Method, device, equipment and storage medium for determining charge and discharge strategy

Abstract

This application relates to a method, apparatus, device, and storage medium for determining a charging and discharging strategy, and pertains to the field of electric vehicle charging technology. The method includes: acquiring time information of the vehicle's charging and discharging phases, battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phase ends; the time information includes the start and end times of the charging and discharging phases; and determining a target charging and discharging strategy based on the time information, battery information, and target SOC; the target charging and discharging strategy is used to control the battery's SOC to reach the target SOC after the charging and discharging phase ends; the target charging and discharging strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the battery's charging and discharging performance and minimizing the cost after the charging and discharging phase ends.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging technology, specifically to a method, apparatus, device, and storage medium for determining a charging and discharging strategy. Background Technology

[0002] Today, electric vehicle grid connection technology can provide ancillary services to the power grid based on peak-valley electricity pricing, such as peak shaving services, to reduce the charging costs of electric vehicles.

[0003] However, in related technologies, optimization is only performed on charging cost as an optimization target, and it is impossible to generate charging and discharging strategies for multiple optimization targets while reducing charging cost. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for determining a charging and discharging strategy, to at least solve the technical problem in related technologies that it is impossible to generate charging strategies for multiple optimization objectives while reducing charging costs. The technical solution of this application is as follows:

[0005] According to a first aspect of this application, a method for determining a charging and discharging strategy is provided, comprising: acquiring time information of a vehicle's charging and discharging phase, battery information of the vehicle's battery, and a target state of charge (SOC) of the battery after the charging and discharging phase ends; the time information includes the start and end times of the charging and discharging phase; determining a target charging and discharging strategy based on the time information, battery information, and target SOC; the target charging and discharging strategy is used to control the battery's SOC to reach the target SOC after the charging and discharging phase ends; the target charging and discharging strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the battery's charging and discharging performance and minimizing the cost after the charging and discharging phase ends.

[0006] Based on the aforementioned technical means, this application can determine a target charging and discharging strategy by acquiring the time information of the vehicle's charging and discharging phases, the battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phases are completed, and by using the time information, battery information, and target SOC. Thus, since the target charging and discharging strategy includes constraints for multiple optimization objectives, it can simultaneously satisfy multiple optimization objectives, and also implements a method for generating charging and discharging strategies for multiple optimization objectives.

[0007] In one possible implementation, determining the target charge / discharge strategy based on time information, battery information, and target SOC includes: generating multiple initial charge / discharge strategies based on time information, battery information, and target SOC; each initial charge / discharge strategy includes the charge / discharge power of each charge / discharge interval in multiple charge / discharge intervals; the multiple charge / discharge intervals are used to form a charge / discharge stage; generating multiple candidate charge / discharge strategies based on the multiple initial charge / discharge strategies and a preset quantum particle swarm optimization algorithm; the multiple candidate charge / discharge strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charge / discharge strategy is less than the constraint violation threshold of the candidate charge / discharge strategy; the infeasibility is used to indicate the distance between the candidate charge / discharge strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charge / discharge strategy is greater than a preset congestion distance threshold; determining the candidate charge / discharge strategy with the highest standard satisfaction from the multiple candidate charge / discharge strategies as the target charge / discharge strategy; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after completing the charge / discharge stage according to the charge / discharge strategy.

[0008] Based on the aforementioned technical means, this application can generate multiple initial charge / discharge strategies according to time information, battery information, and target SOC; and generate multiple candidate charge / discharge strategies based on these initial strategies and a preset quantum particle swarm optimization algorithm. Furthermore, from these candidate strategies, the candidate strategy with the highest standard satisfaction is determined as the target charge / discharge strategy. Thus, by pre-generating multiple initial charge / discharge strategies and processing them, multiple better candidate charge / discharge strategies are obtained. Furthermore, by determining the optimal target charge / discharge strategy from these candidate strategies, the optimal charge / discharge strategy can be obtained.

[0009] In one possible implementation, the battery information includes the battery's maximum charging power, maximum discharging power, rated battery capacity, and initial SOC. Based on time information, battery information, and target SOC, multiple initial charge / discharge strategies are generated, including: determining multiple charge / discharge intervals based on the start and end times of the charge / discharge phase and a preset charge / discharge time interval; for a first charge / discharge strategy, determining whether to execute the first charge / discharge strategy according to the first charge / discharge strategy, the battery's rated capacity, and the initial SOC; and determining whether the battery's SOC reaches the target SOC after the charge / discharge phase ends. The first charge / discharge strategy includes multiple charge / discharge intervals, and the charge / discharge power of each interval is less than the maximum charging power but greater than the maximum discharging power. If the battery's SOC reaches the target SOC after the charge / discharge phase ends, the first charge / discharge strategy is determined as the initial charge / discharge strategy.

[0010] Based on the aforementioned technical means, this application can determine multiple charge / discharge intervals according to the start and end times of the charge / discharge phase and a preset charge / discharge time interval. For a first charge / discharge strategy, based on the first charge / discharge strategy, the battery's rated capacity, and initial SOC, it determines whether the battery's SOC reaches the target SOC after the charge / discharge phase ends. Furthermore, if the battery's SOC reaches the target SOC after the charge / discharge phase ends, the first charge / discharge strategy is determined as the initial charge / discharge strategy. Thus, by pre-determining multiple charge / discharge intervals, generating a first charge / discharge strategy, and determining the first charge / discharge strategy as the initial charge / discharge strategy when the battery's SOC can reach the target SOC, a method for determining an initial charge / discharge strategy is implemented.

[0011] In one possible implementation, the above-mentioned generation of multiple candidate charge-discharge strategies based on multiple initial charge-discharge strategies and a preset quantum particle swarm optimization algorithm includes: iteratively processing the multiple initial charge-discharge strategies as a particle swarm according to the preset quantum particle swarm optimization algorithm; one charge-discharge strategy corresponds to one particle in the particle swarm, and one iteration process includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain a first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain a second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm with the maximum number of particles; after multiple iterations, if the number of times the third intermediate particle swarm is obtained is greater than the preset number, or the number of multiple iterations is equal to the preset maximum number, then the charge-discharge strategy corresponding to the third intermediate particle swarm obtained in the last iteration is used as multiple candidate charge-discharge strategies.

[0012] Based on the aforementioned technical means, this application can iteratively process multiple initial charging and discharging strategies as a particle swarm according to a preset quantum particle swarm algorithm. After multiple iterations, if the number of times the third intermediate particle swarm is identical in consecutive iterations is greater than a preset number, or the number of iterations is equal to a preset maximum number, then the charging and discharging strategy corresponding to the third intermediate particle swarm obtained in the last iteration is selected as multiple candidate charging and discharging strategies. Thus, by iteratively processing multiple initial charging and discharging strategies and determining the number of times the third intermediate particle swarm is identical in consecutive iterations or the number of iterations, it can be guaranteed that the resulting multiple candidate charging and discharging strategies are the optimal charging and discharging strategies in the multiple iterations.

[0013] In one possible implementation, the method further includes: determining multiple fuzzy satisfaction levels corresponding to each candidate charge-discharge strategy among multiple candidate charge-discharge strategies; each fuzzy satisfaction level corresponds to an optimization objective, and the fuzzy satisfaction level is used to indicate the optimization result corresponding to an optimization objective after the charge-discharge stage is completed according to the charge-discharge strategy; determining the standard satisfaction level of each candidate charge-discharge strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charge-discharge strategy, thereby obtaining multiple standard satisfaction levels; each candidate charge-discharge strategy corresponds to one standard satisfaction level.

[0014] Based on the aforementioned technical means, this application can determine multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy among multiple candidate charging and discharging strategies; and determine the standard satisfaction level of each candidate charging and discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, thereby obtaining multiple standard satisfaction levels. In this way, by pre-determining the fuzzy satisfaction level of each candidate charging and discharging strategy for each optimization objective, and determining the standard satisfaction level of each candidate charging and discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, a method for determining the standard satisfaction level of each candidate charging and discharging strategy is realized.

[0015] According to a second aspect provided in this application, a device for determining a charge / discharge strategy is provided, comprising an acquisition unit and a determination unit; the acquisition unit is configured to acquire time information of a vehicle's charge / discharge phase, battery information of the vehicle's battery, and a target SOC of the battery after the charge / discharge phase ends; the time information includes the start and end times of the charge / discharge phase; the determination unit is configured to determine a target charge / discharge strategy based on the time information, battery information, and target SOC; the target charge / discharge strategy is used to control the battery's SOC to reach the target SOC after the charge / discharge phase ends; the target charge / discharge strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the battery's charge / discharge performance and minimizing the cost after the charge / discharge phase ends.

[0016] In one possible implementation, the determining unit is specifically configured to: generate multiple initial charge / discharge strategies based on time information, battery information, and target SOC; each initial charge / discharge strategy includes the charge / discharge power of each charge / discharge interval in multiple charge / discharge intervals; the multiple charge / discharge intervals are used to form a charge / discharge stage; generate multiple candidate charge / discharge strategies based on the multiple initial charge / discharge strategies and a preset quantum particle swarm optimization algorithm; the multiple candidate charge / discharge strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charge / discharge strategy is less than the constraint violation threshold of the candidate charge / discharge strategy; the infeasibility is used to indicate the distance between the candidate charge / discharge strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charge / discharge strategy is greater than a preset congestion distance threshold; determine the candidate charge / discharge strategy with the highest standard satisfaction from the multiple candidate charge / discharge strategies as the target charge / discharge strategy; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charge / discharge stage is completed according to the charge / discharge strategy.

[0017] In one possible implementation, the battery information includes the battery's maximum charging power, maximum discharging power, rated battery capacity, and initial SOC; the determining unit is specifically configured to: determine multiple charging / discharging intervals based on the start and end times of the charging / discharging phase and a preset charging / discharging time interval; for a first charging / discharging strategy, determine whether the battery's SOC reaches the target SOC after the charging / discharging phase ends, based on the first charging / discharging strategy, the battery's rated capacity, and the initial SOC; the first charging / discharging strategy includes multiple charging / discharging intervals, and the charging / discharging power of each interval is less than the maximum charging power and greater than the maximum discharging power; if the battery's SOC reaches the target SOC after the charging / discharging phase ends, then the first charging / discharging strategy is determined as the initial charging / discharging strategy.

[0018] In one possible implementation, the determining unit is specifically used to: iteratively process multiple initial charge-discharge strategies as a particle swarm according to a preset quantum particle swarm algorithm; one charge-discharge strategy corresponds to one particle in the particle swarm, and one iteration process includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain a first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain a second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm with the maximum number of particles; after multiple iterations, if the number of times the third intermediate particle swarm is obtained is greater than the preset number, or the number of multiple iterations is equal to the preset maximum number, then the charge-discharge strategy corresponding to the third intermediate particle swarm obtained in the last iteration is used as multiple candidate charge-discharge strategies.

[0019] In one possible implementation, the determining unit is further configured to: determine multiple fuzzy satisfaction levels corresponding to each candidate charging / discharging strategy among multiple candidate charging / discharging strategies; each fuzzy satisfaction level corresponds to an optimization objective, and the fuzzy satisfaction level is used to indicate the optimization result corresponding to an optimization objective after the charging / discharging stage is completed according to the charging / discharging strategy; determine the standard satisfaction level of each candidate charging / discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging / discharging strategy, thereby obtaining multiple standard satisfaction levels; each candidate charging / discharging strategy corresponds to one standard satisfaction level.

[0020] According to a third aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0021] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0022] According to the fifth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0023] It should be noted that the technical effects of any of the implementation methods in aspects two through five can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application.

[0025] Therefore, the above-mentioned technical features of this application have the following beneficial effects:

[0026] (1) The target charging and discharging strategy can be determined by acquiring the time information of the vehicle's charging and discharging phase, the battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phase. In this way, since the target charging and discharging strategy includes constraints of multiple optimization objectives, the target charging and discharging strategy can simultaneously satisfy multiple optimization objectives, and a method for generating charging and discharging strategies for multiple optimization objectives is also realized.

[0027] (2) Multiple initial charge / discharge strategies can be generated based on time information, battery information, and target SOC. Multiple candidate charge / discharge strategies are then generated based on these initial strategies and a pre-defined quantum particle swarm optimization algorithm. Furthermore, the candidate strategy with the highest standard satisfaction is selected as the target charge / discharge strategy from among these candidates. Thus, by pre-generating multiple initial charge / discharge strategies and processing them, several better candidate charge / discharge strategies are obtained. Finally, the optimal target charge / discharge strategy is determined from among these candidate strategies, resulting in the optimal charge / discharge strategy.

[0028] (3) Multiple charge / discharge intervals can be determined based on the start and end times of the charge / discharge phases and a preset charge / discharge time interval. For the first charge / discharge strategy, based on the first charge / discharge strategy, the battery's rated capacity, and the initial SOC, it is determined whether the battery's SOC reaches the target SOC after the charge / discharge phase ends. Furthermore, if the battery's SOC reaches the target SOC after the charge / discharge phase ends, the first charge / discharge strategy is determined as the initial charge / discharge strategy. Thus, by pre-determining multiple charge / discharge intervals, generating the first charge / discharge strategy, and determining the first charge / discharge strategy as the initial charge / discharge strategy when the battery's SOC can reach the target SOC, a method for determining the initial charge / discharge strategy is implemented.

[0029] (4) Multiple initial charge-discharge strategies can be iteratively processed as a particle swarm according to a preset quantum particle swarm algorithm. After multiple iterations, if the number of times the third intermediate particle swarm is identical in consecutive iterations is greater than a preset number, or the number of iterations is equal to a preset maximum number, then the charge-discharge strategy corresponding to the third intermediate particle swarm obtained in the last iteration is taken as multiple candidate charge-discharge strategies. In this way, by iteratively processing multiple initial charge-discharge strategies and judging the number of times the third intermediate particle swarm is identical in consecutive iterations or the number of iterations, it can be guaranteed that the multiple candidate charge-discharge strategies obtained are the optimal charge-discharge strategies in multiple iterations.

[0030] (5) Multiple fuzzy satisfaction levels can be determined for each candidate charging / discharging strategy among multiple candidate charging / discharging strategies; and based on the multiple fuzzy satisfaction levels for each candidate charging / discharging strategy, the standard satisfaction level for each candidate charging / discharging strategy can be determined, thus obtaining multiple standard satisfaction levels. In this way, by pre-determining the fuzzy satisfaction level of each candidate charging / discharging strategy for each optimization objective, and by determining the standard satisfaction level for each candidate charging / discharging strategy based on the multiple fuzzy satisfaction levels for each candidate charging / discharging strategy, a method for determining the standard satisfaction level of each candidate charging / discharging strategy is realized. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0032] Figure 1 This is a flowchart illustrating a method for determining a charging and discharging strategy according to an exemplary embodiment;

[0033] Figure 2 This is a flowchart illustrating yet another method for determining a charging / discharging strategy according to an exemplary embodiment;

[0034] Figure 3 This is a schematic diagram illustrating yet another method for determining a charging and discharging strategy according to an exemplary embodiment;

[0035] Figure 4 This is a block diagram illustrating a charging / discharging strategy determination device according to an exemplary embodiment;

[0036] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0038] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] For ease of understanding, the method for determining the charging and discharging strategy provided in this application will be described in detail below with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart illustrating a method for determining a charging and discharging strategy according to an exemplary embodiment, such as... Figure 1 As shown, the method for determining the charging and discharging strategy includes the following steps:

[0041] S101, The electronic device acquires the time information of the vehicle's charging and discharging phases, the battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phases are completed.

[0042] The time information includes the start and end times of the charging and discharging phases.

[0043] One possible implementation involves the driver inputting the start and end times of the charging / discharging phase, as well as the target SOC of the battery after the charging / discharging phase, into the vehicle's infotainment system. Correspondingly, the electronic devices acquire the timing information of the vehicle's charging / discharging phases and the target SOC of the battery after the charging / discharging phase.

[0044] The electronic device sends a battery information retrieval request to the vehicle's battery management system. Upon receiving the request, the vehicle's battery management system collects the vehicle's battery information and sends it to the electronic device. The electronic device then obtains the vehicle's battery information.

[0045] S102. The electronic device determines the target charging and discharging strategy based on time information, battery information, and target SOC.

[0046] The target charge / discharge strategy is used to control the battery's SOC to reach the target SOC after the charge / discharge phase ends. The target charge / discharge strategy also satisfies constraints including multiple optimization objectives. These multiple optimization objectives include minimizing the impact on the battery's charge / discharge performance and minimizing the cost after the charge / discharge phase ends.

[0047] As one possible implementation, the electronic device generates multiple initial charge / discharge strategies based on time information, battery information, and target SOC.

[0048] The electronic device generates multiple candidate charging and discharging strategies based on multiple initial charging and discharging strategies and a preset quantum particle swarm algorithm.

[0049] The electronic device selects the candidate charging and discharging strategy with the highest standard satisfaction from multiple candidate charging and discharging strategies as the target charging and discharging strategy.

[0050] It should be noted that each initial charge / discharge strategy includes the charge / discharge power of each charge / discharge interval in multiple charge / discharge intervals; multiple charge / discharge intervals are used to form a charge / discharge stage; multiple candidate charge / discharge strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charge / discharge strategy is less than the constraint violation threshold of the candidate charge / discharge strategy; the infeasibility is used to indicate the distance between the candidate charge / discharge strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charge / discharge strategy is greater than the preset congestion distance threshold; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charge / discharge stage is completed according to the charge / discharge strategy.

[0051] As is understood, the technical solution proposed in this application obtains the time information of the vehicle's charging and discharging phases, the battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phases, and determines the target charging and discharging strategy based on the time information, battery information, and target SOC. Thus, since the target charging and discharging strategy includes constraints for multiple optimization objectives, it can simultaneously satisfy multiple optimization objectives, and also implements a method for generating charging and discharging strategies for multiple optimization objectives.

[0052] In some embodiments, in order to determine a target charge / discharge strategy, such as Figure 2 As shown in the embodiment of this application, the method for determining the charging and discharging strategy, wherein S102 above includes the following steps:

[0053] S201. The electronic device generates multiple initial charge and discharge strategies based on time information, battery information, and target SOC.

[0054] Each initial charge / discharge strategy includes the charge / discharge power of each charge / discharge interval in multiple charge / discharge intervals; multiple charge / discharge intervals are used to form a charge / discharge phase; battery information includes the battery's maximum charging power, maximum discharging power, battery rated capacity, and initial SOC.

[0055] As one possible implementation, the electronic device determines multiple charging and discharging intervals based on the start and end times of the charging and discharging phases and a preset charging and discharging time interval.

[0056] The electronic device generates a first charge / discharge strategy based on the maximum charging power and maximum discharging power of each charge / discharge interval.

[0057] The electronic device, based on the first charge / discharge strategy, the battery's rated capacity, and the initial SOC, determines whether to execute the first charge / discharge strategy and whether the battery's SOC reaches the target SOC after the charge / discharge phase ends.

[0058] If the battery's SOC reaches the target SOC after the charge / discharge phase ends, the electronic device will determine the first charge / discharge strategy as the initial charge / discharge strategy.

[0059] It should be noted that if the battery's SOC does not reach the target SOC after the charge / discharge phase ends, the electronic device will determine the first charge / discharge strategy as a non-initial charge / discharge strategy.

[0060] Multiple charge / discharge intervals are denoted by n, and the start time of each charge / discharge phase is denoted by T. start The end time of the charging and discharging phase is represented by T. end The preset charge / discharge time interval is represented by T. invThis means that the electronic device determines multiple charging and discharging intervals based on the start and end times of the charging and discharging phases and a preset charging and discharging time interval, satisfying the following formula:

[0061]

[0062] Initial SOC using SOC initial The rated capacity of a battery is indicated by S. ev_batmax P represents the total charging and discharging power within the charging and discharging interval t. D (t) represents the SOC of the vehicle's battery after the (t-1)th charge / discharge interval, denoted by SOC(t-1), and the SOC of the vehicle's battery after the tth charge / discharge interval, denoted by SOC(t). The electronic device calculates the SOC of the vehicle's battery after the end of the charge / discharge interval t, which satisfies the following formula:

[0063]

[0064] The maximum charging power of a vehicle's battery is represented by P. cmax The maximum discharge power of a vehicle's battery is represented by P. dcmax This indicates the charge / discharge power P in each charge / discharge interval. D (t) satisfies the following formula three:

[0065]

[0066] Target SOC using SOC exp This indicates that the initial SOC uses SOC. initial This indicates that the electronic device follows the initial charge / discharge strategy, and after the charge / discharge phase ends, it satisfies the following formula:

[0067]

[0068] The charge / discharge power within the charge / discharge interval t is represented by P. D (t) represents the battery capacity after the charging / discharging interval t-1 ends, expressed in S. b (t-1) represents the battery capacity after the charging / discharging interval t ends, expressed in terms of S. b (t) indicates that the following formula five is satisfied:

[0069] S b (t)=S b (t-1)+P D (t)tt∈(1,……n)

[0070] Formula 5 S202: The electronic device generates multiple candidate charging and discharging strategies based on multiple initial charging and discharging strategies and a preset quantum particle swarm algorithm.

[0071] Among them, multiple candidate charging and discharging strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charging and discharging strategy is less than the constraint violation threshold of the candidate charging and discharging strategy; the infeasibility is used to indicate the distance between the candidate charging and discharging strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charging and discharging strategy is greater than the preset congestion distance threshold.

[0072] As one possible implementation, the electronic device iteratively processes multiple initial charge and discharge strategies as a swarm of particles according to a preset quantum particle swarm algorithm.

[0073] After multiple iterations, if the number of times the third intermediate particle group is the same after consecutive iterations is greater than the preset number, or the number of multiple iterations is equal to the preset maximum number, then the electronic device will use the charging and discharging strategy corresponding to the third intermediate particle group obtained in the last iteration as multiple candidate charging and discharging strategies.

[0074] It should be noted that a charge / discharge strategy corresponds to a particle in a particle swarm. One iteration process includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain a first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain a second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm with the number of particles equal to the maximum number of particles.

[0075] M is the population size, P i_best The best position in the history of the i-th particle, M best g represents the average historical best position of all particles in the population. best Let Pi represent the current optimal particle in the global range, where Pi is the update of the position of the i-th particle, and x... i Let λ be the position of the i-th particle, λ and u be random numbers on (0,1), α be the innovation parameter, and β be the contraction-expansion factor. The electronic device updates the position of the parent particle to obtain the child particle, satisfying the following formula six:

[0076]

[0077] Pi = λPi best +(1-λ)g best

[0078]

[0079] The innovation parameter α satisfies the following formula seven:

[0080] α = (-1) 0.5+μ

[0081] Formula 7

[0082] In practical applications, the contraction-expansion factor β can be handled using a linear decreasing weight method to control the convergence speed of particles. A slower rate of decrease in the contraction-expansion factor β during the early stages of optimization improves global search capabilities and makes it easier to find the global optimum. A faster rate of decrease in the contraction-expansion factor β during the later stages of optimization leads to faster convergence and improves the efficiency of particle updates.

[0083] Understandably, the innovation parameter α being an integer power of -1 can enhance the randomness of the electronic device in updating the position of the parent particle to obtain the offspring particle.

[0084] i max i represents the maximum value processed in the iteration. n For the current iteration number, the contraction-expansion factor β satisfies the following formula:

[0085]

[0086] The following details how the electronic device removes particles that do not meet the first condition from the first intermediate particle swarm to obtain the second intermediate particle swarm:

[0087] The infeasibility of a particle is denoted by L(x), and the electronic device calculates the infeasibility of each particle in the first intermediate particle swarm according to the following formula:

[0088]

[0089] The infeasibility of a particle is expressed by L(x), which is the distance from the particle to the feasible region. The closer the particle is to the feasible region, the smaller L(x) is. If the particle is determined to be a feasible solution, then the value of L(x) is 0.

[0090] The constraint violation threshold is represented by ε, and the current iteration count is represented by i. n Indicates that the total number of iterations is represented by i. max This indicates that the electronic device determines the constraint violation threshold corresponding to each particle in the first intermediate particle swarm to satisfy the following formula:

[0091]

[0092] Understandably, the more iterations are performed, the smaller the constraint violation threshold becomes.

[0093] The following details how, if the number of particles in the second intermediate particle group exceeds a preset maximum number of particles, the electronic device removes particles from the second intermediate particle group that do not meet the second condition, thus obtaining a third intermediate particle group with the maximum number of particles:

[0094] d iLet f be the target crowding distance value for the i-th particle, M be the number of targets, and f be the distance between the target and the target. i+1，j and f i-1，j f is the fitness value of the two adjacent particles of the i-th particle in the j-th target. jmax f jmin For the maximum and minimum fitness values ​​corresponding to the j-th target, the electronic device calculates the target crowding distance for each particle according to the following formula eleven:

[0095]

[0096] In practical applications, the electronic device pre-determines multiple first initial charge and discharge strategies that satisfy the first condition from multiple initial charge and discharge strategies, and determines multiple second initial charge and discharge strategies that satisfy the second condition from multiple first initial charge and discharge strategies.

[0097] S203. The electronic device determines multiple fuzzy satisfaction levels for each candidate charging / discharging strategy among multiple candidate charging / discharging strategies.

[0098] Among them, a fuzzy satisfaction level corresponds to an optimization objective. The fuzzy satisfaction level is used to indicate the optimization result corresponding to an optimization objective after the charging and discharging phase is completed according to the charging and discharging strategy.

[0099] As one possible implementation, the electronic device calculates multiple fuzzy satisfaction levels corresponding to each candidate charge / discharge strategy among multiple candidate charge / discharge strategies.

[0100] It should be noted that the first optimization objective among the multiple optimization objectives is the ratio of the number of charge-discharge cycles of the vehicle's battery to the total number of battery cycles of the vehicle.

[0101] C max P represents the total number of battery cycles in the vehicle. D (t) represents the charging and discharging power during the charging and discharging interval t, P D (t-1) represents the charging and discharging power during the charging and discharging interval t-1, M. s (t) represents the number of charge-discharge cycles of the battery within the charge-discharge interval t. The number of charge-discharge cycles of the battery is... The total number of battery cycles for the vehicle is C. max The electronic device calculates the ratio of the number of charge-discharge cycles of the vehicle's battery to the total number of battery cycles, minf1, which satisfies the following formula:

[0102] N(t)=P D (t)*P D (t-1)t∈(1, ...,n)

[0103]

[0104] The first optimization objective among multiple optimization objectives is the expenditure of the vehicle's battery after the charging and discharging phase ends, c Mbat P is the consumption factor during the charging and discharging phase. Mbat (t) represents the power value on the grid side during the charging and discharging phase, c g For off-peak electricity pricing, c p P D (t) represents the peak electricity price. The electronic device calculates the vehicle's battery expenditure after the charging and discharging phase ends, according to the following formula thirteen:

[0105]

[0106] η c The charging efficiency, η, for charging vehicle batteries via the grid side. d The discharge efficiency of the vehicle's battery to the grid side, and the power value P on the grid side during the charging and discharging phase. Mbat (t) satisfies the following formula fourteen:

[0107]

[0108] It should be noted that γ i j The fuzzy satisfaction level for the j-th optimization objective corresponding to the i-th candidate charging / discharging strategy; f i j f represents the fitness value of the i-th candidate charge / discharge strategy corresponding to the j-th optimization objective; jmax with f jmin Let $\mathbf{j}$ be the maximum and minimum values ​​of the j-th optimization objective among multiple candidate charge / discharge strategies.

[0109] The electronic device calculates multiple fuzzy satisfaction levels corresponding to each candidate charging / discharging strategy, which satisfy the following formula fifteen:

[0110]

[0111] S204. The electronic device determines the standard satisfaction of each candidate charging and discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtains multiple standard satisfaction levels.

[0112] Each candidate charge / discharge strategy corresponds to a standard satisfaction level.

[0113] As one possible implementation, the electronic device calculates the standard satisfaction level for each candidate charge / discharge strategy based on multiple satisfaction values ​​corresponding to each candidate strategy. Furthermore, the electronic device calculates multiple standard satisfaction levels.

[0114] It should be noted that γ iLet N be the standardized satisfaction of the i-th candidate charging / discharging strategy; N is the number of candidate charging / discharging strategies, and M is the number of multiple optimization objectives. The electronic device calculates the standardized satisfaction of each candidate charging / discharging strategy based on the multiple satisfaction values ​​corresponding to each candidate charging / discharging strategy, satisfying the following formula sixteen:

[0115]

[0116] S205. The electronic device selects the candidate charging and discharging strategy with the highest standard satisfaction from multiple candidate charging and discharging strategies as the target charging and discharging strategy.

[0117] Among them, the standard satisfaction is used to indicate the overall optimization result of multiple optimization objectives after the charging and discharging phase is completed according to the charging and discharging strategy.

[0118] As one possible implementation, the electronic device ranks the standard satisfaction of multiple candidate charging and discharging strategies and determines the candidate charging and discharging strategy with the highest standard satisfaction as the target charging and discharging strategy.

[0119] Understandably, the technical solution provided in this application generates multiple initial charge / discharge strategies based on time information, battery information, and target SOC; and generates multiple candidate charge / discharge strategies based on these initial strategies and a preset quantum particle swarm optimization algorithm. Further, the candidate charge / discharge strategy with the highest standard satisfaction is determined as the target charge / discharge strategy from among the multiple candidate strategies. Thus, by pre-generating multiple initial charge / discharge strategies and processing them, multiple better candidate charge / discharge strategies are obtained. Furthermore, by determining the optimal target charge / discharge strategy from among the multiple candidate strategies, the optimal charge / discharge strategy can be obtained.

[0120] In practical applications, such as Figure 3 As shown, the method for determining the charging and discharging strategy provided in this application includes the following steps:

[0121] S301. The electronic device acquires the time information of the vehicle's charging and discharging phases, the battery information of the vehicle's battery, the target SOC of the battery after the charging and discharging phases, and the time and price information of off-peak electricity.

[0122] S302. The electronic device determines multiple charging and discharging intervals based on the start and end times of the charging and discharging phases and a preset charging and discharging time interval.

[0123] S303. For the first charge / discharge strategy, based on the first charge / discharge strategy, the battery rated capacity, and the initial SOC, the electronic device determines whether to execute the first charge / discharge strategy and whether the battery's SOC reaches the target SOC after the charge / discharge phase ends.

[0124] S304. If the battery's SOC reaches the target SOC after the charging and discharging phase ends, the electronic device will determine the first charging and discharging strategy as the initial charging and discharging strategy.

[0125] S305. The electronic device iteratively processes multiple initial charging and discharging strategies as a particle swarm according to a preset quantum particle swarm algorithm.

[0126] S306. The electronic device determines multiple fuzzy satisfaction levels for each candidate charging / discharging strategy among multiple candidate charging / discharging strategies.

[0127] S307. The electronic device determines the standard satisfaction of each candidate charging and discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtains multiple standard satisfaction levels.

[0128] S308. After multiple iterations, if the number of times the third intermediate particle group is the same after consecutive iterations is greater than the preset number, or the number of multiple iterations is equal to the preset maximum number, then the electronic device will use the charging and discharging strategy corresponding to the third intermediate particle group obtained in the last iteration as multiple candidate charging and discharging strategies.

[0129] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the charging / discharging strategy determination device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0130] This application embodiment can, based on the above method, exemplarily divide the charging / discharging strategy determination device or electronic device into functional modules. For example, the charging / discharging strategy determination device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0131] Figure 4 This is a block diagram illustrating a charging / discharging strategy determination device according to an exemplary embodiment. (Refer to...) Figure 4The charging and discharging strategy determination device 400 includes an acquisition unit 401 and a determination unit 402.

[0132] The acquisition unit 401 is used to acquire the time information of the charging and discharging phase of the vehicle, the battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phase ends; the time information includes the start time and end time of the charging and discharging phase.

[0133] The determining unit 402 is used to determine a target charge-discharge strategy based on time information, battery information, and target SOC. The target charge-discharge strategy is used to control the battery's SOC to reach the target SOC after the end of the charge-discharge phase. The target charge-discharge strategy also satisfies constraints including multiple optimization objectives. The multiple optimization objectives include minimizing the impact on the battery's charge-discharge performance and minimizing the cost after the end of the charge-discharge phase.

[0134] Optional, such as Figure 4 As shown, the determining unit 402 provided in this embodiment is specifically used for:

[0135] Based on time information, battery information, and target SOC, multiple initial charge and discharge strategies are generated; each initial charge and discharge strategy includes the charge and discharge power of each charge and discharge interval in multiple charge and discharge intervals; multiple charge and discharge intervals are used to form a charge and discharge stage.

[0136] Based on multiple initial charge / discharge strategies and a preset quantum particle swarm optimization algorithm, multiple candidate charge / discharge strategies are generated; the multiple candidate charge / discharge strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charge / discharge strategy is less than the constraint violation threshold of the candidate charge / discharge strategy; the infeasibility is used to indicate the distance between the candidate charge / discharge strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charge / discharge strategy is greater than a preset congestion distance threshold.

[0137] From multiple candidate charging and discharging strategies, the candidate charging and discharging strategy with the highest standard satisfaction is determined as the target charging and discharging strategy; standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charging and discharging stage is completed according to the charging and discharging strategy.

[0138] Optionally, the battery information includes the battery's maximum charging power, maximum discharging power, rated battery capacity, and initial state of charge (SOC); such as... Figure 4 As shown, the determining unit 402 provided in this embodiment is specifically used for:

[0139] Multiple charge / discharge intervals are determined based on the start and end times of the charge / discharge phases and the preset charge / discharge time intervals.

[0140] For the first charge-discharge strategy, based on the first charge-discharge strategy, the battery's rated capacity, and the initial SOC, it is determined whether the battery's SOC reaches the target SOC after the charge-discharge phase ends. The first charge-discharge strategy includes multiple charge-discharge intervals, and the charge-discharge power of each interval is less than the maximum charging power and greater than the maximum discharging power.

[0141] If the battery's SOC reaches the target SOC after the charge / discharge phase ends, then the first charge / discharge strategy will be determined as the initial charge / discharge strategy.

[0142] Optional, such as Figure 4 As shown, the determining unit 402 provided in this embodiment is specifically used for:

[0143] According to the preset quantum particle swarm optimization algorithm, multiple initial charge-discharge strategies are treated as a particle swarm for iterative processing. Each charge-discharge strategy corresponds to a particle in the particle swarm. One iteration process includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain the first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain the second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain the third intermediate particle swarm with the maximum number of particles.

[0144] After multiple iterations, if the number of times the third intermediate particle group is the same after consecutive iterations is greater than the preset number, or the number of multiple iterations is equal to the preset maximum number, then the charging and discharging strategy corresponding to the third intermediate particle group obtained in the last iteration is used as multiple candidate charging and discharging strategies.

[0145] Optional, such as Figure 4 As shown, the determining unit 402 provided in this application embodiment is further used for:

[0146] Multiple fuzzy satisfaction levels are determined for each candidate charge / discharge strategy among multiple candidate charge / discharge strategies; each fuzzy satisfaction level corresponds to an optimization objective, and the fuzzy satisfaction level is used to indicate the optimization result corresponding to an optimization objective after the charge / discharge phase is completed according to the charge / discharge strategy.

[0147] Based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, the standard satisfaction level of each candidate charging and discharging strategy is determined, resulting in multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

[0148] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0149] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 5 As shown, the electronic device 500 includes, but is not limited to, a processor 501 and a memory 502.

[0150] The memory 502 described above is used to store the executable instructions of the processor 501. It is understood that the processor 501 is configured to execute instructions to implement the method for determining the charging and discharging strategy in the above embodiments.

[0151] It should be noted that those skilled in the art will understand that Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 5 This may indicate more or fewer components, or a combination of certain components, or a different arrangement of components.

[0152] Processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 502, and by calling data stored in memory 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 501 may include one or more processing units. Optionally, processor 501 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 501.

[0153] The memory 502 can be used to store software programs and various data. The memory 502 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0154] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions, which can be executed by a processor 501 of an electronic device 500 to implement the method for determining a charging and discharging strategy in the above embodiments.

[0155] In actual implementation, Figure 4 The functions of the acquisition unit 401 and the determination unit 402 can both be provided by Figure 5The processor 501 calls the computer program stored in the memory 502 to implement the process. The specific execution process can be found in the description of the charging / discharging strategy determination method in the previous embodiment, and will not be repeated here.

[0156] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0157] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 501 of an electronic device to complete the method for determining the charging and discharging strategy in the above embodiments.

[0158] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the electronic device, they implement the various processes of the above-described method for determining the charging and discharging strategy, and can achieve the same technical effect as the above-described method for determining the charging and discharging strategy. To avoid repetition, they will not be described again here.

[0159] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0161] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0162] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0164] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining a charging and discharging strategy, characterized in that, include: The system acquires time information of the vehicle's charging and discharging phases, battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phase ends; the time information includes the start and end times of the charging and discharging phases. Based on the time information, the battery information, and the target SOC, multiple initial charge-discharge strategies are generated; each initial charge-discharge strategy includes the charge-discharge power of each charge-discharge interval in multiple charge-discharge intervals; the multiple charge-discharge intervals are used to form the charge-discharge stage; According to a preset quantum particle swarm optimization algorithm, the multiple initial charge-discharge strategies are treated as a particle swarm for iterative processing. Each charge-discharge strategy corresponds to a particle in the particle swarm. One iteration includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain a first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain a second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm with the number of particles equal to the maximum number of particles. After multiple iterations, if the number of times the third intermediate particle group is obtained in consecutive iterations is greater than a preset number, or the number of iterations is equal to a preset maximum number, then the charging / discharging strategy corresponding to the third intermediate particle group obtained in the last iteration is taken as multiple candidate charging / discharging strategies; the multiple candidate charging / discharging strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charging / discharging strategy is less than the constraint violation threshold of the candidate charging / discharging strategy; the infeasibility is used to indicate the distance between the candidate charging / discharging strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charging / discharging strategy is greater than a preset congestion distance threshold; From the multiple candidate charge / discharge strategies, the candidate charge / discharge strategy with the highest standard satisfaction is determined as the target charge / discharge strategy; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charge / discharge stage is completed according to the charge / discharge strategy; the target charge / discharge strategy is used to control the SOC of the battery to reach the target SOC after the end of the charge / discharge stage; the target charge / discharge strategy also satisfies the constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge / discharge performance of the battery and minimizing the cost after the end of the charge / discharge stage.

2. The method according to claim 1, characterized in that, The battery information includes the battery's maximum charging power, maximum discharging power, rated battery capacity, and initial state of charge (SOC). The step of generating multiple initial charge / discharge strategies based on the time information, the battery information, and the target SOC includes: The plurality of charge-discharge intervals are determined based on the start and end times of the charge-discharge phase and the preset charge-discharge time interval. For the first charge-discharge strategy, based on the first charge-discharge strategy, the battery rated capacity, and the initial SOC, it is determined whether the battery's SOC reaches the target SOC after the charge-discharge phase ends; the first charge-discharge strategy includes the plurality of charge-discharge intervals, and the charge-discharge power of each charge-discharge interval is less than the maximum charging power and greater than the maximum discharging power. If the SOC of the battery reaches the target SOC after the charging and discharging phase ends, then the first charging and discharging strategy is determined as the initial charging and discharging strategy.

3. The method according to claim 1, characterized in that, The method further includes: Multiple fuzzy satisfaction levels are determined for each candidate charging and discharging strategy among the multiple candidate charging and discharging strategies; each fuzzy satisfaction level corresponds to an optimization objective, and the fuzzy satisfaction level is used to indicate the optimization result corresponding to the optimization objective after the charging and discharging stage is completed according to the charging and discharging strategy; Based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, the standard satisfaction level of each candidate charging and discharging strategy is determined, resulting in multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

4. A device for determining a charging / discharging strategy, characterized in that, include: Acquiring and determining units; The acquisition unit is used to acquire time information of the vehicle's charging and discharging phase, battery information of the vehicle's battery, and the target SOC of the battery after the charging and discharging phase ends; the time information includes the start time and end time of the charging and discharging phase. The determining unit is configured to generate multiple initial charge-discharge strategies based on the time information, the battery information, and the target SOC; each initial charge-discharge strategy includes the charge-discharge power of each charge-discharge interval in multiple charge-discharge intervals; the multiple charge-discharge intervals are used to form the charge-discharge stage; According to a preset quantum particle swarm optimization algorithm, the multiple initial charge-discharge strategies are treated as a particle swarm for iterative processing. Each charge-discharge strategy corresponds to a particle in the particle swarm. One iteration includes: updating the position of the parent particle to obtain the child particle, and merging the child particle with the parent particle to obtain a first intermediate particle swarm; deleting particles that do not meet the first condition from the first intermediate particle swarm to obtain a second intermediate particle swarm; if the number of particles in the second intermediate particle swarm is greater than the preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm with the number of particles equal to the maximum number of particles. After multiple iterations, if the number of times the third intermediate particle group is obtained in consecutive iterations is greater than a preset number, or the number of iterations is equal to a preset maximum number, then the charging / discharging strategy corresponding to the third intermediate particle group obtained in the last iteration is taken as multiple candidate charging / discharging strategies; the multiple candidate charging / discharging strategies satisfy a first condition and / or a second condition; the first condition includes that the infeasibility of the candidate charging / discharging strategy is less than the constraint violation threshold of the candidate charging / discharging strategy; the infeasibility is used to indicate the distance between the candidate charging / discharging strategy and the feasible region; the second condition includes that the target congestion distance of the candidate charging / discharging strategy is greater than a preset congestion distance threshold; From the multiple candidate charge / discharge strategies, the candidate charge / discharge strategy with the highest standard satisfaction is determined as the target charge / discharge strategy; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charge / discharge stage is completed according to the charge / discharge strategy; the target charge / discharge strategy is used to control the SOC of the battery to reach the target SOC after the end of the charge / discharge stage; the target charge / discharge strategy also satisfies the constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge / discharge performance of the battery and minimizing the cost after the end of the charge / discharge stage.

5. The apparatus according to claim 4, characterized in that, The battery information includes the battery's maximum charging power, maximum discharging power, rated battery capacity, and initial SOC; the determining unit is specifically used for: The plurality of charge-discharge intervals are determined based on the start and end times of the charge-discharge phase and the preset charge-discharge time interval. For the first charge-discharge strategy, based on the first charge-discharge strategy, the battery rated capacity, and the initial SOC, it is determined whether the battery's SOC reaches the target SOC after the charge-discharge phase ends; the first charge-discharge strategy includes the plurality of charge-discharge intervals, and the charge-discharge power of each charge-discharge interval is less than the maximum charging power and greater than the maximum discharging power. If the SOC of the battery reaches the target SOC after the charging and discharging phase ends, then the first charging and discharging strategy is determined as the initial charging and discharging strategy.

6. The apparatus according to claim 4, characterized in that, The determining unit is further configured to: Multiple fuzzy satisfaction levels are determined for each candidate charging and discharging strategy among the multiple candidate charging and discharging strategies; each fuzzy satisfaction level corresponds to an optimization objective, and the fuzzy satisfaction level is used to indicate the optimization result corresponding to the optimization objective after the charging and discharging stage is completed according to the charging and discharging strategy; Based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, the standard satisfaction level of each candidate charging and discharging strategy is determined, and multiple standard satisfaction levels are obtained. Each candidate charge / discharge strategy corresponds to a standard satisfaction level.

7. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 3.

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