Adaptive optimization method and system for energy management of hybrid power supply system

Abstract

The application provides a kind of hybrid energy supply system energy management adaptive optimization method and system, belongs to power system energy distribution technical field, acquires load power, calculates the weight coefficient of optimization target;Based on the weight coefficient of optimization target, determine the constraint condition of energy distribution, randomly generate the initial position and speed of optimization factor, and carry out iterative update, finally calculate the cutoff frequency;Based on the cutoff frequency calculated, the energy distribution of low-frequency power source and high-frequency power source is carried out.The application adds optimization processing to the fluctuation of driving load in the process of hybrid energy supply system energy distribution, which is beneficial to the rationality of energy optimization of hybrid energy supply system under different load levels;At the same time, it can not only reduce the energy loss of hybrid energy supply system, but also can improve the service life of energy storage system, so as to improve the economy of transport equipment transportation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system energy distribution, in particular to an adaptive optimization method and system for energy management of a hybrid energy supply system. BACKGROUND

[0002] The two main problems existing in the current transportation industry are environmental pollution and energy shortage. Among them, fuel consumption is the main source of pollution emission of carrying equipment, which also causes excessive consumption of traditional energy. With the development of new energy technology, all-electric propulsion carrying equipment is gradually replacing traditional fuel propulsion carrying equipment to become the main carrying way for medium and short distance transportation. Among them, in order to fully utilize the advantages of different new energy power sources, a hybrid energy supply system is often used as the power system of all-electric carrying equipment. The hybrid energy supply system mainly relies on energy management strategy to reasonably distribute the energy between different power sources according to the working condition characteristics, so as to realize system optimization. The commonly used energy management strategy of hybrid energy supply system mostly uses low-pass filtering strategy to distribute the energy between different power sources. Although this strategy is easy to implement, in the case of frequent fluctuations of driving load or severe environmental changes, the hybrid energy supply system is difficult to respond quickly, which is not conducive to the stability of the system. SUMMARY

[0003] The purpose of the present application is to provide an adaptive optimization method and system for energy management of a hybrid energy supply system, which can reduce energy loss of the hybrid energy supply system, improve service life of the energy storage system and economic efficiency of the carrying equipment transportation, so as to solve at least one technical problem existing in the background technology.

[0004] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0005] On the one hand, the present application provides an adaptive optimization method for energy management of a hybrid energy supply system, comprising:

[0006] Collecting load power;

[0007] Calculating the weight coefficient of the optimization target according to the load power;

[0008] Determining the constraint condition of energy distribution based on the weight coefficient of the optimization target;

[0009] Based on the constraint condition, randomly generating the initial position and speed of the optimization factor and iteratively updating, finally calculating the cutoff frequency;

[0010] Based on the calculated cutoff frequency, the energy distribution of low-frequency power source and high-frequency power source is carried out; wherein the load power is divided into low-frequency steady-state power component and high-frequency transient power component, and the calculation is carried out according to the following formula:

[0011]

[0012] P l = P load *G f

[0013] P h = P load *(1-G f )

[0014] wherein f c is the cut-off frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are the reference powers of the low-frequency power source and the high-frequency power source, respectively.

[0015] Preferably, the value of the weight coefficient α of the optimization target is determined by the error square value of the load power P load , including:

[0016]

[0017] wherein P load (k+1) represents the load power at k+1 time, and P load (k+1) represents the load power at k time.

[0018] Preferably, the constraint condition of energy distribution is determined based on the weight coefficient of the optimization target, including:

[0019] Due to frequent fluctuations of the load, the energy distribution cannot be adaptively optimized to the load change when the cut-off frequency f c takes a fixed value. The energy loss function of the hybrid energy supply system is calculated according to the following formula:

[0020]

[0021] wherein R o.bat and R o.SC are the equivalent resistances of the low-frequency power source and the high-frequency power source, respectively; T s is the simulation step; V bat and V SC are the output voltages of the low-frequency power source and the high-frequency power source, respectively; P bat and P SC represent the output powers of the low-frequency power source and the high-frequency power source, respectively.

[0022] Preferably, the output powers of the low-frequency power source and the high-frequency power source are calculated according to the following formula:

[0023]

[0024] Preferably, the constraint further comprises: the service life of the power source when optimizing the energy distribution of the hybrid energy supply system, and the multi-objective function of optimization is calculated according to the following formula:

[0025]

[0026] Wherein, I bat is the output current of the low-frequency power source; SOC bat and SOC SC are the state of charge of the low-frequency power source and the high-frequency power source, respectively.

[0027] Preferably, the update of the position and speed of the adaptive optimization algorithm is calculated according to the following formula:

[0028]

[0029] Wherein, ω is the inertia weight; is the historical optimal solution of the i th th optimization factor; is the global optimal solution; and V i (t) represent the position and speed of the i th th optimization factor, respectively; c1 and c2 represent the optimization speed, which are non-negative constants; r1 and r2 are random numbers between 0 and 1.

[0030] In the second aspect, the application provides an adaptive optimization system for energy management of a hybrid energy supply system, comprising:

[0031] A collection module for collecting load power;

[0032] A first calculation module for calculating the weight coefficient of the optimization target according to the load power;

[0033] A construction module for determining the constraint condition of energy distribution based on the weight coefficient of the optimization target;

[0034] A second calculation module for randomly generating the initial position and speed of the optimization factor based on the constraint condition, and iteratively updating to finally calculate the cutoff frequency;

[0035] A distribution module for energy distribution of the low-frequency power source and the high-frequency power source based on the calculated cutoff frequency; wherein the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and calculated according to the following formula:

[0036]

[0037] P l= P load * G f

[0038] P h = P load *(1-G f )

[0039] wherein f c is the cut-off frequency of the energy filter; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are the reference powers of the low-frequency power source and the high-frequency power source, respectively.

[0040] In a third aspect, the present application provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implement the adaptive optimization method for energy management of the hybrid energy supply system as described above.

[0041] In a fourth aspect, the present application provides a computer program product comprising a computer program for implementing the adaptive optimization method for energy management of the hybrid energy supply system as described above when run on one or more processors.

[0042] In a fifth aspect, the present application provides an electronic device comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so as to make the electronic device execute the instructions for implementing the adaptive optimization method for energy management of the hybrid energy supply system as described above.

[0043] The present application has the following advantages: the optimization processing for driving load fluctuation is added in the energy distribution process of the hybrid energy supply system, which is beneficial to the rationality of energy optimization of the hybrid energy supply system under different load levels; at the same time, the energy loss of the hybrid energy supply system can be reduced, and the service life of the energy storage system can be improved, so as to improve the economy of the transportation of the transport equipment.

[0044] The advantages of the additional aspects of the present application will be more apparent from the following description part or be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only show some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0046] Figure 1 The energy distribution flowchart of the hybrid energy supply system described in the embodiments of the present application.

[0047] Figure 2 The energy adaptive optimization management strategy flowchart of the hybrid energy supply system described in the embodiments of the present application.

[0048] Figure 3 The cutoff frequency calculation flowchart of the hybrid energy supply system described in the embodiments of the present application. DETAILED DESCRIPTION

[0049] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with the drawings are exemplary and are only used to explain the present application, and cannot be interpreted as a limitation to the present application.

[0050] Those skilled in the art can understand that, unless otherwise defined, all the terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art in the field of the present application.

[0051] It should also be understood that the terms such as those defined in a general dictionary should be understood to have meanings consistent with those in the context of the prior art, and should not be interpreted with an idealized or overly formal meaning unless otherwise defined as such.

[0052] Those skilled in the art can understand that, unless otherwise stated, the singular forms "a", "an" and "the" used herein also include the plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or groups exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0053] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0054] In order to facilitate the understanding of the present application, the present application will be further explained and described in specific embodiments in combination with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present application.

[0055] The person skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily necessary for the implementation of the present application.

[0056] Embodiment 1

[0057] The present embodiment 1 provides an adaptive optimization system for energy management of a hybrid energy supply system, comprising:

[0058] The acquisition module is used to acquire the load power;

[0059] The first calculation module is used to calculate the weight coefficient of the optimization target according to the load power;

[0060] The construction module is used to determine the constraint condition of energy distribution based on the weight coefficient of the optimization target;

[0061] The second calculation module is used to randomly generate the initial position and speed of the optimization factor based on the constraint condition, and iteratively update, and finally calculate the cutoff frequency;

[0062] The distribution module is used to perform energy distribution of the low-frequency power source and the high-frequency power source based on the calculated cutoff frequency.

[0063] In the present embodiment 1, the above system is used to realize an adaptive optimization method for energy management of a hybrid energy supply system, comprising:

[0064] The acquisition module is used to acquire the load power;

[0065] The first calculation module is used to calculate the weight coefficient of the optimization target according to the load power;

[0066] The construction module is used to determine the constraint condition of energy distribution based on the weight coefficient of the optimization target;

[0067] The second calculation module is used to randomly generate the initial position and speed of the optimization factor based on the constraint condition, and iteratively update, and finally calculate the cutoff frequency;

[0068] Finally, the distribution module is used to distribute the energy of the low-frequency power source and the high-frequency power source based on the calculated cutoff frequency; wherein the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and is calculated according to the following formula:

[0069]

[0070] P l = P load *G f

[0071] P h = P load *(1-G f )

[0072] Where f c is the cutoff frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are the reference power of the low-frequency power source and the high-frequency power source, respectively.

[0073] Specifically, the value of the weight coefficient α of the optimization target is determined by the error square value of the load power P load , including:

[0074]

[0075] Where P load (k+1) represents the load power at time k+1, and P load (k+1) represents the load power at time k.

[0076] Based on the weight coefficient of the optimization target, the constraint condition of energy distribution is determined, including:

[0077] Due to the frequent fluctuation of the load, the energy distribution cannot be adaptively optimized when the cutoff frequency f c takes a fixed value, and the energy loss function of the hybrid energy supply system is calculated according to the following formula:

[0078]

[0079] Where R o.bat and R o.SC are the equivalent resistances of the low-frequency power source and the high-frequency power source, respectively; T sis the simulation step size; V bat and V SC are the output voltages of the low-frequency power source and the high-frequency power source, respectively; P bat and P SC represent the output powers of the low-frequency power source and the high-frequency power source, respectively.

[0080] The output powers of the low-frequency power source and the high-frequency power source are calculated according to the following formula:

[0081]

[0082] The constraint condition also includes the service life of the power source when optimizing the energy distribution of the hybrid power supply system, and the multi-objective function optimized according to the following formula:

[0083]

[0084] wherein I bat is the output current of the low-frequency power source; SOC bat and SOC SC are the state of charge of the low-frequency power source and the high-frequency power source, respectively.

[0085] The update of the position and speed of the adaptive optimization algorithm is calculated according to the following formula:

[0086]

[0087] wherein ω is the inertia weight; is the historical optimal solution of the i th th optimization factor; is the global optimal solution; and V i (t) represent the position and speed of the i th th optimization factor, respectively; c1 and c2 represent the optimization speed, which are non-negative constants; r1 and r2 are random numbers between 0 and 1.

[0088] Embodiment 2

[0089] As shown in FIG. 2, the embodiment 2 provides an energy distribution method of a hybrid power system including two power sources of a battery and a super capacitor.

[0090] First, the load power P load driving the load is collected, and according to the collected load power, the reference power P bat of the battery (i.e., the reference power P l of the low-frequency power source) and the reference power P sc of the super capacitor (i.e., the reference power P h of the high-frequency power source) are obtained respectively by combining the adaptive optimization energy management strategy.h ), the battery and the super capacitor respectively get their respective actual reference power P bat_real and P sc_real , both of which together get the actual load power P load_real .

[0091] As shown in Figure 2 , in this embodiment 2, the adaptive optimization energy management strategy is provided, which includes: firstly, collecting the load power P load , then calculating the weight coefficient a according to the collected load power, and then calculating the cutoff frequency f c according to a in combination with an adaptive optimization algorithm, and finally calculating the reference power P bat of the battery and the reference power P sc of the super capacitor according to the cutoff frequency.

[0092] In this embodiment, the constraint condition established when calculating the cutoff frequency includes:

[0093] The load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and is calculated according to the following formula:

[0094]

[0095] P l = P load *G f (2)

[0096] P h = P load *(1-G f ) (3)

[0097] Wherein, f c is the cutoff frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component. P l and P h are the reference powers of the low-frequency power supply and the high-frequency power supply respectively.

[0098] Due to frequent fluctuations of the load, the energy allocated when the cutoff frequency f c takes a fixed value cannot be adaptively optimized for load changes, which may increase the energy loss of the hybrid energy supply system. The energy loss function is calculated according to the following formula:

[0099]

[0100] Wherein, R o.bat and Ro.SC It represents the equivalent resistance of the two power sources; T s It is the simulation step size; V bat and V SC These are the output voltages of two power sources; P bat and P SC The output power of the battery and supercapacitor is calculated using the following formula:

[0101]

[0102] In optimizing the energy distribution of the hybrid energy supply system, in addition to reducing energy loss, this embodiment also considers battery lifespan. The optimized multi-objective function is calculated according to the following formula:

[0103]

[0104] Among them, I bat It is the battery's output current; SOC bat and SOC SC These are the SOC of the battery and the supercapacitor, respectively; α is the weighting coefficient of the optimization objective.

[0105] In this embodiment, the value of α is determined by the squared error of the load fluctuation, calculated according to the following formula:

[0106]

[0107] When the weighting coefficient α is 0, it indicates that the driving load is stable, and only the service life of the hybrid energy supply system needs to be considered. As α increases, the load fluctuation becomes larger. At this time, the weight of loss optimization of the hybrid energy supply system increases, while the weight of service life optimization decreases, thus prioritizing the stable operation of the hybrid energy supply system.

[0108] like Figure 3 As shown in this embodiment, the adaptive optimization algorithm for calculating the cutoff frequency includes:

[0109] The adaptive optimization algorithm updates the position and velocity of the optimization factors according to the following formula:

[0110]

[0111] Where ω is the inertia weight; It is the i-th th The historical optimal solution of each optimization factor; It is the globally optimal solution; and V i (t) Representing the i-th th The position and velocity of the optimization factors; c1 and c2 represent the optimization velocity, which are non-negative constants; r1 and r2 are random numbers between 0 and 1.

[0112] Embodiment 3

[0113] Embodiment 3 of the present application provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implement an adaptive optimization method for energy management of a hybrid power supply system, the method comprising:

[0114] collecting load power;

[0115] calculating a weight coefficient of an optimization target according to the load power;

[0116] determining a constraint condition of energy distribution based on the weight coefficient of the optimization target;

[0117] generating an initial position and speed of a seeking optimization factor randomly based on the constraint condition, and performing iterative updating to finally calculate a cutoff frequency;

[0118] performing energy distribution of a low-frequency power source and a high-frequency power source based on the calculated cutoff frequency; wherein the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and the following formula is used for calculation:

[0119]

[0120] P l = P load * G f

[0121] P h = P load *(1-G f )

[0122] wherein f c is a cutoff frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are reference powers of the low-frequency power source and the high-frequency power source, respectively.

[0123] Embodiment 4

[0124] Embodiment 4 of the present application provides a computer program (product) comprising a computer program for implementing an adaptive optimization method for energy management of a hybrid power supply system when running on one or more processors, the method comprising:

[0125] collecting load power;

[0126] According to the load power, a weight coefficient of an optimization target is calculated;

[0127] Based on the weight coefficient of the optimization target, a constraint condition of energy distribution is determined;

[0128] Based on the constraint condition, an initial position and speed of an optimization factor are randomly generated and iteratively updated, and finally a cutoff frequency is calculated;

[0129] Based on the calculated cutoff frequency, energy distribution of a low-frequency power source and a high-frequency power source is performed; wherein the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and the following formula is used for calculation:

[0130]

[0131] P l = P load * G f

[0132] P h = P load *(1-G f )

[0133] Wherein, f c is the cutoff frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are reference powers of the low-frequency power source and the high-frequency power source respectively.

[0134] Embodiment 5

[0135] Embodiment 5 of the present application provides an electronic device, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute instructions for realizing an adaptive optimization method of energy management of a hybrid energy supply system, and the method comprises:

[0136] Collecting load power;

[0137] According to the load power, a weight coefficient of an optimization target is calculated;

[0138] Based on the weight coefficient of the optimization target, a constraint condition of energy distribution is determined;

[0139] Based on the constraint condition, an initial position and speed of an optimization factor are randomly generated and iteratively updated, and finally a cutoff frequency is calculated;

[0140] Based on the calculated cut-off frequency, energy distribution of the low-frequency power source and the high-frequency power source is performed; wherein the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, and the following formula is used for calculation:

[0141]

[0142] P l = P load * G f

[0143] P h = P load * (1 - G f )

[0144] Wherein, f c is the cut-off frequency of energy filtering; P load is the collected load power; P l is the low-frequency steady-state power component; P h is the high-frequency transient power component; P l and P h are the reference powers of the low-frequency power source and the high-frequency power source respectively.

[0145] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0147] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed aspects can be implemented on a single device or distributed across several devices. Figure 1

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 The functions of a flow or multiple flows and / or a block or multiple blocks in conjunction with the disclosed aspects can be implemented on a single device or distributed across several devices. Figure 1

[0149] The above description is only a specific implementation of the present application, and is not intended to limit the protection scope of the present application. It should be understood by those skilled in the art that various modifications or changes can be made to the disclosed technical solutions without inventive labor, and all these modifications or changes should be covered within the protection scope of the present application.​​

Claims

1. An adaptive optimization method for energy management of a hybrid energy supply system, characterized in that, include: Collect load power; Calculate the weighting coefficients of the optimization objective based on the load power; Based on the weighting coefficients of the optimization objective, the constraints for energy allocation are determined; Based on the constraints, the initial position and velocity of the optimization factor are randomly generated and iteratively updated to finally calculate the cutoff frequency. Based on the calculated cutoff frequency, energy allocation is performed between the low-frequency and high-frequency power sources; specifically, the load power is divided into a low-frequency steady-state power component and a high-frequency transient power component, calculated according to the following formula: P l =P load *G f P h =P load *(1-G f ) Among them, f c P is the cutoff frequency for energy filtering; load It is the collected load power; P l It is the low-frequency steady-state power component; P h It is a high-frequency transient power component; P l and P h These serve as reference power for the low-frequency power source and the high-frequency power source, respectively.

2. The adaptive optimization method for energy management of a hybrid energy supply system according to claim 1, characterized in that, The value of the weighting coefficient α of the optimization objective is determined by the load power P. load The determination of the squared error value includes: Among them, P load (k+1) represents the load power at time k+1, P load (k+1) represents the load power at time k.

3. The adaptive optimization method for energy management of a hybrid energy supply system according to claim 2, characterized in that, Based on the weighting coefficients of the optimization objective, the constraints for energy allocation are determined, including: Due to frequent load fluctuations, the cutoff frequency f c When a fixed value is used, the allocated energy cannot adaptively optimize for load changes. The energy loss function of the hybrid energy supply system is calculated according to the following formula: Among them, R o.bat and R o.SC These are the equivalent resistances of the low-frequency power source and the high-frequency power source, respectively; T s It is the simulation step size; V bat and V SC These are the output voltages of the low-frequency power source and the high-frequency power source, respectively, P bat and P SC These represent the output power of the low-frequency power source and the high-frequency power source, respectively.

4. The adaptive optimization method for energy management of a hybrid energy supply system according to claim 3, characterized in that, The output power of low-frequency and high-frequency power sources is calculated according to the following formula:

5. The adaptive optimization method for energy management of a hybrid energy supply system according to claim 3, characterized in that, The constraints also include: the lifespan of the power source when optimizing the energy distribution of the hybrid energy supply system, and the multi-objective function for its optimization is calculated according to the following formula: Among them, I bat It is the output current of the low-frequency power source; SOC bat and SOC SC These are the states of charge of the low-frequency power source and the high-frequency power source, respectively.

6. The adaptive optimization method for energy management of a hybrid energy supply system according to claim 5, characterized in that, The adaptive optimization algorithm updates the position and velocity of the optimization factors according to the following formula: Where ω is the inertia weight; It is the i-th th The historical optimal solution of each optimization factor; It is the globally optimal solution; and V i (t) Representing the i-th th The position and velocity of the optimization factors; c1 and c2 represent the optimization velocity, which are non-negative constants; r1 and r2 are random numbers between 0 and 1.

7. An adaptive optimization system for energy management of a hybrid energy supply system, characterized in that, include: The acquisition module is used to acquire load power. The first calculation module is used to calculate the weighting coefficients of the optimization target based on the load power; A module is built to determine the constraints for energy allocation based on the weighting coefficients of the optimization objective; The second calculation module is used to randomly generate the initial position and velocity of the optimization factor based on the constraints, and to perform iterative updates to finally calculate the cutoff frequency. The allocation module is used to allocate energy between low-frequency and high-frequency power sources based on the calculated cutoff frequency; wherein, the load power is divided into low-frequency steady-state power components and high-frequency transient power components, calculated according to the following formula: P l =P load *G f P h =P load *(1-G f ) Among them, f c P is the cutoff frequency for energy filtering; load It is the collected load power; P l It is the low-frequency steady-state power component; P h It is a high-frequency transient power component; P l and P h These serve as reference power for the low-frequency power source and the high-frequency power source, respectively.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the adaptive optimization method for energy management of a hybrid energy supply system as described in any one of claims 1-6.

9. A computer program product, characterized in that, Includes a computer program, which, when run on one or more processors, is used to implement an adaptive optimization method for energy management of a hybrid energy supply system as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the adaptive optimization method for energy management of a hybrid energy supply system as described in any one of claims 1-6.

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