CN119543378A - Energy storage battery charging and discharging control method, device, equipment and storage medium - Google Patents
Energy storage battery charging and discharging control method, device, equipment and storage medium Download PDFInfo
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- 238000013136 deep learning model Methods 0.000 claims abstract description 13
- 238000004590 computer program Methods 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 5
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/007182—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery voltage
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/443—Methods for charging or discharging in response to temperature
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
- H02J7/007194—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature of the battery
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
本申请适用于电能存储系统技术领域，提供了储能电池充放电控制方法、装置、设备及存储介质，该方法包括：获取目标储能电池的历史簇电压、历史电芯电压和历史电芯温度；获取目标储能电池的充电时长上限，并基于充电时长上限确定多个荷电状态分段的最优充电电流；基于历史簇电压、历史电芯电压、历史电芯温度和多个荷电状态分段的最优充电电流训练预设深度学习模型，得到充电电流控制模型；当目标储能电池进行充电时，将实时簇电压、实时电芯电压和实时电芯温度输入充电电流控制模型，得到目标电流，并将目标电流作为目标储能电池的实时充电电流。本申请能准确地控制储能电池的充放电电流，提高储能电池的使用寿命。
The present application is applicable to the technical field of electric energy storage system, and provides a method, device, equipment and storage medium for controlling the charging and discharging of an energy storage battery, the method comprising: obtaining the historical cluster voltage, historical cell voltage and historical cell temperature of the target energy storage battery; obtaining the upper limit of the charging time of the target energy storage battery, and determining the optimal charging current of 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, historical cell voltage, historical cell temperature and multiple state of charge segments to obtain a charging current control model; when the target energy storage battery is charged, inputting the real-time cluster voltage, real-time cell voltage and real-time cell temperature into the charging current control model to obtain the target current, and using the target current as the real-time charging current of the target energy storage battery. The present application can accurately control the charging and discharging current of the energy storage battery and improve the service life of the energy storage battery.
Description
Technical Field
The application belongs to the technical field of electric energy storage systems, and particularly relates to a method, a device, equipment and a storage medium for controlling charge and discharge of an energy storage battery.
Background
In the context of rapid development of new energy, in order to store inexhaustible electricity on the user side as much as possible in time or release the stored electricity in time, energy storage technologies are also being developed in rapid iteration, and energy storage systems are being developed toward a larger storage capacity and a higher charge and discharge rate.
In the energy storage battery market, 95.5% of the energy storage batteries are lithium ion batteries, and the rest of the energy storage batteries also comprise lead-acid batteries, flow batteries, sodium-sulfur batteries and the like. The service life of the energy storage battery is ensured to be very important for the use of a user side. Factors influencing the life of the energy storage battery include temperature, charge-discharge current multiplying power, depth of discharge, charge-discharge cut-off voltage and the like. Although the improvement of the energy storage technology increases the storage capacity of the unit battery, the use technology route of the energy storage battery still adopts constant current charge or discharge, and the charge and discharge are carried out at a rate of 0.5C or 1C, so that more heat is generated in the battery due to high-rate charge and discharge, the aging of the battery is accelerated, and meanwhile, an SEI (solid electrolyte interface) film on the electrode surface of high-rate discharge is thicker than that of low-rate discharge. Moreover, deep discharge can cause damage to the internal structure of the battery, and in addition, the battery is influenced by an improper upper voltage limit and a proper lower voltage limit, so that the internal resistance of the battery is increased, the internal heating of the battery is increased, and the aging of the battery is accelerated.
The improper charge and discharge mode can directly lead to the damage of the battery, reduce the capacity, further influence the next charge or discharge, and repeatedly lead to the great reduction of the service life of the energy storage battery, the reduction of the maximum voltage and the chargeable and dischargeable capacity, influence the storage effect of the electric quantity and increase the use cost of the stored electric energy.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for controlling charge and discharge of an energy storage battery, so as to accurately control the charge and discharge current of the energy storage battery and prolong the service life of the energy storage battery.
The application is realized by the following technical scheme:
In a first aspect, an embodiment of the present application provides a method for controlling charge and discharge of an energy storage battery, including:
And acquiring the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery.
And acquiring the upper limit of the charging time length of the target energy storage battery, and determining the optimal charging currents of the plurality of charge state segments based on the upper limit of the charging time length.
And training a preset deep learning model based on the historical cluster voltage, the historical cell temperature and the optimal charging currents of the plurality of charge state 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 a charging current control model to obtain target current, and the target current is used as the real-time charging current of the target energy storage battery.
With reference to the first aspect, in some possible implementations, the method further includes:
And taking the magnitude of the real-time charging current corresponding to each charge state as the discharging current magnitude when the target energy storage battery is discharged.
With reference to the first aspect, in some possible implementations, determining an optimal charging current for the plurality of state of charge segments based on the upper limit of the charging duration includes:
Dividing the charge state of the target energy storage battery into a plurality of continuous charge state segments, and dividing the upper limit of the charge time period into a plurality of sub charge time periods, wherein the number of the charge state segments is the same as that of the sub charge time periods, and each charge state segment corresponds to one sub charge time period.
And aiming at each charge state segment, acquiring energy efficiency under different charging currents on the basis of sub-charging time length corresponding to the charge state segment, and taking the charging current corresponding to the highest energy efficiency value as the optimal charging current of the charge state segment.
With reference to the first aspect, in some possible implementations, the plurality of consecutive state of charge segments includes at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.
The sub-charging time periods corresponding to the first charge state segment and the third charge state segment are both longer than the sub-charging time period corresponding to the second charge state segment.
With reference to the first aspect, in some possible implementations, taking a charging current corresponding to the highest value of energy efficiency as an optimal charging current for the state of charge segment includes:
if the charging current corresponding to the highest energy efficiency value is a plurality of charging currents, the plurality of charging currents corresponding to the highest energy efficiency value are all recorded as first currents, and the battery life decay percentage corresponding to the plurality of first currents is obtained.
And taking the first current corresponding to the minimum value of the decay percentage of the service life of the battery as the optimal charging current of the charge state segment.
With reference to the first aspect, in some possible implementations, the preset deep learning model is a transducer model, training the preset deep learning model based on the historical cluster voltage, the historical cell temperature and the optimal charging currents of the plurality of state-of-charge segments to obtain a charging current control model, including:
the hyper-parameters of the transducer model are initialized.
And inputting the historical cluster voltage, the historical cell voltage and the historical cell temperature into a transducer model to obtain a predicted current value.
Determining a charge state segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage;
and recording the optimal charging current value of the charge state segment corresponding to the predicted current value as the second current.
And iterating with the minimum difference value between the predicted current value and the value of the second current as a target, adjusting the hyper-parameters of the transducer model, and returning to the step of calculating the predicted current value.
And when the difference value between the predicted current value and the value of the second current is 0 or the iteration number reaches the maximum, ending training, and taking the converter model obtained by training as a charging current control model.
With reference to the first aspect, in some possible implementations, obtaining a historical cluster voltage, a historical cell voltage, and a historical cell temperature of the target energy storage battery includes:
And acquiring original data corresponding to the historical cluster voltage, original data corresponding to the historical cell voltage and original data corresponding to the historical cell temperature of the target energy storage battery.
And respectively carrying out normalization processing on each type of original data to obtain the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery.
In a second aspect, an embodiment of the present application provides a charge and discharge control device for an energy storage battery, including:
the data acquisition module is used for acquiring the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery.
The data calculation module is used for acquiring the upper limit of the charging time length of the target energy storage battery and determining the optimal charging currents of the plurality of charge state segments based on the upper limit of the charging time length.
The model building module is used for training a preset deep learning model based on the historical cluster voltage, the historical cell temperature and the optimal charging currents of the plurality of charge state segments to obtain a charging current control model.
And the current control module is used for inputting 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 to obtain target current, and taking the target current as the real-time charging current of the target energy storage battery.
In a third aspect, an embodiment of the present application provides a terminal device, including a processor and a memory, where the memory is configured to store a computer program, and the processor implements the method for controlling charging and discharging of an energy storage battery according to any one of the first aspects when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for controlling charging and discharging of an energy storage battery according to any one of the first aspects.
It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
According to the application, the training data of the charging current control model is obtained by processing the upper limit of the charging time length, the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery, and finally the charging current control model is trained and obtained. When the energy storage battery is discharged, the magnitude of the real-time charging current corresponding to each charge state is directly used as the magnitude of the discharging current. The scheme has carried out strict control to charging current, compares in traditional scheme, can influence energy storage battery's life-span as little as possible, reduces the degree that energy storage battery storage effect reduced, reduces energy storage battery's use cost.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for controlling charge and discharge of an energy storage battery according to an embodiment of the application;
fig. 2 is a schematic structural diagram of an energy storage battery charging and discharging control device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
An embodiment of the present application provides a method for controlling charge and discharge of an energy storage battery, and fig. 1 is a schematic flow chart of the method for controlling charge and discharge of an energy storage battery according to an embodiment of the present application, and details of the method for controlling charge and discharge of an energy storage battery are as follows with reference to fig. 1:
Step 101, acquiring historical cluster voltage, historical cell voltage and historical cell temperature of a target energy storage battery.
Illustratively, step 101 may include:
And acquiring original data corresponding to the historical cluster voltage, original data corresponding to the historical cell voltage and original data corresponding to the historical cell temperature of the target energy storage battery.
And respectively carrying out normalization processing on each type of original data to obtain the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery.
By way of example, the normalization processing is performed on the original data, so that the stability of the charge current control model after training is improved, the convergence of the charge current control model can be accelerated, and the model gradient disappearance or the model gradient explosion condition is avoided.
Step 102, obtaining an upper limit of the charging time length of the target energy storage battery, and determining optimal charging currents of a plurality of charge state segments based on the upper limit of the charging time length.
For example, determining an optimal charging current for a plurality of state of charge segments based on an upper charge duration limit may include:
Dividing the charge state of the target energy storage battery into a plurality of continuous charge state segments, and dividing the upper limit of the charge time period into a plurality of sub charge time periods, wherein the number of the charge state segments is the same as that of the sub charge time periods, and each charge state segment corresponds to one sub charge time period.
And aiming at each charge state segment, acquiring energy efficiency under different charging currents on the basis of sub-charging time length corresponding to the charge state segment, and taking the charging current corresponding to the highest energy efficiency value as the optimal charging current of the charge state segment.
By way of example, using the charging current corresponding to the highest 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 improved.
The plurality of consecutive state of charge segments, for example, includes at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.
The sub-charging time periods corresponding to the first charge state segment and the third charge state segment are both longer than the sub-charging time period corresponding to the second charge state segment.
For example, the first state of charge segment may be a state with a lower battery capacity, for example, 0% -10% soc, and the third state of charge segment may be a state with a higher battery capacity, for example, 90% -100% soc, where the sub-charging durations corresponding to the first state of charge segment and the third state of charge segment are greater, so as to reduce the attenuation of the battery and improve the service life of the energy storage battery.
Illustratively, 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 the charging current corresponding to the highest energy efficiency value is a plurality of charging currents, the plurality of charging currents corresponding to the highest energy efficiency value are all recorded as first currents, and the battery life decay percentage corresponding to the plurality of first currents is obtained.
And taking the first current corresponding to the minimum value of the decay percentage of the service life of the battery as the optimal charging current of the charge state segment.
And step 103, training a preset deep learning model based on the historical cluster voltage, the historical cell temperature and the optimal charging currents of the plurality of charge state segments to obtain a charging current control model.
The preset deep learning model is illustratively a transducer model.
Illustratively, step 103 may include:
the hyper-parameters of the transducer model are initialized.
And inputting the historical cluster voltage, the historical cell voltage and the historical cell temperature into a transducer model to obtain a predicted current value.
Determining a charge state segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage;
and recording the optimal charging current value of the charge state segment corresponding to the predicted current value as the second current.
And iterating with the minimum difference value between the predicted current value and the value of the second current as a target, adjusting the hyper-parameters of the transducer model, and returning to the step of calculating the predicted current value.
And when the difference value between the predicted current value and the value of the second current is 0 or the iteration number reaches the maximum, ending training, and taking the converter model obtained by training as a charging current control model.
And 104, inputting the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature into a charging current control model to obtain a target current when the target energy storage battery is charged, and taking the target current as the real-time charging current of the target energy storage battery.
The method may further comprise:
And taking the magnitude of the real-time charging current corresponding to each charge state as the discharging current magnitude when the target energy storage battery is discharged.
According to the energy storage battery charge-discharge control method, the training data of the charge current control model is obtained through processing the upper limit of the charge time length, the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery, the charge current control model is finally trained and obtained, when the target energy storage battery is charged, the real-time cluster voltage, the real-time cell voltage and the real-time cell temperature during charging can be directly input into the charge current control model, and the current directly given by the model is the optimal real-time charge current. When the energy storage battery is discharged, the magnitude of the real-time charging current corresponding to each charge state is directly used as the magnitude of the discharging current. The scheme has carried out strict control to charging current, compares in traditional scheme, can influence energy storage battery's life-span as little as possible, reduces the degree that energy storage battery storage effect reduced, reduces energy storage battery's use cost. In addition, the scheme does not need to additionally increase external equipment, and can effectively prolong the service life of the energy storage battery, improve the service efficiency of the battery and reduce the use cost for users on the basis of communication between the original EMS (energy management system ) and the BMS (Building management system, building MANAGEMENT SYSTEM).
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
Corresponding to the method for controlling charge and discharge of the energy storage battery described in the foregoing embodiments, fig. 2 is a block diagram illustrating a structure of the device for controlling charge and discharge of the energy storage battery according to an embodiment of the present application, and for convenience of explanation, only the portions related to the embodiments of the present application are shown.
Referring to fig. 2, the charge and discharge control device for an energy storage battery according to an embodiment of the present application may include:
the data acquisition module 201 is configured to acquire a historical cluster voltage, a historical cell voltage, and a historical cell temperature of the target energy storage battery.
The data calculation module 202 is configured to obtain an upper limit of a charging duration of the target energy storage battery, and determine optimal charging currents of the plurality of charge state segments based on the upper limit of the charging duration.
The model building module 203 is configured to train a preset deep learning model based on the historical cluster voltage, the historical cell temperature, and the optimal charging currents of the plurality of state of charge segments, and obtain a charging current control model.
The current control module 204 is configured to input the real-time cluster voltage, the real-time cell voltage, and the real-time cell temperature into a charging current control model when the target energy storage battery is charged, obtain a target current, and use the target current as the real-time charging current of the target energy storage battery.
For example, the current control module 204 may be configured to:
And taking the magnitude of the real-time charging current corresponding to each charge state as the discharging current magnitude when the target energy storage battery is discharged.
By way of example, the data calculation module 202 may be configured to:
Dividing the charge state of the target energy storage battery into a plurality of continuous charge state segments, and dividing the upper limit of the charge time period into a plurality of sub charge time periods, wherein the number of the charge state segments is the same as that of the sub charge time periods, and each charge state segment corresponds to one sub charge time period.
And aiming at each charge state segment, acquiring energy efficiency under different charging currents on the basis of sub-charging time length corresponding to the charge state segment, and taking the charging current corresponding to the highest energy efficiency value as the optimal charging current of the charge state segment.
The plurality of consecutive state of charge segments, for example, includes at least a first state of charge segment, a second state of charge segment, and a third state of charge segment.
The sub-charging time periods corresponding to the first charge state segment and the third charge state segment are both longer than the sub-charging time period corresponding to the second charge state segment.
By way of example, the data calculation module 202 may be configured to:
if the charging current corresponding to the highest energy efficiency value is a plurality of charging currents, the plurality of charging currents corresponding to the highest energy efficiency value are all recorded as first currents, and the battery life decay percentage corresponding to the plurality of first currents is obtained.
And taking the first current corresponding to the minimum value of the decay percentage of the service life of the battery as the optimal charging current of the charge state segment.
The preset deep learning model is illustratively a transducer model, and the model building module 203 may be illustratively configured to:
the hyper-parameters of the transducer model are initialized.
And inputting the historical cluster voltage, the historical cell voltage and the historical cell temperature into a transducer model to obtain a predicted current value.
Determining a charge state segment corresponding to the predicted current value based on the historical cluster voltage and the historical cell voltage;
and recording the optimal charging current value of the charge state segment corresponding to the predicted current value as the second current.
And iterating with the minimum difference value between the predicted current value and the value of the second current as a target, adjusting the hyper-parameters of the transducer model, and returning to the step of calculating the predicted current value.
And when the difference value between the predicted current value and the value of the second current is 0 or the iteration number reaches the maximum, ending training, and taking the converter model obtained by training as a charging current control model.
By way of example, the data acquisition module 201 may be configured to:
And acquiring original data corresponding to the historical cluster voltage, original data corresponding to the historical cell voltage and original data corresponding to the historical cell temperature of the target energy storage battery.
And respectively carrying out normalization processing on each type of original data to obtain the historical cluster voltage, the historical cell voltage and the historical cell temperature of the target energy storage battery.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The embodiment of the present application further provides a terminal device, referring to fig. 3, the terminal device 300 may include at least one processor 310, a memory 320, where the memory 320 is configured to store a computer program 321, and the processor 310 is configured to invoke and execute the computer program 321 stored in the memory 320 to implement the steps in any of the foregoing method embodiments, for example, steps 101 to 104 in the embodiment shown in fig. 1. Or the processor 310, when executing the computer program, performs the functions of the modules/units in the above-described apparatus embodiments, for example, the functions of the modules shown in fig. 2.
By way of example, the computer program 321 may be partitioned into one or more modules/units that are stored in the memory 320 and executed by the processor 310 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing specific functions for describing the execution of the computer program in the terminal device 300.
It will be appreciated by those skilled in the art that fig. 3 is merely an example of a terminal device and is not limiting of the terminal device and may include more or fewer components than shown, or may combine certain components, or different components, such as input-output devices, network access devices, buses, etc.
The Processor 310 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 320 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. The memory 320 is used for storing 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.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (PERIPHERAL COMPONENT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.
The method for controlling the charge and discharge of the energy storage battery provided by the embodiment of the application can be applied to terminal equipment such as computers, wearable equipment, vehicle-mounted equipment, tablet computers, notebook computers, netbooks and the like, and the specific type of the terminal equipment is not limited.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in each embodiment of the energy storage battery charge and discharge control method when being executed by a processor.
Embodiments of the present application provide a computer program product that, when executed on a mobile terminal, enables the mobile terminal to implement the steps of the embodiments of the energy storage battery charge and discharge control method described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least any entity or device capable of carrying computer program code to a camera device/terminal equipment, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
The foregoing embodiments are merely illustrative of the technical solutions of the present application, and not restrictive, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that modifications may still be made to the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
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