CN113866638A - Battery parameter inference method, device, equipment and medium - Google Patents
Battery parameter inference method, device, equipment and medium Download PDFInfo
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- CN113866638A CN113866638A CN202110977584.8A CN202110977584A CN113866638A CN 113866638 A CN113866638 A CN 113866638A CN 202110977584 A CN202110977584 A CN 202110977584A CN 113866638 A CN113866638 A CN 113866638A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The present disclosure relates to a battery parameter estimation device, a method, a device and a medium, wherein the method is applied to the battery parameter estimation device, and the method comprises: generating a model by using the model generating device and updating the model; reading and storing training data by using the data processing device; performing battery parameter presumption calculation according to the updated model of the model generation device and training data stored by the data processing device and outputting the calculated result; and judging whether the parameters of the current model meet the requirements according to the output battery parameter presumption calculation result, if so, saving the model by using the current parameters, if not, saving the model parameters, and generating new model parameters to update the model after analyzing and calculating the model parameters.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for battery parameter inference.
Background
The service life estimation of the battery is of great significance, and the technical scheme in the prior art cannot meet the requirements of users on the estimation of the state of the battery, such as the service life, and the like, so the present disclosure mainly provides a method, a device, equipment and a medium for solving the estimation of the state of the battery through deep learning/machine learning.
Disclosure of Invention
The technical problem that the prior art cannot meet the battery parameter presumption requirement of a user is solved.
To achieve the above technical object, the present disclosure provides a battery parameter estimation device, including:
a model generation device and a data processing device;
the model generation device specifically includes:
the training data storage module is used for storing training data;
the training data acquisition module is used for reading training data from the training data storage module to perform model training;
the model training module is used for carrying out model training by utilizing the training data read by the training data acquisition module to obtain a battery parameter conjecture model;
the first model storage module is used for storing the battery parameter conjecture model obtained by the training of the model training module;
the model sending module is used for sending the state conjecture model trained by the model training module;
the training data storage module is sequentially connected with the training data acquisition module, the model training module, the first model storage module and the model sending module;
wherein, the data processing device specifically comprises:
the model parameter acquisition module is used for reading the battery parameter conjecture calculation model sent by the model sending module;
the second model storage module is used for storing the model obtained by the model parameter obtaining module;
the input data acquisition module is used for acquiring and storing input data related to a measured object;
the output data generation module is used for calling the model stored in the second model storage module, reading the input data in the input data acquisition module, calculating and outputting the input data;
the input data acquisition module specifically comprises: an input data acquisition submodule and an input data storage submodule;
the output data generation module specifically comprises: the output data calculation submodule and the output data output submodule;
the representation processing module is used for calling the output data of the output data generation module and processing the output data to obtain displayable data;
and the display module is used for displaying the data which can be displayed and is obtained after the processing of the representation processing module.
Further, still include:
and the model training parameter training module is used for training parameters of model training in the model training module.
Further, the model training parameter training module is specifically configured to:
and training parameters in the model training module by using a deep learning model, a mechanical learning model and/or a linear regression analysis method.
Further, the deep learning model specifically includes:
a deep neural network, a convolutional neural network, a recurrent neural network, a long short term memory network, and/or a bidirectional long short term memory network.
In order to achieve the above technical object, the present disclosure provides a battery parameter estimation method applied to the above battery parameter estimation apparatus, the method including:
generating a model by using the model generating device and updating the model;
reading and storing training data by using the data processing device;
performing battery parameter presumption calculation according to the updated model of the model generation device and training data stored by the data processing device and outputting the calculated result;
and judging whether the parameters of the current model meet the requirements according to the output battery parameter presumption calculation result, if so, saving the model by using the current parameters, if not, saving the model parameters, and generating new model parameters to update the model after analyzing and calculating the model parameters.
Further, the performing battery parameter estimation calculation and output according to the model updated by the model generation device and the training data stored by the data processing device specifically includes:
the first battery parameter presumption model group comprises n independent models;
a is toi～ajSegment data are respectively input into n models as input parameters, wherein aiRepresenting the starting time of the battery parameter input field, ajRepresenting the ending time of the battery parameter input section, wherein n is an integer greater than or equal to 2;
the first battery parameter presumption model of each stage respectively saves the ai～ajSegment data, n independent models using said a respectivelyi～ajCalculating the first battery parameter guess for the output phase for which the segment data is responsible, e.g. calculating b for the first modeli1～bj1The second model calculates bi2～bj2… … nth model calculates bin～bjn；
And summarizing, processing and storing the first battery parameter presumption result.
Further, the performing battery parameter estimation calculation and output according to the model updated by the model generation device and the training data stored by the data processing device specifically includes:
the second battery parameter model group comprises m independent models;
a is toi～ajSegment as input data is input into m models in segments as input data, where aiRepresenting the starting time of the battery parameter input field, ajRepresenting the ending time of the battery parameter input section, wherein m is an integer greater than or equal to 2;
each of the second battery parameter estimation models uses the ai～ajTaking the segmented data as input data of each model;
each second battery parameter presumption model respectively calculates and stores second battery parameter data segment presumption output data which is responsible for each model by using the input data of each model;
inputting each group of the second battery parameter presumption output data into an analysis model for analysis;
and storing the analyzed result as an output result of the second battery parameter model group.
Further, the performing battery parameter estimation calculation and output according to the model updated by the model generation device and the training data stored by the data processing device specifically includes:
the third battery parameter model group comprises k independent models;
a is toi～ajInputting the segment data to a first third battery parameter estimation model, said first third battery parameter estimation model storing said ai～ajSegment data in which aiRepresenting the starting time of the battery parameter input field, ajIndicating the end time of the battery parameter input segment;
said first third battery parameter estimation model using said ai～ajAfter the section data are calculated, inputting a calculation result into a second third battery parameter presumption model as input data of the second model, and sequentially circulating until the calculation result of a (k-1) th third battery parameter presumption model is input into a kth third battery parameter presumption model, wherein k is an integer more than or equal to 2;
and processing the calculation results of the k third battery parameter presumption models to obtain a presumption result of a third battery parameter model group.
To achieve the above technical objects, the present disclosure can also provide a computer storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the above battery parameter estimation method.
To achieve the above technical object, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the battery parameter estimation method when executing the computer program.
Drawings
Fig. 1 shows a schematic structural diagram of an apparatus of embodiment 1 of the present disclosure;
fig. 2 shows a schematic configuration diagram of a model generation apparatus of embodiment 1 of the present disclosure;
fig. 3 shows a schematic configuration diagram of a data processing apparatus of embodiment 1 of the present disclosure;
figure 4 shows a flow diagram of the method of embodiment 2 of the present disclosure;
figure 5 shows a flow diagram of the method of embodiment 2 of the present disclosure;
FIG. 6 shows a flow diagram of a method of embodiment 2 of the present disclosure;
figure 7 shows a flow diagram of the method of embodiment 2 of the present disclosure;
figure 8 shows a flow diagram of the method of embodiment 2 of the present disclosure;
fig. 9 shows a schematic structural diagram of embodiment 4 of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
Various structural schematics according to embodiments of the present disclosure are shown in the figures. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers, and relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, or make adjustments to logical relationships and/or topologies in the drawings as actually required.
The first embodiment is as follows:
as shown in fig. 1:
the present disclosure provides a battery parameter estimation device including:
a model generation device 10 and a data processing device 20;
the model generation apparatus 10 specifically includes:
the training data storage module is used for storing training data;
the training data acquisition module is used for reading training data from the training data storage module to perform model training;
the model training module is used for carrying out model training by utilizing the training data read by the training data acquisition module to obtain a battery parameter conjecture model;
the first model storage module is used for storing the battery parameter conjecture model obtained by the training of the model training module;
the model sending module is used for sending the service life conjecture model trained by the model training module;
the training data storage module is sequentially connected with the training data acquisition module, the model training module, the first model storage module and the model sending module;
the data processing device 20 specifically includes:
the model parameter acquisition module is used for reading the battery parameter conjecture calculation model sent by the model sending module;
the second model storage module is used for storing the model obtained by the model parameter obtaining module;
the input data acquisition module is used for acquiring and storing input data related to a measured object;
the output data generation module is used for calling the model stored in the second model storage module, reading the input data in the input data acquisition module, calculating and outputting the input data;
the input data acquisition module specifically comprises: an input data acquisition submodule and an input data storage submodule;
the output data generation module specifically comprises: the output data calculation submodule and the output data output submodule;
the representation processing module is used for calling the output data of the output data generation module and processing the output data to obtain displayable data;
and the display module is used for displaying the data which can be displayed and is obtained after the processing of the representation processing module.
As an example, the input data is test index data of the state of the measurement target 30, and includes at least a part of a plurality of test indexes. The output data is data indicating the current or future state of the measurement target 30. For example, the output data may be an estimated value of the current or future value of the data in the input data portion, which is not included in the plurality of test indexes of the measurement target 30, or may be an estimated value of a future value of at least one index of the measurement target 30. In the latter case, the index included in the output data may appear in the input data.
For example, when the measurement object 30 is a battery, at least one of the output voltage, the output current value, and the temperature of the battery is at least one of the indices included in the input data. However, the object and the index are not limited thereto. The output data includes at least one of the remaining capacity (unit Ah), the state of charge (SOC), and the soh of the measurement target 30, but may include at least one of the output voltage, the output current value, and the temperature.
SOH ═ current full charge capacity (Ah)/initial full charge capacity (Ah) × 100%
When the measurement object 30 is a battery, a part of the functions of the data processing device 20 may be used as a bms (battery management system) of the measurement object 30. In the example shown in the figure, one data processing device 20 is connected to a plurality of measurement objects 30, and performs processing on the plurality of measurement objects 30.
In the case where the measurement object 30 is a battery, the measurement object 30 supplies power to a device, and the device may be a vehicle such as an electric vehicle. However, when the measurement object 30 is a household battery, the device is a household appliance or the like, and in this case, the measurement object 30 is provided outside the device. The measurement target 30 may be connected to a grid power supply system, and in this case, the measurement target 30 is used to average the supply power amount. The specific expression is that the electric quantity is stored when the electric quantity of the machine is remained, and the electric quantity is supplied when the electric quantity of the machine is insufficient.
As described above, the data processing apparatus 20 applies the model when generating the output data. The model generation device 10 generates and updates at least one model to be used by the data processing device 20 through mechanical learning, such as a neural network.
As shown in fig. 2:
fig. 2 is an example of a functional configuration of the model generation apparatus 10.
As shown in fig. 2:
a training data storage module 110 for storing training data;
a training data obtaining module 120, configured to read training data from the training data storage module to perform model training;
a model training module 130, configured to perform model training using the training data read by the training data obtaining module to obtain a battery parameter inference model;
a first model storage module 140, configured to store the battery parameter inference model obtained through training by the model training module;
a model sending module 150, configured to send the lifetime prediction model trained by the model training module.
The training data storage module 110 is sequentially connected to the training data acquisition module 120, the model training module 130, the first model storage module 140, and the model transmission module 150.
The training data acquisition module 120 acquires a plurality of training data, and the model training module 130 performs mechanical learning on a plurality of training data acquired by the training data acquisition module 120, thereby generating a model. The model training module 130 may generate a plurality of different models through a plurality of machine learning algorithms (e.g., LSTM (long short-term memory), DNN (deep Neural network), LR (linear regression), CNN (volumetric Neural networks), GAN (general adaptive networks), etc.).
The training data acquisition module 120 acquires training data from the training data storage module 110. The training data storage module 110 may be a part of the model generating apparatus 10 or may be an external device of the model generating apparatus 10.
The model generated by the model training module 130 is stored in the first model storage module 140, and then the model transmission module 150 transmits the model stored in the first model storage module 140 to the data processing apparatus 20. As shown, the first model storage module 140 and the model transmission module 150 are both part of the model generation apparatus 10. The first model storage module 140 and the model transmission module 150 may be provided outside the model generating apparatus 10.
One example of the functional structure of the data processing device 20 is shown in fig. 3.
The data processing device 20 specifically includes:
a model parameter obtaining module 210, configured to read the battery parameter inference calculation model sent by the model sending module;
a second model storage module 220, configured to store the model obtained by the model parameter obtaining module;
an input data acquisition module 230 configured to acquire and store input data related to a measurement object;
an output data generating module 240, configured to call the model stored in the second model storage module, read the input data in the input data obtaining module, calculate, and output the model;
the input data acquiring module 230 specifically includes: an input data acquisition sub-module 2301 and an input data storage sub-module 2302;
the output data generating module 240 specifically includes: an output data calculation submodule 2401 and an output data output submodule 2402;
the representation processing module 250 is used for calling the output data of the output data generation module and processing the output data to obtain displayable data;
and the display module 260 is used for displaying the data which can be displayed and is obtained after the processing of the representation processing module.
The input data is acquired by the input data acquisition module 230. For example, when the measurement object 30 is a battery, the input data acquisition module 230 acquires input data from a battery state detection detector (e.g., an ammeter, a voltmeter, or a thermometer).
The output data generation module 240 processes the input data using the model generated by the model generation device 10 to generate output data.
The model parameter obtaining module 210 obtains the model from the model generating apparatus 10 and stores the model in the second model storing module 220. When the model parameter acquisition module 210 acquires a parameter for update (for example, a parameter of a model) from the model generation apparatus 10, the model recorded in the second model storage module 220 is updated using this parameter. Such update processing is suitable for repeated operations.
The presentation processing module 250 may display the data generated by the output data generation module 240 at the display module 260. The display module 260 may be disposed at a convenient position according to a user's demand.
Example two:
as shown in figure 4 of the drawings,
the present disclosure can also provide a battery parameter estimation method, including:
s401: generating a model by using the model generating device and updating the model;
s402: reading and storing training data by using the data processing device;
s403: performing battery parameter presumption calculation according to the updated model of the model generation device and training data stored by the data processing device and outputting the calculated result;
s404: and judging whether the parameters of the current model meet the requirements according to the output battery parameter presumption calculation result, if so, saving the model by using the current parameters, if not, saving the model parameters, and generating new model parameters to update the model after analyzing and calculating the model parameters.
In particular, as shown in figure 5,
the S403 is specifically divided into the embodiments S403-a:
the first battery parameter inference model group includes n independent models. A is toi～ajSegment data are respectively input into n models as input parameters, wherein aiRepresenting the starting time of the battery parameter input field, ajRepresenting the ending time of the battery parameter input section, wherein n is an integer greater than or equal to 2;
the first battery parameter presumption model of each stage respectively saves the ai～ajSegment data, n independent models using said a respectivelyi～ajCalculating the first battery parameter guess for the output phase for which the segment data is responsible, e.g. calculating b for the first modeli1～bj1The second model calculates bi2～bj2… … nth model calculates bin～bjn；
And summarizing, processing and storing the first battery parameter presumption result.
In particular, as shown in figure 6,
the S403 is specifically divided into the embodiments S403-b:
the second battery parameter model group comprises m independent models;
a is toi～ajSegment as input data is input into m models in segments as input data, where aiRepresenting the starting time of the battery parameter input field, ajRepresenting the ending time of the battery parameter input section, wherein m is an integer greater than or equal to 2;
each of the second battery parameter estimation models uses the ai～ajTaking the segmented data as input data of each model;
each second battery parameter presumption model respectively calculates and stores second battery parameter data segment presumption output data which is responsible for each model by using the input data of each model;
inputting each group of the second battery parameter presumption output data into an analysis model for analysis;
and storing the analyzed result as an output result of the second battery parameter model group.
In particular, as shown in figure 7,
the S403 is specifically divided into the embodiments S403-c-1:
the third battery parameter model group comprises k independent models;
a is toi～ajInputting the segment data to a first third battery parameter estimation model, said first third battery parameter estimation model storing said ai～ajSegment data in which aiRepresenting the starting time of the battery parameter input field, ajIndicating the end time of the battery parameter input segment;
said first third battery parameter estimation model using said ai～ajAfter the section data are calculated, inputting a calculation result into a second third battery parameter presumption model as input data of the second model, and sequentially circulating until the calculation result of a (k-1) th third battery parameter presumption model is input into a kth third battery parameter presumption model, wherein k is an integer more than or equal to 2;
and processing the calculation results of the k third battery parameter presumption models to obtain a presumption result of a third battery parameter model group.
Further, on the basis of the embodiment S403-c-1, an improvement may be made, as specifically shown in fig. 8:
a is toi～ajInputting the segment data to a first third battery parameter estimation model, said first third battery parameter estimation model storing said ai～ajSegment data in which aiIndicates the starting time of battery parameter estimation, ajIndicating the end time of battery parameter estimation;
after the first third battery parameter inference model is calculated, inputting a calculation result into a second third battery parameter inference model;
importing the result calculated by the first third battery parameter inference model and the result calculated by the second third battery parameter inference model into a third battery parameter inference model;
circulating in sequence
Introducing the result calculated by the first third battery parameter inference model to the result calculated by the (k-1) th third battery parameter inference model into the kth third battery parameter inference model for calculation; wherein k is an integer greater than or equal to 2;
and taking the calculation result of the kth third battery parameter presumption model as a third battery parameter presumption result.
It is emphasized here that other similar variants, such as: the result of the plurality of third battery parameter estimation models is merged as the input of the third battery parameter estimation model in the next stage, because the results belong to the same architecture and similar ideas, and the results also belong to the modification of the present solution. Because the deformation mode is various, it is not described herein.
Example three:
the present disclosure can also provide a computer storage medium having stored thereon a computer program for implementing the steps of the above-described battery parameter estimation method when executed by a processor.
The computer storage medium of the present disclosure may be implemented with a semiconductor memory, a magnetic core memory, a magnetic drum memory, or a magnetic disk memory.
Semiconductor memories are mainly used as semiconductor memory elements of computers, and there are two types, Mos and bipolar memory elements. Mos devices have high integration, simple process, but slow speed. The bipolar element has the advantages of complex process, high power consumption, low integration level and high speed. NMos and CMos were introduced to make Mos memory dominate in semiconductor memory. NMos is fast, e.g. 45ns for 1K bit sram from intel. The CMos power consumption is low, and the access time of the 4K-bit CMos static memory is 300 ns. The semiconductor memories described above are all Random Access Memories (RAMs), i.e. read and write new contents randomly during operation. And a semiconductor Read Only Memory (ROM), which can be read out randomly but cannot be written in during operation, is used to store solidified programs and data. The ROM is classified into a non-rewritable fuse type ROM, PROM, and a rewritable EPROM.
The magnetic core memory has the characteristics of low cost and high reliability, and has more than 20 years of practical use experience. Magnetic core memories were widely used as main memories before the mid 70's. The storage capacity can reach more than 10 bits, and the access time is 300ns at the fastest speed. The typical international magnetic core memory has a capacity of 4 MS-8 MB and an access cycle of 1.0-1.5 mus. After semiconductor memory is rapidly developed to replace magnetic core memory as a main memory location, magnetic core memory can still be applied as a large-capacity expansion memory.
Drum memory, an external memory for magnetic recording. Because of its fast information access speed and stable and reliable operation, it is being replaced by disk memory, but it is still used as external memory for real-time process control computers and medium and large computers. In order to meet the needs of small and micro computers, subminiature magnetic drums have emerged, which are small, lightweight, highly reliable, and convenient to use.
Magnetic disk memory, an external memory for magnetic recording. It combines the advantages of drum and tape storage, i.e. its storage capacity is larger than that of drum, its access speed is faster than that of tape storage, and it can be stored off-line, so that the magnetic disk is widely used as large-capacity external storage in various computer systems. Magnetic disks are generally classified into two main categories, hard disks and floppy disk memories.
Hard disk memories are of a wide variety. The structure is divided into a replaceable type and a fixed type. The replaceable disk is replaceable and the fixed disk is fixed. The replaceable and fixed magnetic disks have both multi-disk combinations and single-chip structures, and are divided into fixed head types and movable head types. The fixed head type magnetic disk has a small capacity, a low recording density, a high access speed, and a high cost. The movable head type magnetic disk has a high recording density (up to 1000 to 6250 bits/inch) and thus a large capacity, but has a low access speed compared with a fixed head magnetic disk. The storage capacity of a magnetic disk product can reach several hundred megabytes with a bit density of 6250 bits per inch and a track density of 475 tracks per inch. The disk set of the multiple replaceable disk memory can be replaced, so that the disk set has large off-body capacity, large capacity and high speed, can store large-capacity information data, and is widely applied to an online information retrieval system and a database management system.
Example four:
the present disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the battery parameter estimation method are implemented.
Fig. 9 is a schematic diagram of an internal structure of an electronic device in one embodiment. As shown in fig. 9, the electronic device includes a processor, a storage medium, a memory, and a network interface connected through a system bus. The storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store control information sequences, and the computer readable instructions can make a processor realize a battery parameter inference method when being executed by the processor. The processor of the electrical device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform a method of battery parameter speculation. The network interface of the computer device is used for connecting and communicating with the terminal. Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The electronic device includes, but is not limited to, a smart phone, a computer, a tablet, a wearable smart device, an artificial smart device, a mobile power source, and the like.
The processor may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing remote data reading and writing programs, etc.) stored in the memory and calling data stored in the memory.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory and at least one processor or the like.
Fig. 9 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 9 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A battery parameter estimation device, comprising:
a model generation device and a data processing device;
the model generation device specifically includes:
the training data storage module is used for storing training data;
the training data acquisition module is used for reading training data from the training data storage module to perform model training;
the model training module is used for carrying out model training by utilizing the training data read by the training data acquisition module to obtain a battery parameter conjecture model;
the first model storage module is used for storing the battery parameter conjecture model obtained by the training of the model training module;
the model sending module is used for sending the service life conjecture model trained by the model training module;
the training data storage module is sequentially connected with the training data acquisition module, the model training module, the first model storage module and the model sending module;
wherein, the data processing device specifically comprises:
the model parameter acquisition module is used for reading the battery parameter conjecture calculation model sent by the model sending module;
the second model storage module is used for storing the model obtained by the model parameter obtaining module;
the input data acquisition module is used for acquiring and storing input data related to a measured object;
the output data generation module is used for calling the model stored in the second model storage module, reading the input data in the input data acquisition module, calculating and outputting the input data;
the input data acquisition module specifically comprises: an input data acquisition submodule and an input data storage submodule;
the output data generation module specifically comprises: the output data calculation submodule and the output data output submodule;
the representation processing module is used for calling the output data of the output data generation module and processing the output data to obtain displayable data;
and the display module is used for displaying the data which can be displayed and is obtained after the processing of the representation processing module.
2. The apparatus of claim 1, further comprising:
and the model training parameter training module is used for training parameters of model training in the model training module.
3. The apparatus of claim 2, wherein the model training parameter training module is specifically configured to:
and training parameters in the model training module by using a deep learning model, a mechanical learning model and/or a linear regression analysis method.
4. The apparatus of claim 3, wherein the deep learning model specifically comprises:
a deep neural network, a convolutional neural network, a recurrent neural network, a long short term memory network, and/or a bidirectional long short term memory network.
5. A battery parameter estimation method applied to the device as claimed in any one of claims 1 to 4, the method comprising:
generating a model by using the model generating device and updating the model;
reading and storing training data by using the data processing device;
performing battery parameter presumption calculation according to the updated model of the model generation device and training data stored by the data processing device and outputting the calculated result;
and judging whether the parameters of the current model meet the requirements according to the output battery parameter presumption calculation result, if so, saving the model by using the current parameters, if not, saving the model parameters, and generating new model parameters to update the model after analyzing and calculating the model parameters.
6. The method according to claim 5, wherein the performing the battery parameter estimation calculation based on the updated model of the model generation device and the training data stored in the data processing device and outputting the battery parameter estimation calculation specifically includes:
a is toi～ajSegment data are inputted to n stages of first battery parameter estimation models, respectively, where aiIndicates the starting time of battery parameter estimation, ajN is an integer of 2 or more, which represents the end time of the battery parameter estimation;
the first battery parameter presumption model of each stage respectively saves the ai～ajSegment data, and respectively calculating the a of the batteryi～ajA first battery parameter inference result of the section data at the stage of the section data;
and summarizing and storing the first battery parameter presumption result.
7. The method according to claim 5, wherein the performing the battery parameter estimation calculation based on the updated model of the model generation device and the training data stored in the data processing device and outputting the battery parameter estimation calculation specifically includes:
a is toi～ajInputting m second battery parameter inference models into m segments of segment data, wherein aiIndicates the starting time of battery parameter estimation, ajM is an integer of 2 or more, which represents the end time of the battery parameter estimation;
saving the a with each of the second battery parameter estimation modelsi～ajSegment data segmented data;
calculating the a by using each second battery parameter estimation modeli～ajSecond battery parameter presumption output data corresponding to the whole data of the segment data are stored;
inputting each group of the second battery parameter presumption output data into an analysis model for analysis;
and storing the analyzed result.
8. The method according to claim 5, wherein the performing the battery parameter estimation calculation based on the updated model of the model generation device and the training data stored in the data processing device and outputting the battery parameter estimation calculation specifically includes:
a is toi～ajInputting the segment data to a first third battery parameter estimation model, said first third battery parameter estimation model storing said ai～ajSegment data in which aiIndicates the starting time of battery parameter estimation, ajIndicating the end time of battery parameter estimation;
after the first third battery parameter inference model is calculated, inputting a calculation result into a second third battery parameter inference model, and sequentially circulating until a calculation result of a (k-1) th third battery parameter inference model is input into a kth third battery parameter inference model, wherein k is an integer greater than or equal to 2;
and taking the calculation result of the kth third battery parameter presumption model as a third battery parameter presumption result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor executes the computer program to perform the steps corresponding to the battery parameter estimation method according to any one of claims 5 to 8.
10. A computer storage medium having computer program instructions stored thereon, wherein the program instructions, when executed by a processor, are adapted to implement the steps corresponding to the battery parameter estimation method according to any of claims 1 to 7.
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