Charging and discharging strategy determination method and device, equipment and storage medium

Abstract

The invention relates to a charging and discharging strategy determination method and device, equipment and a storage medium, and relates to the technical field of electric vehicle charging. The method comprises the steps of obtaining time information of a charging and discharging stage of a vehicle, battery information of a battery of the vehicle and a target state of charge (SOC) of the battery after the charging and discharging stage is ended; the time information comprises the starting time and the ending time of the charging and discharging stage; determining a target charging and discharging strategy according to the time information, the battery information and the target SOC; the target charging and discharging strategy is used for controlling the SOC of the battery to reach the target SOC after the charging and discharging stage is finished; the target charging and discharging strategy also meets a constraint condition comprising a plurality of optimization targets; the plurality of optimization objectives include minimum influence on charge and discharge performance of the battery and minimum cost after the charge and discharge phase is ended.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging, and in particular to a method, device, equipment and storage medium for determining a charging and discharging strategy. Background Art

[0002] Today, electric vehicle grid access technology can provide auxiliary services to the power grid, such as peak-shaving services, based on the price of peak and valley electricity, to reduce the charging cost of electric vehicles.

[0003] However, in the related art, only the charging cost is optimized as the optimization target, and it is impossible to generate charging and discharging strategies for multiple optimization targets while reducing the charging cost. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for determining a charging and discharging strategy, so as to at least solve the technical problem in the related art that it is impossible to generate a charging strategy for multiple optimization objectives while reducing the charging cost. The technical solution of the present application is as follows:

[0005] According to the first aspect of the present application, there is provided a method for determining a charging and discharging strategy, comprising: obtaining time information of a vehicle's charging and discharging phase, battery information of a battery of the vehicle, and a target state of charge (SOC) of the battery after the charging and discharging phase ends; the time information comprises the start time and the end time of the charging and discharging phase; determining a target charging and discharging strategy based on the time information, the battery information, and the target SOC; the target charging and discharging strategy is used to control the SOC of the battery 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 charging and discharging performance of the battery, and minimizing the cost after the charging and discharging phase ends.

[0006] According to the above technical means, the present application can obtain the time information of the vehicle's charging and discharging phase, the battery information of the vehicle's battery, and the target state of charge SOC of the battery after the charging and discharging phase, and determine the target charging and discharging strategy based on the time information, battery information and target SOC. In this way, since the target charging and discharging strategy includes constraints of multiple optimization targets, the target charging and discharging strategy can simultaneously meet multiple optimization targets, and also realizes a method for generating charging and discharging strategies for multiple optimization targets.

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

[0008] According to the above technical means, the present application can generate multiple initial charge and discharge strategies according to time information, battery information, and target SOC; and generate multiple candidate charge and discharge strategies according to the multiple initial charge and discharge strategies and the preset quantum particle swarm algorithm. Further, from the multiple candidate charge and discharge strategies, the candidate charge and discharge strategy with the highest standard satisfaction is determined as the target charge and discharge strategy. In this way, by pre-generating multiple initial charge and discharge strategies and processing the multiple initial charge and discharge strategies, multiple better candidate charge and discharge strategies are obtained. Furthermore, the optimal target charge and discharge strategy is determined from the multiple candidate charge and discharge strategies, and the optimal charge and discharge strategy can be obtained.

[0009] In a possible implementation, the battery information includes the maximum charging power, maximum discharging power, rated capacity, and initial SOC of the battery; multiple initial charging and discharging strategies are generated based on the time information, battery information, and target SOC, including: determining 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; for a first charging and discharging strategy, determining whether the SOC of the battery reaches the target SOC after the charging and discharging phase ends according to the first charging and discharging strategy, the rated capacity of the battery, and the initial SOC; the first charging and discharging strategy includes multiple charging and discharging intervals, and the charging and discharging power of each charging and discharging 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, the first charging and discharging strategy is determined as the initial charging and discharging strategy.

[0010] According to the above technical means, the present application can determine multiple charge and discharge intervals according to the start and end times of the charge and discharge phase, and the preset charge and discharge time interval; and for the first charge and discharge strategy, according to the first charge and discharge strategy and the rated capacity and initial SOC of the battery, determine whether the SOC of the battery reaches the target SOC after the charge and discharge phase ends according to the first charge and discharge strategy. Further, if the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the first charge and discharge strategy is determined as the initial charge and discharge strategy. In this way, by predetermining multiple charge and discharge intervals, generating the first charge and discharge strategy, and when the SOC of the battery can reach the target SOC, judging the first charge and discharge strategy as the initial charge and discharge strategy, a method for determining the initial charge and discharge strategy is implemented.

[0011] In a possible implementation, the method of generating multiple candidate charge and discharge strategies according to multiple initial charge and discharge strategies and a preset quantum particle swarm algorithm includes: iteratively processing multiple initial charge and discharge strategies as a particle swarm according to a preset quantum particle swarm algorithm; one charge and discharge strategy corresponds to one particle in the particle swarm, and one iterative processing includes: updating the position of the parent particle to obtain a 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 a preset maximum number of particles, 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 performing multiple iterative processing, if the number of times that the third intermediate particle swarms are the same in consecutive iterations is greater than a preset number of times, or the number of multiple iterative processing is equal to the preset maximum number of times, then the charge and discharge strategy corresponding to the third intermediate particle swarm obtained in the last iterative processing is used as the multiple candidate charge and discharge strategies.

[0012] According to the above technical means, the present application can iteratively process multiple initial charge and discharge strategies as particle groups according to a preset quantum particle swarm algorithm; and after multiple iterative processes, if the number of times that the third intermediate particle groups are the same in continuous iterations is greater than the preset number of times, or the number of multiple iterative processes is equal to the preset maximum number of times, then the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative process is used as multiple candidate charge and discharge strategies. In this way, by iteratively processing multiple initial charge and discharge strategies and judging the number of times that the third intermediate particle groups are the same in continuous iterations or the number of multiple iterative processes, it can be ensured that the multiple candidate charge and discharge strategies obtained are the optimal charge and discharge strategies in multiple iterations.

[0013] In a possible implementation, the method further includes: determining multiple fuzzy satisfaction levels corresponding to each of multiple candidate charging and discharging strategies; one fuzzy satisfaction level corresponds to one optimization target, and the fuzzy satisfaction level is used to indicate an optimization result corresponding to an optimization target after the charging and discharging stage is completed according to the charging and discharging strategy; according to the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, determining a standard satisfaction level for each candidate charging and discharging strategy, and obtaining multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

[0014] According to the above technical means, the present application can obtain multiple standard satisfaction levels by determining multiple fuzzy satisfaction levels corresponding to each candidate charge and discharge strategy among multiple candidate charge and discharge strategies; and determining the standard satisfaction level of each candidate charge and discharge strategy according to the multiple fuzzy satisfaction levels corresponding to each candidate charge and discharge strategy. In this way, by predetermining the fuzzy satisfaction level of each candidate charge and discharge strategy for each optimization target, and determining the standard satisfaction level of each candidate charge and discharge strategy according to the multiple fuzzy satisfaction levels corresponding to each candidate charge and discharge strategy, a method for determining the standard satisfaction level of each candidate charge and discharge strategy is implemented.

[0015] According to the second aspect provided by the present application, a device for determining a charging and discharging strategy is provided, including an acquisition unit and a determination unit; the acquisition unit is used to acquire time information of a vehicle's charging and discharging phase, battery information of a battery of the vehicle, and a target state of charge SOC of the battery after the charging and discharging phase ends; the time information includes the start time and the end time of the charging and discharging phase; the determination unit is used to determine a target charging and discharging strategy based on the time information, the battery information, and the target SOC; the target charging and discharging strategy is used to control the SOC of the battery 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 charging and discharging performance of the battery, and minimizing the cost after the charging and discharging phase ends.

[0016] In a possible implementation, the determination unit is specifically used to: generate multiple initial charge and discharge strategies according to time information, battery information, and target SOC; 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; generate multiple candidate charge and discharge strategies according to multiple initial charge and discharge strategies and a preset quantum particle swarm algorithm; multiple candidate charge and discharge strategies meet the first condition and / or the second condition; the first condition includes that the infeasibility of the candidate charge and discharge strategy is less than the constraint violation threshold of the candidate charge and discharge strategy; the infeasibility is used to indicate the distance between the candidate charge and discharge strategy and the feasible domain; the second condition includes that the target crowding distance of the candidate charge and discharge strategy is greater than the preset crowding distance threshold; from multiple candidate charge and discharge strategies, determine the candidate charge and discharge strategy with the highest standard satisfaction as the target charge and discharge strategy; the standard satisfaction is used to indicate the optimization result of multiple optimization objectives as a whole after the charge and discharge stage is completed according to the charge and discharge strategy.

[0017] In a possible implementation, the battery information includes the maximum charging power, maximum discharging power, rated capacity, and initial SOC of the battery; the above-mentioned determination unit is specifically used to: determine multiple charging and discharging intervals according to the start and end times of the charging and discharging phases, and a preset charging and discharging time interval; for the first charging and discharging strategy, determine whether the SOC of the battery reaches the target SOC after the charging and discharging phase ends according to the first charging and discharging strategy, the rated capacity of the battery, and the initial SOC; the first charging and discharging strategy includes multiple charging and discharging intervals, and the charging and discharging power of each charging and discharging 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, the first charging and discharging strategy is determined as the initial charging and discharging strategy.

[0018] In a possible implementation, the determination unit is specifically used to: perform iterative processing on multiple initial charge and discharge strategies as a particle group according to a preset quantum particle swarm algorithm; one charge and discharge strategy corresponds to one particle in the particle group, and one iterative processing 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 group; deleting particles that do not meet the first condition from the first intermediate particle group to obtain a second intermediate particle group; if the number of particles in the second intermediate particle group is greater than a preset maximum number of particles, then deleting particles that do not meet the second condition from the second intermediate particle group to obtain a third intermediate particle group with the maximum number of particles; after performing multiple iterative processing, if the number of times that the third intermediate particle group is the same in consecutive iterations is greater than a preset number of times, or the number of multiple iterative processing is equal to the preset maximum number of times, then the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative processing is used as multiple candidate charge and discharge strategies.

[0019] In a possible implementation, the determination unit is further used to: determine multiple fuzzy satisfaction levels corresponding to each of a plurality of candidate charging and discharging strategies; one fuzzy satisfaction level corresponds to an optimization target, and the fuzzy satisfaction level is used to indicate an optimization result corresponding to an optimization target after the charging and discharging stage is completed according to the charging and discharging strategy; determine a standard satisfaction level for each candidate charging and discharging strategy based on the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtain multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

[0020] According to the third aspect provided by the present 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 above-mentioned first aspect and any possible implementation manner thereof.

[0021] According to the fourth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0022] According to the fifth aspect provided by the present application, a computer program product is provided, the computer program product comprising computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0023] It should be noted that the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

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

[0026] (1) The target charge and discharge strategy can be determined by obtaining the time information of the vehicle's charge and discharge phase, the battery information of the vehicle's battery, and the target state of charge SOC of the battery after the charge and discharge phase ends, and according to the time information, the battery information, and the target SOC. In this way, since the target charge and discharge strategy includes constraints of multiple optimization objectives, the target charge and discharge strategy can simultaneously meet multiple optimization objectives, and a method for generating a charge and discharge strategy for multiple optimization objectives is also realized.

[0027] (2) Multiple initial charge and discharge strategies can be generated based on time information, battery information, and target SOC; and multiple candidate charge and discharge strategies can be generated based on the multiple initial charge and discharge strategies and the preset quantum particle swarm algorithm. Further, from the multiple candidate charge and discharge strategies, the candidate charge and discharge strategy with the highest standard satisfaction is determined as the target charge and discharge strategy. In this way, by pre-generating multiple initial charge and discharge strategies and processing the multiple initial charge and discharge strategies, multiple better candidate charge and discharge strategies are obtained. Further, the optimal target charge and discharge strategy is determined from the multiple candidate charge and discharge strategies, and the optimal charge and discharge strategy can be obtained.

[0028] (3) Multiple charge and discharge intervals can be determined based on the start and end times of the charge and discharge phase, and the preset charge and discharge time intervals; and for the first charge and discharge strategy, based on the first charge and discharge strategy, the rated capacity of the battery, and the initial SOC, it is determined whether the SOC of the battery reaches the target SOC after the charge and discharge phase ends according to the first charge and discharge strategy. Furthermore, if the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the first charge and discharge strategy is determined as the initial charge and discharge strategy. In this way, by predetermining multiple charge and discharge intervals, generating the first charge and discharge strategy, and judging the first charge and discharge strategy as the initial charge and discharge strategy when the SOC of the battery can reach the target SOC, a method for determining the initial charge and discharge strategy is implemented.

[0029] (4) It is possible to iterate multiple initial charge-discharge strategies as particle swarms according to a preset quantum particle swarm algorithm; and after multiple iterations, if the number of times that the third intermediate particle swarms are the same in consecutive iterations is greater than a preset number, or the number of multiple 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 used as multiple candidate charge-discharge strategies. In this way, by iteratively processing multiple initial charge-discharge strategies and determining the number of times that the third intermediate particle swarms are the same in consecutive iterations or the number of multiple iterations, it is possible to ensure that the multiple candidate charge-discharge strategies obtained are the optimal charge-discharge strategies in multiple iterations.

[0030] (5) By determining multiple fuzzy satisfactions corresponding to each candidate charge and discharge strategy among multiple candidate charge and discharge strategies; and determining the standard satisfaction of each candidate charge and discharge strategy according to the multiple fuzzy satisfactions corresponding to each candidate charge and discharge strategy, multiple standard satisfactions can be obtained. In this way, by predetermining the fuzzy satisfaction of each candidate charge and discharge strategy for each optimization target, and determining the standard satisfaction of each candidate charge and discharge strategy according to the multiple fuzzy satisfactions corresponding to each candidate charge and discharge strategy, a method for determining the standard satisfaction of each candidate charge and discharge strategy is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0032] Figure 1 is a flow chart of a method for determining a charge and discharge strategy according to an exemplary embodiment;

[0033] Figure 2 is a flow chart of another method for determining a charge and discharge strategy according to an exemplary embodiment;

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

[0035] Figure 4 is a block diagram of a device for determining a charge and discharge strategy according to an exemplary embodiment;

[0036] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0037] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0038] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0039] For ease of understanding, the method for determining the charging and discharging strategy provided in the present application is specifically introduced below with reference to the accompanying drawings.

[0040] Figure 1 is a flow chart of a method for determining a charge and discharge strategy according to an exemplary embodiment. Figure 1 As shown, the method for determining the charge and discharge strategy includes the following steps:

[0041] S101. The electronic device obtains time information of a charging and discharging phase of a vehicle, battery information of a battery of the vehicle, and a target state of charge SOC of the battery after the charging and discharging phase is completed.

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

[0043] As a possible implementation, the driver inputs the start and end time of the charging and discharging phase, as well as the target SOC of the battery after the charging and discharging phase, into the vehicle computer. Accordingly, the electronic device obtains the time information of the charging and discharging phase of the vehicle and the target SOC of the battery after the charging and discharging phase is completed.

[0044] The electronic device sends a battery information acquisition request to the battery management system of the vehicle. After receiving the battery information acquisition request, the battery management system of the vehicle collects the battery information of the battery of the vehicle and sends the battery information of the battery of the vehicle to the electronic device. Further, the electronic device acquires the battery information of the battery of the vehicle.

[0045] S102: The electronic device determines a target charging and discharging strategy according to the time information, the battery information and the target SOC.

[0046] Among them, the target charge and discharge strategy is used to control the SOC of the battery to reach the target SOC after the charge and discharge stage is over; the target charge and discharge strategy also satisfies the constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge and discharge performance of the battery, and minimizing the cost after the charge and discharge stage is over.

[0047] As a possible implementation manner, the electronic device generates multiple initial charge and discharge strategies according to time information, battery information, and target SOC.

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

[0049] The electronic device determines, from among a plurality of candidate charge and discharge strategies, a candidate charge and discharge strategy with the highest standard satisfaction as a target charge and discharge strategy.

[0050] It should be noted that 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; multiple candidate charge and discharge strategies meet the first condition, and / or the second condition; the first condition includes that the infeasibility of the candidate charge and discharge strategy is less than the constraint violation threshold of the candidate charge and discharge strategy; the infeasibility is used to indicate the distance between the candidate charge and discharge strategy and the feasible domain; the second condition includes that the target congestion distance of the candidate charge and 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 and discharge stage is completed according to the charge and discharge strategy.

[0051] It can be understood that the technical solution proposed in the embodiment of the present application obtains the time information of the vehicle's charging and discharging stage, the battery information of the vehicle's battery, and the target state of charge SOC of the battery after the charging and discharging stage, and determines the target charging and discharging strategy based on the time information, battery information and target SOC. In this way, since the target charging and discharging strategy includes constraints of multiple optimization targets, the target charging and discharging strategy can meet multiple optimization targets at the same time, and also realizes a method for generating charging and discharging strategies for multiple optimization targets.

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

[0053] S201 : The electronic device generates a plurality of initial charge and discharge strategies according to time information, battery information, and target SOC.

[0054] Among them, 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; the battery information includes the maximum charge power, maximum discharge power, rated capacity and initial SOC of the battery.

[0055] As a possible implementation, the electronic device determines a plurality of charge and discharge intervals according to the start time and the end time of the charge and discharge phase and a preset charge and discharge time interval.

[0056] The electronic device generates a first charging and discharging strategy according to the maximum charging power and the maximum discharging power in each charging and discharging interval.

[0057] The electronic device determines, for the first charge and discharge strategy, whether the SOC of the battery reaches the target SOC after the charge and discharge phase ends, according to the first charge and discharge strategy, the rated capacity of the battery, and the initial SOC.

[0058] If the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the electronic device determines the first charge and discharge strategy as the initial charge and discharge strategy.

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

[0060] Multiple charge and discharge intervals are represented by n, and the start time of the charge and discharge stage is represented by T start The end time of the charge and discharge phase is represented by T end Indicates that the preset charge and discharge time interval is T invIt means that the electronic device determines multiple charging and discharging intervals satisfying the following formula 1 according to the start and end times of the charging and discharging phases and the preset charging and discharging time intervals:

[0061]

[0062] Initial SOC SOC initial Indicates that the battery rated capacity is expressed in S ev_batmax Indicates that the total charge and discharge power in the charge and discharge interval t is P D (t), the SOC of the battery of the vehicle after the (t-1)th charge and discharge interval is represented by SOC(t-1), and the SOC of the battery of the vehicle after the tth charge and discharge interval is represented by SOC(t). The electronic device calculates that after the charge and discharge interval t ends, the SOC of the battery of the vehicle satisfies the following formula 2:

[0063]

[0064] The maximum charging power of the vehicle's battery is P cmax The maximum discharge power of the vehicle’s battery is represented by P dcmax Indicates. The charge and discharge power P in each charge and discharge interval D (t) satisfies the following formula 3:

[0065]

[0066] Target SOC using SOC exp Indicates that the initial SOC is expressed as SOC initial It means that the electronic device executes according to the initial charging and discharging strategy, and after the charging and discharging stage, the following formula 4 is satisfied:

[0067]

[0068] The charge and discharge power in the charge and discharge interval t is expressed as P D (t) represents the capacity of the battery after the charge and discharge interval t-1 ends, S b (t-1) indicates that the capacity of the battery after the charge and discharge interval t is S b (t) means that the following formula 5 is satisfied:

[0069] S b (t) = S b (t-1)+P D (t)tt∈(1,……,n) Formula 5 S202: The electronic device generates multiple candidate charging and discharging strategies according to multiple initial charging and discharging strategies and a preset quantum particle swarm algorithm.

[0070] Among them, multiple candidate charging and discharging strategies meet the first condition and / or the 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 domain; the second condition includes that the target congestion distance of the candidate charging and discharging strategy is greater than the preset congestion distance threshold.

[0071] As a possible implementation method, the electronic device iteratively processes multiple initial charge and discharge strategies as a particle swarm according to a preset quantum particle swarm algorithm.

[0072] After multiple iterative processes, if the number of times that the third intermediate particle groups are the same in consecutive iterations is greater than a preset number, or the number of multiple iterative processes is equal to a preset maximum number, the electronic device uses the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative process as one of the multiple candidate charge and discharge strategies.

[0073] It should be noted that one charge and discharge strategy corresponds to one particle in a particle group, and one iterative 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 group; deleting particles that do not meet the first condition from the first intermediate particle group to obtain a second intermediate particle group; if the number of particles in the second intermediate particle group is greater than the preset maximum number of particles, deleting particles that do not meet the second condition from the second intermediate particle group to obtain a third intermediate particle group whose number of particles is the maximum number of particles.

[0074] M is the population size, P i_best The best historical position of the i-th particle, M best is the average historical best position of all particles in the population, g best represents the optimal particle in the current global range, Pi is the update of the position of the ith particle, and x i is the position of the i-th particle, λ and u are random numbers on (0,1), α is the innovation parameter, and β is the contraction-expansion factor; the electronic device updates the position of the parent particle to obtain the child particle, which satisfies the following formula 6:

[0075]

[0076] Pi=λPi _best +(1-λ)g best

[0077]

[0078] The innovation parameter α satisfies the following formula 7:

[0079] α=(-1) 0.5+μ Formula 7

[0080] In practical applications, the shrink-expansion factor β can be processed according to the linear decreasing weight method to control the convergence speed of particles. In the early stage of optimization, the shrink-expansion factor β decreases more slowly, which can improve the global search ability and make it easier to find the global optimum. In the later stage of optimization, the shrink-expansion factor β decreases more quickly, which can converge quickly and improve the efficiency of updating particles.

[0081] It can be understood that the innovation parameter α is an integer power of -1, which can enhance the randomness of the electronic device updating the position of the parent particle to obtain the child particle.

[0082] i max is the maximum value of the iterative process, i n is the current iteration number, and the contraction-expansion factor β satisfies the following formula 8:

[0083]

[0084] The following specifically describes how the electronic device deletes particles that do not meet the first condition from the first intermediate particle group to obtain the second intermediate particle group:

[0085] The infeasibility of a particle is represented by L(x), and the electronic device calculates the infeasibility of each particle in the first intermediate particle group to satisfy the following formula 9:

[0086]

[0087] The infeasibility of a particle is represented by L(x), which is the distance from the particle to the feasible domain space. The closer the particle is to the feasible domain space, the smaller L(x). If the particle is determined to be a feasible solution, the value of L(x) is 0.

[0088] The constraint violation threshold is represented by ε, and the current iteration number is represented by i. n Indicates that the total number of iterative processing is i max It indicates that the electronic device determines that the constraint violation threshold corresponding to each particle in the first intermediate particle group satisfies the following formula 10:

[0089]

[0090] It can be understood that if the number of iterative processing increases, the constraint violation threshold will be smaller.

[0091] The following specifically describes the implementation method of, if the number of particles in the second intermediate particle group is greater than the preset maximum number of particles, the electronic device deletes particles that do not meet the second condition from the second intermediate particle group to obtain a third intermediate particle group whose number of particles is the maximum number of particles:

[0092] d iis the target crowding distance value of the i-th particle, M is the number of targets, and f i+1，j and f i-1，j is the fitness value of the two adjacent particles of the ith particle at the jth target, f jmax 、f jmin Corresponding to the maximum and minimum fitness values ​​of the jth target, the electronic device calculates the target crowding distance of each particle to satisfy the following formula 11:

[0093]

[0094] In actual application, the electronic device determines in advance a plurality of first initial charge and discharge strategies satisfying a first condition from a plurality of initial charge and discharge strategies, and determines a plurality of second initial charge and discharge strategies satisfying a second condition from the plurality of first initial charge and discharge strategies.

[0095] S203: The electronic device determines multiple fuzzy satisfaction levels corresponding to each of the multiple candidate charging and discharging strategies.

[0096] Among them, one fuzzy satisfaction degree corresponds to one optimization target, and the fuzzy satisfaction degree is used to indicate the optimization result corresponding to one optimization target after the charging and discharging stage is completed according to the charging and discharging strategy.

[0097] As a possible implementation manner, the electronic device calculates and obtains multiple fuzzy satisfaction levels corresponding to each of the multiple candidate charging and discharging strategies.

[0098] It should be noted that the first optimization target among the multiple optimization targets is the ratio of the number of charge and discharge cycles of the vehicle's battery to the total number of battery cycles of the vehicle.

[0099] C max is the total number of vehicle battery cycles, P D (t) is the charge and discharge power in the charge and discharge interval t, P D (t-1) is the charge and discharge power in the charge and discharge interval t-1, M s (t) is the number of charge and discharge cycles of the battery in the charge and discharge interval t. The number of charge and discharge cycles of the battery is The total number of battery cycles of the vehicle is C max , the electronic device calculates the ratio minf1 of the number of charge and discharge cycles of the vehicle's battery to the total number of cycles of the vehicle's battery to satisfy the following formula twelve:

[0100] N(t)=P D (t)*P D (t-1) t∈(1,……,n)

[0101]

[0102]

[0103] The first optimization target among multiple optimization targets is the expenditure of the vehicle's battery after the charging and discharging phase, c Mbat is the consumption coefficient of the charge and discharge stage, P Mbat (t) is the power value of the grid side during the charging and discharging phase, c g is the valley electricity price, c p P D (t) is the peak electricity price, and the electronic device calculates the expenditure of the vehicle's battery after the charging and discharging stage to satisfy the following formula 13:

[0104]

[0105] η c Charging efficiency for charging the battery of the grid-side vehicle, η d is the discharge efficiency of the vehicle's battery to the grid, and the power value P on the grid during the charging and discharging phase Mbat (t) satisfies the following formula 14:

[0106]

[0107] Need to explain, γ i j is the fuzzy satisfaction of the i-th candidate charging and discharging strategy corresponding to the j-th optimization objective; f i j is the fitness value of the i-th candidate charging and discharging strategy corresponding to the j-th optimization target; f jmax With f jmin is the maximum and minimum value of the jth optimization objective among multiple candidate charging and discharging strategies.

[0108] The electronic device calculates multiple fuzzy satisfactions corresponding to each candidate charging and discharging strategy to satisfy the following formula 15:

[0109]

[0110] S204. The electronic device determines a standard satisfaction level for each candidate charging and discharging strategy according to the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtains multiple standard satisfaction levels.

[0111] Among them, one candidate charging and discharging strategy corresponds to one standard satisfaction.

[0112] As a possible implementation, the electronic device calculates the standard satisfaction level of each candidate charging and discharging strategy according to the multiple satisfaction levels corresponding to each candidate charging and discharging strategy. Further, the electronic device calculates the multiple standard satisfaction levels.

[0113] Need to explain, γ iis the standardized satisfaction of the ith candidate charge and discharge strategy; N is the number of candidate charge and discharge strategies, M is the number of multiple optimization targets, and the electronic device calculates the standard satisfaction of each candidate charge and discharge strategy according to the multiple satisfaction values ​​corresponding to each candidate charge and discharge strategy to satisfy the following formula 16:

[0114]

[0115] S205 . The electronic device determines, from among a plurality of candidate charge and discharge strategies, a candidate charge and discharge strategy with the highest standard satisfaction as a target charge and discharge strategy.

[0116] Among them, the 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.

[0117] As a possible implementation manner, the electronic device ranks the standard satisfaction of multiple candidate charge and discharge strategies, and determines the candidate charge and discharge strategy with the highest standard satisfaction as the target charge and discharge strategy.

[0118] It can be understood that the technical solution provided in the embodiment of the present application generates multiple initial charge and discharge strategies based on time information, battery information, and target SOC; and generates multiple candidate charge and discharge strategies based on multiple initial charge and discharge strategies and a preset quantum particle swarm algorithm. Further, from multiple candidate charge and discharge strategies, the candidate charge and discharge strategy with the highest standard satisfaction is determined as the target charge and discharge strategy. In this way, by pre-generating multiple initial charge and discharge strategies and processing the multiple initial charge and discharge strategies, multiple better candidate charge and discharge strategies are obtained. Furthermore, by determining the optimal target charge and discharge strategy from multiple candidate charge and discharge strategies, the optimal charge and discharge strategy can be obtained.

[0119] In actual application, Figure 3 As shown, the method for determining the charge and discharge strategy provided in the embodiment of the present application includes the following steps:

[0120] S301, the electronic device obtains the time information of the charging and discharging phase of the vehicle, the battery information of the vehicle, the target state of charge SOC of the battery after the charging and discharging phase, and the time information and price information of valley and peak electricity.

[0121] S302: The electronic device determines a plurality of charge and discharge intervals according to the start and end times of the charge and discharge phases and a preset charge and discharge time interval.

[0122] S303: For the first charge and discharge strategy, the electronic device determines whether the battery SOC reaches the target SOC after the charge and discharge phase is completed according to the first charge and discharge strategy, the rated capacity of the battery, and the initial SOC.

[0123] S304: If the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the electronic device determines the first charge and discharge strategy as the initial charge and discharge strategy.

[0124] S305 , the electronic device iteratively processes the multiple initial charge and discharge strategies as a particle swarm according to a preset quantum particle swarm algorithm.

[0125] S306. The electronic device determines multiple fuzzy satisfaction levels corresponding to each of the multiple candidate charging and discharging strategies.

[0126] S307 . The electronic device determines a standard satisfaction level for each candidate charging and discharging strategy according to the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtains multiple standard satisfaction levels.

[0127] S308. After performing multiple iterative processes, if the number of times that the third intermediate particle groups are obtained in continuous iterations is greater than a preset number, or the number of multiple iterative processes is equal to a preset maximum number, the electronic device uses the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative process as one of the multiple candidate charge and discharge strategies.

[0128] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the device for determining the charge and discharge strategy or the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] The embodiment of the present application can, according to the above method, exemplarily divide the functional modules of the device for determining the charge and discharge strategy or the electronic device. For example, the device for determining the charge and discharge strategy or the electronic device may include various functional modules corresponding to the various functional divisions, or two or more functions may be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0130] Figure 4 is a block diagram of a device for determining a charge and discharge strategy according to an exemplary embodiment. Figure 4The device 400 for determining the charge and discharge strategy includes: an acquisition unit 401 and a determination unit 402 .

[0131] The acquisition unit 401 is used to acquire the time information of the charging and discharging stage of the vehicle, the battery information of the battery of the vehicle, and the target state of charge SOC of the battery after the charging and discharging stage ends; the time information includes the start time and the end time of the charging and discharging stage.

[0132] The determination unit 402 is used to determine a target charge and discharge strategy according to time information, battery information and target SOC; the target charge and discharge strategy is used to control the SOC of the battery to reach the target SOC after the charge and discharge stage ends; the target charge and discharge strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge and discharge performance of the battery and minimizing the cost after the charge and discharge stage ends.

[0133] Optional, such as Figure 4 As shown, the determination unit 402 provided in the embodiment of the present application is specifically used for:

[0134] A plurality of initial charge and discharge strategies are generated according to time information, battery information, and target SOC; each initial charge and discharge strategy includes a charge and discharge power of each charge and discharge interval in a plurality of charge and discharge intervals; and a plurality of charge and discharge intervals are used to form a charge and discharge stage.

[0135] According to multiple initial charge and discharge strategies and a preset quantum particle swarm algorithm, multiple candidate charge and discharge strategies are generated; the multiple candidate charge and discharge strategies meet the first condition and / or the second condition; the first condition includes that the infeasibility of the candidate charge and discharge strategy is less than the constraint violation threshold of the candidate charge and discharge strategy; the infeasibility is used to indicate the distance between the candidate charge and discharge strategy and the feasible domain; the second condition includes that the target crowding distance of the candidate charge and discharge strategy is greater than the preset crowding distance threshold.

[0136] From multiple candidate charging and discharging strategies, determine the candidate charging and discharging strategy with the highest standard satisfaction as the target charging and discharging strategy; the 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.

[0137] Optionally, the battery information includes the maximum charging power, maximum discharging power, rated capacity, and initial SOC of the battery; Figure 4 As shown, the determination unit 402 provided in the embodiment of the present application is specifically used for:

[0138] A plurality of charge and discharge intervals are determined according to the start and end times of the charge and discharge phases and the preset charge and discharge time intervals.

[0139] For the first charge and discharge strategy, according to the first charge and discharge strategy, the rated capacity of the battery and the initial SOC, it is determined whether the SOC of the battery reaches the target SOC after the charge and discharge stage is completed according to the first charge and discharge strategy; the first charge and discharge strategy includes multiple charge and discharge intervals, and the charge and discharge power of each charge and discharge interval is less than the maximum charge power and greater than the maximum discharge power.

[0140] If the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the first charge and discharge strategy is determined as the initial charge and discharge strategy.

[0141] Optional, such as Figure 4 As shown, the determination unit 402 provided in the embodiment of the present application is specifically used for:

[0142] According to the preset quantum particle swarm algorithm, multiple initial charge and discharge strategies are iteratively processed as a particle swarm; one charge and discharge strategy corresponds to one particle in the particle swarm, and one iterative 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, 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.

[0143] After multiple iterations, if the number of times that the third intermediate particle groups are the same in consecutive iterations is greater than a preset number, or the number of multiple iterations is equal to a preset maximum number, the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iteration is used as a plurality of candidate charge and discharge strategies.

[0144] Optional, such as Figure 4 As shown, the determining unit 402 provided in the embodiment of the present application is also used for:

[0145] Determine multiple fuzzy satisfaction levels corresponding to each of the multiple candidate charging and discharging strategies; one fuzzy satisfaction level corresponds to one optimization target, and the fuzzy satisfaction level is used to indicate an optimization result corresponding to one optimization target after the charging and discharging stage is completed according to the charging and discharging strategy.

[0146] According to 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 to obtain multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

[0147] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0148] Figure 5 FIG. 1 is a block diagram of 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 .

[0149] The memory 502 is used to store executable instructions of the processor 501. It can be understood that the processor 501 is configured to execute instructions to implement the method for determining the charge and discharge strategy in the above embodiment.

[0150] It should be noted that those skilled in the art can understand that Figure 5 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include Figure 5 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0151] The processor 501 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 502, and calling data stored in the memory 502, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 501 may include one or more processing units. Optionally, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

[0152] The memory 502 can be used to store software programs and various data. The memory 502 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application program required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0153] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions. The instructions can be executed by a processor 501 of an electronic device 500 to implement the method for determining the charge and discharge strategy in the above embodiment.

[0154] In actual implementation, Figure 4 The functions of the acquisition unit 401 and the determination unit 402 in Figure 5The processor 501 in the embodiment calls the computer program stored in the memory 502. The specific execution process can refer to the description of the determination method of the charge and discharge strategy in the above embodiment, which will not be repeated here.

[0155] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0156] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 501 of the electronic device to complete the method for determining the charging and discharging strategy in the above embodiment.

[0157] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the embodiment of the method for determining the above-mentioned charging and discharging strategy are implemented, and the same technical effect as the method for determining the above-mentioned charging and discharging strategy can be achieved. To avoid repetition, they will not be repeated here.

[0158] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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.

[0159] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0161] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0162] If the integrated unit is implemented in the form of 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 embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0163] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining a charge and discharge strategy, characterized in that: include: Acquire time information of the charging and discharging phase of the vehicle, battery information of the battery of the vehicle, and the target state of charge SOC of the battery after the charging and discharging phase ends; the time information includes the start time and the end time of the charging and discharging phase; A target charge and discharge strategy is determined according to the time information, the battery information and the target SOC; the target charge and discharge strategy is used to control the SOC of the battery to reach the target SOC after the charge and discharge stage ends; the target charge and discharge strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge and discharge performance of the battery and minimizing the cost after the charge and discharge stage ends.

2. The method according to claim 1, characterized in that: The determining a target charge and discharge strategy according to the time information, the battery information and the target SOC includes: Generate multiple initial charge and discharge strategies according to the time information, the battery information, and the target SOC; each initial charge and discharge strategy includes a charge and discharge power of each charge and discharge interval in a plurality of charge and discharge intervals; the plurality of charge and discharge intervals are used to constitute the charge and discharge stage; According to the multiple initial charge and discharge strategies and the preset quantum particle swarm algorithm, multiple candidate charge and discharge strategies are generated; the multiple candidate charge and discharge strategies meet the first condition and / or the second condition; the first condition includes that the infeasibility of the candidate charge and discharge strategy is less than the constraint violation threshold of the candidate charge and discharge strategy; the infeasibility is used to indicate the distance between the candidate charge and discharge strategy and the feasible domain; the second condition includes that the target crowding distance of the candidate charge and discharge strategy is greater than the preset crowding distance threshold; From the multiple candidate charging and discharging strategies, determine the candidate charging and discharging strategy with the highest standard satisfaction as the target charging and discharging strategy; the standard satisfaction is used to indicate the optimization result of the multiple optimization objectives as a whole after the charging and discharging stage is completed according to the charging and discharging strategy.

3. The method according to claim 2, characterized in that The battery information includes the maximum charging power, the maximum discharging power, the rated capacity and the initial SOC of the battery; The generating a plurality of initial charge and discharge strategies according to the time information, the battery information, and the target SOC includes: Determining the multiple charge and discharge intervals according to the start time and the end time of the charge and discharge phase and the preset charge and discharge time interval; For a first charge and discharge strategy, determining whether the SOC of the battery reaches the target SOC after the charge and discharge stage is completed according to the first charge and discharge strategy, the rated capacity of the battery, and the initial SOC; the first charge and discharge strategy includes the multiple charge and discharge intervals, and the charge and discharge power of each charge and discharge interval is less than the maximum charge power and greater than the maximum discharge power; If the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the first charge and discharge strategy is determined as the initial charge and discharge strategy.

4. The method according to claim 2, characterized in that: The generating of multiple candidate charge and discharge strategies according to the multiple initial charge and discharge strategies and the preset quantum particle swarm algorithm includes: According to the preset quantum particle swarm algorithm, the multiple initial charge and discharge strategies are iteratively processed as a particle swarm; one charge and discharge strategy corresponds to one particle in the particle swarm, and one iterative 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, deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm whose number of particles is the maximum number of particles; After performing multiple iterative processes, if the number of times that the third intermediate particle group is the same in consecutive iterations is greater than a preset number, or the number of multiple iterative processes is equal to a preset maximum number, the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative process is used as the multiple candidate charge and discharge strategies.

5. The method according to claim 2, characterized in that: The method further comprises: Determine a plurality of fuzzy satisfaction levels corresponding to each of the plurality of candidate charging and discharging strategies; one fuzzy satisfaction level corresponds to one optimization target, and the fuzzy satisfaction level is used to indicate an optimization result corresponding to the one optimization target after the charging and discharging stage is completed according to the charging and discharging strategy; According to 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 to obtain multiple standard satisfaction levels; one candidate charging and discharging strategy corresponds to one standard satisfaction level.

6. A device for determining a charge and discharge strategy, characterized in that: include: Get unit and determine unit; The acquisition unit is used to acquire the time information of the charging and discharging stage of the vehicle, the battery information of the battery of the vehicle, and the target state of charge SOC of the battery after the charging and discharging stage ends; the time information includes the start time and the end time of the charging and discharging stage; The determining unit is used to determine a target charging and discharging strategy according to the time information, the battery information and the target SOC; The target charge and discharge strategy is used to control the SOC of the battery to reach the target SOC after the charge and discharge stage ends; the target charge and discharge strategy also satisfies constraints including multiple optimization objectives; the multiple optimization objectives include minimizing the impact on the charge and discharge performance of the battery and minimizing the cost after the charge and discharge stage ends.

7. The device according to claim 6, characterized in that The determining unit is specifically configured to: Generate multiple initial charge and discharge strategies according to the time information, the battery information, and the target SOC; each initial charge and discharge strategy includes a charge and discharge power of each charge and discharge interval in a plurality of charge and discharge intervals; the plurality of charge and discharge intervals are used to constitute the charge and discharge stage; According to the multiple initial charge and discharge strategies and the preset quantum particle swarm algorithm, multiple candidate charge and discharge strategies are generated; the multiple candidate charge and discharge strategies meet the first condition and / or the second condition; the first condition includes that the infeasibility of the candidate charge and discharge strategy is less than the constraint violation threshold of the candidate charge and discharge strategy; the infeasibility is used to indicate the distance between the candidate charge and discharge strategy and the feasible domain; the second condition includes that the target crowding distance of the candidate charge and discharge strategy is greater than the preset crowding distance threshold; From the multiple candidate charging and discharging strategies, determine the candidate charging and discharging strategy with the highest standard satisfaction as the target charging and discharging strategy; the standard satisfaction is used to indicate the optimization result of the multiple optimization objectives as a whole after the charging and discharging stage is completed according to the charging and discharging strategy.

8. The device according to claim 7, characterized in that The battery information includes the maximum charging power, the maximum discharging power, the rated capacity and the initial SOC of the battery; the determining unit is specifically used to: Determining the multiple charge and discharge intervals according to the start time and the end time of the charge and discharge phase and the preset charge and discharge time interval; For a first charge and discharge strategy, determining whether the SOC of the battery reaches the target SOC after the charge and discharge stage is completed according to the first charge and discharge strategy, the rated capacity of the battery, and the initial SOC; the first charge and discharge strategy includes the multiple charge and discharge intervals, and the charge and discharge power of each charge and discharge interval is less than the maximum charge power and greater than the maximum discharge power; If the SOC of the battery reaches the target SOC after the charge and discharge phase ends, the first charge and discharge strategy is determined as the initial charge and discharge strategy.

9. The device according to claim 7, characterized in that The determining unit is specifically configured to: According to the preset quantum particle swarm algorithm, the multiple initial charge and discharge strategies are iteratively processed as a particle swarm; one charge and discharge strategy corresponds to one particle in the particle swarm, and one iterative 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, deleting particles that do not meet the second condition from the second intermediate particle swarm to obtain a third intermediate particle swarm whose number of particles is the maximum number of particles; After performing multiple iterative processes, if the number of times that the third intermediate particle group is the same in consecutive iterations is greater than a preset number, or the number of multiple iterative processes is equal to a preset maximum number, the charge and discharge strategy corresponding to the third intermediate particle group obtained in the last iterative process is used as the multiple candidate charge and discharge strategies.

10. The device according to claim 7, characterized in that The determining unit is further configured to: Determine a plurality of fuzzy satisfaction levels corresponding to each of the plurality of candidate charging and discharging strategies; one fuzzy satisfaction level corresponds to one optimization target, and the fuzzy satisfaction level is used to indicate an optimization result corresponding to the one optimization target after the charging and discharging stage is completed according to the charging and discharging strategy; Determine a standard satisfaction level of each candidate charging and discharging strategy according to the multiple fuzzy satisfaction levels corresponding to each candidate charging and discharging strategy, and obtain multiple standard satisfaction levels; A candidate charging and discharging strategy corresponds to a standard satisfaction level.

11. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method as claimed in any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that: When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is capable of performing the method as claimed in any one of claims 1 to 5.

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