltonian function expression is:

[0100] H(x(t),u(t),λ(t),t)=L(x(t),u(t),t)+λ T (t)f(x(t),u(t),t)

[0101] Among them, λ(t) is the covariate variable, λ T Indicates that the co-state variable changes with the change of the first torque and / or the second torque;

[0102] From the first Hamiltonian function expression, a control variable that makes the first Hamiltonian function expression obtain a minimum value is determined as an optimal control variable.

[0103] For each moment t, the control variable is selected to minimize the Hamiltonian function. If the control variable at a certain moment makes the Hamiltonian function obtain the minimum value and achieves optimal control, it means that the control variable at that moment is the optimal control variable.

[0104] S140: Obtain a second power requirement of the ship at a previous moment, and determine a torque requirement of the ship based on the second power requirement.

[0105] After determining the optimal control variables, we can start to determine the optimal co-state variables. Only when the optimal variables and the optimal co-state variables are determined at every moment can we obtain the minimum total fuel consumption corresponding to each moment of operation, so as to achieve optimal energy management for the ship.

[0106] The co-state variables at the current moment can be optimized from the previous moment. The second power demand of the ship at the previous moment can be obtained by acquiring the ship's operating data. After determining the second power demand, the ship's torque demand can be calculated. In a ship power system, the main propulsion transmission device can be a marine gearbox. The torque demand is the torque demand at the gearbox's input. Calculating the gearbox's torque demand based on the ship's power demand is common knowledge to those skilled in the art and will not be explained in detail.

[0107] S150. Based on the torque demand, the first torque corresponding to the engine and the second torque corresponding to the electric motor are distributed multiple times to obtain multiple torque combinations, wherein each distribution obtains a torque combination, and the sum of the first torque and the second torque in each torque combination is the torque demand.

[0108] After determining the ship's torque requirements, the torque requirements can be allocated multiple times to obtain multiple torque combinations. During allocation, a first torque can be randomly determined within the torque requirements, and the second torque will be determined at the same time. In another implementation, the allocation can be completed by decreasing the first torque by a fixed tolerance and increasing the second torque by a fixed tolerance.

[0109] S160 : Determine the optimal torque combination at the previous moment and the co-state variables corresponding to the optimal torque combination from the multiple torque combination groups.

[0110] Multiple torque combinations represent multiple distribution situations of first torques and second torques. All torque combinations can be traversed to obtain the energy consumption corresponding to each torque combination. Then, the torque combination corresponding to the most ideal energy consumption situation is used as the optimal torque combination, and the co-state variable corresponding to the optimal torque combination is obtained. The co-state variable can be used for subsequent optimization to obtain the optimal co-state variable at the current moment.

[0111] In one embodiment, step S160 includes the following sub-steps:

[0112] S160-1, based on the PMP algorithm, determine the second Hamiltonian function expression, the second Hamiltonian function expression is:

[0113]

[0114] Among them, m fuel (P bat (t), t) is obtained by querying the universal characteristic diagram corresponding to the engine, μ(t) is the Lagrange factor, which is a scalar. is the vector corresponding to the battery output power, λ(t) is the covariate variable, λT Indicates that the co-state variable changes with the change of the first torque and / or the second torque, SOC (SOC, P bat (t), t) represents the state of charge function related to the current state of charge, battery charging power and time;

[0115] S160-2, respectively substituting the torque combinations into the second Hamiltonian function expression, and obtaining the function value of the Hamiltonian function expression corresponding to each torque combination;

[0116] S160-3, comparing the function values ​​to determine the minimum function value;

[0117] S160-4, taking the torque combination corresponding to the minimum function value as the optimal torque combination;

[0118] S160-5, obtain the co-state variables from the Hamiltonian function expression corresponding to the optimal torque combination.

[0119] The PMP algorithm can transform the global optimization problem into a local analytical formula and a transient minimum problem, significantly reducing the computational effort required to solve the problem and making it more suitable for online applications. Based on the PMP algorithm, the second Hamiltonian function expression can be determined, which can be viewed as the Hamiltonian function for the ship's hybrid power system.

[0120] In one implementation, the Lagrangian factor μ(t) in the second Hamiltonian is a scalar term that introduces an additional term into the Hamiltonian to ensure that the battery SOC reaches SOCmin at the end of the cycle. If the battery operates outside the SOC range, μ(t) is used to penalize it.

[0121] μ l ≥0 and obeys the Kuhn-Ticker condition, then:

[0122]

[0123] Substitute each torque combination into the second Hamiltonian function expression and calculate the function value corresponding to each torque combination. After determining the minimum function value, the torque combination corresponding to the minimum function value is selected as the optimal torque combination. Save the first torque and second torque of the optimal torque combination at this time, as well as the covariate variables in the second Hamiltonian function expression at this time.

[0124] During actual navigation, the ship's torque demand is continuous and rarely changes dramatically. In addition, the control of the engine and motor will also limit the torque from changing significantly in a short period of time. For example, for the engine, the maximum torque increase ΔT will occur in a certain time interval. e,inc,max and the maximum value of torque drop ΔT e,dec,max , the same limit value will also appear in motor control. Based on this consideration, when designing the algorithm, the torque change between two adjacent moments will be limited. Figure 2 A torque limit diagram can be defined as the optimal torque of the engine obtained at the current moment plus ΔT e,inc,max As the maximum value of the engine torque at the next moment, subtract ΔT e,dec,max As the minimum value of the diesel engine torque at the next moment, the optimal calculation is performed in the new interval, which effectively improves the calculation accuracy and also increases the calculation speed. Figure 2 The PMP optimization algorithm in can be regarded as an algorithm that optimizes both the control variables and the co-state variables in the PMP algorithm, that is, the control variables in the PMP optimization algorithm are the optimal control variables, and the co-state variables are the optimal co-state variables.

[0125] S170: Optimize the co-state variables to obtain the optimal co-state variables at the current moment.

[0126] After obtaining the co-state variables in the second Hamiltonian function expression corresponding to the optimal torque combination, the co-state variables can be optimized to obtain a more appropriate co-state variable as the optimal co-state variable at the current moment. For the previous moment, there will be an optimal co-state variable optimized from the co-state variable of the previous moment. The actual co-state variable can be obtained based on the operating data of the previous moment. Based on the difference between the optimal co-state variable corresponding to the previous moment and the co-state variable corresponding to the previous moment, the optimal co-state variable for the current moment can be optimized.

[0127] In one embodiment, step S170 includes the following sub-steps:

[0128] S170-1, inputting the co-state variables into a pre-set adaptive particle swarm algorithm model;

[0129] S170-2, receiving the result output by the adaptive particle swarm optimization model as the optimal co-state variable at the current moment.

[0130] The core of the adaptive particle swarm optimization model is to find the optimal solution through collaboration and information sharing between individuals in the group. Therefore, by inputting the co-state variables into the pre-set adaptive particle swarm optimization model, we can obtain the results output by the adaptive particle swarm optimization model, that is, the optimal co-state variables.

[0131] The process of outputting the results of the adaptive particle swarm algorithm model is as follows:

[0132] After obtaining the co-state variables, the particle position, particle velocity and number of iterations corresponding to each particle in the particle swarm are set, and the individual extreme value and the overall extreme value of the particle swarm are obtained;

[0133] Determine the fitness value of each particle separately;

[0134] Based on each fitness value, the fitness variance of the particle swarm is determined. The fitness variance calculation formula is: Where N is the total number of particles in the particle swarm, f i is the fitness value of the i-th particle, i is a positive integer from 1 to N, f avg is the average fitness value of the particle swarm, and f is the normalization factor used to control the range of fitness variance;

[0135] Based on the fitness, the adaptive weight of each particle is determined. The calculation formula of the adaptive weight is: ω max is the maximum weight of a single particle in the particle swarm, ω min is the minimum weight of a single particle in the particle swarm, f g is the optimal fitness value of the particle swarm;

[0136] Update individual extreme values ​​and overall extreme values;

[0137] The particle position and particle velocity are updated. The process of updating the particle velocity corresponding to the i-th particle is: Among them, ω i is the adaptive weight corresponding to the i-th particle, c1 and c2 are constant learning factors, r1 and r2 are random numbers in the interval [0,1], k is the update number, is the single optimal value in the kth update, is the global optimal value in the kth update; the process of updating the particle velocity corresponding to the i-th particle is:

[0138] Update the fitness variance according to the updated particle position and particle velocity;

[0139] Determine whether the particle swarm meets the preset conditions based on the updated fitness variance, particle velocity, and particle position;

[0140] If it meets the requirements, the result is output; if it does not meet the requirements, the process continues to determine the fitness value of each particle.

[0141] In this process, ω is usually taken max =0.9,ωmin =0.4.

[0142] If the adaptive particle swarm algorithm model falls into a local optimal solution, the current particle population extreme value g best A certain probability p m Mutation, p m The specific calculation is expressed as where q is a random number in the interval [0, 0.4], and It is much smaller than the maximum value of the fitness variance D. According to the particle swarm extreme value g best , perform the following mutation operations.

[0143] (1) Randomly generate a random number r that follows the normal distribution n(0,1).

[0144]

[0145] f=max{1,max{|f i -f avg |}}

[0146] (2) The random number r and p m Compare. If r is greater than p m , the particle mutates.

[0147] The specific calculation formula for mutation operation is as follows

[0148]

[0149] If r is less than p m , no mutation is performed.

[0150] In one embodiment, a PI closed-loop controller is used to optimize the adaptive particle swarm algorithm model. The optimization process of the adaptive particle swarm algorithm model is as follows:

[0151] Get the relationship between the optimized co-state variables and the co-state variables. The relationship is: λ0 is the covariate variable input into the adaptive particle swarm optimization model, is the optimal co-state variable, SOC ref is the reference value of the state of charge at the current moment, the sampling period is T, t=nT, n is an integer, k p is the first parameter, k i is the second parameter, the first parameter and the second parameter are parameters in the PI closed-loop controller;

[0152] The first parameter and the second parameter are optimized, and the adaptive particle swarm optimization model is optimized based on the optimized first parameter and the second parameter.

[0153] Adaptive Particle Swarm Optimization (A-PSO) can be used to optimize the parameters of the PI closed-loop controller to form an A-PSO-PI controller to optimize the "λ-control" in real time. Figure 3 A closed-loop control diagram can improve the flexibility and adaptive characteristics of feedback closed-loop control. Considering the state of charge SOC(t) of each energy storage unit in the diesel-electric hybrid system in a ship, assuming that the reference value of the state of charge at the current moment is SOC ref ,In the simulation control system environment, the sampling period T, t=nT, where n can be 1, 2, 3, ,…, can be set up during simulation and the ,PDEHS simulation model can be established using Matlab / Simulink ,software.

[0154] exist middle,

[0155] Only the k of the PI controller needs to be adjusted p and k i The two parameters can be optimized according to the ITAE (Integral of time multiplied by the absolute value of error) index, which comprehensively considers the performance indicators of steady-state error and adjustment time. The overshoot is small and the transition is smooth, which has good practicality. Therefore, the ITAE criterion can be used to calculate the objective function of the PSO algorithm.

[0156] In addition, each potential optimal solution of the problem to be optimized in the PSO algorithm represents a particle in the solvable space. For example, particle i corresponds to the fitness value of the i-th particle under the fitness function. If the current position x of the particle is introduced i =(x i1 ,x i2 ,…,x id ),i=1,2,…,current speed v i =(v i1 ,ν i2 ,…,ν id ) The best position trajectory of all particles is P i =(p i1 ,P i2 ,…,p id ) Individual extreme value P best,i =(p best,i1 ,p best,i2 ,…,p best,id ), group extreme value P gbest,i =(p gbest,i1 ,p gbest,i2 ,…,p gbest,id ) and the inertia weight h.

[0157] Particles can be updated and iterated according to the following formula:

[0158]

[0159] v id,k+1 =hv id,k +c1r1*(p best,id,k -x id,k )+c2r2*(p gbest,id,k -x id,k )

[0160] x id,k+1 =x id,k +v id,k+1

[0161]

[0162] Where d = 1, 2, ..., D, d is the spatial dimension of the problem to be solved, h is the inertia weight, r1 and r2 are random numbers between (0, 1), c1 and c2 are non-negative constants as evolution factors, and x id,k and v id,k They are the position and velocity of the i-th particle updated at the k-th iteration in the D-dimensional space, h initial is the initial inertia weight, k max is the maximum number of iterations, h end k max The inertia weight when initial =0.9 and h end , ensuring a strong global search capability in the early stage, while facilitating the algorithm to perform local search in the later stage.

[0163] By using the particle swarm optimization (A-PSO) algorithm to optimize the parameters of the PI controller, an A-PSO-PI controller can be formed to optimize the "λ-control" in real time to improve the flexibility and adaptive characteristics of the feedback closed-loop control.

[0164] S180. Determine the minimum total fuel consumption of the ship at the current moment based on the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

[0165] After obtaining the optimal control variables and the optimal co-state variables, the minimum total fuel consumption of the ship at the current moment can be determined based on the optimal control variables and the optimal co-state variables. The distribution of energy provided by the electric motor and the engine at this time can be determined to achieve the goal of minimizing fuel consumption while ensuring the normal navigation of the ship, thereby completing the energy management of the ship.

[0166] An embodiment of the present invention proposes an energy management method for a hybrid electric ship. The method includes: obtaining a first power demand of the ship at the current moment, the first power demand is satisfied by the first real-time power of the engine and / or the second real-time power of the electric motor, the second real-time power is determined according to the battery output power, and the battery output power is the output power corresponding to the battery that provides electrical energy to the electric motor; obtaining a fuel consumption function, wherein the fuel consumption function is related to a control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is the minimum; using the battery output power as a control variable, and determining the optimal control variable based on the battery output power; obtaining the second power demand of the ship at the previous moment .... Based on the second power demand, the torque demand of the ship is determined; according to the torque demand, the first torque corresponding to the engine and the second torque corresponding to the electric motor are distributed multiple times to obtain multiple torque combinations, wherein each distribution obtains a set of torque combinations, and the sum of the first torque and the second torque in each set of torque combinations is the torque demand; from the multiple sets of torque combinations, the optimal torque combination at the previous moment and the comorphic variables corresponding to the optimal torque combination are determined; the comorphic variables are optimized to obtain the optimal comorphic variables at the current moment; according to the optimal control variables and the optimal comorphic variables, the minimum total fuel consumption of the ship at the current moment is determined to manage the energy of the ship, thereby improving the fuel economy of the hybrid ship.

[0167] Example 2

[0168] Figure 4 This is a schematic diagram of the structure of an energy management device for a hybrid electric ship provided in the second embodiment of the present invention. Figure 4 As shown, the device includes:

[0169] A first power demand acquisition module 410 is configured to acquire a first power demand of the vessel at a current moment, where the first power demand is satisfied by the first real-time power of the engine and / or the second real-time power of the motor, where the second real-time power is determined based on a battery output power, where the battery output power is the output power corresponding to a battery that provides electrical energy to the motor;

[0170] A fuel consumption function acquisition module 420 is configured to acquire a fuel consumption function, wherein the fuel consumption function is related to the control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum;

[0171] an optimal control variable determination module 430, configured to use the battery output power as the control variable and determine the optimal control variable based on the battery output power;

[0172] a torque requirement determination module 440 configured to obtain a second power requirement of the ship at a previous moment and determine a torque requirement of the ship based on the second power requirement;

[0173] a distribution module 450 configured to distribute the first torque corresponding to the engine and the second torque corresponding to the electric motor multiple times according to the torque demand to obtain multiple torque combinations, wherein each distribution produces a set of the torque combinations, and the sum of the first torque and the second torque in each set of the torque combinations is the torque demand;

[0174] a co-state variable determination module 460 for determining an optimal torque combination at a previous moment and a co-state variable corresponding to the optimal torque combination from the plurality of torque combinations;

[0175] An optimization module 470 is configured to optimize the co-state variables to obtain the optimal co-state variables at the current moment;

[0176] The energy management module 480 is used to determine the minimum total fuel consumption of the ship at the current moment according to the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

[0177] In one embodiment, the fuel consumption function is: Where J is the fuel consumption, t0-t f is the duration of the cycle condition, is the fuel consumption rate, and u(t) is the control variable.

[0178] In one embodiment, the optimal control variable determination module 430 includes the following submodules:

[0179] a first constraint condition determination submodule, configured to respectively determine first constraint conditions on the engine's operating speed, engine torque, and engine torque change rate;

[0180] a second constraint condition determination submodule, configured to respectively determine second constraint conditions for the current state of charge of the battery, the battery charging power, and the battery output power;

[0181] a third constraint condition determination submodule, configured to respectively determine third constraint conditions on the motor speed, motor torque, and motor torque change rate of the motor;

[0182] The state equation acquisition module is used to obtain the state equation, wherein the state equation is: x(t)=f(x(t),u(t),t), where the terminal state is x(t f )=x f , u(t) is the control variable;

[0183] The fuel consumption function updating module is configured to update the fuel consumption function under the constraints of the first constraint condition, the second constraint condition, and the third constraint condition, wherein the fuel consumption function obtained after the update is: Among them, Φ(x(x f ),x f ) and L(x(t),u(t),t) are continuously differentiable scalar functions;

[0184] The optimal control variable determination submodule is used to determine the optimal control variable based on the updated fuel consumption function using a Hamiltonian function.

[0185] In one embodiment, the optimal control variable determination submodule includes the following units:

[0186] The combining unit is configured to combine the fuel consumption function obtained after the update with the Hamiltonian function to obtain a first Hamiltonian function expression, wherein the first Hamiltonian function expression is:

[0187] H(x(t),u(t),λ(t),t)=L(x(t),u(t),t)+λ T (t)f(x(t),u(t),t)

[0188] Wherein, λ(t) is the covariate variable, λ T Indicates that the co-state variable changes with changes in the first torque and the second torque;

[0189] The optimal control variable determining unit is used to determine, from the first Hamiltonian function expression, a control variable that makes the first Hamiltonian function expression obtain a minimum value as the optimal control variable.

[0190] In one embodiment, the co-state variable determination module 460 includes the following sub-modules:

[0191] The expression determination submodule is used to determine the second Hamiltonian function expression based on the PMP algorithm, where the second Hamiltonian function expression is:

[0192]

[0193] Among them, m fuel (P bat (t), t) is obtained by querying the universal characteristic diagram corresponding to the engine, μ(t) is the Lagrange factor, which is a scalar. is the vector corresponding to the battery output power, λ(t) is the covariate variable, λ TIndicates that the co-state variable changes with the change of the first torque and / or the second torque, SOC (SOC, P bat (t), t) represents a state of charge function related to the current state of charge, the battery charging power and time;

[0194] a function value acquisition submodule, configured to substitute the torque combinations into the second Hamiltonian function expression respectively, and obtain the function value of the Hamiltonian function expression corresponding to each torque combination;

[0195] A comparison submodule, configured to compare the function values ​​and determine a minimum function value;

[0196] an optimal torque combination determining submodule, configured to use the torque combination corresponding to the minimum function value as the optimal torque combination;

[0197] The co-state variable acquisition submodule is used to obtain the co-state variable from the Hamiltonian function expression corresponding to the optimal torque combination.

[0198] In one embodiment, the optimization module 470 includes the following submodules:

[0199] An input submodule, used for inputting the co-state variables into a preset adaptive particle swarm algorithm model;

[0200] The optimal co-state variable determination submodule is used to receive the result output by the adaptive particle swarm algorithm model as the optimal co-state variable at the current moment, wherein the process of the adaptive particle swarm algorithm model outputting the result is as follows:

[0201] After obtaining the co-state variables, setting the particle position, particle velocity and number of iterations corresponding to each particle in the particle swarm, and obtaining the individual extreme value and the overall extreme value of the particle swarm;

[0202] Determining the fitness value of each particle respectively;

[0203] Based on each of the fitness values, the fitness variance of the particle swarm is determined. The fitness variance is calculated as follows: Wherein, N is the total number of particles in the particle group, f i is the fitness value of the i-th particle, i=1,2,...,N, f avg is the average fitness value of the particle swarm, and f is a normalization factor used to control the range of the fitness variance;

[0204] Based on the fitness, the adaptive weight of each particle is determined, and the calculation formula of the adaptive weight is: ω maxis the maximum weight of a single particle in the particle group, ω min is the minimum weight of a single particle in the particle group, f g is the optimal fitness value of the particle swarm;

[0205] Updating the individual extreme value and the overall extreme value;

[0206] The particle position and the particle velocity are updated. The process of updating the particle velocity corresponding to the i-th particle is: Among them, ω i is the adaptive weight corresponding to the i-th particle, c1 and c2 are constant learning factors, r1 and r2 are random numbers in the interval [0,1], k is the update number, is the single optimal value in the kth update, is the global optimal value in the kth update; the process of updating the particle velocity corresponding to the i-th particle is:

[0207] Updating the fitness variance according to the updated particle position and the particle velocity;

[0208] Determining whether the particle swarm meets a preset condition according to the updated fitness variance, the particle velocity, and the particle position;

[0209] If it is consistent, the result is output; if it is not consistent, the step of determining the fitness value of each particle is continued.

[0210] In one embodiment, a PI closed-loop controller is used to optimize the adaptive particle swarm algorithm model. The optimization process of the adaptive particle swarm algorithm model is as follows:

[0211] Obtain a relationship between the optimal co-state variable and the co-state variable, the relationship being: λ0 is the co-state variable input into the adaptive particle swarm optimization model, is the optimal co-state variable, SOC ref is the reference value of the state of charge at the current moment, the sampling period is T, t=nT, n is an integer, k p is the first parameter, k i is a second parameter, wherein the first parameter and the second parameter are parameters in the PI closed-loop controller;

[0212] The first parameter and the second parameter are optimized, and the adaptive particle swarm optimization model is optimized based on the optimized first parameter and the second parameter.

[0213] An energy management device for a hybrid electric ship provided in an embodiment of the present invention can implement an energy management method for a hybrid electric ship provided in the first embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0214] Example 3

[0215] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0216] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12 and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0217] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0218] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, or microcontroller. The processor 11 executes the various methods and processes described above, such as an energy management method for a hybrid electric vessel.

[0219] In some embodiments, a method for managing energy for a hybrid electric vessel can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for managing energy for a hybrid electric vessel described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the method for managing energy for a hybrid electric vessel in any other suitable manner (e.g., via firmware).

[0220] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0221] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0222] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0223] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0224] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0225] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0226] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0227] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. An energy management method for a hybrid electric ship, characterized in that: The method comprises: Obtaining a first power demand of the ship at a current moment, where the first power demand is satisfied by a first real-time power of the engine and / or a second real-time power of the electric motor, where the second real-time power is determined based on a battery output power, where the battery output power is the output power corresponding to a battery that provides electrical energy to the electric motor; Obtaining a fuel consumption function, wherein the fuel consumption function is related to a control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum; using the battery output power as the control variable and determining the optimal control variable based on the battery output power; obtaining a second power requirement of the ship at a previous moment, and determining a torque requirement of the ship based on the second power requirement; According to the torque demand, a first torque corresponding to the engine and a second torque corresponding to the electric motor are distributed multiple times to obtain multiple torque combinations, wherein each distribution obtains a set of the torque combinations, and the sum of the first torque and the second torque in each set of the torque combinations is the torque demand; Determining an optimal torque combination at a previous moment and a co-state variable corresponding to the optimal torque combination from the plurality of torque combinations; Optimizing the co-state variables to obtain the optimal co-state variables at the current moment; The minimum total fuel consumption of the ship at the current moment is determined according to the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

2. The method according to claim 1, characterized in that The fuel consumption function is: Where J is the fuel consumption, t0-t f is the duration of the cycle condition, is the fuel consumption rate, and u(t) is the control variable.

3. The method according to claim 1 or 2, characterized in that The step of using the battery output power as the control variable and determining the optimal control variable based on the battery output power includes: determining first constraints on the engine's operating speed, engine torque, and engine torque change rate, respectively; determining second constraints on the current state of charge of the battery, the battery charging power, and the battery output power respectively; determining third constraints on the motor speed, motor torque, and motor torque change rate of the motor respectively; Obtain the state equation, which is: x(t) = f(x(t), u(t), t), where the terminal state is x(t f )=x f , u(t) is the control variable; Under the constraints of the first constraint, the second constraint, and the third constraint, the fuel consumption function is updated. The updated fuel consumption function is: Among them, Φ(x(x f ),x f ) and L(x(t),u(t),t) are continuously differentiable scalar functions; The optimal control variable is determined based on the updated fuel consumption function using a Hamiltonian function.

4. The method according to claim 3, characterized in that The method of determining the optimal control variable based on the updated fuel consumption function using the Hamiltonian function includes: Based on the updated fuel consumption function and the Hamiltonian function, a first Hamiltonian function expression is obtained. The first Hamiltonian function expression is: H1(x(t),u(t),λ(t),t)=L(x(t),u(t),t)+λ T (t)f(x(t),u(t),t) Wherein, λ(t) is the covariate variable, λ T Indicates that the co-state variable changes with changes in the first torque and the second torque; A control variable that minimizes the first Hamiltonian function expression is determined from the first Hamiltonian function expression as the optimal control variable.

5. The method according to claim 4, characterized in that Determining the optimal torque combination at the previous moment and the co-state variables corresponding to the optimal torque combination from the multiple groups of torque combinations includes: Based on the PMP algorithm, the second Hamiltonian function expression is determined, and the second Hamiltonian function expression is: Among them, m fuel (P bat (t), t) is obtained by querying the universal characteristic diagram corresponding to the engine, μ(t) is the Lagrange factor, which is a scalar. is the vector corresponding to the battery output power, λ(t) is the covariate variable, λ T Indicates that the co-state variable changes with the change of the first torque and / or the second torque, SOC (SOC, P bat (t), t) represents a state of charge function related to the current state of charge, the battery charging power and time; Substituting the torque combinations into the second Hamiltonian function expression respectively, and obtaining the function value of the Hamiltonian function expression corresponding to each torque combination; comparing the function values ​​to determine a minimum function value; taking the torque combination corresponding to the minimum function value as the optimal torque combination; A co-state variable is obtained from the Hamiltonian function expression corresponding to the optimal torque combination.

6. The method according to claim 5, characterized in that Optimizing the co-state variables to obtain the optimal co-state variables at the current moment includes: Inputting the co-state variables into a pre-set adaptive particle swarm algorithm model; The result output by the adaptive particle swarm optimization model is received as the optimal co-state variable at the current moment, wherein the process of the adaptive particle swarm optimization model outputting the result is as follows: After obtaining the co-state variables, setting the particle position, particle velocity and number of iterations corresponding to each particle in the particle swarm, and obtaining the individual extreme value and the overall extreme value of the particle swarm; Determining the fitness value of each particle respectively; Based on each of the fitness values, the fitness variance of the particle swarm is determined. The fitness variance is calculated as follows: Wherein, N is the total number of particles in the particle group, f i is the fitness value of the i-th particle, i is a positive integer from 1 to N, f avg is the average fitness value of the particle swarm, and f is a normalization factor used to control the range of the fitness variance; Based on the fitness, the adaptive weight of each particle is determined, and the calculation formula of the adaptive weight is: ω max is the maximum weight of a single particle in the particle group, ω min is the minimum weight of a single particle in the particle group, f g is the optimal fitness value of the particle swarm; Updating the individual extreme value and the overall extreme value; The particle position and the particle velocity are updated. The process of updating the particle velocity corresponding to the i-th particle is: Among them, ω i is the adaptive weight corresponding to the i-th particle, c1 and c2 are constant learning factors, r1 and r2 are random numbers in the interval [0,1], k is the update number, is the single optimal value in the kth update, is the global optimal value in the kth update; the process of updating the particle velocity corresponding to the i-th particle is: Updating the fitness variance according to the updated particle position and the particle velocity; Determining whether the particle swarm meets a preset condition according to the updated fitness variance, the particle velocity, and the particle position; If it is consistent, the result is output; if it is not consistent, the step of determining the fitness value of each particle is continued.

7. The method according to claim 6, characterized in that The PI closed-loop controller is used to optimize the adaptive particle swarm algorithm model. The optimization process of the adaptive particle swarm algorithm model is as follows: Obtain a relationship between the optimal co-state variable and the co-state variable, the relationship being: λ0 is the co-state variable input into the adaptive particle swarm optimization model, is the optimal co-state variable, SOC ref is the reference value of the state of charge at the current moment, the sampling period is T, t=nT, n is an integer, k p is the first parameter, k i is a second parameter, wherein the first parameter and the second parameter are parameters in the PI closed-loop controller; The first parameter and the second parameter are optimized, and the adaptive particle swarm optimization model is optimized based on the optimized first parameter and the second parameter.

8. An energy management device for a hybrid electric ship, characterized in that: The device comprises: a first power demand acquisition module, configured to acquire a first power demand of the vessel at a current moment, wherein the power demand is satisfied by a first real-time power of the engine and / or a second real-time power of the electric motor, wherein the second real-time power is determined according to a battery output power, wherein the battery output power is the output power corresponding to a battery that provides electrical energy to the electric motor; a fuel consumption function acquisition module, configured to acquire a fuel consumption function, wherein the fuel consumption function is related to a control variable, and when the control variable is an optimal control variable, the fuel consumption output by the fuel consumption function is minimum; an optimal control variable determination module, configured to use the battery output power as the control variable and determine the optimal control variable based on the battery output power; a torque requirement determination module, configured to obtain a second power requirement of the ship at a previous moment, and determine a torque requirement of the ship based on the second power requirement; a distribution module, configured to distribute the first torque corresponding to the engine and the second torque corresponding to the electric motor multiple times according to the torque demand to obtain multiple torque combinations, wherein each distribution obtains a set of the torque combinations, and the sum of the first torque and the second torque in each set of the torque combinations is the torque demand; a co-state variable determination module, configured to determine an optimal torque combination at a previous moment and a co-state variable corresponding to the optimal torque combination from the plurality of torque combinations; An optimization module, configured to optimize the co-state variables to obtain the optimal co-state variables at the current moment; An energy management module is used to determine the minimum total fuel consumption of the ship at a current moment based on the optimal control variables and the optimal co-state variables, so as to perform energy management on the ship.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy management method for a hybrid electric ship according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement an energy management method for a hybrid electric ship according to any one of claims 1 to 7 when executed.

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