CN112782584B - Method, system, medium and device for predicting remaining usage amount of battery electric quantity - Google Patents
Method, system, medium and device for predicting remaining usage amount of battery electric quantity Download PDFInfo
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention discloses a method, a system, a medium and electronic equipment for predicting remaining use limit of battery power, wherein the method for predicting remaining use limit of battery power comprises the following steps: acquiring historical discharge process data of a battery to be predicted; extracting a plurality of groups of discharge variables from the historical discharge process data, and respectively corresponding historical use limits to each group of discharge variables; taking a plurality of groups of discharge variables and the historical use amount as training data, and training to obtain a prediction model; 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 residual usage amount of the battery to be predicted. The technical scheme of the invention improves the accuracy and timeliness of prediction and improves the user experience.
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 residual usage of battery electric quantity.
Background
The electric automobile is one of the energy-saving and environment-friendly transportation tools which are increasingly popular in recent years, and has the advantages of low carbon dioxide emission, low maintenance cost and good user experience. When the user uses the electric vehicle, the most concerned is the use condition of the battery power, so as to judge whether the electric vehicle can not run due to the exhaustion of the battery power in the long-distance running process.
In the prior art, only the SOC of the remaining battery is usually evaluated, and then the SOC value of the remaining battery obtained by the evaluation is displayed to the user. However, after knowing the SOC value of the remaining battery power, the user cannot get the user from the beginning at all, and it is not clear how long or how long the electric vehicle can travel, and only fuzzy judgment can be performed according to the historical traveling condition. In addition, as various variables such as user habit, battery health degree and temperature can influence the residual usage amount of the battery electric quantity to different degrees, the user can subjectively judge the residual usage amount of the battery electric quantity according to the residual electric quantity of the battery, so that the accuracy is poor, the efficiency is low, and the user experience is seriously influenced.
Disclosure of Invention
The invention aims to overcome the defect that the corresponding residual usage amount cannot be obtained only by showing the residual battery power to a user in the prior art, and provides a method, a system, a medium and electronic equipment for predicting the residual usage amount of the battery power.
The invention solves the technical problems by the following technical scheme:
a method of predicting remaining usage of battery power, the method comprising:
acquiring historical discharge process data of a battery to be predicted;
extracting a plurality of groups of discharge variables from the historical discharge process data, and respectively corresponding historical use limits to each group of discharge variables;
taking a plurality of groups of discharge variables and the historical use amount as training data, and training to obtain a prediction model;
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 residual usage amount of the battery to be predicted.
Preferably, different ones of said discharge variables have different weight coefficients;
the step of inputting the current discharge variable into the prediction model to obtain the current residual usage amount 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 residual usage amount by utilizing the weight coefficient.
Preferably, the formula for predicting the current remaining usage amount by using the weight coefficient is:
wherein i is the current moment, i is more than or equal to 1,
the weight coefficients for one or more of the discharge variables,
and->
y i A current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
Preferably, the historical usage amount includes historical remaining usage time or historical remaining usage mileage;
and/or the number of the groups of groups,
the discharge variables include one or more of battery health, user profile, 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 conversion suggestion according to the current residual usage amount;
and sending the current residual use limit and/or the power change suggestion to a preset user terminal.
A prediction system of remaining usage of battery power, the prediction 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 limits corresponding to each group of discharge variables respectively 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 amount 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 residual usage amount of the battery to be predicted.
Preferably, different ones of said discharge variables have different weight 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 residual usage amount by utilizing the weight coefficient.
Preferably, the formula for predicting the current remaining usage amount by using the weight coefficient is:
wherein i is the current moment, i is more than or equal to 1,
the weight coefficients for one or more of the discharge variables,
and->
y i A current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
Preferably, the historical usage amount includes historical remaining usage time or historical remaining usage mileage;
and/or the number of the groups of groups,
the discharge variables include one or more of battery health, user profile, 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 transmission module;
the power-change suggestion generation module is used for generating a power-change suggestion according to the current residual usage amount;
and the sending module is used for sending the current residual use limit and/or the power-changing suggestion to a preset user terminal.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for predicting remaining usage of battery power as described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned method of predicting remaining usage of battery power.
The invention has the positive progress effects that: according to the method, the system, the medium and the electronic equipment for predicting the residual use limit of the battery electric quantity, influence of various influencing factors on the battery use process is considered when the residual use limit is predicted, the trained prediction model is utilized for fitting the data, the use limit of the battery residual electric quantity is intuitively displayed for a user, the accuracy and the timeliness of prediction are improved, and the user experience is improved.
Drawings
Fig. 1 is a flowchart of a method for predicting remaining battery power consumption in embodiment 1 of the present invention.
Fig. 2 is a block diagram of a system for predicting remaining battery power consumption in embodiment 2 of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a method for predicting remaining battery power usage in embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The embodiment provides a method for predicting remaining usage of battery power, which includes the following steps:
step S10: acquiring historical discharge process data of a battery to be predicted;
the historical discharge process data herein 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, or the like.
Step S11: extracting a plurality of groups of discharge variables from the historical discharge process data and historical use limits corresponding to each group of discharge variables respectively;
in this embodiment, the discharge variables may include one or more of battery health, user profile, 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 historical usage amount may include a historical remaining usage time or a historical remaining usage mileage.
Step S12: taking a plurality of groups of discharge variables and historical use amount as training data, and training to obtain a prediction model;
the predictive model may be a trained linear regression model, among other things.
Step S13: acquiring a current discharge variable of a battery to be predicted;
step S14: and inputting the current discharge variable into a prediction model to obtain the current residual usage amount 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 residual usage amount is predicted by using the weight coefficient.
Specifically, the formula for predicting the current remaining usage amount using the weight coefficient may be:
wherein i is the current moment, i is more than or equal to 1,
weight coefficient for one or more discharge variables, +.>
And->
y i The current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
Further, after the historical discharge process data of the battery to be predicted is 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 cleaning refers to "cleaning" data by filling in missing values, smoothing noise data, identifying or deleting outliers (e.g., peak or trough data), and resolving inconsistencies. The data format can be standardized by data cleaning, abnormal data is cleared, erroneous data is corrected, and duplicate data is cleared.
Data integration refers to the integration and unified storage of data in multiple data sources, thereby simplifying data storage space.
The data transformation means that the data is converted into a form suitable for data calculation by means of smooth aggregation, data generalization or normalization and the like, so that the speed of subsequent data calculation is improved.
Data reduction refers to the reduction of a large amount of data to a data set, so that the integrity of the 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 training the model, multiple groups of discharge variables in the training set data are used as input, and historical usage amount is used as output to train the model to obtain a prediction model; and then, the test set data can be used for testing the prediction model, whether the test result meets the preset condition is judged, if so, the prediction model is accurate, the method can be used for subsequent prediction operation, if not, the weight coefficient is adjusted, and the training set data is used for training the model again until the prediction result meets the requirement.
Further, after predicting the current remaining usage amount of the battery to be predicted, the prediction method in this embodiment may further include the following steps:
step S15: generating a power conversion suggestion according to the current residual use limit;
step S16: and sending the current residual use limit and/or the power change suggestion to a preset user terminal.
Specifically, the user terminal may be any type of PC terminal or mobile terminal (e.g., mobile phone, ipad), which is not limited in the embodiment of the present invention.
According to the method for predicting the residual usage amount of the battery electric quantity, provided by the embodiment, the influence of various influencing factors on the battery usage process is considered when the residual usage amount is predicted, the trained prediction model is utilized to carry out fitting processing on data, the usage amount of the battery residual electric quantity is intuitively displayed for a user, the accuracy and timeliness of prediction are improved, and the user experience is improved.
Example 2
The present embodiment provides a prediction system for remaining usage of battery power, as shown in fig. 2, the prediction system 1 may include:
a historical data acquisition module 10 for acquiring historical discharge process data of the battery to be predicted; the historical discharge process data herein 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, or the like.
A data extraction module 11, configured to extract a plurality of groups of discharge variables and historical usage amounts corresponding to each group of discharge variables from historical discharge process data;
in this embodiment, the discharge variables may include one or more of battery health, user profile, 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 historical usage amount may include a historical remaining usage time or a historical remaining usage mileage.
The training module 12 is configured to train to obtain a prediction model by using a plurality of sets of discharge variables and historical usage amount as training data; the predictive model may be a trained linear regression model, among other things.
A current data acquisition module 13, configured to acquire a current discharge variable of the battery to be predicted;
the prediction module 14 is configured to input a current discharge variable into the prediction model to obtain a current remaining usage amount 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 a prediction model to match and determine a weight coefficient corresponding to the current discharge variable, and predict a 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:
wherein i is the current moment, i is more than or equal to 1,
weight coefficient for one or more discharge variables, +.>
And->
y i The current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
Further, the prediction system 1 may further include a preprocessing module 15, where after acquiring the historical discharge process data of the battery to be predicted, the preprocessing module 15 may further perform preprocessing on the historical discharge process data to improve accuracy of subsequent calculation. Preprocessing includes, but is not limited to, data cleansing, data integration, data transformation, and data reduction.
Specifically, data cleaning refers to "cleaning" data by filling in missing values, smoothing noise data, identifying or deleting outliers (e.g., peak or trough data), and resolving inconsistencies. The data format can be standardized by data cleaning, abnormal data is cleared, erroneous data is corrected, and duplicate data is cleared.
Data integration refers to the integration and unified storage of data in multiple data sources, thereby simplifying data storage space.
The data transformation means that the data is converted into a form suitable for data calculation by means of smooth aggregation, data generalization or normalization and the like, so that the speed of subsequent data calculation is improved.
Data reduction refers to the reduction of a large amount of data to a data set, so that the integrity of the 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 train the model with multiple sets of discharge variables in the training set data as inputs and with historical usage as outputs to obtain a predictive model; and then, the test set data can be used for testing the prediction model, whether the test result meets the preset condition is judged, if so, the prediction model is accurate, the method can be used for subsequent prediction operation, if not, the weight coefficient is adjusted, and the training set data is used for training the model again until the prediction result meets the requirement.
Further, the prediction system 1 further comprises a power exchange suggestion generation module 16 and a transmission module 17; the power conversion suggestion generation module 16 is configured to generate a power conversion suggestion according to the current remaining usage amount; the sending module 17 is configured to send the current remaining usage amount and/or the power conversion suggestion to a predetermined user terminal.
Specifically, the user terminal may be any type of PC terminal or mobile terminal (e.g., mobile phone, ipad), which is not limited in the embodiment of the present invention.
The prediction system for the residual usage amount of the battery electric quantity, provided by the embodiment, comprehensively considers the influence of various influence factors on the battery usage process when in operation, and utilizes the trained prediction model to carry out fitting processing on data, so that the usage amount of the battery residual electric quantity is intuitively displayed for a user, the accuracy and timeliness of prediction are improved, and the user experience is improved.
Example 3
The present invention also provides an electronic device, as shown in fig. 3, where the electronic device may include a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor executes the computer program to implement the steps of the method for predicting the remaining usage amount of the battery in the foregoing embodiment 1.
It should be understood that the electronic device shown in fig. 3 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 3, 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, a bus 5 connecting the different 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) 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 or some combination of which may include 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 remaining usage of battery power in embodiment 1 of the present invention by running a computer program stored in the memory 4.
The electronic device 2 may also communicate with one or more external devices 6, such as a keyboard, pointing device, etc. Such communication may be through 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, wide area network, WAN, and/or 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 the bus 5. Those skilled in the art will appreciate that although not shown, other hardware and/or software modules may be used in connection 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, data backup storage systems, and the like.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of an electronic device are mentioned, such a division is only exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of predicting remaining usage of battery power in embodiment 1.
More specific ways in which the computer-readable storage medium may be employed include, but are not limited to: portable disk, 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 invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of the method for predicting the remaining usage of battery charge in embodiment 1, when the program product is run on the terminal device.
Wherein the program code for carrying out the invention may be 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, 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 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 principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (8)
1. A method for predicting remaining usage 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 from the historical discharge process data, and respectively corresponding historical use limits to each group of discharge variables;
taking a plurality of groups of discharge variables and the historical use amount as training data, and training to obtain a prediction model;
acquiring a current discharge variable of the battery to be predicted;
inputting the current discharge variable into the prediction model to obtain the current residual usage amount of the battery to be predicted;
different discharge variables have different weight coefficients;
the step of inputting the current discharge variable into the prediction model to obtain the current residual usage amount of the battery to be predicted comprises the following steps:
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 residual usage amount by utilizing the weight coefficient;
the formula for predicting the current residual 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 weight coefficient being one or more of the discharge variables, +.>
And->
y i A current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
2. The method for predicting remaining battery life as recited in claim 1, wherein,
the historical use limit comprises historical use time or historical use mileage;
and/or the number of the groups of groups,
the discharge variables include one or more of battery health, user profile, 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.
3. The method for predicting remaining battery power usage as defined in any one of claims 1-2, wherein the step of inputting the current discharge variable into the prediction model to obtain the current remaining battery usage to be predicted further comprises:
generating a power conversion suggestion according to the current residual usage amount;
and sending the current residual use limit and/or the power change suggestion to a preset user terminal.
4. A system for predicting remaining usage of 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 limits corresponding to each group of discharge variables respectively 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 amount as training data;
the current data acquisition module is used for acquiring the current discharge variable of the battery to be predicted;
the prediction module is used for inputting the current discharge variable into the prediction model to obtain the current residual usage amount of the battery to be predicted;
different discharge variables have different weight 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 residual usage amount by utilizing the weight coefficient;
the formula for predicting the current residual 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 weight coefficient being one or more of the discharge variables, +.>
And->
y i A current residual usage amount is used; y is i-1 And the residual usage amount obtained for the last prediction from the current time.
5. The system for predicting remaining battery life as recited in claim 4, wherein,
the historical use limit comprises historical use time or historical use mileage;
and/or the number of the groups of groups,
the discharge variables include one or more of battery health, user profile, 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.
6. The prediction system for remaining battery power usage as defined in any one of claims 4-5, further comprising a power-change suggestion generation module and a transmission module;
the power-change suggestion generation module is used for generating a power-change suggestion according to the current residual usage amount;
and the sending module is used for sending the current residual use limit and/or the power-changing suggestion to a preset user terminal.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for predicting the remaining usage of battery power of any one of claims 1-3 when the computer program is executed.
8. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of predicting remaining usage of battery power of any one of claims 1-3.
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| US20110234167A1 (en) * | 2010-03-24 | 2011-09-29 | Chin-Hsing Kao | Method of Predicting Remaining Capacity and Run-time of a Battery Device |
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