CN115291111B - Training method of battery rest time prediction model and rest time prediction method - Google Patents
Training method of battery rest time prediction model and rest time prediction method Download PDFInfo
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Abstract
The invention discloses a training method of a battery standing time prediction model and a standing time prediction method. The method comprises the steps of obtaining sample batteries used for model training, and determining battery type data corresponding to each sample battery respectively; determining battery capacity data corresponding to each sample battery in a capacity test process; training the standing time prediction model based on battery capacity data and battery type data corresponding to each sample battery respectively to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process. The technical scheme disclosed by the invention solves the problem that the standing time estimated in the prior art can influence the efficiency and accuracy of the test, and realizes the provision of the accurate standing time, thereby improving the accuracy of the test result.
Description
Technical Field
The invention relates to the technical field of battery testing, in particular to a training method of a battery standing time prediction model and a standing time prediction method.
Background
The battery management system is one of the core components of the vehicle, and in order for the vehicle to operate properly, the battery must be in a normal operating state, i.e. the battery capacity needs to be testedThereby determining the operating state of the battery。
When testing the capacity of the battery, the battery is discharged until the voltage is ended after the battery is fully charged and then is kept stand for a period of time, and the capacity of the battery is calculated through a formula. Generally, the standing time refers to the time of not less than 30 minutes or not more than 60 minutes in national standard, but because the capacity test time is longer, the standing time directly affects the test time and the test effect, namely, under the same charging system, the standing time is 10 minutes and the standing time is 1 hour, and then the discharge capacity is tested, and the result is also 2% -5% different, which is specific to the self-discharge performance of the battery.
The current static time is determined by approximately estimating a time based on the experience of the test and the materials, types and the like of the battery. The estimated standing time based on the above manner can affect the efficiency and accuracy of the test, thereby reducing the reliability of the test result.
Disclosure of Invention
The invention provides a training method and a standing time prediction method of a battery standing time prediction model, which are used for solving the problem that the standing time estimated in the prior art can influence the testing efficiency and accuracy, and realizing the provision of accurate standing time, thereby improving the accuracy of a testing result.
In a first aspect, an embodiment of the present invention provides a method for training a battery rest time prediction model, where the method includes:
acquiring sample batteries for model training, and determining battery type data corresponding to each sample battery respectively;
determining battery capacity data corresponding to each sample battery in a capacity test process;
training the standing time prediction model based on battery capacity data and battery type data corresponding to each sample battery respectively to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process.
Optionally, the determining battery capacity data corresponding to each of the sample batteries in the capacity test process includes:
acquiring current data and voltage data of each sample battery in a capacity test process;
and determining battery capacity data corresponding to each sample battery respectively based on the current data and the voltage data.
Optionally, the acquiring current data and voltage data of each sample battery in the capacity test process includes:
For any sample battery, reading system current data and system voltage data of the current sample battery based on a preset battery management system;
acquiring current data and voltage data of the current sample battery based on a preset data acquisition device;
and acquiring test current data and test voltage data of the current sample battery based on the charge and discharge equipment in the capacity test process.
Optionally, the determining battery capacity data corresponding to each sample battery based on the current data and the voltage data includes:
for any sample cell, determining system cell capacity data for the current sample cell based on the system current data and the system voltage data;
determining collected battery capacity data of the current sample battery based on the collected current data and the collected voltage data;
determining test battery capacity data of the current sample battery based on the test current data and the test voltage data;
and determining battery capacity data of the current sample battery based on the system battery capacity data, the collected battery capacity data and the test battery capacity data.
Optionally, the battery capacity data of each sample battery in the capacity test process includes charging battery capacity data corresponding to a charging stage and standing battery capacity data corresponding to a standing stage;
correspondingly, the training the standing time prediction model based on the battery capacity data and the battery type data respectively corresponding to each sample battery to obtain a standing time prediction model after training, which comprises the following steps:
for any sample battery, determining charging battery capacity data and standing battery capacity data corresponding to battery type data of the battery sample based on a preset battery corresponding relation;
and training the standing time prediction model by taking the battery type data, the rechargeable battery capacity data and the standing battery capacity data as sample training data of the current sample battery to obtain a standing time prediction model after training.
In a second aspect, an embodiment of the present invention further provides a method for predicting a standing time of a battery, including:
acquiring a target battery and determining battery type data of the target battery;
determining charging battery capacity data corresponding to a charging stage of the target battery in a capacity test process;
Inputting the rechargeable battery capacity data and the battery type data into a pre-trained battery standing time prediction model to obtain the standing time of the target battery in a standing stage in a capacity test process; the battery standing time prediction model is obtained by training based on the training method of the battery standing time prediction model in any embodiment.
In a third aspect, an embodiment of the present invention further provides a training device for a battery standing time prediction model, where the device includes:
the sample battery acquisition module is used for acquiring sample batteries used for model training and determining battery type data corresponding to each sample battery respectively;
the battery capacity data acquisition module is used for determining battery capacity data corresponding to each sample battery in the capacity test process;
the model training module is used for training the standing time prediction model based on battery capacity data and battery type data which correspond to the sample batteries respectively to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process.
In a fourth aspect, an embodiment of the present invention further provides a battery rest time prediction apparatus, including:
the rechargeable battery capacity data acquisition module is used for acquiring rechargeable battery capacity data corresponding to a charging stage of the target battery in a capacity test process;
the standing time obtaining module is used for inputting the rechargeable battery capacity data into a pre-trained battery standing time prediction model to obtain the standing time of the standing stage of the target battery in the capacity test process; the battery standing time prediction model is obtained by training based on the training method of the battery standing time prediction model in any embodiment.
In a fifth aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of training the battery rest time prediction model according to any one of the embodiments of the present invention and/or the method of battery rest time prediction according to any one of the embodiments of the present invention.
In a sixth aspect, an embodiment of the present invention further provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a processor to implement a training method for a battery rest time prediction model according to any one embodiment of the present invention, and/or a battery rest time prediction method according to any one embodiment of the present invention.
The technical scheme of the embodiment of the invention specifically comprises the following steps: acquiring sample batteries for model training, and determining battery type data corresponding to each sample battery respectively; determining battery capacity data corresponding to each sample battery in the capacity test process; training the standing time prediction model based on battery capacity data and battery type data respectively corresponding to each sample battery to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process. According to the technical scheme, the battery standing time prediction model is trained by acquiring the type data of the sample battery and the battery capacity data in the capacity test process, so that the battery standing time prediction model after the training is obtained, the problem that the efficiency and the accuracy of the test are affected by the standing time estimated in the prior art is solved, the accurate standing time is provided, and the accuracy of the test result is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a training method of a battery rest time prediction model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a battery capacity test in accordance with an embodiment of the present invention;
fig. 3 is a flowchart of a battery rest time prediction method according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a training device for a battery rest time prediction model according to a third embodiment of the present invention;
fig. 5 is a schematic structural view of a battery rest time prediction apparatus according to a fourth embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.
As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.
It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a flowchart of a method for training a battery rest time prediction model according to an embodiment of the present invention, where the method may be performed by a training device of the battery rest time prediction model, the training device of the battery rest time prediction model may be implemented in hardware and/or software, and the training device of the battery rest time prediction model may be configured in an intelligent terminal and a cloud server.
The test of the battery capacity is generally divided into a charging stage and a discharging stage, and after the battery is charged, the capacity of the battery can be calculated according to the discharging time and the current. Typically, a rest phase is also included between the charging phase and the discharging phase. In other words, in the process of testing the capacity of the battery, after the battery is charged, the battery needs to be left for a period of time and then discharged. In the same charging process, the battery is allowed to stand for 10min and then allowed to stand for 1h for discharge capacity test, and the obtained battery capacity test results have 2% -5% difference, and the specific error size can be determined according to the self-discharge performance of the battery. The standing time at the present stage is mainly estimated by combining the experience of the test with the materials, types and the like of the battery to approximately estimate one time, namely the standing time of the battery in the capacity test process can not be accurately determined. Based on the above, a certain error exists in the estimated standing time, which may result in inaccurate prediction results, thereby reducing the reliability of the test results. Aiming at the problems, the embodiment of the invention provides a training method of a battery standing time prediction model, so that a prediction model capable of accurately predicting the battery standing time is obtained based on the training method, and the reliability of a prediction result is improved. Specifically, as shown in fig. 1, the training method of the battery standing time prediction model specifically includes:
S110, acquiring sample batteries for model training, and determining battery type data corresponding to each sample battery.
In the embodiment of the invention, since different types of batteries have different battery characteristics, in order for the battery standing time prediction model after training to be capable of predicting the standing time of various batteries in the capacity test process, the sample battery for training needs to comprise different types of batteries. It should be noted that, the different types of batteries may be understood as batteries with different materials, or may be understood as batteries with different nominal capacity values, or may be other different types of batteries, and the embodiment is not limited to the specific type of battery.
Specifically, the battery subjected to the battery capacity test may be used as a sample battery for model training, and of course, different types of batteries may be obtained as sample batteries for model training based on other manners. On the basis of obtaining each sample battery for training, corresponding battery type data can be determined based on battery labels of each sample battery; optionally, the battery may be tested based on a preset battery testing device, so as to obtain type data of the sample battery.
It should be noted that, the above-mentioned manners of obtaining the sample battery and determining the battery type data corresponding to the sample battery are all exemplary embodiments of the present embodiment, and are not limited to the embodiments of the technical solution of the present invention.
S120, determining battery capacity data corresponding to each sample battery in the capacity test process.
In this embodiment, the battery capacity data may be understood as storage data of the battery during the capacity test, and specifically includes, but is not limited to, capacity data such as test time, execution serial number, execution name, voltage, current, power, phase charge energy, phase discharge energy, phase charge capacity, phase discharge capacity, charge energy, discharge energy, charge capacity, and discharge capacity. It is noted that, for each battery capacity data, the directly available data includes current data and voltage data, and other data except the current data and the voltage data are calculated by a preset calculation formula.
Optionally, the method for determining battery capacity data corresponding to each sample battery in the capacity test process in this embodiment includes: acquiring current data and voltage data of each sample battery in the capacity test process; and determining battery capacity data corresponding to each sample battery respectively based on the current data and the voltage data.
Specifically, different battery capacity data needs to be obtained through different calculation formulas. Based on the above, on the basis of acquiring current data and voltage data of the sample battery in the capacity test process, respectively corresponding battery capacity data is acquired based on a corresponding calculation formula.
In this embodiment, in the history process of performing the battery capacity test on the sample battery based on the charging and discharging device, since the connecting line exists between the charging and discharging device and the battery to be tested, there is an error between the charging and discharging device and the acquired voltage and current data and the current data and voltage data inside the battery acquired by the battery management system of the battery to be tested, and the connecting lines with different lengths between the charging and discharging device and the battery to be tested can form different sizes of the line resistances, so that the error is further increased, and thus, errors exist in the battery capacity data calculated based on the current data and the voltage data, and further, the accuracy of the battery standing time prediction model obtained based on the training is reduced.
In order to reduce the error of the obtained battery capacity data, the technical scheme of the embodiment adds an acquisition device between the charge and discharge equipment and the sample battery. Specifically, as shown in fig. 2, the collecting device includes a current collecting device and a voltage collecting device, which are respectively used for collecting voltage data and current data of the sample battery in the capacity test process, and storing the voltage data and the current data obtained by the battery internal and the voltage data and the current data obtained by the charging and discharging equipment into the computer recording software. And calculating each voltage data and each current data based on computer recording software to obtain battery capacity data of the sample battery, and storing the battery capacity data in a database for storage. The method has the advantages that the battery capacity data stored in the database are directly extracted as training data to train the battery standing time prediction model, so that the efficiency and the accuracy of the battery standing time prediction model obtained based on the subsequent training are improved.
Optionally, the method for acquiring the current data and the voltage data of each sample battery in the capacity test process in this embodiment may include: for any sample battery, reading system current data and system voltage data of the current sample battery based on a preset battery management system; acquiring current data and voltage data of a current sample battery based on a preset data acquisition device; and acquiring test current data and test voltage data of the current sample battery based on the charge and discharge equipment in the capacity test process.
Optionally, the method for determining battery capacity data corresponding to each sample battery based on the current data and the voltage data in this embodiment may include: for any sample battery, determining system battery capacity data of the current sample battery based on the system current data and the system voltage data; determining collected battery capacity data of the current sample battery based on the collected current data and the collected voltage data; determining test battery capacity data of the current sample battery based on the test current data and the test voltage data; and determining the battery capacity data of the current sample battery based on the system battery capacity data, the collected battery capacity data and the test battery capacity data.
Specifically, based on the current data and the voltage data corresponding to each acquisition mode, the current data and the voltage data are calculated based on a preset calculation formula to obtain battery capacity data corresponding to each acquisition mode, and it is to be noted that, according to the difference of the calculation formulas, the obtained battery capacity data are correspondingly different.
And S130, training the standing time prediction model based on battery capacity data and battery type data respectively corresponding to each sample battery to obtain a standing time prediction model after training.
In the case of a battery for which the type of the battery is determined, the rest time of the battery may be determined in the case of determining the charge data of the battery, thereby allowing the battery to rest. Therefore, in order to reduce the data calculation amount, the obtained battery capacity data of each sample battery in the capacity test process includes the rechargeable battery capacity data corresponding to the charging stage and the stationary battery capacity data corresponding to the stationary stage.
Based on the above, in this embodiment, the method for training the standing time prediction model based on the battery capacity data and the battery type data corresponding to each sample battery to obtain the standing time prediction model after training may include: for any sample battery, determining charging battery capacity data and standing battery capacity data corresponding to battery type data of a battery sample based on a preset battery corresponding relation; and taking the battery type data, the rechargeable battery capacity data and the standing battery capacity data as sample training data of the current sample battery, and training the standing time prediction model to obtain a standing time prediction model after training.
Specifically, battery type data, rechargeable battery capacity data and standing battery capacity data are used as sample training data of a current sample battery and are input into a battery standing time prediction model to be trained, and a capacity prediction value output by the model is obtained. And acquiring a historical capacity value in a historical capacity test result corresponding to the current sample battery, and taking the historical capacity value as a label of the current sample battery in the training process. And generating a loss function of the model in the iterative training process based on the label and the capacity predicted value output by the model, adjusting model parameters of the battery rest time prediction model based on the loss function, and stopping training when the training meets the iterative stopping condition to obtain the rest time prediction model after the training is completed. Alternatively, the rest time prediction model may be used to predict the rest time of the battery during the rest phase of the capacity test.
Based on the above embodiment, in order to eliminate unit limitation of input data, the embodiment of the invention is convenient for comparing and weighting data of different orders or units, and adopts a maximum and minimum normalization method to normalize battery type data and battery capacity data in the input data. The above operation has the advantages that on one hand, the model convergence speed can be increased, and on the other hand, the prediction accuracy of the model can be improved.
The technical scheme of the embodiment of the invention specifically comprises the following steps: acquiring sample batteries for model training, and determining battery type data corresponding to each sample battery respectively; determining battery capacity data corresponding to each sample battery in the capacity test process; training the standing time prediction model based on battery capacity data and battery type data respectively corresponding to each sample battery to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process. According to the technical scheme, the battery standing time prediction model is trained by acquiring the type data of the sample battery and the battery capacity data in the capacity test process, so that the battery standing time prediction model after the training is obtained, the problem that the efficiency and the accuracy of the test are affected by the standing time estimated in the prior art is solved, the accurate standing time is provided, and the accuracy of the test result is improved.
On the basis of the implementation mode, the embodiment of the invention also provides a preferable embodiment which is used for specifically introducing the training method of the battery standing time prediction model. The steps of this embodiment specifically include:
The charge and discharge equipment performs capacity test by charging and discharging the battery, and an external independent voltage and current acquisition device is newly added, and various data based on voltage and current combination are recorded and stored in the process, wherein the data comprise test time, execution serial number, execution name, voltage, current, power, stage charge energy, stage discharge energy, stage charge capacity, stage discharge capacity, charge energy, discharge energy, charge capacity, discharge capacity, stage time, accumulated time, data content reported by a battery management system and the like. Meanwhile, the voltage and current acquired by the external acquisition device can be subjected to weighted analysis and comparison with the voltage and current acquired by the charging and discharging equipment and the voltage and current reported by the battery management system to offset the voltage inconsistency caused by the length of the line.
Starting from the stored database based on the data of the battery capacity charge and discharge test standing time according to different battery capacity data and different battery types, preprocessing and extracting the content data (charge and standing) of the standing step and the previous step, and taking the corresponding time and voltage in the battery capacity test charge and standing step and the nominal capacity of the battery and the battery type data as input characteristics, if missing data are deleted or interpolated. And training the deep neural network model by adopting different battery capacities and types to obtain a trained model, wherein the trained model can predict all standing time.
Specifically, the training process includes:
step 1: in order to eliminate unit limitation of the data features, data of different magnitudes or units can be conveniently compared and weighted, and all the data features are normalized by adopting a maximum and minimum normalization method. On one hand, the model convergence speed can be increased, and on the other hand, the prediction accuracy of the model can be improved.
Step 2: 80% of the training set was randomly selected, and the rest was the test set.
Step 3: training a deep neural network by BP algorithm using training set data while setting weights (W) and bias values (b)
Step 4: and inputting the test set into a deep neural network prediction model, verifying whether the model has an overfitting problem, and testing the accuracy and applicability of the prediction model.
Example two
Fig. 3 is a flowchart of a battery rest time prediction method provided in a second embodiment of the present invention, where the present embodiment is applicable to a case of determining a rest time of a battery in a rest stage in a capacity test process, the method may be performed by a battery rest time prediction device, the battery rest time prediction device may be implemented in a form of hardware and/or software, and the battery rest time prediction device may be configured in an intelligent terminal and a cloud server. As shown in fig. 3, the method includes:
S210, acquiring a target battery and determining battery type data of the target battery.
In the embodiment of the invention, the target battery can be understood as a battery in which a battery capacity test is being performed. Specifically, the method for determining battery type data of the target battery may include: determining corresponding battery type data based on battery tags of the respective sample batteries; optionally, the determining method may further include: and testing the battery based on preset battery testing equipment to obtain type data of the sample battery.
S220, determining charging battery capacity data corresponding to a charging stage of the target battery in the capacity test process.
Specifically, current data and voltage data of the target battery in the charging stage are determined, and charging battery capacity data is obtained based on the current data and the voltage data. Specifically, reading system current data and system voltage data in a target battery based on a preset battery management system, and determining system battery capacity data of the target battery based on the system current data and the system voltage data; acquiring acquisition current data and acquisition voltage data of a target battery based on a preset data acquisition device, and determining acquisition battery capacity data of the target battery based on the acquisition current data and the acquisition voltage data; acquiring test current data and test voltage data of a target battery based on charge and discharge equipment in a capacity test process, and determining test battery capacity data of the target battery based on the test current data and the test voltage data; and determining battery capacity data of the target battery based on the system battery capacity data, the collected battery capacity data and the test battery capacity data.
And S230, inputting the rechargeable battery capacity data and the battery type data into a pre-trained battery standing time prediction model to obtain the standing time of the target battery in the standing stage in the capacity test process.
According to the technical scheme, the battery standing time prediction model is trained by acquiring the type data of the sample battery and the battery capacity data in the capacity test process, the trained battery standing time prediction model is obtained, the standing time of the target battery in the battery capacity test process is predicted by adopting the trained battery standing time prediction model, the problem that the estimated standing time in the prior art can influence the test efficiency and accuracy is solved, the accurate standing time is provided, and the accuracy of a test result is improved.
Example III
Fig. 4 is a schematic structural diagram of a training device for a battery standing time prediction model according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: a sample battery acquisition module 310, a sample battery acquisition module 320, and a model training module 330; wherein,,
a sample battery obtaining module 310, configured to obtain sample batteries used for model training, and determine battery type data corresponding to each sample battery;
A battery capacity data obtaining module 320, configured to determine battery capacity data corresponding to each of the sample batteries in a capacity test process;
the model training module 330 is configured to train the standing time prediction model based on the battery capacity data and the battery type data corresponding to each of the sample batteries, so as to obtain a standing time prediction model after training is completed; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process.
Optionally, based on the above embodiment, the battery capacity data acquisition module 320 includes:
the data acquisition sub-module is used for acquiring current data and voltage data of each sample battery in the capacity test process;
and the battery capacity data determining submodule is used for determining battery capacity data corresponding to each sample battery respectively based on the current data and the voltage data.
Optionally, on the basis of the foregoing embodiment, the data acquisition sub-module includes:
the system data acquisition unit is used for reading the system current data and the system voltage data of the current sample battery based on a preset battery management system for any sample battery;
The acquisition data acquisition unit is used for acquiring acquisition current data and acquisition voltage data of the current sample battery based on a preset data acquisition device;
and the test data acquisition unit is used for acquiring the test current data and the test voltage data of the current sample battery based on the charge and discharge equipment in the capacity test process.
Optionally, the battery capacity data determining sub-module based on the above embodiment includes:
a system battery capacity data determining unit configured to determine, for any one of the sample batteries, system battery capacity data of the current sample battery based on the system current data and the system voltage data;
the collected battery capacity data determining unit is used for determining collected battery capacity data of the current sample battery based on the collected current data and the collected voltage data;
a test battery capacity data determining unit configured to determine test battery capacity data of the current sample battery based on the test current data and the test voltage data;
and the battery capacity data determining unit is used for determining the battery capacity data of the current sample battery based on the system battery capacity data, the collected battery capacity data and the test battery capacity data.
Optionally, on the basis of the foregoing embodiment, the battery capacity data of each sample battery in the capacity test process includes charging battery capacity data corresponding to a charging stage and standing battery capacity data corresponding to a standing stage;
accordingly, model training module 330 includes:
the battery capacity data determining sub-module is used for determining charging battery capacity data and standing battery capacity data corresponding to battery type data of a battery sample based on a preset battery corresponding relation for any sample battery;
and the model training sub-module is used for taking the battery type data, the rechargeable battery capacity data and the standing battery capacity data as sample training data of the current sample battery, and training the standing time prediction model to obtain a standing time prediction model after training.
The training device for the battery standing time prediction model provided by the embodiment of the invention can execute the training method for the battery standing time prediction model provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 is a schematic structural diagram of a training device for a battery rest time prediction model according to a fourth embodiment of the present invention. As shown in fig. 5, the apparatus includes:
The rechargeable battery capacity data acquisition module 410 is configured to acquire rechargeable battery capacity data corresponding to a charging stage of the target battery in a capacity test process;
the standing time obtaining module 420 is configured to input the rechargeable battery capacity data into a pre-trained battery standing time prediction model, so as to obtain a standing time of the target battery in a standing stage in a capacity test process; the battery standing time prediction model is obtained by training based on the training method of the battery standing time prediction model in any embodiment.
The training device for the battery standing time prediction model provided by the embodiment of the invention can execute the training method for the battery standing time prediction model provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the training method of the battery rest time prediction model and the battery rest time prediction method.
In some embodiments, the training method of the battery rest time prediction model and the battery rest time prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described training method of the battery rest time prediction model and the battery rest time prediction method may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the training method of the battery rest time prediction model and the battery rest time prediction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. The training method of the battery standing time prediction model is characterized by comprising the following steps of:
acquiring sample batteries for model training, and determining battery type data corresponding to each sample battery respectively;
determining battery capacity data corresponding to each sample battery in a capacity test process;
training the standing time prediction model based on battery capacity data and battery type data corresponding to each sample battery respectively to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process;
The battery capacity data of each sample battery in the capacity test process comprises charging battery capacity data corresponding to a charging stage and standing battery capacity data corresponding to a standing stage;
correspondingly, the training the standing time prediction model based on the battery capacity data and the battery type data respectively corresponding to each sample battery to obtain a standing time prediction model after training, which comprises the following steps:
for any sample battery, determining charging battery capacity data and standing battery capacity data corresponding to battery type data of the sample battery based on a preset battery corresponding relation;
and training the standing time prediction model by taking the battery type data, the rechargeable battery capacity data and the standing battery capacity data as sample training data of the current sample battery to obtain a standing time prediction model after training.
2. The method of claim 1, wherein determining battery capacity data for each of the sample batteries during the capacity test comprises:
acquiring current data and voltage data of each sample battery in a capacity test process;
And determining battery capacity data corresponding to each sample battery respectively based on the current data and the voltage data.
3. The method of claim 2, wherein the obtaining current data and voltage data for each of the sample cells during the capacity test comprises:
for any sample battery, reading system current data and system voltage data of the current sample battery based on a preset battery management system;
acquiring current data and voltage data of the current sample battery based on a preset data acquisition device;
and acquiring test current data and test voltage data of the current sample battery based on the charge and discharge equipment in the capacity test process.
4. The method of claim 3, wherein the determining battery capacity data for each of the sample batteries based on the current data and the voltage data comprises:
for any sample cell, determining system cell capacity data for the current sample cell based on the system current data and the system voltage data;
determining collected battery capacity data of the current sample battery based on the collected current data and the collected voltage data;
Determining test battery capacity data of the current sample battery based on the test current data and the test voltage data;
and determining battery capacity data of the current sample battery based on the system battery capacity data, the collected battery capacity data and the test battery capacity data.
5. A battery rest time prediction method, characterized by comprising:
acquiring a target battery and determining battery type data of the target battery;
determining charging battery capacity data corresponding to a charging stage of the target battery in a capacity test process;
inputting the rechargeable battery capacity data and the battery type data into a pre-trained battery standing time prediction model to obtain the standing time of the target battery in a standing stage in a capacity test process; the battery rest time prediction model is obtained by training based on the training method of the battery rest time prediction model in any one of claims 1-4.
6. A training device for a battery rest time prediction model, comprising:
the sample battery acquisition module is used for acquiring sample batteries used for model training and determining battery type data corresponding to each sample battery respectively;
The battery capacity data acquisition module is used for determining battery capacity data corresponding to each sample battery in the capacity test process;
the model training module is used for training the standing time prediction model based on battery capacity data and battery type data which correspond to the sample batteries respectively to obtain a standing time prediction model after training; the static time prediction model is used for predicting static time of a static stage of the battery in the capacity test process;
the battery capacity data of each sample battery in the capacity test process comprises charging battery capacity data corresponding to a charging stage and standing battery capacity data corresponding to a standing stage;
model training module 330, comprising:
the phase battery capacity data determining submodule is used for determining charging battery capacity data and standing battery capacity data corresponding to battery type data of any sample battery based on a preset battery corresponding relation;
and the model training sub-module is used for taking the battery type data, the rechargeable battery capacity data and the standing battery capacity data as sample training data of the current sample battery, and training the standing time prediction model to obtain a standing time prediction model after training.
7. A battery rest time prediction apparatus, characterized by comprising:
the rechargeable battery capacity data acquisition module is used for acquiring rechargeable battery capacity data corresponding to a charging stage of the target battery in a capacity test process;
the standing time obtaining module is used for inputting the rechargeable battery capacity data into a pre-trained battery standing time prediction model to obtain the standing time of the standing stage of the target battery in the capacity test process; the battery rest time prediction model is obtained by training based on the training method of the battery rest time prediction model in any one of claims 1-4.
8. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the training method of the battery rest time prediction model of any one of claims 1-4 and/or the battery rest time prediction method of claim 5.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the training method of the battery rest time prediction model according to any one of claims 1-4 and/or the battery rest time prediction method according to claim 5 when executed.
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