A genetic algorithm-optimized method and device for improving energy efficiency of dynamically reconfigurable batteries

Abstract

The present application discloses a method and device for improving the energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm, which relates to the field of dynamic reconfigurable battery regulation. The method includes: predicting the net energy loss in a preset time period through simulation, and randomly generating multiple groups of control strategies for the next reconstruction cycle as the initial population of the genetic algorithm. Taking minimizing the net energy loss as the objective function, the penalty function method is used to construct the fitness function in combination with the safe operating range constraints of the battery unit working current and terminal voltage. The candidate solutions are iteratively optimized using a genetic algorithm, and the optimal solution is finally selected as the target control strategy for the next reconstruction cycle. The present application uses a genetic algorithm to formulate an accurate target control strategy while meeting safety constraints, and minimizes the system energy loss through intelligent optimization, thereby improving the accuracy of the control strategy of the dynamic reconfigurable battery system, solving the problem of lag in the control process, and thus improving the energy efficiency of the dynamic reconfigurable battery network.

Description

Technical Field

[0001] The present application relates to the field of dynamic reconfigurable battery regulation, and in particular to a method and device for improving the energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm. Background Art

[0002] Energy storage enables rapid and dynamic matching between power generation and load curves, smoothing fluctuations, matching supply and demand, shifting peaks and filling valleys, and improving power quality. It is a core component of the Energy Internet. With the rapid development of energy storage, its safety and economic benefits have also attracted considerable attention. However, traditional energy storage systems, limited by their fixed series-parallel battery network topology, suffer from the "short-board effect," meaning that the overall performance of the battery system depends on the weakest battery module. When the weakest battery module reaches its charge or discharge limit, the entire battery system must shut down, resulting in inadequate utilization of the available energy in other modules and reduced system efficiency. Energy efficiency is defined as the ratio of the maximum available power of a battery energy storage system to its fully charged capacity: energy efficiency of a battery energy storage system = maximum available power consumption / fully charged capacity. Higher system efficiency indicates less vulnerability to the "short-board effect" and better system balancing and consistency.

[0003] To overcome the "short board effect" of battery systems, the Dynamic Reconfigurable Battery Network (DRBN) has garnered widespread attention from researchers. DRBNs deeply couple batteries with power electronic switches and employ time-division multiplexing to achieve battery energy control. Specifically, DRBNs regulate module energy by varying the time each battery module is connected to the network for charging and discharging. For example, when a module's energy is higher than that of other modules, the DRBN prolongs its discharge time, allowing it to release more power, thereby reducing the energy gap between modules. During operation, the DRBN controller must implement functions such as balancing control, safety control, and energy efficiency improvement. However, in some cases, the control strategy of a dynamically reconfigurable battery system lacks accuracy and the control process exhibits lag, resulting in high energy loss and low energy efficiency in the dynamically reconfigurable battery network. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm, which can improve the accuracy of the control strategy of the dynamically reconfigurable battery system, solve the problem of control process lag, and thus improve the energy efficiency of the dynamically reconfigurable battery network.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In the first aspect, the present application provides a method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm, and the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm includes: simulating and predicting a dynamically reconfigurable battery network to obtain the net energy loss of the dynamically reconfigurable battery network in a preset time period; the dynamically reconfigurable battery includes multiple battery cells distributed in a matrix form; the net energy loss in the preset time period is the sum of the net energy losses of multiple reconstruction cycles; randomly generating multiple groups of control strategies for the next reconstruction cycle of the dynamically reconfigurable battery network; using all control strategies as the initialization population to obtain multiple candidate solutions; minimizing the net energy loss in the preset time period as the objective function, and using the safe operating range of the battery cell working current and terminal voltage as constraints, and using the penalty function method to construct the fitness function of the genetic algorithm; based on the fitness function, using the genetic algorithm to iteratively calculate multiple candidate solutions to obtain the optimal solution, and using the optimal solution as the target control strategy for the next reconstruction cycle of the dynamically reconfigurable battery network.

[0007] In the second aspect, the present application provides a dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization, the dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization is connected to the dynamic reconfigurable battery, the dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization includes: a digital energy exchange system and a host computer; the digital energy exchange system is respectively connected to the host computer and the dynamic reconfigurable battery network; the digital energy exchange system is used to apply the dynamic reconfigurable battery energy efficiency improvement method optimized by the genetic algorithm described above to obtain the target control strategy, use the target control strategy to control the switching state of the dynamic reconfigurable battery network, and upload the target control strategy to the host computer.

[0008] In a third aspect, the present application provides a computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned genetic algorithm-optimized dynamic reconfigurable battery energy efficiency improvement method.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned genetic algorithm-optimized dynamic reconfigurable battery energy efficiency improvement method.

[0010] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned genetic algorithm-optimized dynamic reconfigurable battery energy efficiency improvement method.

[0011] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0012] This application predicts the net energy loss of a preset time period through simulation, and randomly generates multiple groups of control strategies for the next reconstruction cycle as the initial population of the genetic algorithm. Taking minimizing the net energy loss as the objective function, combined with the safe operating range constraints of the battery unit working current and terminal voltage, the penalty function method is used to construct the fitness function. On this basis, the genetic algorithm is used to iteratively optimize the candidate solutions, and finally the optimal solution is selected as the target control strategy for the next reconstruction cycle. This application uses a genetic algorithm to formulate an accurate target control strategy while meeting safety constraints, and minimizes the system energy loss through intelligent optimization, thereby improving the accuracy of the control strategy of the dynamically reconfigurable battery system, solving the problem of control process lag, and thus improving the energy efficiency of the dynamically reconfigurable battery network. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A schematic diagram of a method for improving energy efficiency of a dynamically reconfigurable battery using genetic algorithm optimization provided in an embodiment of the present application Figure 1 .

[0015] Figure 2 A schematic diagram of a method for improving energy efficiency of a dynamically reconfigurable battery using genetic algorithm optimization provided in an embodiment of the present application Figure 2 .

[0016] Figure 3 A schematic diagram of the structural connection of a dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization provided in the embodiment of the present application Figure 1 .

[0017] Figure 4 A schematic diagram of the structural connection of a dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization provided in the embodiment of the present application Figure 2 .

[0018] Figure 5 A schematic diagram of the architecture of a dynamically reconfigurable battery network provided in an embodiment of the present application.

[0019] Figure 6 A schematic diagram of the statistical results of the battery internal resistance of the dynamically reconfigurable battery network provided in an embodiment of the present application.

[0020] Figure 7 A schematic diagram of the statistical results of the available battery capacity of the dynamically reconfigurable battery network provided in an embodiment of the present application.

[0021] Figure 8 This is a graph showing the change in terminal voltage of each battery cell over time during the charging process of a fixed series-parallel battery network provided in an embodiment of the present application.

[0022] Figure 9 This is a graph showing the change in terminal voltage of each battery cell over time during the charging process of the dynamically reconfigurable battery network provided by an embodiment of the present application.

[0023] Figure 10 This is a graph showing the change in terminal voltage of each battery cell over time during the discharge process of a fixed series-parallel battery network provided in an embodiment of the present application.

[0024] Figure 11 This is a graph showing the change in terminal voltage of each battery cell over time during the discharge process of the dynamically reconfigurable battery network provided in an embodiment of the present application.

[0025] Figure 12 A comparison chart of the operating time of a fixed series-parallel battery network and a dynamically reconfigurable battery network provided in an embodiment of the present application.

[0026] Figure 13 A schematic diagram of the structure of a computer system provided in an embodiment of the present application.

[0027] Figure numerals: digital energy exchange system-1; host computer-2. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] First, the professional titles in this application are explained as follows.

[0030] Dynamically reconfigurable battery network, different from the traditional fixed series superposition battery management system solution, the dynamic reconfigurable battery network (DRBN) is based on the digitalization of battery energy. It realizes the discretization of battery energy by deeply coupling battery cells with low-voltage and low-power power electronic devices, and realizes digital energy management at the battery module level through a digital energy exchange system. Its architecture is shown in the attached figure. Figure 5As shown. The difference between the DRB energy storage system and the traditional battery energy storage system is that the DRBN achieves the discretization and digitization of battery energy by turning on and off switches, and achieves battery energy balance by controlling the charge and discharge time of different battery cells. Therefore, the DRBN no longer requires the balancing circuit in the traditional battery energy storage system. In addition, the digital energy exchange system (i.e., the control device of the DRBN) can achieve controllable parallel connection between battery cells, thereby eliminating the circulation problem caused by the direct parallel connection of battery cells. In the DRBN, the battery cells are controlled in parallel, and the concept of battery clusters in the traditional sense does not exist. Therefore, the DRBN does not require a DC-DC converter, which greatly reduces the system cost and improves the system efficiency.

[0031] The Digital Energy Switch System (DESS) is the control module of the DRB energy storage system, responsible for battery network status monitoring, consistency control, and safety protection. The DESS provides measurement, calculation, control, and protection capabilities. First, battery data measured by voltage, current, and temperature sensors is transmitted to the DESS via a data bus. Next, the DESS evaluates battery performance based on known battery status information, such as state of charge (SOC) and state of health (SOH). The DESS then develops a charge and discharge plan based on load demand, ensuring battery consistency and electrical and thermal safety while meeting the load. Control signals are then sent to each switch via the data bus. Furthermore, the DESS can promptly monitor abnormal battery network conditions. If a battery experiences electrical, thermal, or power abuse, or other abnormalities, the DESS can manipulate the switch to eliminate the fault immediately.

[0032] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0033] Example 1, as Figure 1-Figure 2 As shown, this embodiment provides a method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm. The method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm includes the following steps.

[0034] S1. Simulate and predict the dynamic reconfigurable battery network to obtain the net energy loss of the dynamic reconfigurable battery network during a preset time period; the dynamic reconfigurable battery includes multiple battery cells distributed in a matrix form; the net energy loss during the preset time period is the sum of the net energy losses of multiple reconfiguration cycles;

[0035] Furthermore, step S1 specifically includes the following steps.

[0036] S11. Simulate and predict the dynamic reconfigurable battery network to obtain the switching state, energy loss, and energy recovery of each battery cell within a preset time period;

[0037] S12. Based on the switching state, energy loss and energy recovery of each battery cell within a preset time period, the switching state matrix, energy loss matrix and energy recovery matrix within each reconstruction cycle are constructed by converting the mapping relationship into a matrix form.

[0038] S13. The net energy loss in each reconstruction cycle is calculated by the Hadamard product according to the switch state matrix, energy loss matrix and energy recovery matrix in each reconstruction cycle;

[0039] S14. Combining the net energy losses in different reconstruction periods to obtain the net energy loss in the preset time period.

[0040] Furthermore, the simulation prediction is obtained through energy loss model prediction.

[0041] In practical applications, the nonlinear effects of battery cells can affect system energy control. These nonlinear effects include current effects and recovery effects. The current effect refers to the decrease in available capacity of a battery cell as the current rate increases, while the recovery effect refers to the small amount of capacity recovery after a sudden drop in current to zero. In a DRBN, each battery cell operates in pulsed discharge mode, making these effects more pronounced. Considering these nonlinear effects during operational control helps improve overall system performance.

[0042] DESS reconstructs the topological connection mode of the battery network by controlling the opening and closing of each MOSDET. The switching state of the battery network can be represented by the switch matrix. Indicates, details are as follows.

[0043] .

[0044] in, Indicates the Rank The switch status of the column, 1 means the switch is on, and 0 means the switch is off.

[0045] The optimization goal of the digital energy exchange system is to achieve energy control and improve the energy efficiency of the system. Assuming that the total working time is T, the entire working interval is divided into N reconstruction cycles, and the time of each reconstruction cycle is At the beginning of the reconfiguration cycle, the digital energy exchange system generates the optimal control strategy based on the current battery state and changes the battery topology through the switch array. Since the reconfiguration cycle is very short, the load can be considered constant during this period. Indicates the The switching state of the reconstruction cycle, represents the energy loss matrix of the battery, Indicates the The first of the reconstruction cycles Rank Energy loss of the battery cells in the column, represents the energy recovery matrix of the battery, Indicates the The first of the reconstruction cycles Rank The energy of the battery cells in the column is recovered, then the The energy dissipation matrix of the entire battery system during a reconstruction cycle can be expressed in the form of a Hadamard product, as follows.

[0046] .

[0047] in, is the number of cycles from the beginning of the current reconstruction cycle. The net energy loss of a reconstruction cycle; For the Switch matrix for each reconstruction cycle; For the switch matrix The inverted matrix; For the The energy loss matrix of For the Energy recovery matrix for each reconstruction cycle; It is Hadamard.

[0048] in, and The form is as follows.

[0049] .

[0050] Note that the above analysis describes the net energy loss during the i-th reconstruction cycle. The formula for the net energy loss during the entire charge and discharge cycle is as follows.

[0051] .

[0052] Therefore, the energy control of a dynamically reconfigurable battery energy storage system can be expressed as an optimization problem. The physical meaning of the two constraints is that the battery cells do not over-current and the battery system output voltage remains within a reasonable range.

[0053] .

[0054] Genetic algorithms can be used to solve optimization problems. By setting appropriate initial values, population size, and iteration times, they can quickly and accurately determine the optimal solution to the optimization problem, the switch state matrix. The DESS then sends the calculated reconstruction strategy (i.e., the on / off state instructions for each switch) to the DRBN for execution, achieving optimal operational control and improving overall system energy efficiency.

[0055] S2. Randomly generate multiple sets of control strategies for the next reconstruction cycle of the dynamically reconfigurable battery network.

[0056] S3. Take all control strategies as the initial population and obtain multiple candidate solutions.

[0057] S4. Minimizing the net energy loss in a preset time period is used as the objective function, and the safe operating range of the battery cell operating current and terminal voltage is used as the constraint condition. The penalty function method is used to construct the fitness function of the genetic algorithm.

[0058] Furthermore, the objective function is as follows.

[0059] .

[0060] Where, is the minimum value of net energy loss in the preset time period; The number of times from the current reconstruction cycle The net energy loss of a reconstruction cycle is, .

[0061] Furthermore, the constraints are as follows.

[0062] .

[0063] Where, is a constraint condition; is the first constraint; is the second constraint; is the third constraint; is the sequence number of the constraint condition; For the The first The current of each battery cell; is the maximum allowable current of the battery cell; is the lower limit constraint value of the total output voltage; Output the total voltage of the battery network; is the upper limit constraint value of the total output voltage.

[0064] Furthermore, the fitness function is as follows.

[0065] .

[0066] Where, is the fitness function; is the minimum value of net energy loss in the preset time period; is the penalty coefficient, which is used to represent the degree of penalty; is the penalty power exponent; is a constraint condition.

[0067] in, Generally, a large positive number is taken. When a solution does not satisfy the inequality constraint, the penalty term of the fitness function will add a large positive number to the fitness function, thereby excluding the solution.

[0068] S5. Based on the fitness function, a genetic algorithm is used to iteratively calculate multiple candidate solutions to obtain the optimal solution, and the optimal solution is used as the target control strategy for the next reconstruction cycle of the dynamic reconfigurable battery network.

[0069] Furthermore, step S5 specifically includes the following steps.

[0070] S51. Calculate the fitness of each candidate solution using the fitness function.

[0071] S52. The candidate solution with the highest fitness in the current generation is retained as the target candidate solution, and the remaining candidate solutions are selected, crossovered, and mutated to regenerate the same number of new generation candidate solutions, and iterative calculation is performed until the number of iterations reaches a threshold.

[0072] In actual applications, the fitness of each of the n candidate solutions is calculated, and the best individuals (target candidate solutions) are selected based on their fitness to advance to the next generation. An elite selection strategy is employed, where the best m individuals of the current generation are retained and directly advanced to the next generation, while the remaining nm individuals are generated through other selection methods, crossover, and mutation.

[0073] Crossover is the process of swapping genes from parent individuals to create diverse offspring. This example uses uniform crossover, meaning that for each pair of genes, a crossover is determined based on a certain probability (typically set to 0.6-0.9). For example, if the two parent segments are 110 and 101, and the mask generated based on the crossover probability is 011 (a mask of 1 results in a swap, while a mask of 0 does not), the offspring after the crossover will be 101 and 110.

[0074] Mutation is to maintain the diversity of the population by randomly changing a small number of genes. The mutation operation is done by setting the mutation probability To achieve this, for each binary bit, with the probability Flip its value (usually set to 0.001-0.1). For example, if the original individual's segment is 110 and the third mutation is selected based on the mutation probability, the mutated segment will be 111.

[0075] After crossover and mutation, a new offspring generation is obtained. The offspring population size is the same as that of the parent generation, which is n. Then, the process returns to step S41 and recalculates the fitness, repeating the process again. The iteration terminates when the maximum number of iterations (e.g., 50 to 100 generations) is reached.

[0076] S53. Select the optimal solution with the highest fitness among the target candidate solutions of different generations, and use the optimal solution as the target control strategy for the next reconstruction cycle of the dynamically reconfigurable battery network.

[0077] During the verification process, 64 battery cells were tested for charge and discharge cycles. The results showed that the internal resistance of each battery varied significantly, with some batteries having an internal resistance value twice that of others. The maximum available capacity of the battery can be estimated using a coulomb counting algorithm. Detailed statistics on battery internal resistance and available capacity are as follows: Figure 6-Figure 7 shown.

[0078] To demonstrate the capability of the proposed method, charge and discharge experiments were carried out under both traditional fixed battery topology and dynamically reconfigurable battery topology, and the voltage of 64 battery cells during the charge and discharge process were measured. Figure 8 It can be observed that when using a fixed battery topology, there are significant differences in battery voltage levels during the charging process. To avoid overcharging, the system stops charging early, resulting in most battery cells not being fully charged. In contrast, under a dynamically reconfigurable topology, fully charged battery cells are isolated while the remaining cells continue to charge. Figure 9 As shown, this process is repeated until each battery cell is fully charged.

[0079] This example also shows the performance comparison during the discharge process. Figure 10-12 As shown in the figure, in a fixed battery topology, the difference in battery voltages increases as discharge progresses. Ultimately, to avoid damage to the weakest battery and potential danger, the discharge process is forced to terminate early. In contrast, in the DRB system, thanks to the proposed energy efficiency improvement method, the battery voltages do not show a significant trend of differentiation, resulting in a 33% increase in runtime.

[0080] To verify the energy conversion efficiency of the proposed algorithm in different application scenarios, the proposed energy efficiency improvement method was applied to the aforementioned system. The results were compared with those under a fixed series-parallel strategy. The results are shown in Table 1. Energy efficiency is defined as the ratio of maximum consumable energy to full charge capacity. The results show that the energy efficiency of the proposed method is consistently higher than that of the traditional method under different load currents. When the DRB system adopts the proposed optimization algorithm, the energy efficiency can be improved by 25-30%.

[0081] Table 1 Comparison of battery system energy efficiency under two strategies

[0082]

[0083] The technical effects of this application are as follows.

[0084] This application predicts the net energy loss of a preset time period through simulation, and randomly generates multiple groups of control strategies for the next reconstruction cycle as the initial population of the genetic algorithm. Taking minimizing the net energy loss as the objective function, combined with the safe operating range constraints of the battery unit working current and terminal voltage, the penalty function method is used to construct the fitness function. On this basis, the genetic algorithm is used to iteratively optimize the candidate solutions, and finally the optimal solution is selected as the target control strategy for the next reconstruction cycle. This application uses a genetic algorithm to formulate an accurate target control strategy while meeting safety constraints, and minimizes the system energy loss through intelligent optimization, thereby improving the accuracy of the control strategy of the dynamically reconfigurable battery system, solving the problem of control process lag, and thus improving the energy efficiency of the dynamically reconfigurable battery network.

[0085] Example 2, as Figure 3-Figure 4 As shown, this embodiment also provides a dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization, the dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization is connected to the dynamic reconfigurable battery, and the dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization includes: a digital energy exchange system 1 and a host computer 2.

[0086] The digital energy exchange system 1 is connected to the host computer 2 and the dynamic reconfigurable battery network respectively; the digital energy exchange system 1 is used to apply the above genetic algorithm optimized dynamic reconfigurable battery energy efficiency improvement method to obtain the target control strategy, use the target control strategy to control the switching state of the dynamic reconfigurable battery network, and upload the target control strategy to the host computer 2.

[0087] The following example uses a lithium cobalt phosphate (LpCO) battery with a rated capacity of 10Ah, a maximum allowable discharge current of 120A, and a cutoff voltage of 2.0V. The switching circuit utilizes N-channel MOSFETs, solid-state switching devices with advantages such as low on-resistance, high energy efficiency, strong reliability, and low cost. The entire system is designed and integrated into a standard 19-inch server rack.

[0088] Example 3, the present application also provides a computer system, which can be a server or a terminal, and its internal structure diagram can be as follows Figure 13As shown. The computer system includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer system is used to provide computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer system is used to store processing data. The input / output interface of the computer system is used to exchange information between the processor and an external device. The communication interface of the computer system is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above-mentioned methods are implemented.

[0089] Those skilled in the art will understand that Figure 13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer system to which the solution of the present application is applied. The specific computer system may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0090] In embodiment 4, the present application further provides a computer-readable storage medium storing a computer program, which implements the above methods when executed by a processor.

[0091] Example 5: The present application also provides a computer program product, including a computer program, which implements the above methods when executed by a processor.

[0092] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0093] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm, characterized in that: The method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm includes: Simulating and predicting a dynamically reconfigurable battery network to obtain a net energy loss of the dynamically reconfigurable battery network during a preset time period; the dynamically reconfigurable battery includes a plurality of battery cells distributed in a matrix form; the net energy loss during the preset time period is the sum of the net energy losses of multiple reconfiguration cycles; Randomly generate multiple control strategies for the next reconfiguration cycle of a dynamically reconfigurable battery network; All control strategies are used as the initial population to obtain multiple candidate solutions; Minimizing the net energy loss in a preset time period is used as the objective function, and the safe operating range of the battery cell operating current and terminal voltage is used as the constraint condition. The penalty function method is used to construct the fitness function of the genetic algorithm. Based on the fitness function, a genetic algorithm is used to iteratively calculate multiple candidate solutions to obtain the optimal solution, which is used as the target control strategy for the next reconstruction cycle of the dynamic reconfigurable battery network. The fitness function is: ； Where, is the fitness function; is the minimum value of net energy loss in the preset time period; is the penalty coefficient, which is used to represent the degree of penalty; is the penalty power exponent; is a constraint condition.

2. The method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm according to claim 1, characterized in that: Based on the fitness function, a genetic algorithm is used to iteratively calculate multiple candidate solutions to obtain the optimal solution, which is then used as the target control strategy for the next reconstruction cycle of the dynamically reconfigurable battery network. Specifically, the following steps are involved: Use the fitness function to calculate the fitness of each candidate solution; The candidate solution with the highest fitness in the current generation is retained as the target candidate solution, and the remaining candidate solutions are selected, crossovered, and mutated to regenerate the same number of new generation candidate solutions, and iterative calculations are performed until the number of iterations reaches the threshold; The optimal solution with the highest fitness among target candidate solutions of different generations is selected, and the optimal solution is used as the target control strategy for the next reconstruction cycle of the dynamic reconfigurable battery network.

3. The method for improving energy efficiency of a dynamic reconfigurable battery optimized by genetic algorithm according to claim 1, characterized in that: The objective function is: ； Where, is the minimum value of net energy loss in the preset time period; is the number of cycles from the beginning of the current reconstruction cycle. The net energy loss of a reconstruction cycle is, .

4. The method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm according to claim 1, characterized in that: The constraints are: ； Where, is a constraint condition; is the first constraint; is the second constraint; is the third constraint; is the sequence number of the constraint condition; For the The first The current of each battery cell; is the maximum allowable current of the battery unit; is the lower limit constraint value of the total output voltage; Output the total voltage of the battery network; is the upper limit constraint value of the total output voltage.

5. A dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization, wherein the dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization is connected to a dynamic reconfigurable battery, characterized in that: The dynamic reconfigurable battery energy efficiency improvement device based on genetic algorithm optimization includes: a digital energy exchange system and a host computer; The digital energy exchange system is connected to the host computer and the dynamically reconfigurable battery network respectively; the digital energy exchange system is used to apply the dynamic reconfigurable battery energy efficiency improvement method optimized by the genetic algorithm described in any one of claims 1-4 to obtain a target control strategy, use the target control strategy to control the switching state of the dynamic reconfigurable battery network, and upload the target control strategy to the host computer.

6. A computer system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the genetic algorithm-optimized dynamic reconfigurable battery energy efficiency improvement method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for improving energy efficiency of a dynamic reconfigurable battery optimized by a genetic algorithm according to any one of claims 1 to 4 is implemented.

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