CN112782584A - Method, system, medium, and device for predicting remaining usage limit of battery power - Google Patents
Method, system, medium, and device for predicting remaining usage limit of battery power Download PDFInfo
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- 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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- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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Abstract
The invention discloses a method, a system, a medium and an electronic device for predicting the remaining use limit of battery power, wherein the prediction method comprises the following steps: acquiring historical discharge process data of a battery to be predicted; extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from the historical discharge process data; training to obtain a prediction model by taking a plurality of groups of discharge variables and the historical use limit as training data; acquiring a current discharge variable of the battery to be predicted; and inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted. According to the technical scheme, the accuracy and timeliness of prediction are improved, and user experience is improved.
Description
Technical Field
The invention relates to the technical field of battery replacement of electric vehicles, in particular to a method, a system, a medium and electronic equipment for predicting the remaining use limit of battery power.
Background
The electric automobile is one of energy-saving and environment-friendly transportation tools which are more and more popular in recent years, and has the advantages of low carbon dioxide emission, low maintenance cost and good user experience. When a user uses an electric automobile, the most concerned is the use condition of the battery power, so as to judge whether the electric automobile cannot run due to the exhaustion of the battery power in the process of long-distance running.
In the prior art, only the SOC of the battery is usually evaluated, and then the SOC value of the battery obtained through evaluation is displayed to the user. However, after the user knows the SOC value of the battery, the user has no idea at all, and it is unclear how long or how long the electric vehicle can travel, and only fuzzy judgment can be performed according to the historical driving condition. Moreover, because various variables such as user habits, battery health degree and temperature can all cause different degrees of influence on the remaining use limit of the battery power, the user subjectively judges the remaining use limit of the battery power according to the battery remaining power, the accuracy is poor, the efficiency is very low, and the user experience is seriously influenced.
Disclosure of Invention
The invention provides a method, a system, a medium and an electronic device for predicting the remaining use limit of battery power, aiming at overcoming the defect that the prior art can not obtain the corresponding remaining use limit only by showing the remaining battery power to a user.
The invention solves the technical problems through the following technical scheme:
a method for predicting a remaining usage amount of a battery power, the method comprising:
acquiring historical discharge process data of a battery to be predicted;
extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from the historical discharge process data;
training to obtain a prediction model by taking a plurality of groups of discharge variables and the historical use limit as training data;
acquiring a current discharge variable of the battery to be predicted;
and inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
Preferably, different ones of said discharge variables have different weighting coefficients;
the step of inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted comprises the following steps:
and inputting the current discharge variable into the prediction model to match and determine the weight coefficient corresponding to the current discharge variable, and predicting the current remaining use limit by using the weight coefficient.
Preferably, the formula for predicting the current remaining usage amount by using the weight coefficient is as follows:
wherein i is the current moment, i is more than or equal to 1,
the weighting coefficients for one or more of the discharge variables,
and is
yiThe current remaining use limit is obtained; y isi-1The remaining usage amount obtained by the last prediction at the current time.
Preferably, the historical using amount comprises historical remaining using time or historical remaining using mileage;
and/or the presence of a gas in the gas,
the discharge variables include one or more of battery health, user portrait, ambient temperature outside the battery pack, ambient humidity outside the battery pack, discharge process voltage, discharge process current, battery pack internal temperature, battery pack internal humidity, and battery internal resistance.
Preferably, the step of inputting the current discharge variable into the prediction model to obtain the current remaining usage amount of the battery to be predicted further includes:
generating a power change suggestion according to the current remaining use limit;
and sending the current remaining use limit and/or the battery replacement suggestion to a preset user terminal.
A system for predicting a remaining usage amount of a battery power, the system comprising:
the historical data acquisition module is used for acquiring historical discharge process data of the battery to be predicted;
the data extraction module is used for extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from the historical discharge process data;
the training module is used for training to obtain a prediction model by taking a plurality of groups of discharge variables and the historical use limit as training data;
the current data acquisition module is used for acquiring the current discharge variable of the battery to be predicted;
and the prediction module is used for inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
Preferably, different ones of said discharge variables have different weighting coefficients;
the prediction module is used for inputting the current discharge variable into the prediction model so as to match and determine the weight coefficient corresponding to the current discharge variable, and predicting the current remaining use amount by using the weight coefficient.
Preferably, the formula for predicting the current remaining usage amount by using the weight coefficient is as follows:
wherein i is the current moment, i is more than or equal to 1,
the weighting coefficients for one or more of the discharge variables,
and is
yiThe current remaining use limit is obtained; y isi-1The remaining usage amount obtained by the last prediction at the current time.
Preferably, the historical using amount comprises historical remaining using time or historical remaining using mileage;
and/or the presence of a gas in the gas,
the discharge variables include one or more of battery health, user portrait, ambient temperature outside the battery pack, ambient humidity outside the battery pack, discharge process voltage, discharge process current, battery pack internal temperature, battery pack internal humidity, and battery internal resistance.
Preferably, the prediction system further comprises a power change suggestion generation module and a sending module;
the battery replacement suggestion generation module is used for generating a battery replacement suggestion according to the current remaining use limit;
the sending module is used for sending the current remaining use amount and/or the battery replacement suggestion to a preset user terminal.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the method for predicting the remaining use limit of the battery capacity when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the aforementioned steps of the method of predicting a remaining usage amount of a battery level.
The positive progress effects of the invention are as follows: the method, the system, the medium and the electronic equipment for predicting the remaining use limit of the battery power take the influence of various influence factors on the use process of the battery into consideration when the remaining use limit is predicted, and perform fitting processing on data by using the trained prediction model, so that the use limit of the battery power is visually displayed for a user, the prediction accuracy and timeliness are improved, and the user experience is improved.
Drawings
Fig. 1 is a flowchart of a method for predicting a remaining usage amount of battery power in embodiment 1 of the present invention.
Fig. 2 is a block diagram of a system for predicting the remaining usage amount of battery power according to embodiment 2 of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device implementing a method for predicting a remaining usage amount of battery power according to embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
The embodiment provides a method for predicting the remaining use limit of battery power, which comprises the following steps:
step S10: acquiring historical discharge process data of a battery to be predicted;
the historical discharge process data may be discharge process data of a preset historical period, for example, data within 1 month from the current time or data within 3 months from the current time, and the like.
Step S11: extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from historical discharge process data;
in this embodiment, the discharge variable may include one or more of a battery health, a user profile, an ambient temperature outside the battery pack, an ambient humidity outside the battery pack, a discharge process voltage, a discharge process current, a battery pack internal temperature, a battery pack internal humidity, and a battery internal resistance.
Preferably, the historical usage amount may include historical remaining usage time or historical remaining usage mileage.
Step S12: training to obtain a prediction model by taking a plurality of groups of discharge variables and historical use limit as training data;
the prediction model may be a trained linear regression model.
Step S13: acquiring a current discharge variable of a battery to be predicted;
step S14: and inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
In this embodiment, different weight coefficients may be set for different discharge variables;
based on the above, after the current discharge variable is input into the prediction model, the weight coefficient corresponding to the current discharge variable can be matched and determined, and then the current remaining use limit is predicted by using the weight coefficient.
Specifically, the formula for predicting the current remaining usage amount by using the weight coefficient may be:
wherein i is the current moment, i is more than or equal to 1,
are weighting factors for one or more discharge variables,
and is
yiThe current remaining use limit; y isi-1The remaining usage amount obtained by the last prediction at the current time.
Furthermore, after the historical discharge process data of the battery to be predicted are obtained, the historical discharge process data can be preprocessed, so that the accuracy of subsequent calculation is improved. Preprocessing includes, but is not limited to, data cleansing, data integration, data transformation, and data reduction.
Specifically, data cleansing refers to "cleansing" data by filling in missing values, smoothing out noisy data, identifying or deleting outliers (e.g., peak or trough data), and resolving inconsistencies. Through data cleaning, the data format can be standardized, abnormal data can be eliminated, error data can be corrected, and repeated data can be eliminated.
Data integration refers to combining and uniformly storing data in a plurality of data sources, so that the data storage space is simplified.
The data transformation is to convert data into a form suitable for data calculation by means of smooth aggregation, data generalization or normalization and the like, so as to improve the speed of subsequent data calculation.
The data reduction means that a large amount of data is reduced to a data set, so that the integrity of original data can be maintained, and the order of data storage can be improved.
In this embodiment, the historical discharge process data may include training set data and test set data;
preferably, when the model is trained, a plurality of groups of discharge variables in training set data can be used as input, and historical use limit is used as output to train the model, so as to obtain a prediction model; and then, testing the prediction model by using the test set data, judging whether the test result meets the preset condition, if so, indicating that the prediction model is more accurate, and then using the prediction model for subsequent prediction operation, otherwise, adjusting the weight coefficient, and training the model by using the training set data again until the prediction result meets the requirement.
Further, after the current remaining usage amount of the battery to be predicted is predicted, the prediction method in this embodiment may further include the following steps:
step S15: generating a power change suggestion according to the current remaining use limit;
step S16: and sending the current remaining use limit and/or the battery replacement suggestion to a preset user terminal.
Specifically, the user terminal may be a PC terminal or a mobile terminal (e.g., a mobile phone, ipad) of any type, which is not limited in this embodiment of the present invention.
The method for predicting the remaining use limit of the battery power provided by the embodiment considers the influence of various influence factors on the battery use process when predicting the remaining use limit, performs fitting processing on data by using the trained prediction model, intuitively displays the use limit of the battery remaining power to a user, improves the accuracy and timeliness of prediction, and improves user experience.
Example 2
The present embodiment provides a system for predicting remaining usage amount of battery capacity, as shown in fig. 2, the system 1 may include:
a historical data acquisition module 10, configured to acquire historical discharge process data of a battery to be predicted; the historical discharge process data may be discharge process data of a preset historical period, for example, data within 1 month from the current time or data within 3 months from the current time, and the like.
The data extraction module 11 is used for extracting a plurality of groups of discharge variables and historical use limit values respectively corresponding to the discharge variables from historical discharge process data;
in this embodiment, the discharge variable may include one or more of a battery health, a user profile, an ambient temperature outside the battery pack, an ambient humidity outside the battery pack, a discharge process voltage, a discharge process current, a battery pack internal temperature, a battery pack internal humidity, and a battery internal resistance.
Preferably, the historical usage amount may include historical remaining usage time or historical remaining usage mileage.
The training module 12 is used for training to obtain a prediction model by taking a plurality of groups of discharge variables and historical use limit as training data; the prediction model may be a trained linear regression model.
A current data obtaining module 13, configured to obtain a current discharge variable of the battery to be predicted;
and the prediction module 14 is used for inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
In this embodiment, different discharge variables have different weight coefficients;
the prediction module 14 is configured to input the current discharge variable into the prediction model to match and determine a weight coefficient corresponding to the current discharge variable, and predict the current remaining usage amount by using the weight coefficient.
Specifically, the formula for predicting the current remaining usage amount by using the weight coefficient is as follows:
wherein i is the current moment, i is more than or equal to 1,
are weighting factors for one or more discharge variables,
and is
yiThe current remaining use limit; y isi-1The remaining usage amount obtained by the last prediction at the current time.
Further, the prediction system 1 may further include a preprocessing module 15, and after the historical discharge process data of the battery to be predicted is obtained, the preprocessing module 15 may also preprocess the historical discharge process data to improve the accuracy of the subsequent calculation. Preprocessing includes, but is not limited to, data cleansing, data integration, data transformation, and data reduction.
Specifically, data cleansing refers to "cleansing" data by filling in missing values, smoothing out noisy data, identifying or deleting outliers (e.g., peak or trough data), and resolving inconsistencies. Through data cleaning, the data format can be standardized, abnormal data can be eliminated, error data can be corrected, and repeated data can be eliminated.
Data integration refers to combining and uniformly storing data in a plurality of data sources, so that the data storage space is simplified.
The data transformation is to convert data into a form suitable for data calculation by means of smooth aggregation, data generalization or normalization and the like, so as to improve the speed of subsequent data calculation.
The data reduction means that a large amount of data is reduced to a data set, so that the integrity of original data can be maintained, and the order of data storage can be improved.
In this embodiment, the historical discharge process data may include training set data and test set data;
preferably, the training module 12 may use a plurality of groups of discharge variables in the training set data as inputs, and use a historical usage amount as an output to train the model, so as to obtain a prediction model; and then, testing the prediction model by using the test set data, judging whether the test result meets the preset condition, if so, indicating that the prediction model is more accurate, and then using the prediction model for subsequent prediction operation, otherwise, adjusting the weight coefficient, and training the model by using the training set data again until the prediction result meets the requirement.
Further, the prediction system 1 further includes a battery replacement suggestion generation module 16 and a sending module 17; the battery replacement suggestion generation module 16 is used for generating a battery replacement suggestion according to the current remaining use limit; the sending module 17 is configured to send the current remaining usage amount and/or the battery replacement suggestion to a predetermined user terminal.
Specifically, the user terminal may be a PC terminal or a mobile terminal (e.g., a mobile phone, ipad) of any type, which is not limited in this embodiment of the present invention.
When the prediction system for the remaining usage amount of the battery power provided by the embodiment operates, the influence of various influence factors on the battery using process is comprehensively considered, the trained prediction model is used for fitting data, the usage amount of the battery remaining power is visually displayed for a user, the prediction accuracy and timeliness are improved, and the user experience is improved.
Example 3
The present invention further provides an electronic device, as shown in fig. 3, the electronic device may include a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method for predicting the remaining usage amount of battery power in embodiment 1 when executing the computer program.
It should be understood that the electronic device shown in fig. 3 is only an example, and should not bring any limitation to the function and the scope of the application of the embodiment of the present invention.
As shown in fig. 3, the electronic device 2 may be embodied in the form of a general purpose computing device, such as: which may be a server device. The components of the electronic device 2 may include, but are not limited to: the at least one processor 3, the at least one memory 4, and a bus 5 connecting the various system components (including the memory 4 and the processor 3).
The bus 5 may include a data bus, an address bus, and a control bus.
The memory 4 may include volatile memory, such as Random Access Memory (RAM)41 and/or cache memory 42, and may further include Read Only Memory (ROM) 43.
The memory 4 may also include a program tool 45 (or utility tool) having a set (at least one) of program modules 44, such program modules 44 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 3 executes various functional applications and data processing, such as the steps of the method for predicting the remaining usage amount of battery power in embodiment 1 of the present invention, by running the computer program stored in the memory 4.
The electronic device 2 may also communicate with one or more external devices 6 (e.g., a keyboard, a pointing device, etc.). Such communication may be via an input/output (I/O) interface 7. Also, the model-generated electronic device 2 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network) via the network adapter 8.
As shown in FIG. 3, the network adapter 8 may communicate with other modules of the model-generated electronic device 2 via a bus 5. It will be appreciated by those skilled in the art that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generated electronic device 2, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the steps of the method for predicting the remaining usage amount of battery power in embodiment 1.
More specific ways in which the computer-readable storage medium may be employed may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the present invention can also be implemented in the form of a program product including program code for causing a terminal device to execute the steps of the method for predicting the remaining usage amount of battery power in embodiment 1 when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (12)
1. A method for predicting the remaining usage amount of battery power is characterized in that the method comprises the following steps:
acquiring historical discharge process data of a battery to be predicted;
extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from the historical discharge process data;
training to obtain a prediction model by taking a plurality of groups of discharge variables and the historical use limit as training data;
acquiring a current discharge variable of the battery to be predicted;
and inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
2. The method of claim 1, wherein different discharging variables have different weighting factors;
the step of inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted comprises the following steps:
and inputting the current discharge variable into the prediction model to match and determine the weight coefficient corresponding to the current discharge variable, and predicting the current remaining use limit by using the weight coefficient.
3. The method of claim 2, wherein the method of predicting the remaining usage amount of the battery power,
the formula for predicting the current remaining use amount by using the weight coefficient is as follows:
wherein i is the current moment, i is more than or equal to 1,
the weighting coefficients for one or more of the discharge variables,
and is
yiThe current remaining use limit is obtained; y isi-1The remaining usage amount obtained by the last prediction at the current time.
4. The method of claim 1, wherein the method of predicting the remaining usage amount of the battery power,
the historical using amount comprises historical using time or historical using mileage;
and/or the presence of a gas in the gas,
the discharge variables include one or more of battery health, user portrait, ambient temperature outside the battery pack, ambient humidity outside the battery pack, discharge process voltage, discharge process current, battery pack internal temperature, battery pack internal humidity, and battery internal resistance.
5. The method for predicting the remaining usage amount of battery power as claimed in any one of claims 1 to 4, wherein the step of inputting the current discharge variable into the prediction model to obtain the current remaining usage amount of the battery to be predicted further comprises:
generating a power change suggestion according to the current remaining use limit;
and sending the current remaining use limit and/or the battery replacement suggestion to a preset user terminal.
6. A system for predicting a remaining usage amount of a battery capacity, the system comprising:
the historical data acquisition module is used for acquiring historical discharge process data of the battery to be predicted;
the data extraction module is used for extracting a plurality of groups of discharge variables and historical use limit respectively corresponding to each group of discharge variables from the historical discharge process data;
the training module is used for training to obtain a prediction model by taking a plurality of groups of discharge variables and the historical use limit as training data;
the current data acquisition module is used for acquiring the current discharge variable of the battery to be predicted;
and the prediction module is used for inputting the current discharge variable into the prediction model to obtain the current remaining use limit of the battery to be predicted.
7. The system of claim 6, wherein different ones of the discharge variables have different weighting factors;
the prediction module is used for inputting the current discharge variable into the prediction model so as to match and determine the weight coefficient corresponding to the current discharge variable, and predicting the current remaining use amount by using the weight coefficient.
8. The system for predicting the remaining usage amount of battery power as set forth in claim 7,
the formula for predicting the current remaining use amount by using the weight coefficient is as follows:
wherein i is the current moment, i is more than or equal to 1,
the weighting coefficients for one or more of the discharge variables,
and is
yiThe current remaining use limit is obtained; y isi-1The remaining usage amount obtained by the last prediction at the current time.
9. The system for predicting the remaining usage amount of battery power as set forth in claim 6,
the historical using amount comprises historical using time or historical using mileage;
and/or the presence of a gas in the gas,
the discharge variables include one or more of battery health, user portrait, ambient temperature outside the battery pack, ambient humidity outside the battery pack, discharge process voltage, discharge process current, battery pack internal temperature, battery pack internal humidity, and battery internal resistance.
10. The system for predicting the remaining usage amount of battery power according to any one of claims 6 to 9, further comprising a power swapping suggestion generation module and a transmission module;
the battery replacement suggestion generation module is used for generating a battery replacement suggestion according to the current remaining use limit;
the sending module is used for sending the current remaining use amount and/or the battery replacement suggestion to a preset user terminal.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for predicting the remaining usage amount of battery power according to any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for predicting the remaining usage amount of battery power according to any one of claims 1 to 5.
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