CN117454107A - Historical data analysis method and system for electric automobile - Google Patents
Historical data analysis method and system for electric automobile Download PDFInfo
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- CN117454107A CN117454107A CN202311219575.8A CN202311219575A CN117454107A CN 117454107 A CN117454107 A CN 117454107A CN 202311219575 A CN202311219575 A CN 202311219575A CN 117454107 A CN117454107 A CN 117454107A
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Abstract
According to the invention, a set of historical data analysis method is constructed by combining understanding of the battery and partial use characteristics of the battery, so that screening of data of a charging section and a discharging section of the electric automobile can be realized quickly and effectively, the problem of data screening during EV operation data analysis is solved, and relevant data with effective and complete numerical values and available selection range and meeting analysis logic requirements are extracted after data cleaning and data classification; and meanwhile, EV driving behaviors are considered and used for counting data charging habits and driving behavior habits.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a historical data analysis method for an electric automobile.
Background
In order to face the increasingly serious environmental problems, the development of clean energy, the guarantee of energy safety and the realization of 'carbon peak' and 'carbon neutralization' become one of the core targets of the energy strategy in China, wherein electric vehicles (Electric Vehicles, EV) are taken as important components of a new energy system, and make great contribution to promoting urban green traffic. Aiming at the management and maintenance of electric automobiles, various manufacturers and platforms of large automobiles construct a large data platform of the Internet of vehicles for monitoring and managing the electric automobiles so as to realize the real-time monitoring and management of the data of the electric automobiles, but the problems of poor data analysis effect, high monitoring cost and the like caused by the characteristics of complex running environment of the current vehicles, large data quantity of the vehicles and low data effectiveness are remarkable, and especially the problems of data loss, error, noise, different formats and the like in the data transmission process are solved, so that the follow-up analysis of the data is difficult and the accuracy of analysis results is reduced.
Disclosure of Invention
In view of the above problems, in a first aspect, the present invention provides a method for analyzing historical data for an electric vehicle, including the following steps:
filtering NAN type invalid data recorded in the historical data;
setting a monitoring threshold value according to the extreme value of the vehicle operation data aiming at the electromagnetic interference data;
determining abnormal data caused by electromagnetic interference in the historical data;
screening out characteristic points in the battery history data based on the extreme points, screening out the data of a preliminary charging section of the battery, and taking data except the charging data as preliminary discharging data;
screening missing charging data in the preliminary discharging data, and excluding electric energy feedback data to obtain screened charging data and discharging data;
aiming at the charging section data screened by the extremum, the adjacent section data are combined to judge, so as to realize the extraction of the charging data outside the extremum section and inside the adjacent section;
classifying the charging data, and counting the charging behavior characteristics in the charging process;
and (5) counting driving behavior characteristics.
Based on the scheme, the method further comprises the following steps:
and labeling the stored charging characteristics of each section of charging data, and preparing for the analysis of the EV battery in the later period.
Based on the scheme, the method further comprises the following steps:
a vehicle history data analysis report is generated.
Based on the scheme, the distinguishing method is as follows:
screening out charging data with negative current by taking the current value as a first screening priority;
extracting charging data segments of the continuous charging segments based on the time interval to obtain continuous single charging data;
and filtering charging current data generated by energy braking recovery in the discharging process according to the judgment of the decrease of the SOC.
In a second aspect, there is provided a historical data analysis system for an electric vehicle, employing the historical data analysis method for an electric vehicle in the first aspect, the analysis system comprising:
the first module is used for filtering NAN type invalid data recorded in the historical data;
the second module is used for setting a monitoring threshold value according to the extreme value of the vehicle operation data aiming at the electromagnetic interference data;
a third module for determining abnormal data caused by electromagnetic interference in the history data;
a fourth module, configured to screen out preliminary charging section data of the battery based on feature points in the historical data of the battery and use data other than the charging data as preliminary discharging data;
the fifth module is used for screening missing charging data in the preliminary discharging data, and the missing charging data does not comprise electric energy feedback data, so that screened charging data and discharging data are obtained;
the sixth module is used for judging according to the extremum screened charging section data and the adjacent section data, so as to extract the charging data outside the extremum section and inside the adjacent section;
the sixth module is used for classifying the charging data and counting the charging behavior characteristics in the charging process;
and a seventh module for counting driving behavior characteristics.
The above aspect is based on the above aspect, further comprising an eighth module for generating a vehicle history data analysis report.
In a third aspect, there is provided an electronic device comprising a memory storing a computer program and a processor implementing the steps of the method described in the first aspect when the computer program is executed.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in the first aspect.
The invention has the beneficial effects that:
according to the invention, a set of historical data analysis method is constructed by combining understanding of the battery and partial use characteristics of the battery, so that screening of data of a charging section and a discharging section of the electric automobile can be realized quickly and effectively, the problem of data screening during EV operation data analysis is solved, and relevant data with effective and complete numerical values and available selection range and meeting analysis logic requirements are extracted after data cleaning and data classification; and meanwhile, EV driving behaviors are considered and used for counting data charging habits and driving behavior habits.
Drawings
The invention has the following drawings:
FIG. 1 is a diagram of a data classification using the method of the present invention;
FIG. 2 is a battery state evaluation curve;
FIG. 3 is a statistical diagram of charging conditions;
FIG. 4 is a vehicle operation data map;
fig. 5 is a running condition analysis chart.
Detailed Description
The present invention will be described in further detail with reference to fig. 1-5 and the detailed description of the invention, in order to make the objects, advantages and features of the invention more apparent.
In a specific embodiment of the present invention, as shown in fig. 1, the code is embedded into the data analysis platform program by using the data classification chart of the method of the present invention, and the method is implemented through the following cleaning and screening steps:
the NAN type invalid data recorded in the historical data is deleted;
step (2) setting a monitoring threshold for common electromagnetic interference data, such as voltage data of 0, voltage value exceeding a voltage range, current data of 5000 and the like, by limiting extreme values of data such as voltage, current, SOC, endurance mileage, vehicle speed data and the like, and comparing the threshold to judge abnormal data caused by electromagnetic interference in historical data;
screening out characteristic points with heavy historical data of the battery based on extreme points, screening out preliminary charging section data of the battery by combining data such as current, SOC and the like and data such as time intervals and the like, and taking data except the charging data as preliminary discharging data;
step (4) carrying out deep cleaning on the preliminary discharge data, further screening out missing charge data in the discharge process, wherein the missing charge data does not comprise electric energy feedback data, and obtaining final screened charge data and discharge data after the deep cleaning; deep cleaning is carried out on the charging section data screened out by the extremum, and the charging data extraction (mainly aiming at voltage reduction caused by current reduction and charging in the battery charging process) outside the extremum section and inside the adjacent section is realized by combining the data discrimination of the adjacent section data such as current, SOC and the like, wherein the discrimination is specifically as follows: and screening out charging data with negative current by taking the current as a first screening priority, extracting the charging data segments of the continuous charging segments based on time intervals to obtain continuous single charging data, and finally deleting the charging current data generated by energy braking recovery in the discharging process based on judging the decrease of the SOC, wherein the obtained data is complete and single charging data.
And (5) classifying the charging data based on data such as the SOC, the charging mode, the charging depth, the charging and discharging start and stop SOC, and counting charging behavior characteristics in the charging process, wherein the charging characteristics comprise the charging mode, the charging depth, the charging and discharging start and stop SOC, a pressure difference extreme value and a pressure difference distribution trend in the charging process, a temperature trend and the like, and are mainly obtained based on statistical analysis of the charging data. This is called charge data analysis, and the charge analysis result is saved.
And (6) counting driving behavior characteristics based on data such as the discharge current magnitude, the discharge current change trend, the discharge current jump pressure difference, the vehicle speed, the service time and the like, which is called discharge data analysis, and storing analysis results.
Step (7) collecting historical data, carrying out charging data analysis and discharging data analysis, and then providing a vehicle historical data analysis report, so as to realize visualization of statistical results and export of reports, which is called a reporting program; the analysis report comprises charging section data display, charging statistical results including the charging characteristics of the step (5), discharging section data display and discharging data analysis results.
The report diagrams are shown, for example, in fig. 2-5.
Preferably, each piece of charging data can be connected to an online data platform for visualization.
Preferably, the discharge data may be visualized by being connected to an on-line data platform.
Preferably, after the data visualization analysis is completed, the visualized graph can be derived using a reporting program as needed.
Preferably, each segment of charging data is stored by data and the charging characteristics are noted in preparation for later EV battery analysis.
In another embodiment, the analysis method of the present invention is applied to a historical data analysis system for an electric vehicle, comprising:
the first module is used for filtering NAN type invalid data recorded in the historical data;
the second module is used for setting a monitoring threshold value according to the extreme value of the vehicle operation data aiming at the electromagnetic interference data;
a third module for determining abnormal data caused by electromagnetic interference in the history data;
a fourth module, configured to screen out preliminary charging section data of the battery based on feature points in the historical data of the battery and use data other than the charging data as preliminary discharging data;
the fifth module is used for screening missing charging data in the preliminary discharging data, and the missing charging data does not comprise electric energy feedback data, so that screened charging data and discharging data are obtained;
the sixth module is used for judging according to the extremum screened charging section data and the adjacent section data, so as to extract the charging data outside the extremum section and inside the adjacent section;
the sixth module is used for classifying the charging data and counting the charging behavior characteristics in the charging process;
and a seventh module for counting driving behavior characteristics.
And an eighth module for generating a vehicle history data analysis report.
The above embodiments are only for illustrating the present invention and not for limiting the present invention, and various changes and modifications may be made by one skilled in the relevant art without departing from the spirit and scope of the present invention, so that all equivalent technical solutions fall within the scope of the present invention, which is defined by the claims. What is not described in detail in this specification is prior art known to those skilled in the art.
Claims (8)
1. The historical data analysis method for the electric automobile is characterized by comprising the following steps of:
filtering NAN type invalid data recorded in the historical data;
setting a monitoring threshold value according to the extreme value of the vehicle operation data aiming at the electromagnetic interference data;
determining abnormal data caused by electromagnetic interference in the historical data;
screening out characteristic points in the battery history data based on the extreme points, screening out the data of a preliminary charging section of the battery, and taking data except the charging data as preliminary discharging data;
screening missing charging data in the preliminary discharging data, and excluding electric energy feedback data to obtain screened charging data and discharging data;
aiming at the charging section data screened by the extremum, the adjacent section data are combined to judge, so as to realize the extraction of the charging data outside the extremum section and inside the adjacent section;
classifying the charging data, and counting the charging behavior characteristics in the charging process;
and (5) counting driving behavior characteristics.
2. The method for analyzing historical data for an electric automobile according to claim 1, further comprising the steps of:
and labeling the stored charging characteristics of each section of charging data, and preparing for the analysis of the EV battery in the later period.
3. The method for analyzing historical data for an electric automobile according to claim 1, further comprising the steps of:
a vehicle history data analysis report is generated.
4. The method for analyzing historical data for an electric automobile according to claim 1, wherein the method for discriminating is:
screening out charging data with negative current by taking the current value as a first screening priority;
extracting charging data segments of the continuous charging segments based on the time interval to obtain continuous single charging data;
and filtering charging current data generated by energy braking recovery in the discharging process according to the judgment of the decrease of the SOC.
5. A historical data analysis system for an electric vehicle, characterized in that a historical data analysis method for an electric vehicle is adopted, the analysis system comprising:
the first module is used for filtering NAN type invalid data recorded in the historical data;
the second module is used for setting a monitoring threshold value according to the extreme value of the vehicle operation data aiming at the electromagnetic interference data;
a third module for determining abnormal data caused by electromagnetic interference in the history data;
a fourth module, configured to screen out preliminary charging section data of the battery based on feature points in the historical data of the battery and use data other than the charging data as preliminary discharging data;
the fifth module is used for screening missing charging data in the preliminary discharging data, and the missing charging data does not comprise electric energy feedback data, so that screened charging data and discharging data are obtained;
the sixth module is used for judging according to the extremum screened charging section data and the adjacent section data, so as to extract the charging data outside the extremum section and inside the adjacent section;
the sixth module is used for classifying the charging data and counting the charging behavior characteristics in the charging process;
and a seventh module for counting driving behavior characteristics.
6. The historical data analysis system for an electric vehicle of claim 5, further comprising an eighth module for generating a vehicle historical data analysis report.
7. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311219575.8A CN117454107A (en) | 2023-09-21 | 2023-09-21 | Historical data analysis method and system for electric automobile |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311219575.8A CN117454107A (en) | 2023-09-21 | 2023-09-21 | Historical data analysis method and system for electric automobile |
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| Publication Number | Publication Date |
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| CN117454107A true CN117454107A (en) | 2024-01-26 |
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| CN202311219575.8A Pending CN117454107A (en) | 2023-09-21 | 2023-09-21 | Historical data analysis method and system for electric automobile |
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2023
- 2023-09-21 CN CN202311219575.8A patent/CN117454107A/en active Pending
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