CN102095953B - A kind of performance of accumulator charger online test method - Google Patents

A kind of performance of accumulator charger online test method Download PDF

Info

Publication number
CN102095953B
CN102095953B CN201010568576.XA CN201010568576A CN102095953B CN 102095953 B CN102095953 B CN 102095953B CN 201010568576 A CN201010568576 A CN 201010568576A CN 102095953 B CN102095953 B CN 102095953B
Authority
CN
China
Prior art keywords
charging set
precision
current
performance
real
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.)
Active
Application number
CN201010568576.XA
Other languages
Chinese (zh)
Other versions
CN102095953A (en
Inventor
梁国坚
陈斌
蔡永智
王林青
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou high special electronic equipment Limited by Share Ltd
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Hangzhou Gold Electronic Equipment Co Ltd
Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hangzhou Gold Electronic Equipment Co Ltd, Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Hangzhou Gold Electronic Equipment Co Ltd
Priority to CN201010568576.XA priority Critical patent/CN102095953B/en
Publication of CN102095953A publication Critical patent/CN102095953A/en
Application granted granted Critical
Publication of CN102095953B publication Critical patent/CN102095953B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)

Abstract

The present invention relates to on-line checkingi and the evaluation field of battery charger, a kind of performance of accumulator charger online test method is specifically provided.The knowledge of its using artificial intelligence carrys out Comprehensive Evaluation charging set performance, by the electric current of Real-time Collection charging set, voltage and ambient temperature data, the precision of voltage regulation of real-time calculating charging set, precision of steady current and current stabilizing factor, and Real-time Alarm can be carried out to the computing information exceeding definite value, by above data input artificial neural network, through learning to generate comprehensive evaluation value, and provide charging set Evaluation results, for the repair based on condition of component of charging set provides key reference data.A kind of performance of accumulator charger online test method of the present invention overcomes electric system charging set and can not realize on-line performance evaluate, Real-time Alarm can not be carried out to degraded performance or the charging set equipment that there is hidden danger, repair based on condition of component can not be realized to charging set and the problems such as effective decision data are provided.

Description

A kind of performance of accumulator charger online test method
Technical field
The present invention relates to on-line checkingi and the evaluation field of battery charger, a kind of performance of accumulator charger online test method is specifically provided.
Background technology
Charging set is the visual plant of production and maintenance free cell, is mainly used in the charging of accumulator.The quality of accumulator, performance, serviceable life, extremely important with charging set quality, property relationship.There is a large amount of charging set equipment in electric system, all the time, the maintenance of charging set is all just carry out after failure, lacks a kind of online appraisal procedure, and the operation duty based on charging set estimates the performance of charging set, accomplishes repair based on condition of component.
Summary of the invention
The present invention is exactly for above problem, a kind of performance of accumulator charger online test method is provided, which overcome electric system charging set and can not realize on-line performance evaluate, Real-time Alarm can not be carried out to degraded performance or the charging set equipment that there is hidden danger, repair based on condition of component can not be realized to charging set and the problems such as effective decision data are provided.
The technical solution adopted in the present invention is as follows:
A kind of performance of accumulator charger online test method, comprises the following steps:
The real-time running data of A, collection charging set;
B, the precision of voltage regulation calculating charging set according to the real-time running data gathered, precision of steady current and current stabilizing factor;
C, the precision of voltage regulation of the charging set calculated, precision of steady current and current stabilizing factor input artificial neural network is saved as charging set operational factor table, and make contrast with the data in established charging set operational factor table, draw charging set Evaluation results.
The accumulated time gathering real-time running data in steps A exceedes half an hour.
Real-time running data comprises the voltage of charging set, electric current or environment temperature.
Steps A also comprises the step real-time running data gathered being arranged to alarm restriction, thus whether the current operation of real-time judge reaches alarm border, and provides alarm signal.
Step B specifically comprises:
The real-time running data of the charging set that B1, basis collect judges the running status of charging set;
B2, to calculate respectively according to the running status of charging set, if the running status of charging set is for all to fill state, then calculate the precision of voltage regulation, precision of steady current and current stabilizing factor; If the running status of charging set is floating charge state, then calculate the precision of voltage regulation.
A kind of performance of accumulator charger online test method of the present invention, it is using the input of the parameter of multiple reflection charging set performance as artificial neural network, through learning the Comprehensive Evaluation of the experts database generated, export the performance number etc. of charging set, and provide the important evidence of the repair based on condition of component to charging set with this.
Another feature of the present invention is the function of adaptive learning to above-mentioned Design on Artificial Neural Networks, and when charging set is under different charge modes, network will change learning sample automatically, relearn training, form a kind of new Indentification model.The function of this dynamic corrections experts database, makes system draw charging set results of performance analysis more accurately.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of a kind of performance of accumulator charger online test method of the present invention;
Fig. 2 is the concrete topology diagram of a kind of performance of accumulator charger online test method of the present invention Multi-layered Feedforward Networks used;
Fig. 3 is that the charging set duty of a kind of performance of accumulator charger online test method of the present invention differentiates process flow diagram.
Embodiment
Below in conjunction with the drawings and specific embodiments, a kind of performance of accumulator charger online test method of the present invention is further described.
As shown in Figure 1, a kind of performance of accumulator charger online test method of the present invention, it gathers the real-time running data of charging set by charging set performance analysis system, as parameters such as voltage, electric current, temperature, and be uploaded to background computer simultaneously, enter software systems treatment scheme by application input interface.
Charging set real-time running data is gathered, as charging set voltage, current parameters by charging set performance analysis system.(have 2 frame data accumulation) when the data frame number uploaded reaches requirement, enter charging set running status discrimination model, namely Fig. 2 is that charging set running status differentiates flow process, wherein
Z 1: all fill state;
Z 2: floating charge state;
Z 3: other states.
Be described as follows:
Current acquisition unit of error A, C10 are accumulator 10 hours discharge capacities (unit is Ah).
1), all state Z is filled 1:
Electric current > (0.01C10+ current acquisition error);
2), floating charge state Z 2:
-(current acquisition error)≤electric current≤(0.01C10+ current acquisition error);
3), other state Z 3:
Be left after removing above state.
When the data uploaded reach requirement (having the data accumulation of half an hour), enter charging set performance analysis models.
When battery condition is in Z 1time, and meet when having data accumulation half an hour, system following (1) formula calculates precision of steady current:
δ I = I M - I Z I Z × 100 % - - - ( 1 )
System background arranges precision of steady current alarm setting value, judges whether to exceed alarm setting value, if exceeded, provide warning message immediately after each calculating.
Precision of steady current data are transmitted by the SMV service message of IEC61850 standard.
When calculating precision of steady current, record the current value of each module, system following (2) formula calculates current stabilizing factor simultaneously.
δ i = I m - I z Iz × 100 % - - - ( 2 )
System background arranges current stabilizing factor alarm setting value, judges whether to exceed alarm setting value, if exceeded, provide warning message immediately after each calculating.
Current stabilizing factor data are transmitted by the SMV service message of IEC61850 standard.
Replace its original initial value with up-to-date precision of steady current, current stabilizing factor and the Current Temperatures obtained, substitute into network operations, obtain the performance number y of charging set.The preliminary formula provided according to artificial neural network:
Input: net=δ iω 1+ δ iω 2+ t 0ω 3
Export: y = f ( net ) = 1 1 + e - net × 100 %
Wherein: y represents charging set performance number, δ irepresent precision of steady current, δ irepresent current stabilizing factor, t 0represent current temperature value, ω irepresent network weight weight values.
When battery condition is in Z 2time, and meet have data accumulation half an hour after, system with following (3) formula calculate the precision of voltage regulation.
δ U = U M - U Z U Z × 100 % - - - ( 3 )
System background arranges precision of voltage regulation alarm setting value, judges whether to exceed alarm setting value, if exceeded, provide warning message immediately after each calculating.
Precision of voltage regulation data, warning message are transmitted by the SMV service message of IEC61850 standard.
Replace its original initial value by the up-to-date precision of voltage regulation that obtains and Current Temperatures, substitute into network operations, draw charging set runnability value.The preliminary formula provided according to artificial neural network:
Input: net=δ uω 1+ t 0ω 2
Export: y = f ( net ) = 1 1 + e - net × 100 %
Wherein: y represents charging set performance number, δ urepresent the precision of voltage regulation, t 0represent current temperature value, ω irepresent network weight weight values.
In sum, a kind of performance of accumulator charger online test method of the present invention, the knowledge of its using artificial intelligence carrys out Comprehensive Evaluation charging set performance, by the electric current of Real-time Collection charging set, voltage and ambient temperature data, the precision of voltage regulation of real-time calculating charging set, precision of steady current and current stabilizing factor, and Real-time Alarm can be carried out to the computing information exceeding definite value, by above data input artificial neural network, through learning to generate comprehensive evaluation value, and provide charging set Evaluation results, for the repair based on condition of component of charging set provides key reference data.
Above-described embodiment, the just one of the present invention's more preferably embodiment, the usual change that those skilled in the art carries out within the scope of technical solution of the present invention and replacing all should be included in protection scope of the present invention.

Claims (3)

1. a performance of accumulator charger online test method, comprises the following steps:
The real-time running data of A, collection charging set; The accumulated time gathering real-time running data exceedes half an hour;
B, the precision of voltage regulation calculating charging set according to the real-time running data gathered, precision of steady current and current stabilizing factor;
Step B specifically comprises:
The real-time running data of the charging set that B1, basis collect judges the running status of charging set;
B2, to calculate respectively according to the running status of charging set, if the running status of charging set is for all to fill state, then calculate the precision of voltage regulation, precision of steady current and current stabilizing factor; If the running status of charging set is floating charge state, then calculate the precision of voltage regulation;
When battery condition be in all fill state time, and meet when having data accumulation half an hour, system following (1) formula calculates precision of steady current:
δ I = I M - I Z I Z × 100 % - - - ( 1 )
When calculating precision of steady current, record the current value of each module, system following (2) formula calculates current stabilizing factor simultaneously:
δ i = I m - I z Iz × 100 % - - - ( 2 )
Replace its original initial value with the precision of steady current obtained, current stabilizing factor and Current Temperatures, substitute into network operations, obtain the performance number y of charging set:
Input: net=δ iω 1+ δ iω 2+ t 0ω 3
Export: y = f ( net ) = 1 1 + e - net × 100 %
Wherein: y represents charging set performance number, δ irepresent precision of steady current, δ irepresent current stabilizing factor, t 0represent current temperature value, ω irepresent network weight weight values;
When battery condition is in Z 2time, and meet have data accumulation half an hour after, system with following (3) formula calculate the precision of voltage regulation:
δ U = U M - U Z U Z × 100 % - - - ( 3 )
Replace its original initial value by the precision of voltage regulation obtained and Current Temperatures, substitute into network operations, draw charging set runnability value:
Input: net=δ uω 1+ t 0ω 2
Export: y = f ( net ) = 1 1 + e - net × 100 %
Wherein: y represents charging set runnability value, δ urepresent the precision of voltage regulation, t 0represent current temperature value, ω irepresent network weight weight values;
C, the precision of voltage regulation of the charging set calculated, precision of steady current and current stabilizing factor input artificial neural network is saved as charging set operational factor table, and make contrast with the data in established charging set operational factor table, draw charging set Evaluation results.
2. a kind of performance of accumulator charger online test method according to claim 1, is characterized in that, described real-time running data comprises the voltage of charging set, electric current or environment temperature.
3. according to a kind of performance of accumulator charger online test method in claim 1-2 described in any, it is characterized in that, wherein steps A also comprises the step real-time running data gathered being arranged to alarm restriction, thus whether the current operation of real-time judge reaches alarm border, and provide alarm signal.
CN201010568576.XA 2010-11-26 2010-11-26 A kind of performance of accumulator charger online test method Active CN102095953B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201010568576.XA CN102095953B (en) 2010-11-26 2010-11-26 A kind of performance of accumulator charger online test method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201010568576.XA CN102095953B (en) 2010-11-26 2010-11-26 A kind of performance of accumulator charger online test method

Publications (2)

Publication Number Publication Date
CN102095953A CN102095953A (en) 2011-06-15
CN102095953B true CN102095953B (en) 2015-11-25

Family

ID=44129125

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201010568576.XA Active CN102095953B (en) 2010-11-26 2010-11-26 A kind of performance of accumulator charger online test method

Country Status (1)

Country Link
CN (1) CN102095953B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107462823A (en) * 2017-07-19 2017-12-12 安徽中能电源有限公司 Battery of electric vehicle is internalized into charger communication maintenance and check system
CN109782097B (en) * 2019-03-07 2021-01-29 深圳市计量质量检测研究院 Charging facility remote metering system and metering method thereof
CN110646706B (en) * 2019-09-12 2022-06-07 国电南瑞科技股份有限公司 Method, device and system for detecting differential protection fault of super capacitor charging device of energy storage tramcar
CN115201616B (en) * 2022-09-16 2022-12-16 智洋创新科技股份有限公司 Charger operation online monitoring method based on big data
CN115508736B (en) * 2022-11-15 2023-02-28 智洋创新科技股份有限公司 Direct-current power supply online charging performance testing system and method based on big data

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201255766Y (en) * 2008-05-17 2009-06-10 陈书欣 Portable charger characteristic test device
CN101557121A (en) * 2008-04-11 2009-10-14 中国北车集团大同电力机车有限责任公司 Control device and method thereof for locomotive charger
CN101706558A (en) * 2009-07-20 2010-05-12 深圳市普禄科智能检测设备有限公司 On-line monitoring system for direct-current power supply and storage battery

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07120539A (en) * 1993-10-25 1995-05-12 Nippon Electric Ind Co Ltd Method and device for judging life of storage battery
JP3524280B2 (en) * 1996-08-20 2004-05-10 株式会社東芝 DC power supply

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101557121A (en) * 2008-04-11 2009-10-14 中国北车集团大同电力机车有限责任公司 Control device and method thereof for locomotive charger
CN201255766Y (en) * 2008-05-17 2009-06-10 陈书欣 Portable charger characteristic test device
CN101706558A (en) * 2009-07-20 2010-05-12 深圳市普禄科智能检测设备有限公司 On-line monitoring system for direct-current power supply and storage battery

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
充电机性能检测试验台;李东义 等;《铁道车辆》;20080331;第46卷(第3期);29-32 *
直流充电机稳流稳压精度和纹波系数测试的探讨;唐志军 等;《福建电力与电工》;20060331;第26卷(第1期);23-25 *

Also Published As

Publication number Publication date
CN102095953A (en) 2011-06-15

Similar Documents

Publication Publication Date Title
Hu et al. A review of second-life lithium-ion batteries for stationary energy storage applications
CN104134999B (en) Distribution network based on multi-data source measures the practical method of calculation of efficiency analysis
CN107870306A (en) A kind of lithium battery charge state prediction algorithm based under deep neural network
CN107656216A (en) A kind of lead-acid accumulator on-line monitoring is safeguarded and early warning system and performance estimating method
CN107037370A (en) Residual quantity calculation method of electric vehicle battery based on monitoring data
CN102095953B (en) A kind of performance of accumulator charger online test method
CN114372417A (en) Electric vehicle battery health state and remaining life evaluation method based on charging network
CN103576096A (en) Real-time assessment method and device for residual capacity of power battery of electric automobile
CN102779230A (en) State analysis and maintenance decision judging method of power transformer system
CN112255560B (en) Battery cell health degree prediction method
CN109446661A (en) A kind of method for predicting residual useful life considering lithium battery degradation characteristics
WO2021052540A1 (en) Condition value for rechargeable batteries
CN112883634B (en) DC measurement system state prediction method and system based on multi-dimensional analysis
CN108205114A (en) The Forecasting Methodology and system of battery life
CN117013606A (en) Intelligent energy storage control system for photovoltaic power generation based on artificial intelligence
CN116365066B (en) BMS module-based power management system
CN111738573A (en) Health evaluation method based on electric energy meter full life cycle data
CN116845391A (en) Lithium battery energy storage management system
CN115219913A (en) Power battery full-life-cycle management system based on capacity increment method
CN117394412A (en) Scheduling control system and method for power grid energy storage system
CN115236523A (en) Power battery fault diagnosis and prediction method based on digital twinning
CN204030697U (en) Based on the battery management system of dynamic SOC estimating system
CN111698304B (en) Battery remote service and intelligent management system
CN117767462A (en) Automatic generation method and device for universal BMS control model
CN116305741B (en) Updating method and device for digital twin model of power equipment

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
C56 Change in the name or address of the patentee
CP01 Change in the name or title of a patent holder

Address after: Yue Lai Road No. 13 518400 Guangdong province Zhongshan City Shiqi District

Patentee after: Zhongshan Power Supply Bureau of Guangzhong Power Ltd.

Patentee after: Hangzhou high special electronic equipment Limited by Share Ltd

Address before: Yue Lai Road No. 13 518400 Guangdong province Zhongshan City Shiqi District

Patentee before: Zhongshan Power Supply Bureau of Guangzhong Power Ltd.

Patentee before: Hangzhou Gaote Electronic Equipment Co., Ltd.