Comprehensive power system energy configuration parameter optimization design method, system and medium
By improving the Golden Jackal optimization algorithm and combining it with the position update mechanism of the mountaineering team and moss growth optimization algorithms, the problems of local optima and slow convergence of the Golden Jackal optimization algorithm in the energy configuration parameter optimization design of integrated power systems are solved, and efficient energy configuration parameter optimization and transmission efficiency are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINESE PEOPLES LIBERATION ARMY ARMY SERVICES UNIVERSITY
- Filing Date
- 2026-01-27
- Publication Date
- 2026-05-08
AI Technical Summary
Existing Golden Jackal optimization algorithms are prone to getting stuck in local optima and have low convergence accuracy in the optimization design of integrated power system energy configuration parameters, failing to obtain the optimal energy configuration parameters and resulting in unsatisfactory design results.
The location update mechanism of the mountaineering team optimization algorithm and the moss growth optimization algorithm is introduced to improve the location update method of the golden jackal optimization algorithm in the exploration and development stages. The global search capability and convergence speed are improved by comprehensively considering factors such as switching probability, average golden jackal location, propagation distance and intensity.
It enables the rapid generation of optimal integrated power system energy configuration parameters, improves transmission efficiency and design effectiveness, avoids local optima and premature convergence problems, and enhances the algorithm's global search capability and convergence speed.
Smart Images
Figure CN121997746A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated power system technology, specifically to a method, system, and medium for optimizing the design of energy configuration parameters of an integrated power system. Background Technology
[0002] Vehicle Integrated Power Systems (VIPs) are systems that integrate multiple electrical energy sources and transmission mechanisms to support the power needs of vehicles such as electric vehicles (EVs) or hybrid electric vehicles (HEVs). To ensure efficient vehicle operation, system design involves multiple fundamental parameters and energy configuration parameters to be optimized. Optimization of VIP energy configuration parameters is one of the key technologies for victory on land-based platforms; therefore, in-depth research into the characteristics of VIP energy configurations and conducting research on the optimization design of VIP energy configuration parameters is urgent and necessary.
[0003] Intelligent optimization algorithms are a hot topic in artificial intelligence research and have been widely applied in the optimization design of the power industry. Among them, the Golden Jackal Optimization Algorithm (GJOA), a novel metaheuristic algorithm proposed in 2022, is a new type of intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the integrated matching design problem of comprehensive power systems. However, the Golden Jackal Optimization Algorithm still has some drawbacks, making it prone to getting trapped in local optima and exhibiting low convergence accuracy. When optimizing the energy configuration parameters of comprehensive power systems, it often fails to obtain the optimal energy configuration parameters in the optimization direction, resulting in suboptimal design outcomes. Summary of the Invention
[0004] This invention provides a method for optimizing the energy configuration parameters of an integrated power system. This method can quickly generate the optimal design scheme for the energy configuration parameters of an integrated power system. The integrated power system constructed by this design scheme can achieve the optimal transmission efficiency.
[0005] Specifically, the following steps are included: Determine the energy configuration parameters to be optimized based on the basic parameters of the integrated power system; The constraints of the energy configuration parameters are determined, and the energy transmission efficiency of the integrated power system is used as the objective function. The fitness value is obtained according to the objective function. The design parameters are optimized by an improved Golden Jackal optimization algorithm to obtain the energy configuration parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanism of the mountaineering team optimization algorithm and the moss growth optimization algorithm in the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of the Golden Jackal and comprehensively considers the factors of selecting different position update modes according to the switching probability, the optimal position of the Golden Jackal in this iteration, the average position of the Golden Jackal, the propagation distance of the Golden Jackal, and the intensity of the Golden Jackal for the next generation of position update. The optimal energy configuration parameters of the integrated power system are determined based on the optimal energy configuration parameters.
[0006] Preferably, the location update during the exploration phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals is as follows: ; ; In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; ; ; ; In the formula: This represents the average position of the golden jackal in the current t-th iteration; The spread distance of the golden jackal; The parameter is a constant, set to 2; A random number within the range [0,1]; Q The strength of the golden jackal; The formula for updating the position of the golden jackal has been improved as follows: ; In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
[0007] Preferably, the position update during the development phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals is as follows: ; ; In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; The formula for updating the golden jackal's position is as follows: ; In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
[0008] Preferably, the basic parameters of the integrated power system include power supply system parameters, motor system parameters, power electronic system parameters, and integrated management system parameters; The power system parameters include battery capacity, battery voltage, charging power, battery charging and discharging efficiency, and battery life. The motor system parameters include motor power, motor efficiency, and motor torque; The power electronic system parameters include inverter efficiency and converter efficiency; The integrated management system parameters include energy management system parameters and thermal management system parameters.
[0009] Preferably, the optimization of the energy configuration parameters includes matching battery power parameters and motor power parameters, optimizing thermal management system parameters, controlling energy output and charging process parameters of power system parameters and other multi-source energy system parameters, and controlling energy distribution parameters of the energy management system.
[0010] Preferably, the construction of the objective function includes the following steps: Based on the basic parameters and energy configuration parameters of the integrated power system, determine the charging and discharging efficiency model of the battery, the efficiency model of the motor, the efficiency model of the inverter, the efficiency model of the converter, and the energy recovery efficiency model of the regenerative braking. A total transmission efficiency calculation model is constructed based on the battery charging and discharging efficiency model, the motor efficiency model, the inverter efficiency model, the converter efficiency model, and the regenerative braking energy recovery efficiency model. Based on the overall transmission efficiency calculation model, maximizing transmission efficiency is used as the optimization objective for energy configuration parameters.
[0011] This invention also proposes a design system for an integrated power system, the system comprising: processor; Memory, on which computer programs that can run on a processor are stored; Among them, the steps of the integrated power system energy configuration parameter optimization design method implemented when the computer program is executed by the processor.
[0012] The present invention also proposes a computer-readable storage medium storing a data processing program, wherein the steps of a comprehensive power system energy configuration parameter optimization design method are implemented when the data processing program is executed by a processor.
[0013] The beneficial effects of this invention are: This invention proposes an optimization design method for integrated power system energy configuration parameters. This method uses an improved Golden Jackal optimization algorithm to optimize the integrated power system energy configuration parameters, and constructs the optimal integrated power system energy configuration parameter optimization design scheme based on the optimization results. Based on this system architecture, the best transmission efficiency can be achieved.
[0014] In the exploration phase of the improved Golden Jackal optimization algorithm, to more effectively enhance the algorithm's global search capability, a position update mechanism combining the mountaineering team optimization algorithm and the moss growth optimization algorithm is introduced. This improves the Golden Jackal position update method by comprehensively considering factors such as selecting different position update modes based on switching probabilities, the optimal position of the Golden Jackal in this iteration, the average position of the Golden Jackal, the Golden Jackal's propagation distance, and the Golden Jackal's intensity when updating the Golden Jackal position. This avoids local optima in each iteration, thereby improving the algorithm's global search capability, effectively covering the entire problem search space, and enabling the acquisition of better comprehensive power system target parameters.
[0015] Furthermore, during the development of the improved Golden Jackal optimization algorithm, in order to more effectively improve the convergence speed of the algorithm, the position update mechanism of the mountaineering team optimization algorithm and the moss growth optimization algorithm was introduced to improve the Golden Jackal position update method, avoid the rapid loss of population diversity, and prevent the algorithm from converging to a local optimum too early, thereby improving the convergence speed of the algorithm and quickly and efficiently locating the optimal integrated power system energy configuration parameters. Attached Figure Description
[0016] Figure 1 This is a flowchart of the integrated power system energy configuration parameter optimization design method according to an embodiment of the present invention; Figure 2 This is an execution flowchart of the improved golden jackal optimization algorithm according to an embodiment of the present invention; Figure 3 This is the iterative process curve of an embodiment of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0018] Example 1 The Golden Jackal Optimization Algorithm (GJOA) is a novel metaheuristic algorithm proposed in 2022. It is a new intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the integrated power system matching design problem. However, the GJOA still has some shortcomings: (1) Exploration stage: The GJOA cannot effectively cover the entire problem search space, which may lead to the algorithm getting stuck in local optima. (2) Development stage: The GJOA converges slowly due to its overly conservative local search strategy. For example, it over-relies on the optimal solution, and the population diversity may be rapidly lost, causing the algorithm to converge to a local optimum prematurely, resulting in poor adaptability to solving complex problems. Based on these two key shortcomings, the GJOA cannot achieve the best design effect when used for integrated power system matching design. Therefore, this invention proposes an integrated power system energy configuration parameter optimization design method, the flowchart of which is as follows: Figure 1 As shown, the specific steps are as follows:
[0019] S1: Determine the energy configuration parameters to be optimized based on the basic parameters of the integrated power system.
[0020] S2: Determine the constraints on the energy configuration parameters, using the energy transmission efficiency of the integrated power system as the objective function. Calculate the fitness value based on the objective function, and optimize the design parameters using an improved Golden Jackal optimization algorithm to obtain the energy configuration parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm incorporates the location update mechanisms of the mountaineering team optimization algorithm and the moss growth optimization algorithm from the exploration and development stages of the original Golden Jackal optimization algorithm. This improves the Golden Jackal location update method and comprehensively considers factors such as selecting different location update modes based on the switching probability, the optimal location of the Golden Jackal in this iteration, the average location of the Golden Jackal, the propagation distance of the Golden Jackal, and the intensity of the Golden Jackal for the next generation of location updates.
[0021] S3: Determine the optimal design scheme for the energy configuration parameters of the integrated power system based on the optimal energy configuration parameters.
[0022] Specifically, such as Figure 2 As shown, the improved golden jackal optimization algorithm includes the following steps to optimize the parameters: S2.1: Determine the energy configuration parameters to be optimized based on the basic parameters of the integrated power system. Specifically, the basic parameters of the integrated power system include power system parameters, motor system parameters, power electronic system parameters, and integrated management system parameters. Power system parameters include battery capacity, battery voltage, charging power, battery charging and discharging efficiency, and battery life; motor system parameters include motor power, motor efficiency, and motor torque; power electronic system parameters include inverter efficiency and converter efficiency; and integrated management system parameters include energy management system parameters and thermal management system parameters.
[0023] Optimization of energy configuration parameters includes matching battery power parameters and motor power parameters, optimizing thermal management system parameters, power system parameters and other multi-source energy system parameters, control parameters for energy output and charging process, and control parameters for energy distribution in the energy management system.
[0024] S2.2: Construct the objective function, specifically including: S2.2.1: Determine the battery charging and discharging efficiency model, the motor efficiency model, the inverter efficiency model, the converter efficiency model, and the regenerative braking energy recovery efficiency model based on the basic parameters and energy configuration parameters of the integrated power system.
[0025] S2.2.2: Construct a total transmission efficiency calculation model based on the battery charging and discharging efficiency model, the motor efficiency model, the inverter efficiency model, the converter efficiency model, and the regenerative braking energy recovery efficiency model.
[0026] S2.2.3: Based on the overall transmission efficiency calculation model, the goal of optimizing the energy configuration parameters is to maximize the transmission efficiency.
[0027] S2.3: Configure parameters, mainly including: population size (i.e., the number of golden jackals) N; maximum number of iterations (i.e., the condition for stopping the iteration). T ; Lower boundary of prey optimization ; The upper boundary of prey optimization .
[0028] S2.4: Initialize the location of the golden jackal population: (1); In the formula: This indicates the location of the initial golden jackal population. It is a random number in the range [0,1]. and These are the upper and lower boundaries of the problem to be solved; In the GJOA algorithm, the prey matrix is represented as: (2); In the formula: For the prey matrix; Let j be the position of the i-th prey in the j-th dimension; The winners of the first and second place (the one with the best and second best fitness values) are paired together as the golden jackal pair; n is the number of prey; d is the dimension of the problem solution.
[0029] S2.5: Calculate the fitness value of the prey based on the objective function. The prey with the best fitness value is designated as the male golden jackal, and the prey with the second best fitness value is designated as the female golden jackal.
[0030] During the optimization process, the fitness value of each prey is estimated using a fitness (objective) function, and the fitness value matrix of all prey is represented as follows: (3); In the formula: This is the fitness value matrix of the prey; The fitness function or objective function is used; the jackal with the best fitness value is designated as the male, and the jackal with the second best fitness value is designated as the female. The location of the prey obtained by each jackal is determined.
[0031] S2.6: Calculate the prey escape energy E. If the prey escape energy |E|≥ 1, proceed to the exploration stage and calculate the prey's location; otherwise, proceed to the development stage.
[0032] S2.6.1: Searching for Prey (Exploration Phase) Just as is their nature, golden jackals know how to sense and follow prey, but occasionally the prey escapes unnoticed. Therefore, the golden jackals wait and search for other prey. The hunting is led by the male golden jackal. The female golden jackal follows the male:
[0033] To more effectively improve the algorithm's global search capability, a position update mechanism combining the mountaineering team optimization algorithm and the moss growth optimization algorithm is introduced to improve the golden jackal position update method. This method comprehensively considers factors such as selecting different position update modes based on switching probabilities, the optimal position of the golden jackal in this iteration, the average position of the golden jackal, the propagation distance of the golden jackal, and the intensity of the golden jackal when updating its position. This avoids local optima in each iteration, thereby improving the algorithm's global search capability and effectively covering the entire problem search space.
[0034] The mountaineering team optimization algorithm simulates a group of "team members" collaboratively searching the solution space. Each member not only chooses a suitable path based on the current solution but also leverages the cooperation of other members to improve search efficiency. Compared to other traditional optimization algorithms, the mountaineering team optimization algorithm exhibits unique advantages in exploring the search space. Its most significant feature is its ability to broadly explore the solution space through the collaborative efforts of multiple searchers (i.e., "team members"). Traditional single-searcher optimization methods are often susceptible to local search traps, making it difficult to escape local optima. However, by simulating the collective action of multiple team members, the mountaineering team optimization algorithm fully utilizes the search capabilities of each member at different positions and states, avoiding over-reliance on a fixed path. This distributed search approach enhances the algorithm's global exploration capability, enabling it to find potential excellent solutions in a larger solution space.
[0035] The moss growth optimization algorithm exhibits significant advantages in terms of search space. This algorithm simulates the biological processes by which mosses expand their populations in the natural environment through various means, including spore dispersal, asexual reproduction, and sexual reproduction. During the search process, the algorithm generates multiple initial solutions and simulates the asexual reproduction mechanism of moss, allowing these solutions to rapidly spread throughout the search space and form extensive coverage. This multi-starting point, multi-path search strategy effectively avoids the pitfalls of traditional optimization algorithms that are prone to getting trapped in local optima. Furthermore, the moss growth optimization algorithm introduces a sexual reproduction mechanism, further enhancing its exploration capabilities within the search space through crossover and mutation operations. This mechanism enables the algorithm to continuously approach better regions while maintaining population diversity, thereby achieving comprehensive coverage and efficient utilization of the search space.
[0036] Therefore, during the exploration phase of GJOA, the mountaineering team optimization algorithm and the moss growth optimization algorithm were introduced. The update mechanism of the team collaboration rescue phase in the mountaineering team optimization algorithm was incorporated into the algorithm, taking into account the average value of all team members, that is, the average value of the golden jackal position in the t-th iteration. The core parameter of the wind-borne diffusion characteristics of moss spores in the moss growth optimization algorithm, namely the propagation distance, was also incorporated into the algorithm to balance the global exploration and local development capabilities of the algorithm.
[0037] The improved formula for updating the positions of male and female golden jackals is as follows: (4); (5); In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let t be the position of the golden jackal after the t-th iteration.
[0038] (6); (7); (8); In the formula: This represents the average position of the golden jackal in the current t-th iteration; The spread distance of the golden jackal; The parameter is a constant, set to 2; A random number within the range [0,1]; Q The strength of the golden jackal.
[0039] The escape energy of prey can be calculated using the following formula: (9); This indicates the process of the prey's energy decreasing. This indicates the initial energy state of the prey.
[0040] (10); In the formula: r is a random number in the range [0,1].
[0041] (11); In the formula: T is the maximum number of iterations; Let be a constant, taking the value 1.5; t is the current iteration number. Throughout the entire iteration process, It decreases linearly from 1.5 to 0.
[0042] In formulas (4) and (5), A random number based on the Lévy distribution can be calculated using the following formula: (12); This is the Lévy flight function, and its calculation method is as follows: (13); In the formula: and A random number within the range (0,1); This is a default constant with a value of 1.5.
[0043] In summary, the formula for updating the position of the golden jackal is improved as follows: (14); In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
[0044] S2.6.2: Development Phase During the development of GJOA, in order to more effectively improve the convergence speed of the algorithm, the position update mechanism of the mountaineering team optimization algorithm and the moss growth optimization algorithm was introduced to improve the golden jackal position update method. The update mechanism comprehensively considers factors such as selecting different position update modes based on the switching probability, the optimal position of the golden jackal in this iteration, the average position of the golden jackal, the propagation distance of the golden jackal, and the strength of the golden jackal to update the position. This avoids the possibility of rapid loss of population diversity, which could cause the algorithm to converge to a local optimum too early, thereby improving the convergence speed of the algorithm.
[0045] During the development phase of GJOA, the mountaineering team optimization algorithm and the moss growth optimization algorithm were introduced. The update mechanism of the team collaborative rescue phase in the mountaineering team optimization algorithm was incorporated into the algorithm, considering the average value of all team members, i.e., the average value of the golden jackal's position in the t-th iteration. The core parameter of the wind-borne diffusion characteristics of moss spores in the moss growth optimization algorithm, i.e., the propagation distance, was also incorporated into the algorithm, balancing the algorithm's global exploration and local exploitation capabilities. The improved position update formulas for male and female golden jackals are as follows:
[0046] (15); (16); In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; The formula for updating the golden jackal's position is as follows: (17); In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
[0047] S2.7: Determine if the stopping condition is met. If not, repeat steps S2.5-S2.7. Otherwise, output the optimal prey, i.e. the optimal integrated power system matching design scheme.
[0048] In this embodiment: Using MATLAB as the simulation platform, the Golden Jackal Optimization Algorithm (GJOA) was compared with the IGJOA method proposed in this paper. To ensure the fairness of the experiment, the population size of all algorithms was set to 30, and the maximum number of iterations was set to 300. Figure 3 The curve represents the iterative process. From... Figure 3 The simulation results clearly show that the IGJOA method converges faster than the GJOA algorithm, and its convergence accuracy is superior. Simulation results also demonstrate that the IGJOA algorithm has stronger search capabilities and yields a better integrated power system energy configuration parameter optimization design scheme, thus validating the algorithm's effectiveness.
[0049] The above is an embodiment of the integrated power system energy configuration parameter optimization design method provided in this example. Based on the same idea, this embodiment also provides a corresponding integrated power system design system. Specific limitations of the integrated power system design system can be found in the limitations of the integrated power system energy configuration parameter optimization design method described above, and will not be repeated here. Each module in the above integrated power system design system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0050] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1The proposed method is an optimization design method for the energy configuration parameters of a comprehensive power system.
[0051] Those skilled in the art will understand that implementing all or part of the processes in the methods of the above embodiments can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0052] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for optimizing the energy configuration parameters of a comprehensive power system, characterized in that, Includes the following steps: Determine the energy configuration parameters to be optimized based on the basic parameters of the integrated power system; The constraints of the energy configuration parameters are determined, and the energy transmission efficiency of the integrated power system is used as the objective function. The fitness value is obtained according to the objective function. The design parameters are optimized by an improved Golden Jackal optimization algorithm to obtain the energy configuration parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanism of the mountaineering team optimization algorithm and the moss growth optimization algorithm in the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of the Golden Jackal and comprehensively considers the factors of selecting different position update modes according to the switching probability, the optimal position of the Golden Jackal in this iteration, the average position of the Golden Jackal, the propagation distance of the Golden Jackal, and the intensity of the Golden Jackal for the next generation of position update. The optimal energy configuration parameters of the integrated power system are determined based on the optimal energy configuration parameters.
2. The method for optimizing the energy configuration parameters of a comprehensive power system according to claim 1, characterized in that, The location update during the exploration phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals is as follows: ； ； In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; ； ； ； In the formula: This represents the average position of the golden jackal in the current t-th iteration; The spread distance of the golden jackal; The parameter is a constant, set to 2; A random number within the range [0,1]; Q The strength of the golden jackal; The final formula for updating the position of the golden jackal is improved as follows: ； In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
3. The method for optimizing the energy configuration parameters of a comprehensive power system according to claim 2, characterized in that, The position update during the development phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals is as follows: ； ； In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; The formula for updating the golden jackal's position is as follows: ； In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].
4. The integrated power system energy configuration parameter optimization design method according to claim 3, characterized in that, The basic parameters of the integrated power system include power supply system parameters, motor system parameters, power electronic system parameters, and integrated management system parameters; The power system parameters include battery capacity, battery voltage, charging power, battery charging and discharging efficiency, and battery life. The motor system parameters include motor power, motor efficiency, and motor torque; The power electronic system parameters include inverter efficiency and converter efficiency; The integrated management system parameters include energy management system parameters and thermal management system parameters.
5. The integrated power system energy configuration parameter optimization design method according to claim 4, characterized in that, The optimization of energy configuration parameters includes matching battery power parameters and motor power parameters, optimizing thermal management system parameters, controlling energy output and charging process parameters with other multi-source energy system parameters, and controlling energy distribution parameters of the energy management system.
6. The method for optimizing the energy configuration parameters of a comprehensive power system according to claim 5, characterized in that, The construction of the objective function includes the following steps: Based on the basic parameters and energy configuration parameters of the integrated power system, determine the charging and discharging efficiency model of the battery, the efficiency model of the motor, the efficiency model of the inverter, the efficiency model of the converter, and the energy recovery efficiency model of the regenerative braking. A total transmission efficiency calculation model is constructed based on the battery charging and discharging efficiency model, the motor efficiency model, the inverter efficiency model, the converter efficiency model, and the regenerative braking energy recovery efficiency model. Based on the overall transmission efficiency calculation model, maximizing transmission efficiency is used as the optimization objective for energy configuration parameters.
7. A design system for an integrated power system, characterized in that the system... include: processor; A memory on which computer programs that can run on the processor are stored; When the computer program is executed by the processor, it implements the steps of the integrated power system energy configuration parameter optimization design method as described in any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the steps of the integrated power system energy configuration parameter optimization design method as described in any one of claims 1 to 6.