Energy storage battery charging and discharging control method, device and equipment and storage medium

Abstract

The invention is suitable for the technical field of electric energy storage systems, and provides an energy storage battery charging and discharging control method, device and equipment and a storage medium, and the method comprises the steps: obtaining a historical cluster voltage, a historical cell voltage and a historical cell temperature of a target energy storage battery; obtaining the charging duration upper limit of the target energy storage battery, and determining the optimal charging current of the plurality of charge state segments based on the charging duration upper limit; training a preset deep learning model based on the historical cluster voltage, the historical cell voltage, the historical cell temperature and the optimal charging current of the plurality of charge state segments to obtain a charging current control model; and when the target energy storage battery is charged, inputting the real-time cluster voltage, the real-time battery cell voltage and the real-time battery cell temperature into the charging current control model to obtain a target current, and taking the target current as a real-time charging current of the target energy storage battery. The charging and discharging current of the energy storage battery can be accurately controlled, and the service life of the energy storage battery is prolonged.

Description

Technical Field

[0001] The present application belongs to the technical field of electric energy storage systems, and in particular to energy storage battery charge and discharge control methods, devices, equipment and storage media. Background Art

[0002] Against the backdrop of the rapid development of new energy, in order to store as much unused electricity as possible on the user side or release the stored electricity in a timely manner, energy storage technology is also developing rapidly, and energy storage systems are moving towards larger storage capacity and higher charging and discharging rates.

[0003] In the energy storage battery market, 95.5% of energy storage batteries are lithium-ion batteries, and the rest include lead-acid batteries, flow batteries, and sodium-sulfur batteries. Ensuring the service life of energy storage batteries is crucial for user-side use. Factors that affect the life of energy storage batteries include temperature, charge and discharge current rate, discharge depth, and charge and discharge cut-off voltage. Although the improvement of energy storage technology has increased the storage capacity of unit batteries, the technical route for the use of energy storage batteries is still to use constant current charging or discharging, and mostly use 0.5C or 1C rates for charging and discharging. High-rate charging and discharging will cause more heat to be generated inside the battery, accelerating battery aging. At the same time, the SEI (Solid Electrolyte Interphase) film on the electrode surface of high-rate discharge is thicker than that of low-rate discharge. Moreover, deep discharge will cause damage to the internal structure of the battery. Not only that, inappropriate upper and lower voltage limits have an impact on the battery, which will increase the internal resistance of the battery, increase the internal heat of the battery, and accelerate battery aging.

[0004] The above-mentioned inappropriate charging and discharging methods will directly cause damage to the battery and reduce its capacity, which will in turn affect the next charge or discharge. Repeatedly doing so will greatly reduce the service life of the energy storage battery, reduce the maximum voltage and charge and discharge capacity, affect the storage effect of electricity, and increase the cost of using stored electricity. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and storage medium for controlling the charge and discharge of an energy storage battery, so as to accurately control the charge and discharge current of the energy storage battery and improve the service life of the energy storage battery.

[0006] This application is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for controlling charging and discharging of an energy storage battery, comprising: Obtain the historical cluster voltage, historical cell voltage, and historical cell temperature of the target energy storage battery.

[0007] The upper limit of the charging time of the target energy storage battery is obtained, and the optimal charging current of multiple state of charge segments is determined based on the upper limit of the charging time.

[0008] A preset deep learning model is trained based on the optimal charging current of historical cluster voltage, historical cell voltage, historical cell temperature and multiple state of charge segments to obtain a charging current control model.

[0009] When the target energy storage battery is charged, the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature are input into the charging current control model to obtain the target current, and the target current is used as the real-time charging current of the target energy storage battery.

[0010] In combination with the first aspect, in some possible implementations, the method further includes: The magnitude of the real-time charging current corresponding to each state of charge is used as the magnitude of the discharge current when the target energy storage battery is discharged.

[0011] In combination with the first aspect, in some possible implementations, determining the optimal charging current of multiple state of charge segments based on the upper limit of the charging time includes: The state of charge of the target energy storage battery is divided into a plurality of continuous state of charge segments, and the upper limit of the charging time is divided into a plurality of sub-charging time periods; wherein the number of the state of charge segments is the same as the number of the sub-charging time periods, and each state of charge segment corresponds to a sub-charging time period.

[0012] For each state of charge segment, based on the sub-charging time corresponding to the state of charge segment, the energy efficiency under different charging currents is obtained, and the charging current corresponding to the highest energy efficiency value is used as the optimal charging current for the state of charge segment.

[0013] In combination with the first aspect, in some possible implementations, the plurality of continuous state of charge segments include at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.

[0014] The sub-charging durations corresponding to the first state of charge segment and the third state of charge segment are both greater than the sub-charging duration corresponding to the second state of charge segment.

[0015] In combination with the first aspect, in some possible implementations, taking the charging current corresponding to the maximum energy efficiency value as the optimal charging current for the state of charge segment includes: If there are multiple charging currents corresponding to the maximum energy efficiency value, the multiple charging currents corresponding to the maximum energy efficiency value are all recorded as first currents, and the battery life attenuation percentages corresponding to the multiple first currents are obtained.

[0016] The first current corresponding to the minimum battery life decay percentage is used as the optimal charging current for the state of charge segment.

[0017] In combination with the first aspect, in some possible implementations, the preset deep learning model is a Transformer model; the preset deep learning model is trained based on the optimal charging current of the historical cluster voltage, the historical cell voltage, the historical cell temperature, and multiple state of charge segments to obtain a charging current control model, including: Initialize the hyperparameters of the Transformer model.

[0018] The historical cluster voltage, historical cell voltage, and historical cell temperature are input into the Transformer model to obtain the predicted current value.

[0019] Determine the state of charge segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage; The optimal charging current value of the state of charge segment corresponding to the predicted current value is recorded as the second current.

[0020] The goal is to iterate with the minimum difference between the predicted current value and the second current value, adjust the hyperparameters of the Transformer model, and return to the step of calculating the predicted current value.

[0021] When the difference between the predicted current value and the second current value is 0 or the number of iterations reaches the maximum, the training is terminated and the trained Transformer model is used as the charging current control model.

[0022] In combination with the first aspect, in some possible implementations, obtaining the historical cluster voltage, historical cell voltage, and historical cell temperature of the target energy storage battery includes: The original data corresponding to the historical cluster voltage, the original data corresponding to the historical cell voltage, and the original data corresponding to the historical cell temperature of the target energy storage battery are obtained.

[0023] Each type of raw data is normalized respectively to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

[0024] In a second aspect, an embodiment of the present application provides a storage battery charge and discharge control device, comprising: The data acquisition module is used to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

[0025] The data calculation module is used to obtain the upper limit of the charging time of the target energy storage battery, and determine the optimal charging current of multiple charge state segments based on the upper limit of the charging time.

[0026] The model building module is used to train a preset deep learning model based on historical cluster voltage, historical cell voltage, historical cell temperature and optimal charging current of multiple state of charge segments to obtain a charging current control model.

[0027] The current control module is used to input the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature into the charging current control model when the target energy storage battery is charged, obtain the target current, and use the target current as the real-time charging current of the target energy storage battery.

[0028] In a third aspect, an embodiment of the present application provides a terminal device, including: a processor and a memory, the memory is used to store a computer program, and when the processor executes the computer program, it implements the energy storage battery charging and discharging control method as described in any one of the first aspects.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the energy storage battery charging and discharging control method as described in any one of the first aspects is implemented.

[0030] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0031] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This application processes the upper limit of the charging time, historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery to obtain the training data of the charging current control model, and finally trains and obtains the charging current control model. When the target energy storage battery is charged, the real-time cluster voltage, real-time cell voltage and real-time cell temperature during charging can be directly input into the charging current control model, and the current directly given by the model is the optimal real-time charging current. When the energy storage battery is discharged, the size of the real-time charging current corresponding to each state of charge is directly used as the size of the discharge current. This solution strictly controls the charging current. Compared with the traditional solution, it can minimize the impact on the life of the energy storage battery, reduce the degree of reduction in the storage effect of the energy storage battery, and reduce the cost of using the energy storage battery.

[0032] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 It is a flow chart of a method for controlling charging and discharging of an energy storage battery provided in one embodiment of the present application; Figure 2 It is a structural schematic diagram of an energy storage battery charging and discharging control device provided in one embodiment of the present application; Figure 3 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0036] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0037] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0039] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0040] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0041] The present application provides a method for controlling the charging and discharging of an energy storage battery. Figure 1 is a schematic diagram of a method for controlling the charging and discharging of an energy storage battery provided in an embodiment of the present application, with reference to Figure 1 , the details of the energy storage battery charging and discharging control method are as follows: Step 101 : Obtain the historical cluster voltage, historical cell voltage, and historical cell temperature of the target energy storage battery.

[0042] Exemplarily, step 101 may include: The original data corresponding to the historical cluster voltage, the original data corresponding to the historical cell voltage, and the original data corresponding to the historical cell temperature of the target energy storage battery are obtained.

[0043] Each type of raw data is normalized respectively to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

[0044] For example, normalizing the original data can help improve the stability of the trained charging current control model, accelerate the convergence of the charging current control model, and avoid the disappearance of the model gradient or the explosion of the model gradient.

[0045] Step 102: Obtain an upper limit of the charging time of the target energy storage battery, and determine the optimal charging current of multiple state of charge segments based on the upper limit of the charging time.

[0046] Exemplarily, determining the optimal charging current for a plurality of state of charge segments based on the upper limit of the charging time may include: The state of charge of the target energy storage battery is divided into a plurality of continuous state of charge segments, and the upper limit of the charging time is divided into a plurality of sub-charging time periods; wherein the number of the state of charge segments is the same as the number of the sub-charging time periods, and each state of charge segment corresponds to a sub-charging time period.

[0047] For each state of charge segment, based on the sub-charging time corresponding to the state of charge segment, the energy efficiency under different charging currents is obtained, and the charging current corresponding to the highest energy efficiency value is used as the optimal charging current for the state of charge segment.

[0048] Exemplarily, using the charging current corresponding to the maximum energy efficiency value as the optimal charging current means that the energy efficiency of the energy storage battery can be improved, and the amount of electricity stored and released by the energy storage battery can be increased.

[0049] Exemplarily, the plurality of continuous state of charge segments include at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.

[0050] The sub-charging durations corresponding to the first state of charge segment and the third state of charge segment are both greater than the sub-charging duration corresponding to the second state of charge segment.

[0051] Exemplarily, the first state of charge segment can be a state of lower battery capacity, such as 0%-10% SOC, and the third state of charge segment can be a state of higher battery capacity, such as 90%-100% SOC. The longer sub-charging time corresponding to the first state of charge segment and the third state of charge segment is beneficial to reducing battery attenuation and improving the service life of the energy storage battery.

[0052] Exemplarily, taking the charging current corresponding to the highest energy efficiency value as the optimal charging current for the state of charge segment may include: If there are multiple charging currents corresponding to the maximum energy efficiency value, the multiple charging currents corresponding to the maximum energy efficiency value are all recorded as first currents, and the battery life attenuation percentages corresponding to the multiple first currents are obtained.

[0053] The first current corresponding to the minimum battery life decay percentage is used as the optimal charging current for the state of charge segment.

[0054] Step 103 , training a preset deep learning model based on the historical cluster voltage, the historical cell voltage, the historical cell temperature, and the optimal charging current of multiple state of charge segments to obtain a charging current control model.

[0055] Exemplarily, the preset deep learning model is a Transformer model.

[0056] Exemplarily, step 103 may include: Initialize the hyperparameters of the Transformer model.

[0057] The historical cluster voltage, historical cell voltage, and historical cell temperature are input into the Transformer model to obtain the predicted current value.

[0058] Determine the state of charge segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage; The optimal charging current value of the state of charge segment corresponding to the predicted current value is recorded as the second current.

[0059] The goal is to iterate with the minimum difference between the predicted current value and the second current value, adjust the hyperparameters of the Transformer model, and return to the step of calculating the predicted current value.

[0060] When the difference between the predicted current value and the second current value is 0 or the number of iterations reaches the maximum, the training is terminated and the trained Transformer model is used as the charging current control model.

[0061] Step 104 , when the target energy storage battery is being charged, the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature are input into a charging current control model to obtain a target current, and the target current is used as the real-time charging current of the target energy storage battery.

[0062] Exemplarily, the method may further include: The magnitude of the real-time charging current corresponding to each state of charge is used as the magnitude of the discharge current when the target energy storage battery is discharged.

[0063] The above energy storage battery charge and discharge control method obtains the training data of the charging current control model through the upper limit of the charging time, historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery, and finally trains and obtains the charging current control model. When the target energy storage battery is charged, the real-time cluster voltage, real-time cell voltage and real-time cell temperature during charging can be directly input into the charging current control model, and the current directly given by the model is the optimal real-time charging current. When the energy storage battery is discharged, the magnitude of the real-time charging current corresponding to each state of charge is directly used as the magnitude of the discharge current. This solution strictly controls the charging current. Compared with the traditional solution, it can minimize the impact on the life of the energy storage battery, reduce the degree of reduction in the storage effect of the energy storage battery, and reduce the use cost of the energy storage battery. In addition, this solution does not require additional external equipment. On the basis of the original EMS (Energy Management System) and BMS (Building Management System) communication, it can effectively improve the service life of the energy storage battery, improve the battery use efficiency, and reduce the use cost for users.

[0064] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] Corresponding to the energy storage battery charging and discharging control method described in the above embodiment, Figure 2 The structural block diagram of the energy storage battery charge and discharge control device provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown.

[0066] See also Figure 2 , the energy storage battery charging and discharging control device in the embodiment of the present application may include: The data acquisition module 201 is used to acquire the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

[0067] The data calculation module 202 is used to obtain the upper limit of the charging time of the target energy storage battery, and determine the optimal charging current of multiple state of charge segments based on the upper limit of the charging time.

[0068] The model building module 203 is used to train a preset deep learning model based on the historical cluster voltage, the historical cell voltage, the historical cell temperature and the optimal charging current of multiple state of charge segments to obtain a charging current control model.

[0069] The current control module 204 is used to input the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature into the charging current control model when the target energy storage battery is charged, obtain the target current, and use the target current as the real-time charging current of the target energy storage battery.

[0070] Exemplarily, the current control module 204 may be used to: The magnitude of the real-time charging current corresponding to each state of charge is used as the magnitude of the discharge current when the target energy storage battery is discharged.

[0071] Exemplarily, the data calculation module 202 may be used to: The state of charge of the target energy storage battery is divided into a plurality of continuous state of charge segments, and the upper limit of the charging time is divided into a plurality of sub-charging time periods; wherein the number of the state of charge segments is the same as the number of the sub-charging time periods, and each state of charge segment corresponds to a sub-charging time period.

[0072] For each state of charge segment, based on the sub-charging time corresponding to the state of charge segment, the energy efficiency under different charging currents is obtained, and the charging current corresponding to the highest energy efficiency value is used as the optimal charging current for the state of charge segment.

[0073] Exemplarily, the plurality of continuous state of charge segments include at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.

[0074] The sub-charging durations corresponding to the first state of charge segment and the third state of charge segment are both greater than the sub-charging duration corresponding to the second state of charge segment.

[0075] Exemplarily, the data calculation module 202 may be used to: If there are multiple charging currents corresponding to the maximum energy efficiency value, the multiple charging currents corresponding to the maximum energy efficiency value are all recorded as first currents, and the battery life attenuation percentages corresponding to the multiple first currents are obtained.

[0076] The first current corresponding to the minimum battery life decay percentage is used as the optimal charging current for the state of charge segment.

[0077] Exemplarily, the preset deep learning model is a Transformer model; Exemplarily, the model building module 203 can be used to: Initialize the hyperparameters of the Transformer model.

[0078] The historical cluster voltage, historical cell voltage, and historical cell temperature are input into the Transformer model to obtain the predicted current value.

[0079] Determine the state of charge segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage; The optimal charging current value of the state of charge segment corresponding to the predicted current value is recorded as the second current.

[0080] The goal is to iterate with the minimum difference between the predicted current value and the second current value, adjust the hyperparameters of the Transformer model, and return to the step of calculating the predicted current value.

[0081] When the difference between the predicted current value and the second current value is 0 or the number of iterations reaches the maximum, the training is terminated and the trained Transformer model is used as the charging current control model.

[0082] Exemplarily, the data acquisition module 201 may be used for: The original data corresponding to the historical cluster voltage, the original data corresponding to the historical cell voltage, and the original data corresponding to the historical cell temperature of the target energy storage battery are obtained.

[0083] Each type of raw data is normalized respectively to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

[0084] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0085] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0086] The present application also provides a terminal device, see Figure 3 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 is used to store a computer program 321, and the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 104 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The functions of each module are shown.

[0087] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and are executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0088] Those skilled in the art will understand that Figure 3 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0089] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0090] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0091] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0092] The energy storage battery charging and discharging control method provided in the embodiment of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0093] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each embodiment of the above-mentioned energy storage battery charge and discharge control method can be implemented.

[0094] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned energy storage battery charging and discharging control method when executing the computer program product.

[0095] 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 computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0096] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software 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.

[0098] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, 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.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for controlling charging and discharging of an energy storage battery, characterized in that: include: Obtain the historical cluster voltage, historical cell voltage, and historical cell temperature of the target energy storage battery; Obtaining an upper limit of the charging time of the target energy storage battery, and determining the optimal charging current for multiple state of charge segments based on the upper limit of the charging time; Training a preset deep learning model based on the optimal charging current of the historical cluster voltage, the historical battery cell voltage, the historical battery cell temperature, and the multiple state of charge segments to obtain a charging current control model; When the target energy storage battery is charged, the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature are input into the charging current control model to obtain a target current, and the target current is used as the real-time charging current of the target energy storage battery.

2. The energy storage battery charge and discharge control method according to claim 1, characterized in that: The method further comprises: The magnitude of the real-time charging current corresponding to each state of charge is used as the magnitude of the discharge current when the target energy storage battery is discharged.

3. The energy storage battery charge and discharge control method according to claim 1, characterized in that: The method of determining the optimal charging current for a plurality of state of charge segments based on the upper limit of the charging time includes: Divide the state of charge of the target energy storage battery into a plurality of continuous state of charge segments, and divide the upper limit of the charging time into a plurality of sub-charging time periods; wherein the number of the state of charge segments is the same as the number of the sub-charging time periods, and each state of charge segment corresponds to one sub-charging time period; For each state of charge segment, based on the sub-charging time corresponding to the state of charge segment, the energy efficiency under different charging currents is obtained, and the charging current corresponding to the highest energy efficiency value is used as the optimal charging current for the state of charge segment.

4. The energy storage battery charge and discharge control method according to claim 3, characterized in that: The plurality of continuous state-of-charge segments include at least a first state-of-charge segment, a second state-of-charge segment, and a third state-of-charge segment; The sub-charging durations corresponding to the first state of charge segment and the third state of charge segment are both greater than the sub-charging duration corresponding to the second state of charge segment.

5. The energy storage battery charge and discharge control method according to claim 3, characterized in that: The method of using the charging current corresponding to the maximum energy efficiency value as the optimal charging current for the state of charge segment includes: If there are multiple charging currents corresponding to the maximum energy efficiency value, all of the multiple charging currents corresponding to the maximum energy efficiency value are recorded as first currents, and battery life attenuation percentages corresponding to the multiple first currents are obtained; The first current corresponding to the minimum battery life decay percentage is used as the optimal charging current for the state of charge segment.

6. The energy storage battery charge and discharge control method according to claim 3, characterized in that: The preset deep learning model is a Transformer model; the optimal charging current based on the historical cluster voltage, the historical battery cell voltage, the historical battery cell temperature and the multiple state of charge segments is trained on the preset deep learning model to obtain a charging current control model, including: Initialize the hyperparameters of the Transformer model; Inputting the historical cluster voltage, the historical cell voltage and the historical cell temperature into the Transformer model to obtain a predicted current value; Determining a state of charge segment corresponding to a predicted current value based on the historical cluster voltage and the historical cell voltage; Recording the optimal charging current value of the state of charge segment corresponding to the predicted current value as the second current; Iterate with the goal of minimizing the difference between the predicted current value and the second current value, adjust the hyperparameters of the Transformer model, and return to the step of calculating the predicted current value; When the difference between the predicted current value and the value of the second current is 0 or the number of iterations reaches a maximum, the training is terminated, and the trained Transformer model is used as the charging current control model.

7. The energy storage battery charge and discharge control method according to claim 1, characterized in that: The obtaining of the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery includes: Obtaining original data corresponding to historical cluster voltage, original data corresponding to historical cell voltage, and original data corresponding to historical cell temperature of a target energy storage battery; Each type of raw data is normalized respectively to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery.

8. A storage battery charge and discharge control device, characterized in that: include: A data acquisition module, used to obtain the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery; A data calculation module is used to obtain the upper limit of the charging time of the target energy storage battery, and determine the optimal charging current of multiple state of charge segments based on the upper limit of the charging time; A model building module, configured to train a preset deep learning model based on the historical cluster voltage, the historical cell voltage, the historical cell temperature and the optimal charging current of the multiple state of charge segments to obtain a charging current control model; The current control module is used to input the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature into the charging current control model when the target energy storage battery is charged, obtain the target current, and use the target current as the real-time charging current of the target energy storage battery.

9. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, and wherein when the processor executes the computer program, the energy storage battery charging and discharging control method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the energy storage battery charging and discharging control method according to any one of claims 1 to 7 is implemented.

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