CN108387850A - A kind of battery detection statistical system and its method based on Internet of Things - Google Patents
A kind of battery detection statistical system and its method based on Internet of Things Download PDFInfo
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- G—PHYSICS
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- 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]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The present invention provides a kind of battery detection statistical system and its method based on Internet of Things, belong to battery life management technical field.It solves battery detection difficulty, battery counts the problems such as inaccurate using experimental data.The present invention includes server end and the monitored end with server end communication connection；Data acquisition module reads the voltage value and voltage value corresponding time point, formation data detection signal of battery；Data transmission blocks receive data detection signal；Data reception module receives data detection signal；Data temporary storage module receives, stores data detection signal and for reading；Data computation module reads data detection signal, to data detection signal calculate and by the data-signal after calculating；It stores contrast module and stores correction data, receive the data-signal after calculating and be compared, judge whether failure and count with correction data；Display module shows statistics or reports barrier signal.The present invention has the advantages that monitoring is simple, statistics is accurate.
Description
Technical field
The invention belongs to battery life management technical field, more particularly to a kind of battery detection department of statistic based on Internet of Things
System and its method.
Background technology
Battery refers to the device that chemical energy can be converted to electric energy.Energy source is utilized the battery as, can be had
Burning voltage, stabling current, long-time stable power supply are limited smaller, the design of low-power consumption, performance easy to maintain by external environment
Reliable and stable, various aspects are widely used in modern society's life.In product course of normal operation, need to make battery
It is counted with data, different battery models can influence true service life, often by all kinds of accelerated tests
Distortion, so, most true data source is in the statistics to field data, but this data statistics workload is very huge,
Although also there is the statistical system for being directed to battery in the prior art, statistical is imperfect, and statistical magnitude is limited.And battery exists
During use, in fact it could happen that failure, existing statistical system can not also reject fault data, and statistical result, which exists, to be missed
Difference.
Invention content
The purpose of the present invention is being directed to the above-mentioned problems in the prior art, provide it is a kind of it is complete, be accurately based on
The battery detection statistical system and its method of Internet of Things.
Object of the invention can be realized by the following technical scheme：A kind of battery detection department of statistic based on Internet of Things
System, which is characterized in that the monitored end including server end and with server end communication connection；
The monitored end includes battery, data acquisition module, data transmission blocks；
The server end includes data reception module, data temporary storage module, data computation module, storage comparison mould
Block, statistical module, report barrier module, display module；
The data acquisition module is used to read the voltage value and voltage value corresponding time point, formation testing number of battery
It is believed that number and data detection signal is sent to data transmission blocks；
The data transmission blocks are for receiving data detection signal and being forwarded to data reception module；
The data reception module is for receiving data detection signal and being forwarded to data temporary storage module；
The data temporary storage module is for receiving, storing data detection signal and for reading；
The data computation module is for reading data detection signal, data detection signal calculate and will be calculated
Data-signal afterwards is sent to storage contrast module；
The storage contrast module is for storing correction data, after the storage contrast module is for receiving calculating
Data-signal is simultaneously compared with correction data, judge monitored end battery whether failure, if battery is judged as normally, institute
The statistical module stated reads data detection signal, is counted and generated statistical data, described if battery is judged as failure
Barrier module is reported to generate report barrier signal；
The display module is for showing statistics or reporting barrier signal.
Operation principle：After battery installation, the data acquisition module for being monitored end reads the initial voltage value of battery and initial
Voltage value corresponding time point, and final voltage value and the final voltage value corresponding time point of battery are read, form testing number
It is believed that number and be sent to data transmission blocks, data transmission blocks receive the data that server end is forwarded to after data detection signal
Receiving module, data reception module are forwarded to data temporary storage module after receiving data detection signal, at this time the data of server end
Computing module reads the data detection signal in data temporary storage module and calculating, and the data-signal for calculating gained is sent to and is deposited
Contrast module is stored up, data detection signal is compared with correction data for storage contrast module, and then judges that battery is normal or former
Barrier, if it is determined that battery is normal, then Instruction Statistics module reads data detection signal to data temporary storage module, by statistical module
Statistical data is formed after being detected data statistics；If it is determined that battery is malfunction, then barrier module is reported to generate report barrier signal,
Last display module receives statistical data or report barrier signal and shows.The present invention can carry out large-scale battery data system
Meter, convenience and high-efficiency, and the low cost of Cell Experimentation An data is greatly obtained, reduce experiment cost of labor.It can also carry out big model
Certain one kind of the monitoring enclosed is equipped with the use state of battery apparatus, and report barrier is carried out when monitoring failure, facilitates staff timely
Plant maintenance is carried out, testing cost has been saved.
In the above-mentioned battery detection statistical system based on Internet of Things, the data acquisition module reads the first of battery
Beginning voltage value is set as V1, record V1Read access time point be set as T1, the final voltage value for reading battery is V2, record V2Reading when
Between point be set as T2, the data computation module is used to calculate the slope of battery discharge curve and is set as K, and the battery discharge is bent
The slope K of line is calculated by the following formula：K=(V1-V2)/(T2-T1)。
In the above-mentioned battery detection statistical system based on Internet of Things, the storage contrast module is preset with battery and puts
Electric slope of a curve KIn advance, the KIn advanceFor preset battery discharge curve slope range,
When K is in KIn advanceWhen in range, battery is judged as normally,
When K exceeds KIn advanceWhen range, battery is judged as failure.
In the above-mentioned battery detection statistical system based on Internet of Things, the data acquisition module is also associated with threshold value
Determination module, the threshold-discrimination module are used to be arranged the minimum operating voltage V at monitored endThreshold, the V2=VThreshold。
In the above-mentioned battery detection statistical system based on Internet of Things, the statistical module is also associated with data rejecting
Module, the data reject module and are equipped with voltage value VJust, work as VJustFor the preset initial voltage range of battery, the V1Exceed
VJustRange when, that is, reject this group of detection data.
In the above-mentioned battery detection statistical system based on Internet of Things, address information, the number are contained in monitored end
It is additionally operable to send the address information of monitored battery according to sending module to data reception module, the data reception module forwards
Address information is to data temporary storage module, and data temporary storage module receives, storage address information and for reading, be judged as when battery therefore
When barrier, display module reads address information and shows.
In the above-mentioned battery detection statistical system based on Internet of Things, the battery is refered in particular in for measuring instrument
Alkaline battery or/and carbon battery.
In the above-mentioned battery detection statistical system based on Internet of Things, the KIn advanceIncluding KAlkaliAnd KCarbon, the KAlkaliFor
Alkaline cell discharge slope of a curve range, the KCarbonFor the slope range of carbon battery discharge curve, the statistics mould
Block includes alkaline statistical module and carbon statistical module,
The alkaline statistical module is for counting alkaline battery, when K is in KAlkaliWhen in range, alkaline statistical module is read
Data detection signal simultaneously counts,
The carbon statistical module is for counting carbon battery, when K is in KCarbonWhen in range, carbon statistical module is read
Data detection signal simultaneously counts.
Another aspect of the present invention additionally provides a kind of battery detection statistical method based on Internet of Things, including following step
Suddenly：
Step A data acquire, and the initial voltage value that data acquisition module reads battery is set as V1, record V1Read access time
Point is set as T1, the final voltage value for reading battery is V2, record V2Read access time point be set as T2；
Step B data is collected, and monitored end includes address information, data transmission blocks by V1, T1, V2, T2 and
Address information is sent to server end；
Step C data calculates, and the slope that battery discharge curve is calculated in data computation module is set as K, and passes through formula：K
=(V1-V2)/(T2-T1) calculate battery discharge curve slope K；
Step D data comparisons, storage contrast module are preset with the slope K of battery discharge curveIn advance, the KIn advanceIt is preset
Battery discharge curve slope range, when K is in KIn advanceWhen in range, battery is judged as normally, when K exceeds KIn advanceWhen range, battery judges
For failure；
Step E fault traces, when battery is judged as failure, display module display report barrier signal and fail battery
Address information.
Further include step F data statistics, when battery is judged in the above-mentioned battery detection statistical method based on Internet of Things
Break to be normal, statistical module reads data detection signal, counted and generated statistical data, and display module shows statistical number
According to.
Further include step G, step G data before step F in the above-mentioned battery detection statistical method based on Internet of Things
It rejects, data reject module and are equipped with voltage value VJust, work as VJustFor the preset initial voltage range of battery, V1Beyond VJustRange when,
Reject this group of detection data.
In the above-mentioned battery detection statistical method based on Internet of Things, step D further includes step H, step H data point
Class, KIn advanceIncluding KAlkaliAnd KCarbon, KAlkaliFor alkaline cell discharge slope of a curve range, KCarbonFor the slope model of carbon battery discharge curve
It encloses, when K is in KAlkaliWhen in range, alkaline statistical module reads data detection signal and counts, when K is in KCarbonWhen in range, carbon system
Meter module reads data detection signal and counts.
Compared with prior art, the present invention has the following advantages：
1, the present invention can be directed to the huge monitored end of radix, and each monitored end will most really be sent out using data
The case where sending to server end, being counted for server end, eliminate data distortion occurs.
2, the present invention can detect the gas meter, flow meter at monitored end and whether there is failure, can when gas meter, flow meter is there are when failure
The gas meter, flow meter of positioning failure, and this group of data are rejected, facilitate maintenance personal to repair failure gas meter, flow meter in time, and improve statistics
Accuracy.
3, the present invention can be directed to different types of battery, and the monitored end of automatic identification uses the type of battery, and classifies
It is counted so that statistics is more accurate and applied widely.
4, the present invention can also be rejected due to the abnormal data that battery initial voltage itself is not up to standard and generates, and further be dropped
The low error of statistical result.
Description of the drawings
Fig. 1 is the system principle schematic diagram of the present invention.
Fig. 2 is the method flow schematic diagram of the present invention.
In figure, 1, server end；2, it is monitored end；3, battery；4, data acquisition module；5, data transmission blocks；6, number
According to receiving module；7, data temporary storage module；8, data computation module；9, contrast module is stored；10, alkaline statistical module；11, carbon
Property statistical module；12, barrier module is reported；13, display module；14, statistical module；15, data reject module；16, threshold determination mould
Block.
Specific implementation mode
Following is a specific embodiment of the present invention in conjunction with the accompanying drawings, technical scheme of the present invention will be further described,
However, the present invention is not limited to these examples.
As shown in Figure 1, based on the battery detection statistical system of Internet of Things include server end 1 and with server end 1
Multiple monitored ends 2 of connection are communicated, it includes battery 3, data acquisition module 4, data transmission blocks 5, server to be monitored end 2
End 1 includes data reception module 6, data temporary storage module 7, data computation module 8, storage contrast module 9, statistical module 14, report
Barrier module 12, display module 13；Data acquisition module 4 is used to read voltage value and the voltage value corresponding time point of battery 3, tool
Body, the initial voltage value that data acquisition module 4 reads battery 3 is set as V1, record V1Read access time point be set as T1, read electricity
The final voltage value in pond 3 is V2, record V2Read access time point be set as T2, to form data detection signal and by the testing number
It is believed that number being sent to data transmission blocks 5, data transmission blocks 5 are forwarded to data reception module 6 after receiving data detection signal,
Data reception module 6 is forwarded to data temporary storage module 7 after receiving data detection signal, and it is temporary that data detection signal is stored in data
Module 7 can therefrom be read later for other modules.Data computation module 8 read testing number from data temporary storage module 7 it is believed that
The slope K of battery discharge curve is calculated after number, the slope K of battery discharge curve is calculated by the following formula：K=(V1-V2)/
(T2-T1), to obtain the data-signal after calculating and be transmitted to storage contrast module 9.Storage contrast module 9 is preset with battery
The slope K of discharge curveIn advance, KIn advanceFor preset battery discharge curve slope range.It stores contrast module 9 and receives the data after calculating
Signal and correction data KIn advanceIt is compared, when K is in KIn advanceWhen in range, battery 3 is judged as that normal rear Instruction Statistics module 14 is read
Data detection signal is counted and is generated statistical data, when K exceeds KIn advanceWhen range, battery 3 is judged as failure and report is instructed to hinder
Module 12 generates report barrier signal, and the statistical data generated later or report barrier signal are shown in display module 13.
It further describes in detail, data acquisition module 4 is connected with threshold-discrimination module 16, and threshold-discrimination module 16 is for being arranged quilt
The minimum operating voltage V of monitoring side 2Threshold, V2=VThreshold.The minimum operating voltage of different equipment requirements, therefore have must at monitored end 2
Final voltage is adjusted according to equipment, and then the slope K for calculating the discharge curve of battery 3 under the equipment use situation is used
In judgement.Threshold-discrimination module 16 is set in the present invention, and system can adjust the minimum work electricity at monitored end 2 by the module
Press VThreshold, adjust the minimum operating voltage V of detection2=VThreshold, the read access time point T at this time of data acquisition module 42Practical is exactly VThresholdIt is corresponding
Time point, i.e., detection adjustment after minimum operating voltage VThresholdCorresponding time point, and data computation module 8 keeps in mould from data
The slope for calculating battery discharge curve in block 7 after reading data detection signal, is exactly from initial voltage to minimum operating voltage VThreshold
Battery discharge curve slope in the corresponding period.
It further describes in detail, statistical module 14 is also associated with data and rejects module 15, and data reject module 15 and are equipped with voltage value
VJust, work as VJustFor 3 preset initial voltage range of battery, V1Beyond VJustRange when, that is, reject this group of detection data.Actually make
In, the battery used is not all filled with the battery of electricity, and the battery that discontented battery detection obtains that charges is compared using duration
It is shorter in normal battery, experimental data can be had an impact.In addition, it is non-full of electricity condition battery meter calculate slave initial voltage to most
Low-work voltage corresponds to the reference significance and little in R＆D process of the battery discharge curve slope in the period, it is necessary to reject
The partial data, and then ensure the validity of experimental data statistics.
It further describes in detail, address information is contained at monitored end 2, and data transmission blocks 5 are additionally operable to send monitored battery 3
Address information is to data reception module 6,6 forwarding address information of data reception module to data temporary storage module 7, data temporary storage module
7 receive, storage address information and for read, when battery 3 is judged as failure, display module 13 read address information simultaneously show
Show.Include unique address information in the information at monitored end 2, includes in the information that data transmission blocks 5 are sent to server end 1
The address information, the display module 13 of server end 1 shows monitored end 2 in time after monitored end 2 is judged failure
Address information carries out maintenance and repair to facilitate staff timely to arrive specified address.
It further describes in detail, battery 3 is refered in particular in alkaline battery or/and carbon battery on measuring instrument.
It further describes in detail, KIn advanceIncluding KAlkaliAnd KCarbon, KAlkaliFor alkaline cell discharge slope of a curve range, KCarbonIt is put for carbon battery
Electric slope of a curve range, statistical module 14 include alkaline statistical module 10 and carbon statistical module 11, alkaline statistical module 10
For counting alkaline battery, when K is in KAlkaliWhen in range, alkaline statistical module 10 reads data detection signal and counts, carbon system
Meter module 11 is for counting carbon battery, when K is in KCarbonWhen in range, carbon statistical module 11 reads data detection signal and unites
Meter.In monitored end 2 used in everyday, the dry cell used includes mainly alkaline battery and carbon battery.For this purpose, for alkalinity
The use information of battery and carbon battery carries out special statistics, facilitates the management and use when later experiments.
As shown in Fig. 2, another aspect of the present invention, additionally provides a kind of battery detection statistical method based on Internet of Things,
Include the following steps：
Step A data acquire, and the initial voltage value that data acquisition module 4 reads battery 3 is set as V1, record V1Reading when
Between point be set as T1, the final voltage value for reading battery 3 is V2, record V2Read access time point be set as T2；
Step B data is collected, and monitored end 2 includes address information, data transmission blocks 5 by V1, T1, V2, T2 with
And address information is sent to server end 1；
Step C data calculates, and the slope that battery discharge curve is calculated in data computation module 8 is set as K, and passes through formula：K
=(V1-V2)/(T2-T1) calculate battery discharge curve slope K；
Step D data comparisons, storage contrast module 9 are preset with the slope K of battery discharge curveIn advance, the KIn advanceIt is default
Battery discharge curve slope range, when K is in KIn advanceWhen in range, battery 3 is judged as normally, when K exceeds KIn advanceWhen range, battery 3
It is judged as failure；
Step E fault traces, when battery 3 is judged as failure, the display report barrier signal of display module 13 and failure electricity
The address information in pond；
Step G data is rejected, and data reject module 15 and are equipped with voltage value VJust, work as VJustFor the preset initial voltage model of battery
It encloses, V1Beyond VJustRange when, that is, reject this group of detection data；
Step F data statistics, when battery 3 is judged as normally, statistical module 14 reads data detection signal, is counted
And statistical data is generated, display module 13 shows statistics.
In order to carry out statistic of classification to battery, step D further includes step H, the classification of step H data, KIn advanceIncluding KAlkaliAnd KCarbon, KAlkali
For alkaline cell discharge slope of a curve range, KCarbonFor the slope range of carbon battery discharge curve, when K is in KAlkaliWhen in range,
Alkaline statistical module 10 reads data detection signal and counts, when K is in KCarbonWhen in range, carbon statistical module 11 reads testing number
It is believed that number and count.
In the present invention, the slope of battery discharge curve is detected first whether in normal range, you can show that offer should
Whether failure while can also pass through battery discharge to the gas meter, flow meter of group data if there are failure and Times barrier and rejecting this group of data
Slope of a curve judges the type of battery, can classify to the data of different type battery, then, by detecting battery
Initial voltage rejects the too low abnormal data of initial voltage, by only leaving normal data, statistical module after multiple rejecting
It records the service life of the battery of each piece of normal use and is counted.
Application examples：This system statistical monitoring is applied to the gas meter, flow meter battery in Internet of Things, and this field gas meter, flow meter battery is usual
Using alkaline battery and carbon battery, the manufacture initial voltage value of gas meter, flow meter battery is typically 6.5V, minimum operating voltage
For 5V, this system is by calculating from initial voltage to the slope of the discharge curve of minimum operating voltage and default discharge curve slope
Comparison, to judge whether battery failure and carries out statistic of classification.Existing default discharge curve slope is：Alkaline gas meter, flow meter battery from
It is 0.08 that initial voltage, which works to 5V its battery discharge slope, and carbon gas meter, flow meter battery is put from initial voltage its battery that works to 5V
Electric slope is 0.2, may determine that failure occur when the battery discharge slope at monitored end 2 is other bigger numericals.Specifically：
After installing battery 3, the initial voltage value that data acquisition module 4 reads gas meter, flow meter battery 3 is V1, record time point is T1, in battery
3 when being discharged to minimum operating voltage 5V, and record time point is T2, to form data detection signal and by the data detection signal
Data transmission blocks 5 are sent to, data transmission blocks 5 are forwarded to data reception module 6 after receiving data detection signal, and data connect
It receives after module 6 receives data detection signal and is forwarded to data temporary storage module 7, data detection signal is stored in data temporary storage module 7
It can therefrom be read for other modules later.Data computation module 8 is counted after reading data detection signal in data temporary storage module 7
The slope K of battery discharge curve is calculated, the slope K of battery discharge curve is calculated as K=(V1-5)/(T2-T1), it is calculated to obtain
Rear data-signal is simultaneously transmitted to storage contrast module 9, and storage contrast module 9 is preset with the slope K of battery discharge curveAlkali=
0.08、KCarbon=0.2, storage contrast module 9 is counted or is reported according to the comparison result of slope K value and preset correction data
Barrier carries out the data detection signal stored in data temporary storage module 7 selectively to reject hash, number before statistics
It is equipped with voltage value V according to module 15 is rejectedJust, work as VJustFor 3 preset initial voltage range of battery, V1Beyond VJustRange when, that is, pick
Except this group of detection data.The specific of storage contrast module 9 is judged as：When K values are equal to KAlkaliOr KCarbonWhen, battery 3 is judged as normally,
The data detection signal in 9 Instruction Statistics module 14 of contrast module reading data temporary storage module 7 is stored, when K values are equal to KAlkaliShi Jian
Measured data signal enters alkaline statistical module 10 and is detected data-signal statistics, when K values are equal to KCarbonWhen data detection signal into
Enter carbon statistical module 11 and is detected data-signal statistics；When K values are not equal to KAlkaliOr KCarbonWhen, it represents gas meter, flow meter battery capacity and exists
It has been leaked in short time, there are failures to generate report barrier signal for battery 3.In addition, when monitored end 2 uses the combustion gas of other models
Table, when minimum operating voltage is not 5V, it is V to adjust minimum operating voltage using threshold-discrimination module 16Threshold, data acquisition module
4 read 3 voltage of battery as minimum operating voltage VThreshold, record time point is T2, data acquisition module 4 sends out the data detection signal
It send to data reception module 6 to repeat the above steps and is judged and counted.
Specific embodiment described herein is only an example for the spirit of the invention.Technology belonging to the present invention is led
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Although server end 1, monitored end 2, battery 3, data acquisition module 4, data transmission is used more herein
Module 5, data reception module 6, data temporary storage module 7, data computation module 8, storage contrast module 9, alkaline statistical module 10,
Carbon statistical module 11, report barrier module 12, display module 13, statistical module 14, data reject module 15, threshold-discrimination module 16
Equal terms, but it does not preclude the possibility of using other terms.The use of these items is only for more easily describe to conciliate
Release the essence of the present invention；Any one of the additional limitations is construed as all to disagree with spirit of that invention.
Claims (10)
1. a kind of battery detection statistical system based on Internet of Things, which is characterized in that including server end (1) and and server
Hold multiple monitored ends (2) of (1) communication connection；
The monitored end (2) includes battery (3), data acquisition module (4), data transmission blocks (5)；
The server end (1) includes data reception module (6), data temporary storage module (7), data computation module (8), storage
Contrast module (9), statistical module (14), report barrier module (12), display module (13)；
The data acquisition module (4) is used to read the voltage value time point corresponding with voltage value of battery (3), forms inspection
Data detection signal is simultaneously sent to data transmission blocks (5) by measured data signal；
The data transmission blocks (5) are for receiving data detection signal and being forwarded to data reception module (6)；
The data reception module (6) is for receiving data detection signal and being forwarded to data temporary storage module (7)；
The data temporary storage module (7) is for receiving, storing data detection signal and for reading；
The data computation module (8) is for reading data detection signal, data detection signal calculate and will be calculated
Data-signal afterwards is sent to storage contrast module (9)；
The storage contrast module (9) is for storing correction data, after the storage contrast module (9) is for receiving calculating
Data-signal and be compared with correction data, judge monitored end (2) battery (3) whether failure, if battery (3) is judged to
Break to be normal, the statistical module (14) reads data detection signal, counted and generated statistical data, if battery (3)
It is judged as failure, the report barrier module (12) generates report barrier signal；
The display module (13) is for showing statistics or reporting barrier signal.
2. a kind of battery detection statistical system based on Internet of Things according to claim 1, which is characterized in that the number
The initial voltage value that battery (3) is read according to acquisition module (4) is set as V1, record V1Read access time point be set as T1, read battery
(3) final voltage value is V2, record V2Read access time point be set as T2, the data computation module (8) is for calculating battery
The slope of discharge curve is simultaneously set as K, and the slope K of the battery discharge curve is calculated by the following formula：K=(V1-V2)/(T2-
T1)。
3. a kind of battery detection statistical system based on Internet of Things according to claim 2, which is characterized in that described deposits
Storage contrast module (9) is preset with the slope K of battery discharge curveIn advance, the KIn advanceFor preset battery discharge curve slope range,
When K is in KIn advanceWhen in range, battery (3) is judged as normally,
When K exceeds KIn advanceWhen range, battery (3) is judged as failure.
4. a kind of battery detection statistical system based on Internet of Things according to claim 2, which is characterized in that the number
It is also associated with threshold-discrimination module (16) according to acquisition module (4), the threshold-discrimination module (16) is for being arranged monitored end
(2) minimum operating voltage VThreshold, the V2=VThreshold。
5. a kind of battery detection statistical system based on Internet of Things according to claim 2, which is characterized in that the system
Meter module (14) is also associated with data and rejects module (15), and the data reject module (15) and are equipped with voltage value VJust, work as VJustFor
Battery (3) preset initial voltage range, the V1Beyond VJustRange when, that is, reject this group of detection data.
6. a kind of battery detection statistical system based on Internet of Things according to claim 1, which is characterized in that monitored end
(2) contain address information, the data transmission blocks (5) are additionally operable to send the address information of monitored battery (3) to data
Receiving module (6), the data reception module (6) forwarding address information to data temporary storage module (7), data temporary storage module
(7) receive, storage address information and for read, when battery (3) is judged as failure, display module (13) read address information
And it shows.
7. a kind of battery detection statistical system based on Internet of Things according to claim 3, which is characterized in that the KIn advance
Including KAlkaliAnd KCarbon, the KAlkaliFor alkaline cell discharge slope of a curve range, the KCarbonFor carbon battery discharge curve
Slope range, the statistical module (14) include alkaline statistical module (10) and carbon statistical module (11),
The alkaline statistical module (10) is for counting alkaline battery, when K is in KAlkaliWhen in range, alkaline statistical module (10)
It reads data detection signal and counts,
The carbon statistical module (11) is for counting carbon battery, when K is in KCarbonWhen in range, carbon statistical module (11)
It reads data detection signal and counts.
8. a kind of battery detection statistical method based on Internet of Things, which is characterized in that include the following steps：
Step A data acquire, and the initial voltage value that data acquisition module (4) reads battery (3) is set as V1, record V1Reading when
Between point be set as T1, the final voltage value for reading battery (3) is V2, record V2Read access time point be set as T2；
Step B data is collected, and monitored end (2) includes address information, data transmission blocks (5) by V1, T1, V2, T2 with
And address information is sent to server end (1)；
Step C data calculates, and the slope that battery discharge curve is calculated in data computation module (8) is set as K, and passes through formula：K=
(V1-V2)/(T2-T1) calculate battery discharge curve slope K；
Step D data comparisons, storage contrast module (9) are preset with the slope K of battery discharge curveIn advance, the KIn advanceIt is preset
Battery discharge curve slope range, when K is in KIn advanceWhen in range, battery (3) is judged as normally, when K exceeds KIn advanceWhen range, battery
(3) it is judged as failure；
Step E fault traces, when battery (3) is judged as failure, display module (13) display report barrier signal and failure electricity
The address information in pond.
9. a kind of battery detection statistical method based on Internet of Things according to claim 8, which is characterized in that further include with
Lower step：
Step F data statistics, when battery (3) is judged as normally, statistical module (14) reads data detection signal, is counted
And statistical data is generated, display module (13) shows statistics.
10. a kind of battery detection statistical method based on Internet of Things according to claim 9, which is characterized in that step F it
Before further include step G, step G data is rejected, and data reject module (15) and are equipped with voltage value VJust, work as VJustIt is preset initial for battery
Voltage range, V1Beyond VJustRange when, that is, reject this group of detection data, step D further includes step H, the classification of step H data,
KIn advanceIncluding KAlkaliAnd KCarbon, KAlkaliFor alkaline cell discharge slope of a curve range, KCarbonFor the slope range of carbon battery discharge curve, when
K is in KAlkaliWhen in range, alkaline statistical module (10) is read data detection signal and is counted, when K is in KCarbonWhen in range, carbon statistics
Module (11) reads data detection signal and counts.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109887121A (en) * | 2018-12-30 | 2019-06-14 | 中银（宁波）电池有限公司 | A kind of system and method that battery quality is examined |
| CN110361659A (en) * | 2019-08-13 | 2019-10-22 | 江苏健雄电气安装工程有限公司 | A kind of remote storage battery monitoring system based on Internet of Things |
| CN111260087A (en) * | 2020-01-13 | 2020-06-09 | 上海威派格智慧水务股份有限公司 | Management system for pipe network abnormity |
| CN111487540A (en) * | 2019-12-26 | 2020-08-04 | 浙江苏泊尔家电制造有限公司 | Detection method of cooking appliance and cooking appliance |
| CN113791350A (en) * | 2021-08-06 | 2021-12-14 | 陕西汽车集团股份有限公司 | Battery failure prediction method |
| CN114818831A (en) * | 2022-05-27 | 2022-07-29 | 深圳先进技术研究院 | Bidirectional lithium ion battery fault detection method and system based on multi-source perception |
| CN116049502A (en) * | 2023-01-31 | 2023-05-02 | 广东中设智控科技股份有限公司 | Method and device for processing read data of accumulated industrial instrument |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2300455A1 (en) * | 1999-03-12 | 2000-09-12 | Toyota Jidosha Kabushiki Kaisha | Fault determination apparatus and fault determination method for a battery set |
| US6404164B1 (en) * | 2001-05-14 | 2002-06-11 | Hewlett-Packard Company | Method of battery chemistry identification through analysis of voltage behavior |
| CN102016617A (en) * | 2008-05-07 | 2011-04-13 | 原子能和代替能源委员会 | Method of estimation of the state of charge of a battery |
| US20120253713A1 (en) * | 2011-04-01 | 2012-10-04 | Continental Automotive Systems, Inc. | Estimating the capacity of a li-ion battery based on initial part of the discharge curve |
| CN103138017A (en) * | 2011-12-02 | 2013-06-05 | 日产自动车株式会社 | Battery control apparatus |
| US20140115858A1 (en) * | 2012-10-25 | 2014-05-01 | Bayerische Motoren Werke Aktiengesellschaft | Systems and Methods to Determine the Condition of a Battery |
| CN106796846A (en) * | 2014-08-08 | 2017-05-31 | 康宁股份有限公司 | Pore volume utilization rate carbon high and double-layer capacitor |
| CN208672773U (en) * | 2018-05-04 | 2019-03-29 | 金卡智能集团股份有限公司 | A kind of battery statistics system based on Internet of Things |
| CN208672779U (en) * | 2018-05-04 | 2019-03-29 | 金卡智能集团股份有限公司 | A kind of battery monitoring system based on Internet of Things |
| CN211785999U (en) * | 2018-05-04 | 2020-10-27 | 金卡智能集团股份有限公司 | Battery monitoring statistical system based on Internet of things |
-
2018
- 2018-05-04 CN CN201810420053.7A patent/CN108387850B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2300455A1 (en) * | 1999-03-12 | 2000-09-12 | Toyota Jidosha Kabushiki Kaisha | Fault determination apparatus and fault determination method for a battery set |
| US6404164B1 (en) * | 2001-05-14 | 2002-06-11 | Hewlett-Packard Company | Method of battery chemistry identification through analysis of voltage behavior |
| CN102016617A (en) * | 2008-05-07 | 2011-04-13 | 原子能和代替能源委员会 | Method of estimation of the state of charge of a battery |
| US20120253713A1 (en) * | 2011-04-01 | 2012-10-04 | Continental Automotive Systems, Inc. | Estimating the capacity of a li-ion battery based on initial part of the discharge curve |
| CN103138017A (en) * | 2011-12-02 | 2013-06-05 | 日产自动车株式会社 | Battery control apparatus |
| US20140115858A1 (en) * | 2012-10-25 | 2014-05-01 | Bayerische Motoren Werke Aktiengesellschaft | Systems and Methods to Determine the Condition of a Battery |
| CN106796846A (en) * | 2014-08-08 | 2017-05-31 | 康宁股份有限公司 | Pore volume utilization rate carbon high and double-layer capacitor |
| CN208672773U (en) * | 2018-05-04 | 2019-03-29 | 金卡智能集团股份有限公司 | A kind of battery statistics system based on Internet of Things |
| CN208672779U (en) * | 2018-05-04 | 2019-03-29 | 金卡智能集团股份有限公司 | A kind of battery monitoring system based on Internet of Things |
| CN211785999U (en) * | 2018-05-04 | 2020-10-27 | 金卡智能集团股份有限公司 | Battery monitoring statistical system based on Internet of things |
Non-Patent Citations (2)
| Title |
|---|
| 朱向明;: "基于数理统计的蓄电池特性评估方法研究", 电气试验, no. 05, 15 October 2008 (2008-10-15), pages 1 - 3 * |
| 朱向明;: "基于数理统计的蓄电池特性评估方法研究", 电气试验, no. 05, pages 1 - 3 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109887121A (en) * | 2018-12-30 | 2019-06-14 | 中银（宁波）电池有限公司 | A kind of system and method that battery quality is examined |
| CN110361659A (en) * | 2019-08-13 | 2019-10-22 | 江苏健雄电气安装工程有限公司 | A kind of remote storage battery monitoring system based on Internet of Things |
| CN111487540A (en) * | 2019-12-26 | 2020-08-04 | 浙江苏泊尔家电制造有限公司 | Detection method of cooking appliance and cooking appliance |
| CN111260087A (en) * | 2020-01-13 | 2020-06-09 | 上海威派格智慧水务股份有限公司 | Management system for pipe network abnormity |
| CN113791350A (en) * | 2021-08-06 | 2021-12-14 | 陕西汽车集团股份有限公司 | Battery failure prediction method |
| CN113791350B (en) * | 2021-08-06 | 2024-05-14 | 陕西汽车集团股份有限公司 | Battery fault prediction method |
| CN114818831A (en) * | 2022-05-27 | 2022-07-29 | 深圳先进技术研究院 | Bidirectional lithium ion battery fault detection method and system based on multi-source perception |
| CN116049502A (en) * | 2023-01-31 | 2023-05-02 | 广东中设智控科技股份有限公司 | Method and device for processing read data of accumulated industrial instrument |
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