CN109656975A - Fault early warning system and method based on big data - Google Patents
Fault early warning system and method based on big data Download PDFInfo
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- 238000004458 analytical method Methods 0.000 claims abstract description 14
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- 239000000284 extract Substances 0.000 claims abstract description 4
- 230000005611 electricity Effects 0.000 claims description 8
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Abstract
The present invention relates to a kind of fault early warning system and method based on big data, the fault pre-alarming suitable for new-energy automobile battery pack, wherein data extracting unit identifies from enterprise platform and extracts critical data；Data retrieval unit is retrieved by search condition；Data statistic analysis unit is for statistical analysis, and statistic analysis result is graphically showed.The present invention can be effectively reduced after cost, product is allowed preferably to meet client with the failure of look-ahead battery deeply the reason of excavation battery failures.
Description
Technical field
It is the present invention relates to the fault pre-alarming for being suitable for new-energy automobile battery pack, in particular to a kind of based on big data
Fault early warning system and method.
Background technique
Along with the wideling popularize at home of new-energy automobile in recent years, the new energy vapour caused by battery pack security risk
Vehicle safety accident is simultaneously not within minority, although many vehicle enterprises and operator emphasize to possess complete intelligent monitoring platform always,
The magnanimity that continuous, dynamic can be provided battery pack, real-time, effectively monitor, but acquired at present by intelligent monitoring platform
Data are relatively rough, and the analysis done to it is not deep enough, are unable to satisfy multi-level, multi-angle requirement, are not enough to capture
The potential fault message of battery pack.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of fault early warning system and method based on big data, using based on big
The statistical analysis mode of data, it is contemplated that influence of various factors to battery pack health status, look-ahead battery pack
Incipient fault improves the service awareness of product.
In order to achieve the above objectives, The technical solution adopted by the invention is as follows:
A kind of fault early warning system based on big data, it is described suitable for the fault pre-alarming of new-energy automobile battery pack
Fault early warning system includes: data retrieval unit, data extracting unit, data analysis unit；
The data extracting unit, identifies from enterprise platform and extracts critical data；
The data retrieval unit carries out precise search or fuzzy search by search condition to the critical data extracted,
It obtains retrieving data accordingly；
The data statistic analysis unit, it is for statistical analysis to retrieval data, to the letter selected from retrieval data
Breath carries out recycle ratio pair, and the result of statistical analysis is pressed the exhibition in a manner of chart of different dimensions the comparison model of setting
It is existing.
Optionally, the data extracting unit shows the critical data of extraction in table form；
The data retrieval unit shows the result of retrieval in table form.
Optionally, the search condition include Start Date, the Close Date, license plate number, SOC range, ceiling voltage range,
Minimum voltage range, maximum temperature range, minimum temperature range.
Optionally, the critical data, comprising:
License plate number, for identification identity of vehicle；
Data acquisition time, for determining the data of specified time, and for counting setting data hair in designated time period
Raw probability；
Vehicle position data, longitude, dimension including vehicle, for the position of designated vehicle to be accurately positioned；
Accumulated distance, for assessing the frequency of use of battery pack；
SOC, for assessing the electricity of battery pack in use；
Total current, for assessing the operating condition of vehicle operation；
Extreme value data, including ceiling voltage, minimum voltage, maximum temperature, minimum temperature, ceiling voltage Sub-System Number, most
High voltage monomer code name, minimum voltage Sub-System Number, minimum voltage monomer code name, maximum temperature Sub-System Number, maximum temperature are visited
Needle serial number, minimum temperature Sub-System Number, minimum temperature probe serial number.
Optionally, the extreme value data include several groups ceiling voltage, respectively correspond different ceiling voltage Sub-System Numbers；
The extreme value data include several groups minimum voltage, respectively correspond different minimum voltage Sub-System Numbers；
The extreme value data include several groups maximum temperature, respectively correspond different maximum temperature Sub-System Numbers；
The extreme value data include several groups minimum temperature, respectively correspond different minimum temperature Sub-System Numbers.
Optionally, the chart includes curve graph, histogram, table.
Optionally, the curve graph, comprising:
Voltage tendency chart, for showing the corresponding battery pack of specified license plate number at the appointed time in section at different SOC
Minimum voltage value is the minimum voltage correlation curve of every month in designated time period；
Mileage tendency chart, for showing the corresponding battery pack of specified license plate number at the appointed time in section in different SOC downlinks
The mileage sailed is the mileage correlation curve of every month in designated time period；
Voltage distribution graph is supported for showing minimum voltage value of the battery pack in charging, discharge process under difference SOC
According to condition show, selectable condition includes: Start Date, the Close Date, single license plate number, all license plate numbers, charges, puts
Electricity, charge and discharge comparison；
Mileage distribution map, for rendering under difference SOC vehicle mileage travelled number, to show one or more or all vehicles
Mileage distribution situation.
Optionally, the histogram includes SOC histogram, electric current histogram, temperature histogram.
Optionally, the table content in the chart, including license plate number, minimum voltage value, minimum voltage position, lowest order
Set the minimum difference of accounting, minimum SOC, highest, accumulated distance.
A kind of fault early warning method based on big data, the fault pre-alarming system using any one of the above based on big data
System, the fault early warning method include following procedure:
Connect the database of enterprise platform；The enterprise platform obtains vehicle driving, charging from electric car car-mounted terminal
Relevant operation data is stored in database；
Critical data is identified and extracted from enterprise platform, is retrieved and is inquired；
Query result is returned in tabular form；
It is for statistical analysis to data, analysis result is graphically shown.
Compared with the prior art, the beneficial effects of the present invention are embodied in:
Can be with flexible choice data source, multiple recycle ratio pair, flexible setting comparison model, at the same can to result data into
The profound analysis of row, is quickly obtained valuable information, data is presented to enterprise by visual mode, depth excavates electricity
The potential fault message of pond group, reduces service cost.
Detailed description of the invention
Fig. 1 is the schematic diagram of the fault early warning system of the present invention based on big data；
Fig. 2 is the provided fault early warning system flow chart based on big data according to embodiments of the present invention；
Fig. 3 is the schematic diagram of the provided fault early warning system based on big data according to embodiments of the present invention.
Specific embodiment
The invention will be further described with embodiment with reference to the accompanying drawing.
As shown in Figure 1, a kind of trouble analysis system based on big data provided by the invention, comprising: data retrieval unit,
Data extracting unit and data analysis unit.
The data retrieval unit carries out precise search or fuzzy search formula by search condition, generates to the data of extraction
Corresponding search result, and the search result is shown in table form；Search condition includes Start Date, closing day
Phase, license plate number, SOC range, ceiling voltage range, minimum voltage range, maximum temperature range, minimum temperature range.
The data extracting unit can identify the critical data for including in enterprise platform and extract critical data；It is crucial
Data include license plate number, data acquisition time, vehicle position data, accumulated distance, SOC, total current, extreme value data.
Wherein, the identity of license plate number vehicle for identification；Data acquisition time is used in combination for determining data sometime
In the probability that a certain data occur in statistics certain time period；Vehicle position data, longitude, dimension including vehicle, for standard
Determine the position of a certain vehicle in position；Accumulated distance, for assessing the frequency of use of battery pack；SOC makes for assessing battery pack
With electricity in the process；Total current, for assessing the operating condition of vehicle operation；Extreme value data, including ceiling voltage, minimum voltage,
Maximum temperature, minimum temperature, ceiling voltage Sub-System Number, ceiling voltage monomer code name, minimum voltage Sub-System Number, minimum voltage
Monomer code name, maximum temperature Sub-System Number, maximum temperature probe serial number, minimum temperature Sub-System Number, minimum temperature probe serial number.
Exemplary ceiling voltage, minimum voltage, maximum temperature, minimum temperature have four groups respectively, respectively correspond to different
Ceiling voltage Sub-System Number, minimum voltage Sub-System Number, maximum temperature Sub-System Number, minimum temperature Sub-System Number.
The data statistic analysis unit can analyze data with various dimensions, multi-angle, can with flexible choice number
According to, multiple recycle ratio pair, flexible setting comparison model.It, can be by the result of statistical analysis by difference by the comparison model
Dimension showed in a manner of chart；The chart includes curve graph, histogram, table.
Wherein, the curve graph includes voltage tendency chart, mileage tendency chart, voltage distribution graph, mileage distribution map: the electricity
Tendency chart is pressed, for showing minimum voltage value of the corresponding battery pack of a certain license plate number within certain a period of time at different SOC,
For the minimum voltage correlation curve of every month in certain a period of time；The mileage tendency chart, it is corresponding for showing a certain license plate number
Battery pack within certain a period of time in the mileage of different SOC downward drivings, be every month in certain a period of time mileage compare it is bent
Line；The voltage distribution graph can be by for showing battery pack in charging, the minimum voltage value in discharge process under difference SOC
Condition shows, selectable condition include: Start Date, the Close Date, single license plate number, all license plate numbers, charging, electric discharge,
Charge and discharge comparison；The mileage distribution map, for rendering under difference SOC vehicle mileage travelled number, to show one or more
Or the mileage distribution situation of all vehicles；
The histogram includes SOC histogram, electric current histogram, temperature histogram, can it is intuitive by way of figure,
The quickly working condition of assessment battery pack.
The content of the table includes license plate number, minimum voltage value, minimum voltage position, extreme lower position accounting, minimum
The minimum difference of SOC, highest, accumulated distance；By table, battery pack incipient fault information can be quickly obtained, while can be excavated
Battery failure reason out.
As shown in Fig. 2, the fault early warning system of the present invention based on big data, implementing procedure includes:
Step 101, pass through C# connection enterprise platform Mysql database.
In one embodiment, enterprise platform obtains the operation datas such as vehicle driving, charging from electric car car-mounted terminal,
And data are stored in Mysql database.
Step 102, critical data is retrieved from database and is inquired.
Step 103, query result returns in tabular form.
Step 104, for statistical analysis to data, analysis result is graphically shown.
In one embodiment, curve graph and histogram are drawn using Livecharts.
In embodiment as shown in Figure 2, the main composition of fault early warning system analysis theme based on big data has:
1. querying condition: inquiring the search condition of critical data i.e. from database；
2. complete set data are downloaded: according to search condition, query result being returned with csv file；
3. drawing: it is for statistical analysis to data, analysis result is showed in a manner of patterned.
Fig. 3 input inquiry condition " Start Date: on January 1st, 2018 ", " Close Date: on August 10th, 2018 ", " license plate
Selection: Shanghai A07317D " is clicked " downloading of complete set data ", generates search result, the data retrieved are in a manner of csv file
Show；
Fig. 3 input inquiry condition " Start Date: on July 1st, 2018 ", " Close Date: on August 10th, 2018 " are clicked
" SOC histogram ", after the data statistical analysis retrieved, analysis result such as Fig. 3 is showed in the form of histogram, in figure, X
Axis is battery pack SOC value, and Y-axis is the number that some SOC occurs in Query Dates, can quickly learn electricity by SOC histogram
SOC range when pond group works.
By using voltage, electric current, SOC, the temperature etc. of the network analysis new-energy automobile battery pack that the present invention designs
The reason of parameter can deeply excavate the potential fault message of battery pack, be quickly found out battery failure, saves the time after sale,
Reduce after cost.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, for those skilled in the art, the invention may be variously modified and varied.It is all in spirit of the invention
With any modification, equivalent replacement, improvement and so within principle, should be included within scope of the invention.
Claims (10)
1. a kind of fault early warning system based on big data, suitable for the fault pre-alarming of new-energy automobile battery pack, feature
It is, the fault early warning system includes: data retrieval unit, data extracting unit, data analysis unit；
The data extracting unit, identifies from enterprise platform and extracts critical data；
The data retrieval unit carries out precise search or fuzzy search by search condition, obtains to the critical data extracted
Corresponding retrieval data；
The data statistic analysis unit, it is for statistical analysis to retrieval data, to from retrieval data the information selected into
Row recycle ratio pair is showed the result of statistical analysis by different dimensions the comparison model of setting in a manner of chart.
2. according to claim 1 based on the fault early warning system of big data, which is characterized in that
The data extracting unit shows the critical data of extraction in table form；
The data retrieval unit shows the result of retrieval in table form.
3. according to claim 1 based on the fault early warning system of big data, which is characterized in that
The search condition includes Start Date, Close Date, license plate number, SOC range, ceiling voltage range, minimum voltage model
It encloses, maximum temperature range, minimum temperature range.
4. according to claim 1 based on the fault early warning system of big data, which is characterized in that
The critical data, comprising:
License plate number, for identification identity of vehicle；
Data acquisition time, for determining the data of specified time, and occur for counting setting data in designated time period
Probability；
Vehicle position data, longitude, dimension including vehicle, for the position of designated vehicle to be accurately positioned；
Accumulated distance, for assessing the frequency of use of battery pack；
SOC, for assessing the electricity of battery pack in use；
Total current, for assessing the operating condition of vehicle operation；
Extreme value data, including ceiling voltage, minimum voltage, maximum temperature, minimum temperature, ceiling voltage Sub-System Number, highest electricity
Press monomer code name, minimum voltage Sub-System Number, minimum voltage monomer code name, maximum temperature Sub-System Number, maximum temperature probe sequence
Number, minimum temperature Sub-System Number, minimum temperature probe serial number.
5. according to claim 4 based on the fault early warning system of big data, which is characterized in that
The extreme value data include several groups ceiling voltage, respectively correspond different ceiling voltage Sub-System Numbers；
The extreme value data include several groups minimum voltage, respectively correspond different minimum voltage Sub-System Numbers；
The extreme value data include several groups maximum temperature, respectively correspond different maximum temperature Sub-System Numbers；
The extreme value data include several groups minimum temperature, respectively correspond different minimum temperature Sub-System Numbers.
6. according to claim 1 based on the fault early warning system of big data, which is characterized in that
The chart includes curve graph, histogram, table.
7. according to claim 6 based on the fault early warning system of big data, which is characterized in that
The curve graph, comprising:
Voltage tendency chart, for show the corresponding battery pack of specified license plate number at the appointed time in section it is minimum at different SOC
Voltage value is the minimum voltage correlation curve of every month in designated time period；
Mileage tendency chart, for showing the corresponding battery pack of specified license plate number at the appointed time in section in different SOC downward drivings
Mileage is the mileage correlation curve of every month in designated time period；
Voltage distribution graph is supported to press item for showing minimum voltage value of the battery pack in charging, discharge process under difference SOC
Part shows, and selectable condition includes: Start Date, the Close Date, single license plate number, all license plate numbers, charging, discharges, fills
Electric discharge comparison；
Mileage distribution map, for rendering under difference SOC vehicle mileage travelled number, to show one or more or all vehicles
Mileage distribution situation.
8. according to claim 6 based on the fault early warning system of big data, which is characterized in that
The histogram includes SOC histogram, electric current histogram, temperature histogram.
9. according to claim 6 based on the fault early warning system of big data, which is characterized in that
Table content in the chart, including it is license plate number, minimum voltage value, minimum voltage position, extreme lower position accounting, minimum
The minimum difference of SOC, highest, accumulated distance.
10. a kind of fault early warning method based on big data, using described in any one of claim 1-9 based on big data
Fault early warning system, which is characterized in that the fault early warning method includes following procedure:
Connect the database of enterprise platform；The enterprise platform obtains vehicle driving, charging correlation from electric car car-mounted terminal
Operation data be stored in database；
Critical data is identified and extracted from enterprise platform, is retrieved and is inquired；
Query result is returned in tabular form；
It is for statistical analysis to data, analysis result is graphically shown.
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| CN201811523353.4A CN109656975A (en) | 2018-12-13 | 2018-12-13 | Fault early warning system and method based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111241154A (en) * | 2020-01-02 | 2020-06-05 | 浙江吉利新能源商用车集团有限公司 | Storage battery fault early warning method and system based on big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102968107A (en) * | 2012-11-28 | 2013-03-13 | 北京理工大学 | Method and system for remotely monitoring electric vehicle |
| CN104731814A (en) * | 2013-12-23 | 2015-06-24 | 北京宸瑞科技有限公司 | System and method for flexibly comparing and analyzing data |
| CN105005222A (en) * | 2015-06-12 | 2015-10-28 | 山东省科学院自动化研究所 | New-energy electric automobile overall performance improving system and method based on big data |
| CN105955146A (en) * | 2016-06-27 | 2016-09-21 | 中国科学技术大学 | Remote monitoring system for power battery pack of electric vehicle |
| CN106776940A (en) * | 2016-12-01 | 2017-05-31 | 中国电力科学研究院 | A kind of magnanimity battery data statistics look-up table building method and system |
| CN108710084A (en) * | 2018-06-01 | 2018-10-26 | 王泽霖 | A kind of monitoring of electric car power supply and energy management system |
-
2018
- 2018-12-13 CN CN201811523353.4A patent/CN109656975A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102968107A (en) * | 2012-11-28 | 2013-03-13 | 北京理工大学 | Method and system for remotely monitoring electric vehicle |
| CN104731814A (en) * | 2013-12-23 | 2015-06-24 | 北京宸瑞科技有限公司 | System and method for flexibly comparing and analyzing data |
| CN105005222A (en) * | 2015-06-12 | 2015-10-28 | 山东省科学院自动化研究所 | New-energy electric automobile overall performance improving system and method based on big data |
| CN105955146A (en) * | 2016-06-27 | 2016-09-21 | 中国科学技术大学 | Remote monitoring system for power battery pack of electric vehicle |
| CN106776940A (en) * | 2016-12-01 | 2017-05-31 | 中国电力科学研究院 | A kind of magnanimity battery data statistics look-up table building method and system |
| CN108710084A (en) * | 2018-06-01 | 2018-10-26 | 王泽霖 | A kind of monitoring of electric car power supply and energy management system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111241154A (en) * | 2020-01-02 | 2020-06-05 | 浙江吉利新能源商用车集团有限公司 | Storage battery fault early warning method and system based on big data |
| CN111241154B (en) * | 2020-01-02 | 2024-04-12 | 浙江吉利远程新能源商用车集团有限公司 | Storage battery fault early warning method and system based on big data |
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