CN105372600A - Storage battery fault diagnosis system and method based on big data - Google Patents
Storage battery fault diagnosis system and method based on big data Download PDFInfo
- Publication number
- CN105372600A CN105372600A CN201510828470.1A CN201510828470A CN105372600A CN 105372600 A CN105372600 A CN 105372600A CN 201510828470 A CN201510828470 A CN 201510828470A CN 105372600 A CN105372600 A CN 105372600A
- Authority
- CN
- China
- Prior art keywords
- battery pack
- data
- diagnostic
- analysis
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
Abstract
The technical scheme of the invention comprises a storage battery pack fault diagnosis system based on big data. The system comprises an interpreter which is used for human-computer interaction; an inference machine which is used for analyzing a storage battery pack fault and performing maintenance; an expert model component which is used for storing storage battery pack configuration and maintenance recommendations; a database which is used for storing historical and real-time data; and an automatic learning machine which is used for automatically learning fault characteristics. The system also comprises a storage battery pack fault diagnosis method based on the big data. The method comprises the steps that instruction transmission is performed on a human-computer interface, the inference machine performs data access and analysis according to instructions, the fault is stored if the fault is a new fault, and the interpreter receives the result and displays the result to a user. The beneficial effects of the storage battery pack fault diagnosis system and method based on the big data are that judgment and analysis are performed according to a model knowledge base by adopting an intelligent summery data, degree of expertise of maintenance personnel is reduced, efficiency is enhanced and thus the system and the method are convenient and rapid.
Description
Technical field
The present invention relates to a kind of system and method for the battery pack fault diagnosis based on large data, belong to technical field of electric power.
Background technology
In the current energy development time rapidly, the management of the energy and process are seemed extremely important.In current battery pack failure diagnostic process, usual use manually carries out image data, comparison, maintenance, not only very loaded down with trivial details but also complicated, and efficiency is very low, and the personnel of needing repairing have very high professional knowledge.And for existing battery pack fault diagnosis, cannot based on the process of mass data, be therefore badly in need of a kind of based on to the cognitive depth of industry and will possess big data quantity analysis ability, this requirement relate to specialist field and can meet actual conditions.
Summary of the invention
Loaded down with trivial details for prior art, low accumulator failure diagnosis efficiency, technical scheme of the present invention provides a kind of system and method for the battery pack fault diagnosis based on large data, smart sink total data is adopted to know storehouse decision analysis according to model intelligence, reduce the professional knowledge degree of maintainer, raise the efficiency, convenient and swift.
A kind of system of the battery pack fault diagnosis based on large data, this system comprises interpreter, expert model assembly, data-carrier store, deduce machine, automatic learning device, it is characterized in that: interpreter is used for end user's machine interactive interface and user carries out alternately, according to user's request instruction transmission request information, and by automatic for request results loopback to user; Expert model assembly for storing configuration and the diagnostic message of battery pack, also for receiving the amendment of configuration to stored battery pack and diagnostic message; Data-carrier store, for storing battery pack historical data and real time data, can receive transferring of data; Deduce machine is used for the failure message of called data storehouse battery pack, reads configuration and the diagnostic message of the battery pack that described expert model stores, implements corresponding maintenance measure to battery pack fault; Automatic learning device is used for according to existing battery failure information, adds configuration and the diagnostic message of new battery pack in described expert model.
A kind of system of the battery pack fault diagnosis based on large data, described interpreter comprises: man-machine interface: for realizing the real time human-machine interaction with user, instruction according to user sends fault diagnosis request to described deduce machine, accepts analysis report and is shown by analysis report; Intelligent assistance device: for using word to carry out man-to-man dialogue, the instruction that intelligent assistance device inputs according to user, feeds back the battery pack fault analysis information of correspondence in man-machine interface with written form; Accounting logging: for receiving the inferred results that described estimator sends, and inferred results being carried out gathering for analysis report, then analysis report being presented in man-machine interface.
Based on a system for the battery pack fault diagnosis of large data, model parameter storehouse, for storing parameter and the information of accumulator and battery pack, can receive the accumulator of storage and transferring and revising of battery pack information; Models repository, for storing the conditions for diagnostics and diagnostic recommendations that solve battery pack fault, can receive the conditions for diagnostics of battery pack fault to storing and transferring and revising of diagnostic recommendations.
A kind of system of the battery pack fault diagnosis based on large data, described deduce machine comprises: real time data estimator, carry out diagnostic analysis for the battery pack configuration information transferred in data and expert model assembly that in described data-carrier store, battery pack produces in real time, and corresponding maintenance suggestion is provided after diagnostic analysis; Historical data estimator, carries out diagnostic analysis for the battery pack configuration information transferred in data and expert model assembly that battery pack history in described data-carrier store produces, and after diagnostic analysis, provides corresponding maintenance suggestion; Performance evaluation estimator, diagnostic analysis is carried out by the battery pack configuration information transferred in data and expert model assembly that battery pack history in described data-carrier store produces, analyze the historical trend of battery performance, intelligent predicting goes out the change in future of battery performance, and provides solution according to pre-direction finding user; Real-time Alarm estimator, for the alarm event produced in real time according to battery pack, finds the source of trouble of battery pack and diagnoses, and provides corresponding diagnostic recommendations according to source of trouble information.
A kind of system of the battery pack fault diagnosis based on large data, described automatic learning device comprises: the battery pack configuration information transferring battery pack failure message and correspondence in deduce machine in real time, when monitoring the new battery pack failure message occurred in deduce machine, then obtain battery pack configuration information and the solution of this failure message, and this failure message battery pack configuration information and solution are stored on expert model.
Based on a method for the battery pack fault diagnosis of large data, it is characterized in that, the method comprises: end user's machine interactive interface and user carry out alternately, according to user's request instruction transmission request information, and by automatic for request results loopback to user; Store the configuration of battery pack and diagnostic message, also for receiving the amendment of configuration to stored battery pack and diagnostic message; Store battery pack historical data and real time data, transferring of data can be received; The failure message of called data storehouse battery pack, reads configuration and the diagnostic message of the battery pack that described expert model stores, implements corresponding maintenance measure to battery pack fault; According to existing battery failure information, in described expert model, add configuration and the diagnostic message of new battery pack.
Based on a method for the battery pack fault diagnosis of large data, the method also comprises: realize the real time human-machine interaction with user, and the instruction according to user sends fault diagnosis request to described deduce machine, accepts analysis report and is shown by analysis report; Use word to carry out man-to-man dialogue, the instruction that intelligent assistance device inputs according to user, the battery pack fault analysis information of correspondence is fed back in man-machine interface with written form; Receive the inferred results that described estimator sends, and inferred results is carried out gathering for analysis report, then analysis report is presented in man-machine interface.
Based on a method for the battery pack fault diagnosis of large data, the method also comprises: the parameter and the information that store accumulator and battery pack, can receive the accumulator of storage and transferring and revising of battery pack information; Storing the conditions for diagnostics and diagnostic recommendations that solve battery pack fault, the conditions for diagnostics of battery pack fault to storing and transferring and revising of diagnostic recommendations can being received;
A kind of method of the battery pack fault diagnosis based on large data, the method also comprises: the battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack produces in real time carries out diagnostic analysis, and after diagnostic analysis, provide corresponding maintenance suggestion; The battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack history produces carries out diagnostic analysis, and after diagnostic analysis, provides corresponding maintenance suggestion; The battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack history produces carries out diagnostic analysis, analyze the historical trend of battery performance, intelligent predicting goes out the change in future of battery performance, and provides solution according to pre-direction finding user; According to the alarm event that battery pack produces in real time, the source of trouble of battery pack found and diagnoses, and providing corresponding diagnostic recommendations according to source of trouble information.
A kind of method of the battery pack fault diagnosis based on large data, the method also comprises: the battery pack configuration information transferring battery pack failure message and correspondence in deduce machine in real time, when monitoring the new battery pack failure message occurred in deduce machine, then obtain battery pack configuration information and the solution of this failure message, and this failure message battery pack configuration information and solution are stored on expert model.
Beneficial effect of the present invention is: adopt smart sink total data to know storehouse decision analysis according to model intelligence, reduce the professional knowledge degree of maintainer, raise the efficiency, convenient and swift.
Accompanying drawing explanation
Figure 1 shows that the overall construction drawing according to embodiment of the present invention;
Figure 2 shows that the battery pack expert system structure figure according to embodiment of the present invention;
Figure 3 shows that the historical data analysis process flow diagram according to embodiment of the present invention;
Figure 4 shows that the real-time data analysis process flow diagram according to embodiment of the present invention;
Figure 5 shows that the data analysis flowcharts according to embodiment of the present invention.
Embodiment
In order to make the object, technical solutions and advantages of the present invention clearly, describe the present invention below in conjunction with the drawings and specific embodiments.Interface Behavior-Based control system of the present invention is applicable to the exploitation of the game such as single-play game, mobile phone games, web game, is particularly useful for the process of playing the part of the interface behavior realized in class game in control role.
Figure 1 shows that the overall construction drawing according to embodiment of the present invention.This system comprises interpreter, expert model assembly, data-carrier store, deduce machine, automatic learning device, wherein interpreter carries out alternately for end user's machine interactive interface and user, according to user's request instruction transmission request information, and by automatic for request results loopback to user; Expert model assembly for storing configuration and the diagnostic message of battery pack, also for receiving the amendment of configuration to stored battery pack and diagnostic message; Data-carrier store, for storing battery pack historical data and real time data, can receive transferring of data; Deduce machine is used for the failure message of called data storehouse battery pack, reads configuration and the diagnostic message of the battery pack that described expert model stores, implements corresponding maintenance measure to battery pack fault; Automatic learning device is used for according to existing battery failure information, adds configuration and the diagnostic message of new battery pack in described expert model.
Figure 2 shows that the battery pack expert system structure figure according to embodiment of the present invention.Wherein in figure, step 1-6 represents the flow process of battery pack breakdown maintenance.Concrete assembly comprises:
(1) interpreter, by man-machine interface, report record, the part such as intelligent assistance device composition.Primary responsibility data interpretation is used for interface, is intended to for user provides a presentation mode intuitively.Wherein man-machine interface part then concentrates calling for data interaction and the application of various subfunction, be also present to user unique can operation part; And the main expression of report record is after analysis data, various result is aggregated into analysis report, then transfers to man-machine interface to show; It is finally intelligent assistance device, it is mainly used in man-machine interactively and assists, user completes man-to-man word dialog by it, Breakdown Maintenance schemes as various in battery pack etc., be different from analysis report, analysis report more pays close attention to the diagnosis after Data Management Analysis or performance evaluation, and intelligent assistance device does not then have, it is exactly man-to-man answer formula word dialog, describes maintenance scheme with mode word.
(2) expert model, is made up of the part such as model parameter, model knowledge.Model parameter is for storing some relevant configured parameters of battery pack brand parameter and battery pack, and model knowledge then more pays close attention to the various condition of fault diagnosis and corresponding maintenance suggestion, and what it presented is a battery pack standard reference model.
(3) automatic learning device, knowledge base is because early stage or other reason are as the shortcoming partial condition judgement of technical support aspect, precise effect is not reached to during data analysis, automatic learning device can when known fault, intelligence adds decision condition to knowledge base automatically according to fault signature, finally reach the object improving knowledge base, reduce maintenance workload.
(4) deduce machine, is analyzed the parts such as estimator formed, for performance and fault diagnosis provide possibility by real time data estimator, historical data estimator, Real-time Alarm estimator, intelligent behaviour.Real time data estimator mainly carries out Analysis on Fault Diagnosis according to real time data, then provides corresponding maintenance suggestion according to the fault after diagnosis again.Historical data estimator and real time data estimator type, unique difference is it is real time data first, and the data of historical data estimator act on historical record.Real-time Alarm estimator mainly carries out source of trouble diagnosis according to Real-time Alarm event, and provides corresponding maintenance suggestion.It is then carry out performance evaluation judgement by the development trend of historical data that intelligent behaviour analyzes estimator, life-span degree etc. as current in healthy battery, provides a kind of reasonably solution, is convenient to user and implements.
(5) database, by history library, the part such as real-time database composition, is intended to store historical record and real time data, is the data direct sources ground of data analysis.
Figure 3 shows that the historical data analysis process flow diagram according to embodiment of the present invention.Specifically as step S301 ~
S307：
Step S301 starts historical data analysis: to the data analysis of range of choice time period;
Step S302 adds up the discharge and recharge data in the time period: gather (one complete discharge and recharge set of records ends) to the discharge and recharge data in the time period and add up and draw a result after carrying out initial analysis;
Step S303 forms initial analysis report: form an initial analysis report according to step S302 analysis result;
Step S304 forms analysis report to the data analysis in discharge and recharge: obtain it according to a complete discharge and recharge record successively and to fill accordingly or discharge data collection entirely carries out tabulate statistics and draws a result after carrying out initial analysis;
Step S305 gathers initial analysis report and forms new analysis report: be aggregated into a new analysis report according to step S304 analysis result and step S303 analysis report;
There is alarm event in step S306 time range: only no within the scope of retrieval time existed alarm event, and without then arriving step S308, having, jumping to step step S307;
Data analysis before and after when step S307 occurs event forms analysis report: carry out general analyzes analysis report according to frame historgraphic data recording a few before and after alarm time section successively and form final report: be aggregated into a new analysis report according to step S307 analysis result and step S305 analysis report;
The report of step S308 Macro or mass analysis forms final report: be aggregated into a new analysis report according to step S307 analysis result and step S305 analysis report.
Figure 4 shows that the real-time data analysis process flow diagram according to embodiment of the present invention.Implement as shown in step S401 ~ S407:
Step S401 starts real-time data analysis;
Step S402 forms preliminary report to after the data analysis of real-time statistics: draw a result after carrying out initial analysis to the data record of real-time statistics;
Step S403, to real-time data analysis, forms analysis report: form analysis report to after the data analysis of current real time execution;
Step S404 gathers initial analysis report and forms new analysis report: be aggregated into a new analysis report according to step S402 analysis result and step S403 analysis report;
Step S405 whether alarm: currently whether be in alarm status, without then step S407, has then step S406;
The type retrieval knowledge storehouse that step S406 is corresponding according to alarm, forms analysis report;
The report of step S407 Macro or mass analysis forms final report: be aggregated into a new analysis report according to step S407 analysis result and step S405 analysis report.
Figure 5 shows that the data analysis flowcharts according to embodiment of the present invention.For out-of-limit event, concrete step is as shown in S501 ~ S507:
Step S501 out-of-limit event: obtain the out-of-limit event time period;
Step S502 obtains event frame equal state and returns and drill data acquisition, obtains and is in Frame set of records ends before and after under same state (all fill, floating charge, electric discharge, leaves standstill);
Step S503 obtains corresponding source of trouble list according to event frame equal state and event type: the state corresponding according to event frame and event type obtain the source of trouble list in knowledge base;
Step S504 judges whether the source of trouble exists: mate the source of trouble successively according to Frame record, if mate then step S505, and nothing then step S506;
Step S505 judges whether this source of trouble will change battery: judge whether battery has necessity of replacing;
Step S506 adds this source of trouble to existing in source of trouble list of will returning: add the source of trouble of current judgement to already present source of trouble list;
Step S506 is without the next source of trouble: whether decision list travels through end, has then step S504, nothing then step S507;
Step S507 returns already present source of trouble list: return the source of trouble list after having diagnosed, for analysis report or next step process.
The above, just preferred embodiment of the present invention, the present invention is not limited to above-mentioned embodiment, as long as it reaches technique effect of the present invention with identical means, all should belong to protection scope of the present invention.In protection scope of the present invention, its technical scheme and/or embodiment can have various different modifications and variations.
Claims (10)
1., based on a system for the battery pack fault diagnosis of large data, it is characterized in that this system comprises interpreter, expert model assembly, data-carrier store, deduce machine and automatic learning device, wherein:
Interpreter is used for end user's machine interactive interface and user carries out alternately, according to user's request instruction transmission request information, and by automatic for request results loopback to user;
Expert model assembly for storing configuration and the diagnostic message of battery pack, also for receiving the amendment of configuration to stored battery pack and diagnostic message;
Data-carrier store, for storing battery pack historical data and real time data, can receive transferring of data;
Deduce machine is used for the failure message of called data storehouse battery pack, reads configuration and the diagnostic message of the battery pack that described expert model stores, implements corresponding maintenance measure to battery pack fault;
Automatic learning device is used for according to existing battery failure information, adds configuration and the diagnostic message of new battery pack in described expert model.
2. the system of a kind of battery pack fault diagnosis based on large data according to claim 1, it is characterized in that, described interpreter comprises:
Man-machine interface, for realizing the real time human-machine interaction with user, the instruction according to user sends fault diagnosis request to described deduce machine, accepts analysis report and is shown by analysis report;
Intelligent assistance device, for using word to carry out man-to-man dialogue, the instruction that intelligent assistance device inputs according to user, feeds back the battery pack fault analysis information of correspondence in man-machine interface with written form;
Accounting logging, for receiving the inferred results that described deduce machine sends, and being undertaken gathering for analysis report by inferred results, being then presented in man-machine interface by analysis report.
3. the system of a kind of battery pack fault diagnosis based on large data according to claim 1, it is characterized in that, described expert model assembly comprises:
Model parameter storehouse, for storing parameter and the information of accumulator and battery pack, can receive the accumulator of storage and transferring and revising of battery pack information;
Models repository, for storing the conditions for diagnostics and diagnostic recommendations that solve battery pack fault, can receive the conditions for diagnostics of battery pack fault to storing and transferring and revising of diagnostic recommendations.
4. the system of a kind of battery pack fault diagnosis based on large data according to claim 1, it is characterized in that, described deduce machine comprises:
Real time data estimator, carries out diagnostic analysis for the battery pack configuration information transferred in data and expert model assembly that in described data-carrier store, battery pack produces in real time, and after diagnostic analysis, provides corresponding maintenance suggestion;
Historical data estimator, carries out diagnostic analysis for the battery pack configuration information transferred in data and expert model assembly that battery pack history in described data-carrier store produces, and after diagnostic analysis, provides corresponding maintenance suggestion;
Performance evaluation estimator, diagnostic analysis is carried out by the battery pack configuration information transferred in data and expert model assembly that battery pack history in described data-carrier store produces, analyze the historical trend of battery performance, intelligent predicting goes out the change in future of battery performance, and provides solution according to pre-direction finding user;
Real-time Alarm estimator, for the alarm event produced in real time according to battery pack, finds the source of trouble of battery pack and diagnoses, and provides corresponding diagnostic recommendations according to source of trouble information.
5. the system of a kind of battery pack fault diagnosis based on large data according to claim 1, it is characterized in that, described automatic learning device is configured to:
Transfer the battery pack configuration information of battery pack failure message and correspondence in deduce machine in real time, when monitoring the new battery pack failure message occurred in deduce machine, then obtain battery pack configuration information and the solution of this failure message, and this failure message battery pack configuration information and solution are stored on expert model.
6., based on a method for the battery pack fault diagnosis of large data, it is characterized in that, the method comprises:
End user's machine interactive interface and user carry out alternately, according to user's request instruction transmission request information, and by automatic for request results loopback to user;
Store the configuration of battery pack and diagnostic message, also for receiving the amendment of configuration to stored battery pack and diagnostic message;
Store battery pack historical data and real time data, transferring of data can be received;
The failure message of called data storehouse battery pack, reads configuration and the diagnostic message of the battery pack that described expert model stores, implements corresponding maintenance measure to battery pack fault;
According to existing battery failure information, in described expert model, add configuration and the diagnostic message of new battery pack.
7. the method for a kind of battery pack fault diagnosis based on large data according to claim 6, it is characterized in that, the method also comprises:
Realize the real time human-machine interaction with user, the instruction according to user sends fault diagnosis request to described deduce machine, accepts analysis report and is shown by analysis report;
Use word to carry out man-to-man dialogue, the instruction that intelligent assistance device inputs according to user, the battery pack fault analysis information of correspondence is fed back in man-machine interface with written form;
Receive the inferred results that described estimator sends, and inferred results is carried out gathering for analysis report, then analysis report is presented in man-machine interface.
8. the method for a kind of battery pack fault diagnosis based on large data according to claim 6, it is characterized in that, the method also comprises:
Store parameter and the information of accumulator and battery pack, can receive the accumulator of storage and transferring and revising of battery pack information;
Storing the conditions for diagnostics and diagnostic recommendations that solve battery pack fault, the conditions for diagnostics of battery pack fault to storing and transferring and revising of diagnostic recommendations can being received.
9. the method for a kind of battery pack fault diagnosis based on large data according to claim 6, it is characterized in that, the method also comprises:
The battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack produces in real time carries out diagnostic analysis, and after diagnostic analysis, provide corresponding maintenance suggestion;
The battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack history produces carries out diagnostic analysis, and after diagnostic analysis, provides corresponding maintenance suggestion;
The battery pack configuration information transferred in the data and expert model assembly that in described data-carrier store, battery pack history produces carries out diagnostic analysis, analyze the historical trend of battery performance, intelligent predicting goes out the change in future of battery performance, and provides solution according to pre-direction finding user;
According to the alarm event that battery pack produces in real time, the source of trouble of battery pack found and diagnoses, and providing corresponding diagnostic recommendations according to source of trouble information.
10. the method for a kind of battery pack fault diagnosis based on large data according to claim 6, it is characterized in that, the method also comprises:
Transfer the battery pack configuration information of battery pack failure message and correspondence in deduce machine in real time, when monitoring the new battery pack failure message occurred in deduce machine, then obtain battery pack configuration information and the solution of this failure message, and this failure message battery pack configuration information and solution are stored on expert model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510828470.1A CN105372600A (en) | 2015-11-24 | 2015-11-24 | Storage battery fault diagnosis system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510828470.1A CN105372600A (en) | 2015-11-24 | 2015-11-24 | Storage battery fault diagnosis system and method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105372600A true CN105372600A (en) | 2016-03-02 |
Family
ID=55374959
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510828470.1A Pending CN105372600A (en) | 2015-11-24 | 2015-11-24 | Storage battery fault diagnosis system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105372600A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383951A (en) * | 2016-09-20 | 2017-02-08 | 北京理工大学 | Fault diagnosis method and system for electric driven traffic tool |
| CN106443475A (en) * | 2016-10-21 | 2017-02-22 | 国网山东省电力公司电力科学研究院 | Retired power battery dismounting-free reuse screening method based on operation big data |
| CN107450030A (en) * | 2017-08-07 | 2017-12-08 | 国网山东省电力公司信息通信公司 | A kind of communication storage battery telemetry data reduction processing method, apparatus and system |
| CN107526044A (en) * | 2017-09-08 | 2017-12-29 | 国网山东省电力公司信息通信公司 | A kind of communication storage battery Telemetry Data Acquisition monitoring method and system |
| CN107860102A (en) * | 2017-10-18 | 2018-03-30 | 深圳市中电电力技术股份有限公司 | A kind of method and device for controlling central air-conditioning |
| CN108045230A (en) * | 2017-11-15 | 2018-05-18 | 苏州工业园区职业技术学院 | A kind of cell managing device with automatic learning device |
| 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 |
| CN114325402A (en) * | 2021-11-26 | 2022-04-12 | 北京百度网讯科技有限公司 | Storage battery management method, device, system, terminal, electronic device and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004004280A1 (en) * | 2004-01-27 | 2005-08-18 | Audi Ag | Car battery diagnosis procedure uses neural network with Lemal algorithm and learning from reference battery to construct model for comparison with actual battery status |
| CN103078403A (en) * | 2012-12-28 | 2013-05-01 | 中国电力科学研究院 | On-line state evaluation method for secondary system of intelligent substation |
| CN103116135A (en) * | 2012-12-14 | 2013-05-22 | 惠州市亿能电子有限公司 | Automatic diagnosis system of battery system fault |
| CN103293415A (en) * | 2013-05-23 | 2013-09-11 | 江西中能电气科技有限公司 | Mini-inverter fault detecting method based on neural network expert system |
| CN103995237A (en) * | 2014-05-09 | 2014-08-20 | 南京航空航天大学 | Satellite power supply system online fault diagnosis method |
| CN104714175A (en) * | 2013-12-12 | 2015-06-17 | 北京有色金属研究总院 | Battery system fault diagnosis method and system |
-
2015
- 2015-11-24 CN CN201510828470.1A patent/CN105372600A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004004280A1 (en) * | 2004-01-27 | 2005-08-18 | Audi Ag | Car battery diagnosis procedure uses neural network with Lemal algorithm and learning from reference battery to construct model for comparison with actual battery status |
| CN103116135A (en) * | 2012-12-14 | 2013-05-22 | 惠州市亿能电子有限公司 | Automatic diagnosis system of battery system fault |
| CN103078403A (en) * | 2012-12-28 | 2013-05-01 | 中国电力科学研究院 | On-line state evaluation method for secondary system of intelligent substation |
| CN103293415A (en) * | 2013-05-23 | 2013-09-11 | 江西中能电气科技有限公司 | Mini-inverter fault detecting method based on neural network expert system |
| CN104714175A (en) * | 2013-12-12 | 2015-06-17 | 北京有色金属研究总院 | Battery system fault diagnosis method and system |
| CN103995237A (en) * | 2014-05-09 | 2014-08-20 | 南京航空航天大学 | Satellite power supply system online fault diagnosis method |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383951A (en) * | 2016-09-20 | 2017-02-08 | 北京理工大学 | Fault diagnosis method and system for electric driven traffic tool |
| CN106383951B (en) * | 2016-09-20 | 2019-08-06 | 北京理工大学 | A kind of method for diagnosing faults being driven by electricity the vehicles and system |
| CN106443475A (en) * | 2016-10-21 | 2017-02-22 | 国网山东省电力公司电力科学研究院 | Retired power battery dismounting-free reuse screening method based on operation big data |
| CN107450030A (en) * | 2017-08-07 | 2017-12-08 | 国网山东省电力公司信息通信公司 | A kind of communication storage battery telemetry data reduction processing method, apparatus and system |
| CN107450030B (en) * | 2017-08-07 | 2021-08-31 | 国网山东省电力公司信息通信公司 | Communication storage battery telemetry data processing method, device and system |
| CN107526044A (en) * | 2017-09-08 | 2017-12-29 | 国网山东省电力公司信息通信公司 | A kind of communication storage battery Telemetry Data Acquisition monitoring method and system |
| CN107860102A (en) * | 2017-10-18 | 2018-03-30 | 深圳市中电电力技术股份有限公司 | A kind of method and device for controlling central air-conditioning |
| CN107860102B (en) * | 2017-10-18 | 2020-12-01 | 深圳市中电电力技术股份有限公司 | Method and device for controlling central air conditioner |
| CN108045230A (en) * | 2017-11-15 | 2018-05-18 | 苏州工业园区职业技术学院 | A kind of cell managing device with automatic learning device |
| 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 |
| CN114325402A (en) * | 2021-11-26 | 2022-04-12 | 北京百度网讯科技有限公司 | Storage battery management method, device, system, terminal, electronic device and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105372600A (en) | Storage battery fault diagnosis system and method based on big data | |
| CN105225049B (en) | High-expandability fault diagnosis expert system | |
| CN103778044B (en) | Method and device for diagnosing system faults | |
| CN102122431B (en) | Method and system for intelligently diagnosing faults of electricity information collection terminal | |
| CN110568838B (en) | Vehicle quiescent current acquisition method and system, electronic device and storage medium | |
| CN106301522A (en) | The Visual method of fault diagnosis of Remote Sensing Ground Station data receiver task and system | |
| CN110677286B (en) | Power failure alarm monitoring method, device and system | |
| CN109871963A (en) | Operation troubles restoring method, device, electronic equipment and storage medium | |
| CN107807872A (en) | A kind of power transmission and transformation system method for monitoring operation states | |
| CN108667927A (en) | Method for remote management, device, system and the storage medium of engineering machinery failure | |
| CN104317695A (en) | Self-service equipment monitoring method and self-service equipment monitoring device | |
| CN103544095A (en) | Server program monitoring method and system of server program | |
| CN109559054B (en) | Electric power engineering construction information processing system | |
| CN114715139A (en) | Automatic parking abnormal data acquisition method and device, storage medium and product | |
| CN112016708A (en) | Multi-dimensional data display method and device for intelligent cable | |
| CN108399303A (en) | A kind of analogy method and device of charging pile | |
| CN106407102A (en) | A fuzz testing method, device and system for application programs | |
| CN104424099B (en) | Method for testing software and system | |
| CN116360377A (en) | Data processing method for battery cell formation component capacity and distributed control system | |
| CN103616877B (en) | Monitoring diagnostic method and system for energy pipe network | |
| CN106899436A (en) | A kind of cloud platform failure predication diagnostic system | |
| CN113591951A (en) | Remote operation and maintenance method and system for smart television | |
| CN114125585A (en) | Monitoring and analyzing system and method for charging process of electric automobile charging pile | |
| CN110333694A (en) | Numerical control device method for diagnosing faults based on Fuzzy Petri Net | |
| CN103684917A (en) | Multi-target-machine system monitoring system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160302 |
|
| RJ01 | Rejection of invention patent application after publication |