CN116780004A - Battery charging optimization method, electronic device and readable storage medium - Google Patents
Battery charging optimization method, electronic device and readable storage medium Download PDFInfo
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Abstract
The application discloses a battery charging optimization method, electronic equipment and a readable storage medium, which are applied to the technical field of charging and comprise the following steps: acquiring charging data of a target battery during charging; constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery; predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy; and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged. The application solves the technical problem of longer service life of the battery during charging.
Description
Technical Field
The present application relates to the field of charging technologies, and in particular, to a battery charging optimization method, an electronic device, and a readable storage medium.
Background
With the rapid development of technology, the charging technology of devices is also mature, and at present, when devices configured with lithium ion batteries are charged, due to the target battery characteristics of the lithium batteries, a three-stage charging strategy is adopted: the pre-charging stage, the constant-current charging stage and the constant-voltage charging stage are easy to accelerate the reaction of the electrolyte of the battery due to the fact that the actual charging scene is complex and changeable, and the service life of the battery is large when the battery is charged if a fixed three-stage charging strategy is still adopted.
Disclosure of Invention
The application mainly aims to provide a battery charging optimization method, electronic equipment and a readable storage medium, and aims to solve the technical problem of longer service life loss of a battery during battery charging in the prior art.
In order to achieve the above object, the present application provides a battery charge optimization method, including:
acquiring charging data of a target battery during charging;
constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery;
predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy;
and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged.
In order to achieve the above object, the present application also provides a battery charge optimizing apparatus comprising:
the acquisition module is used for acquiring charging data of the target battery during charging;
the construction module is used for constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery;
the prediction module is used for predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy;
and the return module is used for returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged.
The application also provides an electronic device comprising: the battery charging optimization system comprises a memory, a processor and a program of the battery charging optimization method stored on the memory and capable of running on the processor, wherein the program of the battery charging optimization method can realize the steps of the battery charging optimization method when being executed by the processor.
The present application also provides a computer-readable storage medium having stored thereon a program for implementing a battery charge optimization method, which when executed by a processor implements the steps of the battery charge optimization method as described above.
The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a battery charge optimization method as described above.
The application provides a battery charging optimization method, electronic equipment and a readable storage medium, wherein charging data of a target battery during charging are obtained; constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery; predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy; and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged, so that a proper charging strategy is matched according to the charging data, the charging environment information and the user use data of the target battery at any time, the technical defect of high service life loss of the battery during charging due to the accelerated reaction of the electrolyte of the battery is avoided, and the service life of the battery is further prolonged.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flowchart of a battery charge optimization method according to a first embodiment of the present application;
FIG. 2 is a flowchart of a battery charge optimization method according to a second embodiment of the present application;
fig. 3 is a schematic diagram of a device structure related to a battery charging optimization method according to an embodiment of the present application;
fig. 4 is a schematic device structure diagram of a hardware operating environment related to a battery charging optimization method according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the above objects, features and advantages of the present application more comprehensible, the following description of the embodiments accompanied with the accompanying drawings will be given in detail. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
An embodiment of the present application provides a battery charging optimization method, in a first embodiment of the battery charging optimization method of the present application, referring to fig. 1, the battery charging optimization method includes:
step S10, acquiring charging data of a target battery during charging;
in this embodiment, the target battery is a battery waiting for charge optimization. The charging data includes at least one of a charging current, a charging voltage, a battery temperature, and a battery charge. The charging strategy adopted when the target battery is charged is not limited, that is, may be a three-stage charging strategy, or may be a charging strategy predicted according to a preset charging prediction model.
Illustratively, if it is detected that a target battery is in a charged state, charging data of the target battery is acquired.
Step S20, constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery;
optionally, before step S20, the method further includes: and carrying out data cleaning on the charging data, and carrying out normalization processing on the charging data after data cleaning.
It can be appreciated that the obtained charging data may have incomplete or damaged condition, so that the incomplete or damaged data is deleted or repaired by performing data cleaning on the charging data; by carrying out normalization processing on the charging data, the various charging data are unified in scale and range, and subsequent feature construction is facilitated.
In step S20, the step of constructing a target battery charging feature corresponding to the target battery based on the charging data, the charging environment information of the target battery, and the user usage data corresponding to the target battery includes:
step S21, extracting and obtaining target charging characteristics based on the charging data of the target battery through a convolutional neural network;
the charge data is mapped to a target charge characteristic, illustratively by a convolutional neural network.
Step S22, extracting a target charging environment characteristic based on charging environment information of the target battery through a preset characteristic extractor, and extracting a target user use characteristic based on user use data corresponding to the target battery;
the preset feature extractor includes an environmental feature extractor and a user usage feature extractor, wherein charging environment information of the target battery is mapped to the target charging environment feature through the environmental feature extractor, and user usage data corresponding to the target battery is mapped to the target user usage feature through the user usage feature extractor.
And step S23, the target charging characteristics, the target charging environment characteristics and the target user using characteristics are spliced into the target battery charging characteristics.
Step S30, according to the charging characteristics of the target battery and a preset charging strategy prediction model, predicting a charging strategy corresponding to the target battery, and charging the target battery by using the charging strategy;
the target battery charging characteristics are mapped to charging strategies corresponding to the target battery through the preset charging strategy prediction model, and the target battery is charged through the charging strategies.
As one example, the charging strategy includes a charging current and/or a charging voltage at which the target battery is charged.
In step S30, the step of predicting a charging policy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging policy prediction model, and charging the target battery with the charging policy includes:
step S31, if a plurality of target batteries exist, acquiring target battery information of each target battery;
for example, if there are multiple target batteries powering the same device, target battery information of each of the target batteries is obtained.
Step S32, selecting a rechargeable battery from the target batteries based on the target battery information;
in this embodiment, the number of the rechargeable batteries may be one or more.
Wherein, in step S32, the step of selecting a rechargeable battery from the target batteries based on the target battery information includes:
step S321, if the target battery information includes a remaining power, selecting a battery with a corresponding remaining power greater than a preset power threshold value from the target batteries as the rechargeable battery; and/or the number of the groups of groups,
step S322, if the target battery information includes a battery health degree, selecting a battery with a corresponding battery health degree greater than a preset health degree threshold value from the target batteries as the rechargeable battery.
If the target battery information includes a remaining power and a battery health, a battery with a corresponding battery health greater than a preset health threshold and a corresponding remaining power greater than a preset power threshold is selected from the target batteries as the rechargeable battery.
It can be understood that the battery with higher battery health degree in the plurality of target batteries is charged preferentially, so that potential safety hazards caused by battery aging when the battery with lower battery health degree is charged are avoided, namely, the safety of battery charging is improved.
It can be understood that the battery with higher residual capacity in the plurality of target batteries is charged preferentially, and the charged battery has stronger power supply capability for the equipment, so that the power supply of the battery is ensured.
And step S33, predicting a charging strategy corresponding to the rechargeable battery according to the target battery charging characteristics corresponding to the rechargeable battery and the preset charging strategy prediction model, and charging the rechargeable battery according to the charging strategy corresponding to the rechargeable battery.
The target battery charging characteristics corresponding to the rechargeable battery are mapped into the charging strategies corresponding to the rechargeable battery through the preset charging strategy prediction model; and charging the rechargeable battery according to a charging strategy corresponding to the rechargeable battery.
And returning to the step S10 until the target battery is charged.
The embodiment of the application provides a battery charging optimization method, which is implemented by acquiring charging data of a target battery during charging; constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery; predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy; and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged, so that a proper charging strategy is matched according to the charging data, the charging environment information and the user use data of the target battery at any time, the technical defect of high service life loss of the battery during charging due to the accelerated reaction of the electrolyte of the battery is avoided, and the service life of the battery is further prolonged.
Example two
Further, in another embodiment of the present application, the same or similar content as that of the first embodiment may be referred to the above description, and will not be repeated. On this basis, referring to fig. 2, in step S30, before the step of predicting the charging policy corresponding to the target battery according to the target battery charging feature and the preset charging policy prediction model, the method further includes:
step A10, a charging strategy prediction model to be trained and a plurality of training samples are obtained, wherein one training sample comprises input characteristic data and a training label corresponding to the input characteristic data, the input characteristic data comprises training battery charging characteristics of a training battery, the training battery charging characteristics are obtained by splicing training charging characteristics of the training battery, training charging environment characteristics and training user using characteristics, the charging characteristics are obtained by extracting a convolutional neural network based on charging data when the training battery is charged, and the training label comprises a charging strategy of the training battery;
wherein, in step a10, before obtaining the plurality of training samples, the method further comprises:
step A01, determining a training charging strategy which enables the charging speed of the training battery to be the fastest and enables the battery health to be the highest according to the training charging characteristics;
illustratively, according to the training charging characteristics, generating a charging speed and a battery health of the training battery under each preset charging strategy; and selecting a training charging strategy which enables the charging speed of the training battery to be the highest and the battery health to be the highest from the preset charging strategies.
And step A02, adjusting the training charging strategy according to the training charging environment characteristics and/or the training user using characteristics of the training battery, and taking the adjusted training charging strategy as the charging strategy of the training battery.
Wherein, in step A02, the charging strategy comprises a charging voltage and/or a charging current,
according to the training charging environment characteristics of the training battery, the training charging strategy is adjusted, and the method comprises the following steps:
and B10, if the training charging environment characteristics indicate that the environmental temperature of the charging environment where the training battery is located is greater than a first preset temperature threshold, or indicate that the environmental temperature of the charging environment where the training battery is located is less than a second preset temperature threshold, regulating down the charging voltage and/or the charging current of the training battery, wherein the first preset temperature threshold is greater than the second preset temperature threshold.
It will be appreciated that if the temperature of the environment in which the training battery is located is too high or too low, then dangerous phenomena such as battery swelling and even explosion may easily occur if the charging voltage and/or charging current of the training battery is set to be high. Therefore, if the training charging environment features indicate that the environmental temperature of the charging environment where the training battery is located is greater than a first preset temperature threshold, or indicate that the environmental temperature of the charging environment where the training battery is located is less than a second preset temperature threshold, the charging voltage and/or charging current of the training battery are adjusted down, and the training charging strategy is reasonably adjusted, so that the charging strategy predicted by the preset charging strategy prediction model obtained through training is used, and when the temperature of the environment where the battery is located is too high or too low, the lower charging battery and/or charging voltage is adopted, so that the charging safety of the battery is ensured.
In step a02, according to the usage characteristics of the training user of the training battery, the training charging strategy is adjusted, including:
and step C10, if the target use characteristic comprises the user use time corresponding to the equipment powered by the training battery, adjusting the training charging strategy according to the user use time.
In this embodiment, it should be noted that the usage time of the user may be a usage time point or a usage time period, which is not limited herein, and the usage time of the user may be consistent with or inconsistent with data in the user usage data corresponding to the target battery, which is not limited herein.
For example, a time of the accumulated use time length of the use time of each user is smaller than a preset duration threshold is taken as a low-frequency use time, and the charging current and/or the charging voltage corresponding to the low-frequency use time are/is regulated down.
It will be appreciated that the greater the charging current and/or charging voltage, the faster the charging speed of the battery and the longer the life loss of the battery, and therefore, the charging current and/or charging voltage used at a shorter time point or period when the user accumulates use, that is, when the user's use requirement for the device is lower, the charging speed of the battery can be appropriately reduced, thereby reducing the life loss of the battery.
And step A20, performing iterative optimization on the charging strategy prediction model to be trained according to the plurality of training samples to obtain the preset charging strategy prediction model.
The input feature data of the training sample is input to the charging strategy prediction model to be trained to obtain an output label, the difference degree between the output label and the training label is determined, model loss corresponding to the charging strategy prediction model to be trained is calculated according to the difference degree, whether the model loss is converged is further judged, if the model loss is converged, the charging strategy prediction model to be trained is used as the preset charging strategy prediction model, if the model loss is not converged, the charging strategy prediction model to be trained is updated through a preset model updating method based on the gradient calculated by the model loss, and the preset model updating method comprises a gradient descent method, a gradient ascent method and the like.
Optionally, in a possible embodiment, the method for updating the charging strategy prediction model to be trained by using a preset model updating method includes: and adjusting the super-parameters of the charging strategy prediction model to be trained, wherein the preset model updating method comprises an adaptive matrix algorithm.
The embodiment of the application provides a battery charging optimization method, which is characterized in that a to-be-trained charging strategy prediction model and a plurality of training samples are obtained, wherein one training sample comprises input characteristic data and a training label corresponding to the input characteristic data, the input characteristic data comprises training battery charging characteristics of a training battery, the training battery charging characteristics are obtained by splicing training charging characteristics of the training battery, training charging environment characteristics and training user using characteristics, the charging characteristics are obtained by extracting a convolutional neural network based on charging data when the training battery is charged, and the training label comprises the charging strategy of the training battery; and carrying out iterative optimization on the charging strategy prediction model to be trained according to the plurality of training samples to obtain the preset charging strategy prediction model, and taking charging characteristics, charging environment characteristics and user use characteristics as the basis for model training of the charging strategy prediction model to be trained, so that the preset charging strategy prediction model obtained by training can be matched with a proper charging strategy for the battery, and the prediction accuracy of the charging strategy of the battery is ensured.
Example III
The embodiment of the application also provides a battery charging optimization device, referring to fig. 3, the battery charging optimization device comprises:
the acquisition module is used for acquiring charging data of the target battery during charging;
the construction module is used for constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery;
the prediction module is used for predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy;
and the return module is used for returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged.
Optionally, the building module is further configured to:
extracting and obtaining target charging characteristics based on charging data of the target battery through a convolutional neural network;
extracting a target charging environment characteristic based on charging environment information of the target battery through a preset characteristic extractor, and extracting a target user use characteristic based on user use data corresponding to the target battery;
and splicing the target charging characteristic, the target charging environment characteristic and the target user using characteristic into the target battery charging characteristic.
Optionally, the prediction module is further configured to:
if a plurality of target batteries exist, acquiring target battery information of each target battery;
selecting a rechargeable battery from the target batteries based on the target battery information;
and predicting a charging strategy corresponding to the rechargeable battery according to the target battery charging characteristics corresponding to the rechargeable battery and the preset charging strategy prediction model, and charging the rechargeable battery according to the charging strategy corresponding to the rechargeable battery.
Optionally, the prediction module is further configured to:
if the target battery information comprises the residual electric quantity, selecting a battery with the corresponding residual electric quantity larger than a preset electric quantity threshold value from the target batteries as the rechargeable battery; and/or the number of the groups of groups,
and if the target battery information comprises the battery health degree, selecting a battery with the corresponding battery health degree larger than a preset health degree threshold value from the target batteries as the rechargeable battery.
Optionally, before the step of predicting the charging policy corresponding to the target battery according to the target battery charging characteristic and a preset charging policy prediction model, the battery charging optimization device further includes:
acquiring a charging strategy prediction model to be trained and a plurality of training samples, wherein one training sample comprises input characteristic data and training labels corresponding to the input characteristic data, the input characteristic data comprises training battery charging characteristics of a training battery, the training battery charging characteristics are obtained by splicing training charging characteristics of the training battery, training charging environment characteristics and training user using characteristics, the charging characteristics are obtained by extracting a convolutional neural network based on charging data when the training battery is charged, and the training labels comprise charging strategies of the training battery;
and carrying out iterative optimization on the charging strategy prediction model to be trained according to the plurality of training samples to obtain the preset charging strategy prediction model.
Optionally, before acquiring the plurality of training samples, the battery charge optimization device further includes:
determining a training charging strategy which enables the charging speed of the training battery to be the fastest and enables the battery health to be the highest according to the training charging characteristics;
and adjusting the training charging strategy according to the training charging environment characteristics and/or the training user using characteristics of the training battery, and taking the adjusted training charging strategy as the charging strategy of the training battery.
Optionally, the charging strategy includes a charging voltage and/or a charging current, and the battery charging optimization device further includes:
according to the training charging environment characteristics of the training battery, the training charging strategy is adjusted, and the method comprises the following steps:
and if the training charging environment characteristics indicate that the environmental temperature of the charging environment where the training battery is located is greater than a first preset temperature threshold, or indicate that the environmental temperature of the charging environment where the training battery is located is less than a second preset temperature threshold, regulating down the charging voltage and/or the charging current of the training battery, wherein the first preset temperature threshold is greater than the second preset temperature threshold.
Optionally, the battery charge optimizing device further includes:
and if the target use characteristic comprises the user use time corresponding to the equipment powered by the training battery, adjusting the training charging strategy according to the user use time.
The battery charging optimization device provided by the application solves the technical problem of larger service life loss of the battery during battery charging by adopting the battery charging optimization method in the embodiment. Compared with the prior art, the battery charging optimization device provided by the embodiment of the application has the same beneficial effects as the battery charging optimization method provided by the embodiment, and other technical features in the battery charging optimization device are the same as the features disclosed by the method of the embodiment, and are not repeated herein.
Example IV
An embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the battery charge optimization method of the above embodiments.
Referring now to fig. 4, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers PDAs (Personal Digital Assistant, personal digital assistants), PADs (tablet computers), PMPs (Portable Media Player, portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 4, the electronic apparatus may include a processing device (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a ROM (Read-Only Memory) or a program loaded from a storage device into a RAM (Random Access Memory ). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM and RAM are connected to each other via a bus. Input/output (I/O) ports are also connected to the bus.
In general, the following systems may be connected to I/O ports: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid Crystal Displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tape, hard disk, etc.; a communication device. The communication means may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While electronic devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by a processing device.
The electronic equipment provided by the application solves the technical problem of larger service life loss of the battery during battery charging by adopting the battery charging optimization method in the embodiment. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the application are the same as those of the battery charging optimization method provided by the embodiment, and other technical features of the electronic device are the same as those disclosed by the method of the embodiment, so that the description is omitted herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Example five
The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method of the battery charge optimization method in the above-described embodiments.
The computer readable storage medium according to the embodiments of the present application may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Erasable Programmable Read Only Memory, erasable programmable read-only memory) or flash memory, an optical fiber, a CD-ROM (compact disc read-only memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (Radio Frequency), and the like, or any suitable combination thereof.
The above-described computer-readable storage medium may be contained in an electronic device; or may exist alone without being assembled into an electronic device.
The computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: acquiring charging data of a target battery during charging; constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery; predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy; and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a LAN (Local Area Network ) or WAN (Wide Area Network, wide area network), or it may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented in software or hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The computer readable storage medium provided by the application stores the computer readable program instructions for executing the battery charging optimization method, and solves the technical problem of longer service life of the battery during battery charging. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the embodiment of the application are the same as those of the battery charging optimization method provided by the implementation, and are not described in detail herein.
Example six
The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a battery charge optimization method as described above.
The computer program product provided by the application solves the technical problem of larger service life loss of the battery during battery charging. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the application are the same as those of the battery charging optimization method provided by the embodiment, and are not described in detail herein.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein, or any application, directly or indirectly, within the scope of the application.
Claims (10)
1. A battery charge optimization method, characterized in that the battery charge optimization method comprises:
acquiring charging data of a target battery during charging;
constructing and obtaining a target battery charging characteristic corresponding to the target battery based on the charging data, the charging environment information of the target battery and the user use data corresponding to the target battery;
predicting a charging strategy corresponding to the target battery according to the charging characteristics of the target battery and a preset charging strategy prediction model, and charging the target battery according to the charging strategy;
and returning to the step of acquiring the charging data of the target battery during charging until the target battery is charged.
2. The battery charge optimization method according to claim 1, wherein the step of constructing a target battery charge characteristic corresponding to the target battery based on the charge data, the charge environment information of the target battery, and the user usage data corresponding to the target battery includes:
extracting and obtaining target charging characteristics based on charging data of the target battery through a convolutional neural network;
extracting a target charging environment characteristic based on charging environment information of the target battery through a preset characteristic extractor, and extracting a target user use characteristic based on user use data corresponding to the target battery;
and splicing the target charging characteristic, the target charging environment characteristic and the target user using characteristic into the target battery charging characteristic.
3. The battery charge optimization method according to claim 1, wherein the step of predicting a charge policy corresponding to the target battery according to the target battery charge characteristic and a preset charge policy prediction model, and charging the target battery with the charge policy comprises:
if a plurality of target batteries exist, acquiring target battery information of each target battery;
selecting a rechargeable battery from the target batteries based on the target battery information;
and predicting a charging strategy corresponding to the rechargeable battery according to the target battery charging characteristics corresponding to the rechargeable battery and the preset charging strategy prediction model, and charging the rechargeable battery according to the charging strategy corresponding to the rechargeable battery.
4. The battery charge optimization method of claim 3, wherein the step of selecting a rechargeable battery among the target batteries based on the target battery information comprises:
if the target battery information comprises the residual electric quantity, selecting a battery with the corresponding residual electric quantity larger than a preset electric quantity threshold value from the target batteries as the rechargeable battery; and/or the number of the groups of groups,
and if the target battery information comprises the battery health degree, selecting a battery with the corresponding battery health degree larger than a preset health degree threshold value from the target batteries as the rechargeable battery.
5. The battery charge optimization method according to claim 1, further comprising, before the step of predicting a charging strategy corresponding to the target battery according to the target battery charging characteristic and a preset charging strategy prediction model:
acquiring a charging strategy prediction model to be trained and a plurality of training samples, wherein one training sample comprises input characteristic data and training labels corresponding to the input characteristic data, the input characteristic data comprises training battery charging characteristics of a training battery, the training battery charging characteristics are obtained by splicing training charging characteristics of the training battery, training charging environment characteristics and training user using characteristics, the charging characteristics are obtained by extracting a convolutional neural network based on charging data when the training battery is charged, and the training labels comprise charging strategies of the training battery;
and carrying out iterative optimization on the charging strategy prediction model to be trained according to the plurality of training samples to obtain the preset charging strategy prediction model.
6. The battery charge optimization method of claim 5, further comprising, prior to obtaining the plurality of training samples:
determining a training charging strategy which enables the charging speed of the training battery to be the fastest and enables the battery health to be the highest according to the training charging characteristics;
and adjusting the training charging strategy according to the training charging environment characteristics and/or the training user using characteristics of the training battery, and taking the adjusted training charging strategy as the charging strategy of the training battery.
7. The battery charge optimization method of claim 6, wherein the charging strategy comprises a charging voltage and/or a charging current,
according to the training charging environment characteristics of the training battery, the training charging strategy is adjusted, and the method comprises the following steps:
and if the training charging environment characteristics indicate that the environmental temperature of the charging environment where the training battery is located is greater than a first preset temperature threshold, or indicate that the environmental temperature of the charging environment where the training battery is located is less than a second preset temperature threshold, regulating down the charging voltage and/or the charging current of the training battery, wherein the first preset temperature threshold is greater than the second preset temperature threshold.
8. The battery charge optimization method of claim 6, wherein adjusting the training charge strategy based on training user usage characteristics of the training battery comprises:
and if the target use characteristic comprises the user use time corresponding to the equipment powered by the training battery, adjusting the training charging strategy according to the user use time.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the battery charge optimization method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for realizing a battery charge optimization method, the program for realizing the battery charge optimization method being executed by a processor to realize the steps of the battery charge optimization method according to any one of claims 1 to 8.
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| CN117318254A (en) * | 2023-11-30 | 2023-12-29 | 深圳航天科创泛在电气有限公司 | Wireless charging method, wireless charging device, electronic equipment and readable storage medium |
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| CN117318254B (en) * | 2023-11-30 | 2024-03-19 | 深圳航天科创泛在电气有限公司 | Wireless charging method, wireless charging device, electronic equipment and readable storage medium |
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