Genetic algorithm optimized dynamic reconfigurable battery energy efficiency improvement method and device

Abstract

The invention discloses a genetic algorithm optimized dynamic reconfigurable battery energy efficiency improvement method and device, and relates to the field of dynamic reconfigurable battery regulation, and the method comprises the steps: predicting the net energy loss of a preset time period through simulation, and randomly generating a plurality of groups of control strategies of a next reconstruction period as an initial population of a genetic algorithm; taking minimum net energy loss as an objective function, combining safe operation interval constraints of working current and terminal voltage of a battery unit, and adopting a penalty function method to construct a fitness function; and performing iterative optimization on the candidate solution by using a genetic algorithm, and finally selecting an optimal solution as a target control strategy of the next reconstruction period. According to the method, the accurate target control strategy is formulated through the genetic algorithm under the condition that the security constraint condition is met, the energy loss of the system is minimized through intelligent optimization, the accuracy of the control strategy of the dynamic reconfigurable battery system is improved, the problem of control process lag is solved, and the energy efficiency of the dynamic reconfigurable battery network is further improved.

Description

Technical Field

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

[0002] Energy storage can achieve a fast dynamic matching between the power generation curve and the load curve, and has the functions of suppressing fluctuations, matching supply and demand, shaving peaks and filling valleys, and improving power supply quality. It is the core device for building an energy Internet. With the rapid development of energy storage, issues such as its safety and economy have also attracted much attention. However, traditional energy storage systems are limited by their fixed series-parallel battery network topology and have the problem of the "short board effect", that is, the overall performance of the battery system depends on the weakest battery module. When the weakest battery module reaches its charge and discharge limit, the entire battery system must stop operating, which will cause the effective battery energy in other battery modules not to be fully utilized, thereby reducing the system energy efficiency. The energy efficiency is defined as the ratio of the maximum available power of the battery energy storage system to the full charge amount, that is, the energy efficiency of the battery energy storage system = the maximum power that the system can consume / the full charge amount of the system. The higher the system energy efficiency, the lower the impact of the "short board effect" on the system, and the better the balance effect and consistency of the system.

[0003] To overcome the "short board effect" of the battery system, the dynamic reconfigurable battery network (DRBN) has received extensive attention from researchers. DRBN deeply couples the battery with power electronic switches and uses the idea of time division multiplexing to achieve energy control of the battery. Specifically, DRBN adjusts the energy of each module by changing the time for each battery module to participate in charge and discharge in the network. For example, when the energy of a module is higher than that of other modules, DRBN will extend the discharge duration of this module to make it discharge more power, thereby reducing the energy gap between modules. The controller of DRBN needs to achieve functions such as balance control, safety control, and energy efficiency improvement during operation. However, in some cases, the control strategy of the dynamic reconfigurable battery system has low accuracy and there is a lag in the control process, which in turn leads to large energy losses and low energy efficiency in the dynamic reconfigurable battery network. Summary of the Invention

[0004] The purpose of the present 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 dynamic reconfigurable battery system, solve the problem of lag in the control process, and thus improve the energy efficiency of the dynamic reconfigurable battery network.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm. 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 a plurality of battery monomers distributed in a matrix form; the net energy loss in the preset time period is the sum of the net energy losses of multiple reconfiguration cycles; randomly generating multiple sets of control strategies for the next reconfiguration cycle of the dynamically reconfigurable battery network; taking all control strategies as an initial population to obtain multiple candidate solutions; taking the minimization of the net energy loss in the preset time period as an objective function, and using the safe operating ranges of the working current and terminal voltage of the battery cells as constraint conditions, constructing a fitness function of the genetic algorithm by using the penalty function method; based on the fitness function, using the genetic algorithm to perform iterative calculations on multiple candidate solutions, obtaining an optimal solution, and taking the optimal solution as the target control strategy for the next reconfiguration cycle of the dynamically reconfigurable battery network.

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

[0008] In a third aspect, the present application provides a computer system, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm described above.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm described above is implemented.

[0010] In a fifth aspect, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm described above is implemented.

[0011] According to the specific embodiments provided by the present application, the following technical effects are disclosed by the present application.

[0012] This application predicts the net energy loss in a preset time period through simulation, and randomly generates multiple groups of control strategies for the next reconstruction period as the initial population of the genetic algorithm. Taking the minimization of the net energy loss as the objective function, combined with the safety operation interval constraints of the battery cell working current and terminal voltage, a fitness function is constructed using the penalty function method. 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 period. This application formulates an accurate target control strategy under the condition of meeting safety constraints through the genetic algorithm, realizes the minimization of the system energy loss through intelligent optimization, improves the accuracy of the control strategy of the dynamic reconfigurable battery system, solves the problem of control process lag, and further improves the energy efficiency of the dynamic 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 the related art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 Flow schematic of a method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm provided by an embodiment of the present application Figure 1 .

[0015] Figure 2 Flow schematic of a method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm provided by an embodiment of the present application Figure 2 .

[0016] Figure 3 Structural connection schematic of a device for improving the energy efficiency of a dynamically reconfigurable battery based on genetic algorithm optimization provided by an embodiment of the present application Figure 1 .

[0017] Figure 4 Structural connection schematic of a device for improving the energy efficiency of a dynamically reconfigurable battery based on genetic algorithm optimization provided by an embodiment of the present application Figure 2 .

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

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

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

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

[0022] Figure 9 This is a graph showing the variation of the terminal voltage of each battery cell with time during the charging process of the dynamic reconfigurable battery network provided by the embodiment of the present application.

[0023] Figure 10 This is a graph showing the variation of the terminal voltage of each battery cell with time during the discharging process of the fixed series-parallel battery network provided by the embodiment of the present application.

[0024] Figure 11 This is a graph showing the variation of the terminal voltage of each battery cell with time during the discharging process of the dynamic reconfigurable battery network provided by the embodiment of the present application.

[0025] Figure 12 This is a comparison graph of the operating durations of the fixed series-parallel battery network and the dynamic reconfigurable battery network provided by the embodiment of the present application.

[0026] Figure 13 This is a schematic structural diagram of a computer system provided by the embodiment of the present application.

[0027] Reference numerals: Digital energy exchange system - 1; Host computer - 2. Detailed implementation manners

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0029] First, the professional terms in the present application are explained as follows.

[0030] Different from the traditional fixed series-connected superimposed 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 through the deep coupling of battery cells and low-voltage and low-power electronic devices, and realizes the digital energy management and control at the battery module level through the digital energy exchange system. Its architecture is as shown in the appendix Figure 5As shown in the figure. The difference between the DRB energy storage system and the traditional battery energy storage system is that the DRBN realizes the discretization and digitization of battery energy through the on-off of switches, and realizes the battery energy balance by controlling the charging and discharging time of different battery units. Therefore, the DRBN no longer requires the equalization circuit in the traditional battery energy storage system. In addition, the digital energy exchange system (i.e., the control device of the DRBN) can realize the controllable parallel connection between battery units, thus eliminating the circulating current problem caused by the direct parallel connection of battery units. In the DRBN, the controllable parallel connection between battery units is realized, and there is no concept of traditional battery clusters. Therefore, the DRBN does not require a DC-DC converter, greatly reducing the system cost and improving the system efficiency.

[0031] Digital Energy Switch System (DESS), the digital energy exchange system is the control module of the DRB energy storage system, which is used to realize functions such as state monitoring, consistency control, and safety protection of the battery network. The DESS has functions such as measurement, calculation, control, and protection. First, the battery data measured by voltage, current, and temperature sensors is transmitted to the DESS through the data bus; then, the DESS evaluates the performance of the battery according to the known battery state information, such as the State of Charge (SOC) and the State of Health (SOH); then, the DESS formulates a charging and discharging plan according to the load demand, so that the system can achieve battery consistency and electrothermal safety control on the premise of meeting the load, and send the control signal to each switch through the data bus. In addition, the DESS can timely monitor the abnormal state of the battery network. If the battery has electrical, thermal, mechanical abuse or other abnormal conditions, the DESS can control the switch to cut off the fault in time.

[0032] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

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

[0034] S1. Perform simulation prediction on the 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 a plurality of battery monomers distributed in a matrix form; the net energy loss in the preset time period is the sum of the net energy losses of multiple reconfiguration cycles; Further, step S1 specifically includes the following steps.

[0035] S11. Simulate and predict the dynamically reconfigurable battery network to obtain the switching states, energy losses, and energy recoveries of each battery cell within a preset time period. S12. Based on the switching states, energy losses, and energy recoveries of each battery cell within the preset time period, respectively transform them into matrix forms through mapping relationships to construct the switching state matrix, energy loss matrix, and energy recovery matrix for each reconfiguration period.

[0036] S13. According to the switching state matrix, energy loss matrix, and energy recovery matrix for each reconfiguration period, calculate the net energy loss for each reconfiguration period through the Hadamard product. S14. Combine the net energy losses in different reconfiguration periods to obtain the net energy loss within the preset time period.

[0037] Furthermore, the simulation prediction is obtained through an energy loss model.

[0038] In the actual application process, the non - linear effects of battery monomers will affect the energy control of the system. The non - linear effects include the current effect and the recovery effect. The current effect means that the available capacity of the battery monomer decreases as the current rate increases, and the recovery effect means that when the current suddenly drops to zero, the capacity of the battery monomer will recover slightly. In the DRBN, each battery monomer operates in a pulsed discharge mode, so the current effect and the recovery effect are more significant. Considering the non - linear effects of the battery during the operation control process helps to improve the overall performance of the system.

[0039] DESS realizes the reconfiguration of the battery network topology by controlling the on - off of each MOSDET. The switching state of the battery network can be represented by a switching matrix as follows.

[0040] .

[0041] Where represents the switching state of the th row and th column, equal to 1 indicates that the switch is on, and equal to 0 indicates that the switch is off.

[0042] The optimization goal of the digital energy exchange system is to achieve energy control and improve the energy efficiency of the system. Assume that the total working duration is T, and the entire working interval is divided into N reconfiguration periods, and the time of each reconfiguration period is . At the beginning of the reconfiguration period, the digital energy exchange system will generate an optimal control strategy according to the current battery state and change the battery topology through the switch array. Since the reconfiguration period is very short, the load can be considered constant during this time period. Let represent the switching state of the th reconfiguration period. represents the energy loss matrix of the battery, represents the th row and th column of the battery cell energy loss in the represents the energy recovery matrix of the battery, represents the th row and th column of the battery cell energy recovery in the th reconstruction period, then the energy dissipation matrix of the entire battery system during the

[0043] .

[0044] where, is the net energy loss from the current reconstruction period to the th reconstruction period; is the switching matrix for each reconstruction period; is the matrix after taking the inverse of the switching matrix ; is the energy loss matrix of the th; is the energy recovery matrix of the th reconstruction period; is the Hadamard product.

[0045] where, and are in the following form.

[0046] .

[0047] Note that the above analysis describes the net energy loss during the i-th reconstruction period. The formula for the net energy loss during the entire charge-discharge cycle is as follows.

[0048] .

[0049] Therefore, the energy control of the dynamically reconfigurable battery energy storage system can be expressed as an optimization problem. The physical meanings of the two constraint conditions are that the battery cells do not overcurrent and the output voltage of the battery system remains within a reasonable range.

[0050] .

[0051] The optimization problem can be solved using a genetic algorithm. By setting appropriate initial values, population size, and number of iterations, the genetic algorithm can quickly and accurately find the optimal solution to the optimization problem, the switching state matrix. Subsequently, DESS issues the calculated reconstruction strategy (i.e., the on / off state instructions for each switch) to the DRBN for execution, thereby achieving optimal operation control and improving the overall energy efficiency of the system.

[0052] S2. Randomly generate multiple control strategies for the next reconstruction period of the dynamically reconfigurable battery network.

[0053] S3. Use all control strategies as the initial population to obtain multiple candidate solutions.

[0054] S4. Take the minimization of the net energy loss in a preset time period as the objective function, and use the safe operating ranges of the battery cell working current and terminal voltage as the constraint conditions. Adopt the penalty function method to construct the fitness function of the genetic algorithm.

[0055] Furthermore, the objective function is as follows.

[0056] .

[0057] In the formula, is the minimum value of the net energy loss in the preset time period; is the net energy loss in the th reconstruction period starting from the current reconstruction period, .

[0058] Furthermore, the constraint conditions are as follows.

[0059] .

[0060] In the formula, is the constraint condition; is the first constraint condition; is the second constraint condition; is the third constraint condition; is the serial number of the constraint condition; is the th current of the th battery cell in the th reconstruction period; is the lower limit constraint value of the output total voltage; is the output total voltage of the battery network; is the upper limit constraint value of the output total voltage.

[0061] Furthermore, the fitness function is as follows.

[0062] .

[0063] In the formula, is the fitness function; is the minimum value of the net energy loss during the preset time period; is the penalty coefficient, used to characterize the degree of penalty; is the penalty power exponent; is the constraint condition.

[0064] Among them, is generally taken as a very large positive number. When a certain solution does not satisfy the inequality constraint, the penalty term of the fitness function will add a very large positive number to the fitness function, thereby excluding this solution.

[0065] S5. Based on the fitness function, use the genetic algorithm to perform iterative calculations on multiple candidate solutions, obtain the optimal solution, and use the optimal solution as the target control strategy for the next reconstruction period of the dynamically reconfigurable battery network.

[0066] Furthermore, step S5 specifically includes the following steps.

[0067] S51. Calculate the fitness of each candidate solution using the fitness function.

[0068] S52. Retain the candidate solution with the highest fitness in the current generation as the target candidate solution, select, cross, and mutate the remaining candidate solutions to regenerate the same number of new-generation candidate solutions, and perform iterative calculations until the number of iterations reaches the threshold.

[0069] In the actual application process, calculate the fitness of n candidate solutions respectively, and select excellent individuals (target candidate solutions) to enter the next generation according to the fitness. Adopt the elite selection strategy, that is, forcefully retain the optimal m individuals in the current generation to directly enter the next generation, and the remaining n - m individuals are generated through other selection methods, crossover, and mutation.

[0070] Crossover is to generate diverse offspring by exchanging part of the genes of the parental individuals. The crossover method adopted in this embodiment is uniform crossover, that is, for each pair of genes, it is determined whether to exchange according to a certain probability (crossover probability, usually set to 0.6 - 0.9). For example, there are two parental segments 110 and 101, and the mask generated according to the crossover probability is 011 (exchange when the mask is 1, do not exchange when it is 0), then the offspring after crossover are 101 and 110.

[0071] Mutation is to maintain the diversity of the population by randomly changing a small number of genes. The mutation operation is achieved by setting the mutation probability For each binary bit, flip its value with probability (usually set to 0.001 - 0.1). For example, the original individual segment is 110, and the 3rd bit is selected for mutation according to the mutation probability, then the mutated segment is 111.

[0072] After crossover and mutation, new offspring are obtained. The population size of the offspring is the same as that of the parent generation, both being n. Then, return to step S41 to recalculate the fitness, and iterate in this way. When the maximum number of iterations is reached (such as 50 - 100 generations), the iteration terminates.

[0073] 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 period of the dynamically reconfigurable battery network.

[0074] During the verification process, the 64 battery cells used have passed the charge-discharge cycle test. The results show that there are significant differences in the internal resistance between the batteries, and the internal resistance values of some batteries may be twice that of other batteries. The maximum available capacity of the battery can be estimated by the coulomb counting algorithm. The detailed statistical data on the battery internal resistance and available capacity are as Figures 6 - 7 shown.

[0075] To demonstrate the capabilities of the proposed method, charge-discharge experiments are carried out respectively under the traditional fixed battery topology and the dynamically reconfigurable battery topology, and the voltages of the 64 battery cells during charging and discharging are measured. From Figure 8 it can be observed that when the fixed battery topology is adopted, there are obvious differences in the battery voltage levels during charging. To avoid overcharging, the system stops charging in advance, resulting in most of the battery cells not being fully charged. In contrast, under the dynamically reconfigurable topology, the fully charged battery cells will be isolated, and the remaining batteries continue to charge. As Figure 9 shown, this process will be repeated until each battery cell is fully charged.

[0076] This embodiment also demonstrates the performance comparison during the discharge process. As Figures 10 - 12 shown, under the fixed battery topology, as the discharge progresses, the differences between the battery voltages continue to increase. Eventually, to avoid damage to the weakest battery and potential hazards, the discharge process is forced to terminate in advance. In the DRB system, thanks to the proposed energy efficiency improvement method, there is no obvious divergence trend in the battery voltages, thus increasing the operating time by 33%.

[0077] To verify the energy conversion efficiency of the proposed algorithm in different application scenarios, the proposed energy efficiency improvement method is applied in the above system, and the operation results are compared with those under the fixed series-parallel strategy. The effects are shown in Table 1. Among them, the energy efficiency is defined as the ratio of the maximum available energy to the full charge capacity. The results show that under different load currents, the energy efficiency of the proposed method is always higher than that of the traditional method. When the DRB system adopts the proposed optimization algorithm, the energy efficiency can be increased by 25 - 30%.

[0078] Table 1 Comparison Table of Battery System Energy Efficiency under Two Strategies

[0079] The technical effects of this application are as follows.

[0080] This application predicts the net energy loss in 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. With minimizing the net energy loss as the objective function, combined with the safety operation range constraints of the battery cell working current and terminal voltage, a fitness function is constructed using the penalty function method. 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 formulates an accurate target control strategy through the genetic algorithm under the condition of meeting safety constraints, realizes the minimization of system energy loss through intelligent optimization, improves the accuracy of the control strategy of the dynamic reconfigurable battery system, solves the problem of control process lag, and further improves the energy efficiency of the dynamic reconfigurable battery network.

[0081] Example 2, as Figures 3 - 4 shown, this embodiment also provides a device for improving the energy efficiency of a dynamically reconfigurable battery optimized based on the genetic algorithm. The device for improving the energy efficiency of a dynamically reconfigurable battery optimized based on the genetic algorithm is connected to the dynamically reconfigurable battery. The device for improving the energy efficiency of a dynamically reconfigurable battery optimized based on the genetic algorithm includes: a digital energy exchange system 1 and a host computer 2.

[0082] The digital energy exchange system 1 is respectively connected to the host computer 2 and the dynamically reconfigurable battery network; the digital energy exchange system 1 is used to obtain the target control strategy by applying the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the above genetic algorithm, control the switching state of the dynamically reconfigurable battery network using the target control strategy, and upload the target control strategy to the host computer 2.

[0083] Further, the model of the digital energy exchange system 1 is: SWI01B22070500104.

[0084] Taking the lithium cobalt phosphate (LpCO) battery as an example below, its rated capacity is 10 Ah, the maximum allowable discharge current is 120 A, and the cut-off voltage is 2.0 V. The switching circuit is constructed using N-channel MOSFETs. As solid-state switching devices, they have the advantages of 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.

[0085] Example 3, this application also provides a computer system. The computer system can be a server or a terminal, and its internal structure diagram can be as Figure 13As shown in the figure. The computer system includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the computer program in the non-volatile storage medium. The database of the computer system is used to store processed data. The input / output interface of the computer system is used to exchange information between the processor and external devices. The communication interface of the computer system is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the above-mentioned various methods.

[0086] Those skilled in the art can understand that Figure 13 the structure shown in the figure is only a block diagram of some structures 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 those shown in the figure, or combine some components, or have a different component layout.

[0087] In Embodiment 4, the present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned various methods.

[0088] In Embodiment 5, the present application also provides a computer program product including a computer program, which, when executed by a processor, implements the above-mentioned various methods.

[0089] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0090] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered to be within the scope described in this specification.

[0091] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for improving the energy efficiency of dynamically reconfigurable batteries optimized by a genetic algorithm, characterized in that, The method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm includes: Performing simulation prediction on the 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 a plurality of battery monomers distributed in a matrix form; the net energy loss in the preset time period is the sum of the net energy losses of multiple reconfiguration cycles; Randomly generating multiple sets of control strategies for the next reconfiguration cycle of the dynamically reconfigurable battery network; Taking all control strategies as the initial population to obtain multiple candidate solutions; Taking the minimization of the net energy loss in the preset time period as the objective function, and using the safe operating intervals of the working current and terminal voltage of the battery cells as the constraint conditions, and constructing the fitness function of the genetic algorithm by using the penalty function method; Based on the fitness function, using the genetic algorithm to perform iterative calculation on multiple candidate solutions to obtain the optimal solution, and taking the optimal solution as the target control strategy for the next reconfiguration cycle of the dynamically reconfigurable battery network.

2. The method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm according to claim 1, wherein Based on the fitness function, using the genetic algorithm to perform iterative calculation on multiple candidate solutions to obtain the optimal solution, and taking the optimal solution as the target control strategy for the next reconfiguration cycle of the dynamically reconfigurable battery network, specifically including: Calculating the fitness of each candidate solution by using the fitness function; Retaining the candidate solution with the highest fitness in the current generation as the target candidate solution, performing selection, crossover, and mutation on the remaining candidate solutions to regenerate the same number of new-generation candidate solutions, and performing iterative calculation until the number of iterations reaches the threshold; Selecting the optimal solution with the highest fitness among the target candidate solutions of different generations, and taking the optimal solution as the target control strategy for the next reconfiguration cycle of the dynamically reconfigurable battery network.

3. The method for improving the energy efficiency of dynamically reconfigurable batteries optimized by a genetic algorithm according to claim 1, characterized in that, The objective function is: ； Wherein, is the minimum value of the net energy loss in a preset time period; is the net energy loss in the -th reconstruction period counted from the start of the current reconstruction period, .

4. The method for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm according to claim 1, characterized in that, The constraint conditions are: ； In the formula, is the constraint condition; is the first constraint condition; is the second constraint condition; is the third constraint condition; is the serial number of the constraint condition; is the current of the th battery cell in the th reconstruction period; is the lower limit constraint value of the total output voltage; is the total output voltage of the battery network; is the upper limit constraint value of the total output voltage.

5. The method for improving the energy efficiency of dynamically reconfigurable batteries optimized by a genetic algorithm according to claim 1, characterized in that, The fitness function is: ； In the formula, is the fitness function; is the minimum value of the net energy loss during the preset time period; is the penalty coefficient, used to characterize the degree of penalty; is the penalty power exponent; is the constraint condition.

6. A device for improving the energy efficiency of a dynamically reconfigurable battery optimized based on a genetic algorithm, the device for improving the energy efficiency of a dynamically reconfigurable battery optimized based on a genetic algorithm is connected to the dynamically reconfigurable battery, and is characterized in that, The device for improving the energy efficiency of a dynamically reconfigurable battery optimized by a genetic algorithm includes: a digital energy exchange system and a host computer; The digital energy exchange system is respectively connected to the host computer and the dynamically reconfigurable battery network; the digital energy exchange system is used to obtain the target control strategy by applying the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm according to any one of claims 1-6, control the switching state of the dynamically reconfigurable battery network by using the target control strategy, and upload the target control strategy to the host computer.

7. The method for improving the energy efficiency of dynamically reconfigurable batteries optimized by a genetic algorithm according to claim 1, characterized in that The model of the digital energy exchange system is: SWI01B22070500104.

8. A computer system, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm according to any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for improving the energy efficiency of a dynamically reconfigurable battery optimized by the genetic algorithm according to any one of claims 1-5.

Images