CN107290683A - The detection method and device of remaining battery capacity - Google Patents
The detection method and device of remaining battery capacity Download PDFInfo
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- CN107290683A CN107290683A CN201710597734.6A CN201710597734A CN107290683A CN 107290683 A CN107290683 A CN 107290683A CN 201710597734 A CN201710597734 A CN 201710597734A CN 107290683 A CN107290683 A CN 107290683A
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- 238000001514 detection method Methods 0.000 title claims abstract description 62
- 238000005259 measurement Methods 0.000 claims abstract description 64
- 210000005036 nerve Anatomy 0.000 claims abstract description 44
- 238000005457 optimization Methods 0.000 claims abstract description 24
- 239000002245 particle Substances 0.000 claims abstract description 15
- 238000002847 impedance measurement Methods 0.000 claims description 22
- 238000007667 floating Methods 0.000 claims description 21
- 230000010287 polarization Effects 0.000 claims description 21
- 238000012360 testing method Methods 0.000 claims description 16
- 238000001914 filtration Methods 0.000 claims description 6
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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Abstract
The present invention relates to a kind of detection method of remaining battery capacity and device, the multiple capacitance prediction values and multiple residual capacity predicted values of battery to be measured are worth to using the measurement of the influence factor of artificial nerve network model storehouse and residual capacity, fitting is weighted to multiple capacitance prediction values, using measurement capacitance as desired value, electric capacity match value is optimized by particle cluster algorithm, obtain optimization weight coefficient, fitting is weighted to multiple residual capacity predicted values using the optimization weight coefficient, the remaining capacity value of battery to be measured is obtained.Artificial nerve network model storehouse has been used in this scheme, accurate multiple residual capacity predicted values can be generated, multiple capacitance prediction values are weighted after fitting, optimization weight coefficient is obtained using particle cluster algorithm, make the real surplus capability value that battery to be measured is more approached after multiple residual capacity predicted value weighted fittings, further improve the detection accuracy of remaining capacity value.
Description
Technical field
The present invention relates to technical field of electric power, the detection method and dress of more particularly to a kind of remaining battery capacity
Put.
Background technology
Battery is a kind of cheap, easy to use, simple in construction power-supply device, be widely used in electric power,
In the every profession and trades such as communication.Operation of the stabilization, reliably working of battery to whole system is most important.In order to be fully understood by electric power storage
The working condition in pond, it is necessary to carry out the detection of residual capacity (SOC, State of Charge, state-of-charge) to battery, with
Just corresponding control measure are taken.
The detection of general remaining battery capacity is using discharge test method, internal resistance detection method and open circuit voltage method etc..Electric discharge
Test method(s) is it needs to be determined that the initial capacity and efficiency for charge-discharge of battery, and it is more difficult to obtain accurate efficiency for charge-discharge, internal resistance
Detection method and open circuit voltage method largely can be influenceed by other influences factor, therefore, using general method to electric power storage
The accuracy that residual capacity detection is entered to exercise in pond is relatively low.
The content of the invention
Based on this, it is necessary to for traditional remaining battery capacity detection accuracy it is relatively low the problem of stored there is provided one kind
The detection method and device of battery remaining power.
A kind of detection method of remaining battery capacity, comprises the following steps：
Obtain the measured value of the influence factor of the residual capacity of battery to be measured and the measurement capacitance of battery to be measured；
The measured value of influence factor is inputted to preset artificial nerve network model storehouse, passes through preset artificial neural network mould
Type storehouse obtains the multiple capacitance prediction values and multiple residual capacity predicted values of battery to be measured, wherein, multiple capacitance prediction values and
Multiple residual capacity predicted values are corresponded；
Fitting is weighted to multiple capacitance prediction values according to default weight coefficient, electric capacity match value is obtained；
Using measurement capacitance as desired value, electric capacity match value is optimized by particle cluster algorithm, optimization weight is obtained
Coefficient；
Fitting is weighted to multiple residual capacity predicted values according to optimization weight coefficient, the residue of battery to be measured is obtained
Capability value.
A kind of detection means of remaining battery capacity, including dc source, current measuring device, voltage measuring apparatus,
Internal resistance instrument, ac impedance measurement instrument, temperature measuring equipment and microprocessor；
Detection means possesses two the first binding posts, and two the are connected to after dc source and current measuring device series connection
Between one binding post, voltage measuring apparatus, internal resistance instrument, ac impedance measurement instrument are connected in parallel between two the first binding posts；
Two the first binding posts are used for the both positive and negative polarity for connecting battery to be measured respectively；
Microprocessor respectively with dc source, current measuring device, voltage measuring apparatus, internal resistance instrument, ac impedance measurement
Instrument, temperature measuring equipment connection；
Microprocessor receives dc source, current measuring device, voltage measuring apparatus, internal resistance instrument, ac impedance measurement instrument
With the measurement data of temperature measuring equipment, the measurement of the influence factor of the residual capacity of battery to be measured is obtained according to measurement data
The measurement capacitance of value and battery to be measured；The measured value of influence factor is inputted to preset artificial nerve network model storehouse, led to
Multiple capacitance prediction values and multiple residual capacity predicted values that preset artificial nerve network model storehouse obtains battery to be measured are crossed, its
In, multiple capacitance prediction values and multiple residual capacity predicted values are corresponded；According to default weight coefficient to multiple capacitance predictions
Value is weighted fitting, obtains electric capacity match value；Using measurement capacitance as desired value, by particle cluster algorithm to electric capacity match value
Optimize, obtain optimization weight coefficient；Fitting is weighted to multiple residual capacity predicted values according to optimization weight coefficient, obtained
Obtain the remaining capacity value of battery to be measured.
According to the detection method and device of the remaining battery capacity of the invention described above, it is to obtain remaining for battery to be measured
The measurement capacitance of the measured value of the influence factor of covolume amount and battery to be measured, utilizes artificial nerve network model storehouse and residue
The measurement of the influence factor of capacity is worth to the multiple capacitance prediction values and multiple residual capacity predicted values of battery to be measured, to many
Individual capacitance prediction value is weighted fitting, using measurement capacitance as desired value, and electric capacity match value is carried out by particle cluster algorithm
Optimization, obtains optimization weight coefficient, is weighted fitting to multiple residual capacity predicted values using the optimization weight coefficient, obtains
The remaining capacity value of battery to be measured.Artificial nerve network model storehouse has been used in this scheme, can have been generated accurately many
Individual residual capacity predicted value, pair multiple capacitance prediction values obtained simultaneously are weighted after fitting, are obtained using particle cluster algorithm
Optimize weight coefficient, make the real surplus capacity that battery to be measured is more approached after multiple residual capacity predicted value weighted fittings
Value, further increases the detection accuracy of remaining capacity value.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the detection method of remaining battery capacity in one of embodiment；
Fig. 2 is the structural representation of the detection means of remaining battery capacity in one of embodiment；
Fig. 3 is the part-structure schematic diagram of the detection means of remaining battery capacity in one of embodiment；
Fig. 4 is the structural representation of the detection means of remaining battery capacity in one of embodiment；
Fig. 5 is the structured flowchart of the detection means of remaining battery capacity in one of specific embodiment；
Fig. 6 is signal processing unit structural representation in one of specific embodiment；
Fig. 7 is the schematic diagram of DC power supply circuit in one of specific embodiment；
Fig. 8 is microprocessor and each part annexation square in signal processing unit in one of specific embodiment
Figure；
Fig. 9 be in one of specific embodiment signal processing unit while measuring the measurement terminal signal of multiple batteries
Figure；
Figure 10 is artificial nerve network model structure diagram in one of specific embodiment；
Figure 11 is the process schematic of battery model storehouse weight coefficient fitting algorithm in one of specific embodiment；
Figure 12 is the schematic equivalent circuit of battery in one of specific embodiment.
Embodiment
For the objects, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with drawings and Examples, to this
Invention is described in further detail.It should be appreciated that embodiment described herein is only to explain the present invention,
Do not limit protection scope of the present invention.
It is shown in Figure 1, it is the schematic flow sheet of the detection method of the remaining battery capacity of one embodiment of the invention.
The detection method of remaining battery capacity in the embodiment comprises the following steps：
Step S101：Obtain the measured value of the influence factor of the residual capacity of battery to be measured and the electric capacity of battery to be measured
Measured value；
In this step, remaining capacity value is not obtained directly, acquisition be residual capacity influence factor measured value,
Influence factor can produce considerable influence to residual capacity, and the measured value of influence factor is easily obtained；
Step S102：The measured value of influence factor is inputted to preset artificial nerve network model storehouse, by preset artificial
Neural network model storehouse obtains the multiple capacitance prediction values and multiple residual capacity predicted values of battery to be measured, wherein, Duo Ge electricity
Hold predicted value and multiple residual capacity predicted values are corresponded；
In this step, preset artificial nerve network model storehouse is advance trained model library, for the shadow of input
The measured value of the factor of sound, can export corresponding capacitance prediction value and residual capacity predicted value, due to the shadow of residual capacity
The factor of sound can also influence the electric capacity of battery, and therefore, preset artificial nerve network model storehouse can also be predicted to electric capacity；In advance
A variety of predictions can be carried out by putting artificial nerve network model storehouse, obtain one-to-one multiple capacitance prediction values and multiple remaining appearances
Measure predicted value；
Step S103：Fitting is weighted to multiple capacitance prediction values according to default weight coefficient, electric capacity match value is obtained；
In this step, electric capacity match value is the product and value of multiple capacitance prediction values and default weight coefficient；
Step S104：Using measurement capacitance as desired value, electric capacity match value is optimized by particle cluster algorithm, obtained
Optimize weight coefficient；
In this step, optimization weight coefficient is obtained using particle cluster algorithm, the electric capacity match value after optimization is at utmost
Approach measurement capacitance；
Step S105：Fitting is weighted to multiple residual capacity predicted values according to optimization weight coefficient, to be measured store is obtained
The remaining capacity value of battery；
In this step, multiple residual capacity predicted values and the product and value of optimization weight coefficient are regard as electric power storage to be measured
The remaining capacity value in pond.
In the present embodiment, the measured value and battery to be measured of the influence factor of the residual capacity of battery to be measured are obtained
Measurement capacitance, battery to be measured is worth to using the measurement of the influence factor of artificial nerve network model storehouse and residual capacity
Multiple capacitance prediction values are weighted fitting, with measurement capacitance by multiple capacitance prediction values and multiple residual capacity predicted values
For desired value, electric capacity match value is optimized by particle cluster algorithm, optimization weight coefficient is obtained, utilizes the optimization weight system
It is several that fitting is weighted to multiple residual capacity predicted values, obtain the remaining capacity value of battery to be measured.Used in this scheme
Artificial nerve network model storehouse, can generate accurate multiple residual capacity predicted values, pair multiple electric capacity obtained simultaneously
Predicted value is weighted after fitting, is obtained optimization weight coefficient using particle cluster algorithm, is weighted multiple residual capacity predicted values
The real surplus capability value of battery to be measured is more approached after fitting, the detection accuracy of remaining capacity value is further increased.
Optionally, the number for presetting weight coefficient is identical with the number of capacitance prediction value, the sum of all default weight coefficients
It is worth for 1.
In one of the embodiments, preset artificial nerve network model storehouse includes multiple preset artificial neural network moulds
Type；
The measured value of influence factor is inputted to preset artificial nerve network model storehouse, passes through preset artificial neural network mould
The step of type storehouse obtains the multiple capacitances and multiple remaining capacity value of battery comprises the following steps：
The measured value of influence factor is separately input into each preset artificial nerve network model, each preset artificial neuron
Network model exports a capacitance prediction value and a residual capacity predicted value.
In the present embodiment, preset artificial nerve network model storehouse includes multiple preset artificial nerve network models, each
Preset artificial nerve network model is different to the prediction process of influence factor measured value, so as to obtain different prediction knots
Really, the dynamic characteristic of battery individual is taken into full account, accurately and effectively prediction data is provided for follow-up process of fitting treatment.
In one of the embodiments, by influence factor parameter input to the step of preset artificial nerve network model storehouse it
It is preceding further comprising the steps of：
To multiple sample batteries carry out charge and discharge electric test, obtain respectively each sample battery residual capacity sample value,
The influence factor sample value of electric capacity sample value and residual capacity；
Set up and the one-to-one multiple artificial nerve network models of each sample battery, for any one artificial neuron
Network model, using the residual capacity sample value and electric capacity sample value of corresponding sample battery as output, is stored with corresponding sample
The influence factor sample value of the residual capacity of battery is input, and the artificial nerve network model is trained, and obtains preset people
Artificial neural networks model.
In the present embodiment, sample battery is the existing battery for being used to train artificial nerve network model, is passed through
Complete charge and discharge electric test, can obtain the residual capacity sample value, electric capacity sample value and residual capacity of sample battery
Influence factor sample value, can be trained by the various sample values of acquisition to artificial neural network, obtain preset artificial god
Through network model, the prediction for the residual capacity of battery to be measured.
Optionally, multiple sample batteries can include the sample battery that remaining capacity value is equal to or higher than predetermined threshold value
It is less than the sample battery of predetermined threshold value with remaining capacity value, selects the sample battery of different residual capacities to ANN
Network is trained, and can improve accuracy of the preset artificial nerve network model to the prediction of the residual capacity of battery to be measured,
In addition, sample battery can also include new samples battery and old sample battery, new samples battery includes residual capacity
Value is less than the new samples battery of predetermined threshold value, old sample equal to or higher than the new samples battery and remaining capacity value of predetermined threshold value
This battery includes remaining capacity value and is equal to or higher than the old sample battery and remaining capacity value of predetermined threshold value less than default threshold
The old sample battery of value, predetermined threshold value can be the remaining capacity value or according to actual needs of testing standard requirement
The remaining capacity value of setting.
In one of the embodiments, the measured value and storage to be measured of the influence factor of the residual capacity of battery to be measured are obtained
The step of measurement capacitance of battery, comprises the following steps：
Battery to be measured is taken multiple measurements, the influence factor for obtaining multigroup residual capacity of battery to be measured is initially surveyed
Value and multigroup electric capacity initial measurement, and the influence factor initial measurement and multigroup electric capacity of multigroup residual capacity are initially surveyed
Value carries out Kalman filtering, obtains the measured value of the influence factor of the residual capacity of battery to be measured and the electricity of battery to be measured
Hold measured value.
In the present embodiment, the measured value and battery to be measured of the influence factor of the residual capacity of battery to be measured are obtained
During measurement capacitance, multi-group data can be continuously measured, multi-group data is handled by Kalman filtering, to reduce error
Influence, improves the measured value and the accuracy of measurement capacitance of the influence factor of residual capacity.
In one of the embodiments, influence factor include open-circuit voltage, ohmic internal resistance, polarization resistance, floating current or
Environment temperature.
In the present embodiment, influence factor includes open-circuit voltage, ohmic internal resistance, polarization resistance, floating current or environment temperature
Degree, one or more therein, above-mentioned open-circuit voltage, ohmic internal resistance, polarization resistance, floating charging can be utilized when actually detected
Stream or environment temperature can influence the residual capacity of battery, and residual capacity is predicted using various factors can be big
The big accuracy for improving prediction.
In one of the embodiments, the measured value and storage to be measured of the influence factor of the residual capacity of battery to be measured are obtained
The step of measurement capacitance of battery, comprises the following steps：
The open-circuit voltage of battery to be measured is measured by voltage measuring apparatus, is surveyed by dc source and current measuring device
The floating current of battery to be measured is measured, the environment temperature of battery to be measured is measured by temperature measuring equipment, is surveyed by internal resistance instrument
Total internal resistance of battery to be measured is measured, the equiva lent impedance of battery to be measured is measured by ac impedance measurement instrument；
Ohmic internal resistance, polarization resistance and the electric capacity of battery to be measured are calculated according to total internal resistance and equiva lent impedance.
In the present embodiment, open-circuit voltage, floating current and environment temperature can be by different equipment to electric power storage to be measured
Pond is measured and obtained, and ohmic internal resistance, polarization resistance and electric capacity can be surveyed by internal resistance instrument and ac impedance measurement instrument
Measure and calculate acquisition, during actually detected, according to the influence factor of selection corresponding equipment can be selected to measure.
According to the detection method of above-mentioned remaining battery capacity, the embodiment of the present invention also provides a kind of remaining battery capacity
Detection means, just the embodiment of detection means of the remaining battery capacity of the present invention is described in detail below.
It is shown in Figure 2, it is the structural representation of the detection means of the remaining battery capacity of one embodiment of the invention.
The detection means of remaining battery capacity in the embodiment includes dc source 210, current measuring device 220, voltage measurement
Device 230, internal resistance instrument 240, ac impedance measurement instrument 250, temperature measuring equipment 260 and microprocessor 270；
Detection means possesses two the first binding posts, and dc source 210 and current measuring device 220 are connected to after connecting
Between two the first binding posts, voltage measuring apparatus 230, internal resistance instrument 240, ac impedance measurement instrument 250 are connected in parallel on two
Between one binding post；Two the first binding posts are used for the both positive and negative polarity for connecting battery to be measured respectively；
Microprocessor 270 respectively with dc source 210, current measuring device 220, voltage measuring apparatus 230, internal resistance instrument
240th, ac impedance measurement instrument 250, temperature measuring equipment 260 are connected；
Microprocessor 270 receive dc source 210, current measuring device 220, voltage measuring apparatus 230, internal resistance instrument 240,
The measurement data of ac impedance measurement instrument 250 and temperature measuring equipment 260, the electric capacity of battery to be measured is obtained according to measurement data
The measured value of the influence factor of measured value and residual capacity；The measured value of influence factor is inputted to preset artificial neural network mould
Type storehouse, the multiple capacitance prediction values and multiple residual capacities for obtaining battery to be measured by preset artificial nerve network model storehouse are pre-
Measured value, wherein, multiple capacitance prediction values and multiple residual capacity predicted values are corresponded；According to default weight coefficient to multiple electricity
Hold predicted value and be weighted fitting, obtain electric capacity match value；Using measurement capacitance as desired value, by particle cluster algorithm to electric capacity
Match value is optimized, and obtains optimization weight coefficient；Multiple residual capacity predicted values are weighted according to optimization weight coefficient
Fitting, obtains the remaining capacity value of battery to be measured.
In one of the embodiments, microprocessor 270 connects the switch module and data mould of dc source 210 respectively
It is block, the switch module of current measuring device 220 and data module, the switch module of voltage measuring apparatus 230 and data module, interior
Hinder the switch module and data module, the switch module of ac impedance measurement instrument 250 and data module of instrument 240.
In the present embodiment, microprocessor 270 is opened by the switch module of dc source 210, current measuring device 220
Close module, the switch module of the switch module of voltage measuring apparatus 230 and internal resistance instrument 240 can control dc source 210 respectively,
The working condition of current measuring device 220, voltage measuring apparatus 230 and internal resistance instrument 240, passes through the data mould of dc source 210
Block, the data module of current measuring device 220, the data module of the data module of voltage measuring apparatus 230 and internal resistance instrument 240 can
To receive dc source 210, current measuring device 220, voltage measuring apparatus 230 and the measurement data of internal resistance instrument 240 respectively, lead to
Influencing each other between each measurement apparatus can be avoided by crossing control respectively.
In one of the embodiments, as shown in figure 3, detection means is also equipped with multigroup second binding post and multiple wiring
Switch module 280, the binding post of each group second includes two the second binding posts, and the binding post of each group second passes through corresponding
Pull-switch module 280 is connected with two the first binding posts, and all pull-switch modules 280 are connected with microprocessor 270,
The binding post of each group second is used for the both positive and negative polarity for connecting different batteries to be measured respectively.
In the present embodiment, two the first binding posts connect multigroup second wiring by multiple pull-switch modules 280
Terminal, the binding post of each group second can connect the both positive and negative polarity of different batteries to be measured respectively, and microprocessor 270 is opened by control
The closure state of module 280 is closed, residual capacity can be carried out to multiple batteries to be measured of connection and detected successively.
In one of the embodiments, as shown in figure 4, detection means also includes the control end being connected with microprocessor 270
End 290, detection control signal to the microprocessor 270, and reception microprocessor 270 for sending remaining battery capacity is returned
The testing result of the remaining battery capacity returned.
In the present embodiment, a control terminal 290 can be set to be connected with microprocessor 270, for controlling microprocessor
Device 270 carries out the detection of remaining battery capacity and receives testing result data, and control terminal 290 can also intuitively show inspection
Result is surveyed, in order to which user uses.
In one of the embodiments, preset artificial nerve network model storehouse includes multiple preset artificial neural network moulds
Type；
The measured value of influence factor is separately input into each preset artificial nerve network model by microprocessor 270, each
Preset artificial nerve network model exports a capacitance prediction value and a residual capacity predicted value.
In one of the embodiments, charge and discharge electric test is carried out to multiple sample batteries, microprocessor 270 is obtained respectively
The influence factor sample value of the residual capacity sample value of each sample battery, electric capacity sample value and residual capacity；Set up and each
The one-to-one multiple artificial nerve network models of sample battery, for any one artificial nerve network model, with correspondence
Sample battery residual capacity sample value and electric capacity sample value for output, with the residual capacity of corresponding sample battery
Influence factor sample value is input, and the artificial nerve network model is trained, and obtains preset artificial nerve network model.
In one of the embodiments, multiple sample batteries include the sample that remaining capacity value is equal to or higher than predetermined threshold value
This battery and remaining capacity value are less than the sample battery of predetermined threshold value.
In one of the embodiments, battery to be measured is taken multiple measurements, microprocessor 270 obtains battery to be measured
Multigroup residual capacity influence factor initial measurement and multigroup electric capacity initial measurement, and to the influence of multigroup residual capacity
Factor initial measurement and multigroup electric capacity initial measurement carry out Kalman filtering, obtain the shadow of the residual capacity of battery to be measured
The measured value of the factor of sound and the measurement capacitance of battery to be measured.
In one of the embodiments, influence factor include open-circuit voltage, ohmic internal resistance, polarization resistance, floating current or
Environment temperature.
In one of the embodiments, microprocessor 270 measures the open circuit electricity of battery to be measured by voltage measuring apparatus
Pressure, the floating current of battery to be measured is measured by dc source and current measuring device, is treated by temperature measuring equipment measurement
The environment temperature of battery is surveyed, total internal resistance of battery to be measured is measured by internal resistance instrument, is treated by ac impedance measurement instrument measurement
Survey the equiva lent impedance of battery；Ohmic internal resistance, polarization resistance and the electricity of battery to be measured are calculated according to total internal resistance and equiva lent impedance
Hold.
The detection means of the remaining battery capacity of the present invention and the detection method phase of the remaining battery capacity of the present invention
Correspondence, the technical characteristic and its advantage illustrated in the embodiment of the detection method of above-mentioned remaining battery capacity is applicable
In the embodiment of the detection means of remaining battery capacity.
In a specific embodiment, the detection means of remaining battery capacity can apply to the surplus of lead-acid accumulator
In the scene of remaining capacity check.
Fig. 5 is remaining battery capacity detection means block diagram, and wherein signal processing unit is used for the open circuit for measuring battery
Voltage, ohmic internal resistance, polarization resistance, floating current, environment temperature, battery capacitor parameter, signal processing unit, which is used to receive, to be controlled
The instruction of terminal processed and the relevant data progress related operation processing for gathering battery, obtain the testing result and biography of residual capacity
It is defeated by control terminal.
Fig. 6 is 1 binding post in signal processing unit structural representation in remaining battery capacity detection means, figure, is used
It is connected in battery to be measured, 2 binding posts, for being connected with battery to be measured, 3 signal processing unit signal ports,
It is connected with control terminal, the switch module of 4 DC power supply circuits, the switch module in 5 current measuring device loops, 6 voltages
The switch module in measurement apparatus loop, the switch module in 7 accumulator internal resistance instrument loops, the switch in 8 ac impedance measurement instrument loops
Module, 9 dc sources, 10 current measuring devices, 11 voltage measuring apparatus, 12 accumulator internal resistance instrument, 13 ac impedance measurement instrument,
14 microprocessors, 15 temperature measuring equipments, 16 measuring loops, 17 measuring loops.
Fig. 7 is the schematic diagram of DC power supply circuit, wherein 4 may be designed as double-point double-throw switch, it can be carried out by relay
Break-make is controlled, and 16 and 17 measuring loops to realize the access of 9 dc sources or break.
Fig. 8 is signal processing unit microprocessor and each part annexation block diagram, microprocessor 14 and 3,9,10,
11st, there is wire to be connected between 12,13,15, wherein by 3 control signals for receiving control terminals, receive 9,10,11,12,
13rd, 15 measurement signal, by related operation, obtains the testing result of residual capacity, testing result is transferred into control by 3
Terminal.Microprocessor 14 is by that can realize 9,10,11,12,13 distinct devices access or disconnected to 4,5,6,7,8 instructions sent
Go out 16 and 17 measuring loops.Because battery only has positive pole, two Wiring ports of negative pole, what signal processing unit was designed is and storage
Corresponding two Wiring ports of battery plus-negative plate, (relay can be used by designing signal processing unit internal each switch module
Realize the function) control so that according to measurement need different equipment is linked among measuring loop.6 are for example connected, is broken
Open 4,5,7,8, you can voltage measuring apparatus is linked into 16 and 17 measuring loops by realization.
Fig. 9 signal processing units measure the measurement terminal schematic diagram of multiple batteries simultaneously.18 switch modules, 19 are opened in figure
Pass module, 20 switch modules, 21 switch modules, 22 binding posts, 23 binding posts, 24 binding posts, 25 binding posts, 26 connect
Line terminals, 27 binding posts, 28 binding posts, 29 binding posts.By increasing a series of switch modules, it is possible to achieve disposable
Measure the residual capacity of multiple batteries.For example, microprocessor 14 is by sending control signal so that 18 connections, 19 disconnections, 20
Disconnect, 21 disconnect, it is possible to achieve be linked into measuring loop, pair measured 22,23 with 22,23 batteries being connected.
Microprocessor 14 is by sending control signal so that 19 connect, and 18 disconnect, 20 disconnect, 21 disconnect, it is possible to achieve by 24,25 accesses
Into measuring loop, pair measured with 24,25 batteries being connected.
Figure 10 is accumulator capacity BP model structure sketches, wherein BP (Back Propagation Artificial
Neural Network) input signal of artificial nerve network model is：Open-circuit voltage, ohmic internal resistance, polarization resistance, floating charging
Stream, environment temperature, output signal are residual capacity, battery capacitor.
Figure 11 is battery model storehouse weight coefficient fitting algorithm, wherein has L to train the electric power storage finished in model library
Tankage BP models.For a new battery, its open-circuit voltage, ohmic internal resistance, polarization resistance, floating current, ring are measured
Border temperature parameter, is entered into accumulator capacity BP model libraries, can obtain the battery capacitor of L BP models output, will
It carries out product with model library weight coefficient, can obtain theoretical cell electric capacity, i.e., different weight coefficients can obtain difference
Theoretical cell electric capacity, when adjustment weight coefficient so that theoretical cell electric capacity approaches measured battery electric capacity to the full extent, lead to
Crossing PSO (Particle Swarm Optimization) particle cluster algorithm can be in the hope of optimal weight coefficient.Obtain optimal
After weight coefficient, the residual capacity for obtaining battery to be measured can be fitted by model library.
Figure 12 is the schematic equivalent circuit of battery, wherein UocFor the open-circuit voltage of battery, RoFor the Europe of battery
Nurse resistance, RpFor the polarization resistance of battery, I is the floating current of battery, CpFor the battery capacitor of battery.
The working condition to the remaining battery capacity check device is described further below
Detection device involved by each measurement parameter is as follows：
Open-circuit voltage：Voltage measuring apparatus；
Ohmic internal resistance：Internal resistance instrument, ac impedance measurement instrument；
Polarization resistance：Internal resistance instrument, ac impedance measurement instrument；
Floating current：Dc source, current measuring device；
Environment temperature：Temperature measuring equipment；
Battery capacitor：Ac impedance measurement instrument, internal resistance instrument.
Reference picture 12, detection method used in each measurement parameter is as follows：
(1) open-circuit voltage
In off-line case, using the open-circuit voltage U of voltage measuring apparatus direct measurement batteryoc。
(2) floating current
In off-line case, float charge voltage U is added to battery two ends, measures the floating current I of battery.
(3) environment temperature
By temperature measuring equipment, environment temperature when measurement battery is used.
(4) ohmic internal resistance/polarization resistance
In off-line case, using internal resistance instrument, the total internal resistance R, wherein R=R of battery are measuredo+Rp(equation one).It is logical
Further calculating is crossed, the ohmic internal resistance and polarization resistance of measurement can be respectively obtained.
(5) battery capacitor
The equiva lent impedance Z of battery is measured with equipment in the case where being applied with alternating current.Twice employed in detection
Ac frequency f is respectively 10Hz and 20Hz (detection twice can also other frequencies), and alternating current is being consequently exerted at battery just
On negative the two poles of the earth.In the case where being applied with alternating current, the equiva lent impedance of battery and the electric capacity C of batterypBetween relation such as
Shown in lower：(equation two), impedance when to make f be 10Hz is Z10, impedance when f is 20Hz is Z20。
Following relation can be obtained according to equation one and equation two：
Before handling measurement data, it is necessary first to first set up an accumulator capacity BP model library offline.
Selection for accumulator capacity BP models needs to meet following two features：It is all standing type first.Different batteries individual
Dynamic model distribution it is very wide, the dynamic that a good typical model set should cover most batteries individual enough is special
Point, so can just find one group of rational combination coefficient, so as to approach actual dynamic.In fact, it should be recognized that truly
" all standing " be impossible.So, it would be desirable to some balances are done between level of coverage and Number of Models；Secondly
It is his different property between battery.In order to reduce as far as possible should have between the number of typical model, any two typical model compared with
Big difference, so as to improve the representative ability of each typical model.Polygonal end points can be obtained by linear fit in theory
To the arbitrfary point of the polygonal internal, as long as therefore set up a good model library enough, the dynamic of any newly-increased battery is special
Property can be come out by the model library linear fit.
For example model library can select 10 typical batteries, and can including three pieces of new accumulators, (capacity is qualified, its capacity etc.
In or higher than testing standard requirement capacity requirement), (capacity is unqualified, and its capacity will less than testing standard for two pieces of new accumulators
The capacity requirement asked), three pieces of old batteries (capacity is qualified, and its capacity is equal to or higher than the capacity requirement of testing standard requirement),
Two pieces of old batteries (capacity is unqualified, and its capacity is less than the capacity requirement of testing standard requirement).This 10 pieces of batteries are carried out
Complete charge and discharge electric test, measure the open-circuit voltages of these batteries, ohmic internal resistance, polarization resistance, floating current, environment temperature,
Residual capacity, battery capacitor parameter.The accumulator capacity BP models in Figure 10 can be set up by these data, wherein opening a way
Voltage, ohmic internal resistance, polarization resistance, floating current, environment temperature for input, residual capacity, battery capacitor for output, make this 10
Individual model is respectively M1 M2 … M10, therefore a model library [M can be set up1 M2 … M10], once model library has been set up
Into as long as output open circuit voltage, ohmic internal resistance, polarization resistance, floating current, environment temperature parameter, it is possible to pass through model library
Obtain corresponding residual capacity, battery capacitor parameter.
In addition, when actually detected, if the accuracy of detection to residual capacity is less demanding, being carried out to artificial neural network
During training, one in the open-circuit voltage of battery to be measured, ohmic internal resistance, polarization resistance, floating current, environment temperature can be chosen
Kind or several be trained as input.
When remaining battery capacity detection means works, the wiring of battery to be measured and field apparatus need to be first disconnected, will be treated
Survey battery with the signal processing unit of detection means to be connected, measure the open-circuit voltage, ohmic internal resistance, polarization of battery to be measured
Internal resistance, floating current, environment temperature, battery capacitor parameter (to improve the accuracy of measurement data, can continuously measure multigroup number
According to, and measurement data is handled by methods such as Kalman filterings (Kalman filtering)), it is updated to previously
BP model libraries [the M of foundation1 M2 … M10], it is possible thereby to obtain electric power storage by the accumulator capacity BP models in BP model libraries
Theoretical residual capacity [the SOC in pond1 SOC2 … SOC10], battery capacity [Cp1 Cp2 … Cp10].Each BP model is given herein
Set up a weight coefficient [w1 w2 … w10], wherein there is following relation w in this 10 weight coefficients1+w2+…+w10=1, its
Initial value may be set to [0.1 0.1 ... 0.1], with reference to Figure 11, and the different weight coefficient by adjusting can be obtained different
Theoretical cell electric capacity match valueBy the way that accumulator capacity BP model libraries are calculated into obtained battery capacitor match valueCompared with the battery capacitor of actual battery to be measured, optimization weight coefficient [w is gone by PSO optimized algorithms1 w2
… w10], calculate optimal weight coefficient [w1 w2 … w10] so that it is fitted what is obtained by accumulator capacity BP model libraries
Battery capacitor approaches measured battery electric capacity to the full extent, now thinks the battery to be measured being fitted to using the weight coefficient
BP capacity modelsThe dynamic characteristic of actual battery to be measured has been approached to the full extent, that is, can consider
Calculate what is obtainedThe closely residual capacity of actual battery to be measured.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope of this specification record is all considered to be.
Can be with one of ordinary skill in the art will appreciate that realizing that all or part of step in above-described embodiment method is
The hardware of correlation is instructed to complete by program.Described program can be stored in a computer read/write memory medium.
The program upon execution, including the step described in the above method.Described storage medium, including：ROM/RAM, magnetic disc, CD
Deng.
Embodiment described above only expresses the several embodiments of the present invention, and it describes more specific and detailed, but simultaneously
Can not therefore it be construed as limiting the scope of the patent.It should be pointed out that coming for one of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
1. a kind of detection method of remaining battery capacity, it is characterised in that comprise the following steps：
Obtain the measured value of the influence factor of the residual capacity of battery to be measured and the measurement capacitance of the battery to be measured；
The measured value of the influence factor is inputted to preset artificial nerve network model storehouse, passes through the preset ANN
Network model library obtains the multiple capacitance prediction values and multiple residual capacity predicted values of the battery to be measured, wherein, it is the multiple
Capacitance prediction value and the multiple residual capacity predicted value are corresponded；
Fitting is weighted to the multiple capacitance prediction value according to default weight coefficient, electric capacity match value is obtained；
Using the measurement capacitance as desired value, the electric capacity match value is optimized by particle cluster algorithm, optimized
Weight coefficient；
Fitting is weighted to the multiple residual capacity predicted value according to the optimization weight coefficient, the electric power storage to be measured is obtained
The remaining capacity value in pond.
2. the detection method of remaining battery capacity according to claim 1, it is characterised in that the preset artificial neuron
Network model storehouse includes multiple preset artificial nerve network models；
The measured value by the influence factor is inputted to preset artificial nerve network model storehouse, passes through the preset artificial god
The step of obtaining the multiple capacitances and multiple remaining capacity value of the battery through network model storehouse comprises the following steps：
The measured value of the influence factor is separately input into each preset artificial nerve network model, each preset artificial neuron
Network model exports a capacitance prediction value and a residual capacity predicted value.
3. the detection method of remaining battery capacity according to claim 2, it is characterised in that it is described by the influence because
The measured value of element inputs further comprising the steps of to before the step of preset artificial nerve network model storehouse：
To multiple sample batteries carry out charge and discharge electric test, obtain respectively each sample battery residual capacity sample value,
The influence factor sample value of electric capacity sample value and residual capacity；
Set up and the one-to-one multiple artificial nerve network models of each sample battery, for any one artificial neuron
Network model, using the residual capacity sample value and electric capacity sample value of corresponding sample battery as output, is stored with corresponding sample
The influence factor sample value of the residual capacity of battery is input, and the artificial nerve network model is trained, and is obtained described pre-
Put artificial nerve network model.
4. the detection method of remaining battery capacity according to claim 3, it is characterised in that the multiple sample electric power storage
Pond includes remaining capacity value and is equal to or higher than the sample battery and remaining capacity value of predetermined threshold value less than the predetermined threshold value
Sample battery.
5. the detection method of remaining battery capacity according to claim 1, it is characterised in that the acquisition electric power storage to be measured
The step of measurement capacitance of the measured value of the influence factor of the residual capacity in pond and the battery to be measured, comprises the following steps：
The battery to be measured is taken multiple measurements, the influence factor for obtaining multigroup residual capacity of battery to be measured is initially surveyed
Value and multigroup electric capacity initial measurement, and at the beginning of the influence factor initial measurement and multigroup electric capacity of multigroup residual capacity
Beginning measured value carries out Kalman filtering, obtains the measured value of the influence factor of the residual capacity of the battery to be measured and described treats
Survey the measurement capacitance of battery.
6. the detection method of remaining battery capacity according to claim 1, it is characterised in that the influence factor includes
Open-circuit voltage, ohmic internal resistance, polarization resistance, floating current or environment temperature.
7. the detection method of remaining battery capacity according to claim 6, it is characterised in that the acquisition electric power storage to be measured
The step of measurement capacitance of the measured value of the influence factor of the residual capacity in pond and the battery to be measured, comprises the following steps：
The open-circuit voltage of the battery to be measured is measured by voltage measuring apparatus, is surveyed by dc source and current measuring device
The floating current of the battery to be measured is measured, the environment temperature of the battery to be measured is measured by temperature measuring equipment, is passed through
Internal resistance instrument measures total internal resistance of the battery to be measured, and the equivalent resistance of the battery to be measured is measured by ac impedance measurement instrument
It is anti-；
Ohmic internal resistance, polarization resistance and the electric capacity of the battery to be measured are calculated according to total internal resistance and the equiva lent impedance.
8. a kind of detection means of remaining battery capacity, it is characterised in that including dc source, current measuring device, voltage
Measurement apparatus, internal resistance instrument, ac impedance measurement instrument, temperature measuring equipment and microprocessor；
The detection means possesses two the first binding posts, is connected after the dc source and current measuring device series connection
Between two first binding posts, the voltage measuring apparatus, the internal resistance instrument, the ac impedance measurement instrument are in parallel
Between two first binding posts；Two first binding posts are used to connect the positive and negative of battery to be measured respectively
Pole；
The microprocessor respectively with the dc source, the current measuring device, the voltage measuring apparatus, the internal resistance
Instrument, the ac impedance measurement instrument, temperature measuring equipment connection；
The microprocessor receives the dc source, the current measuring device, the voltage measuring apparatus, the internal resistance
The measurement data of instrument, the ac impedance measurement instrument and the temperature measuring equipment, is treated according to being obtained the measurement data
Survey the measured value and the measurement capacitance of the battery to be measured of the influence factor of the residual capacity of battery；By the influence because
The measured value of element is inputted to preset artificial nerve network model storehouse, obtains described by the preset artificial nerve network model storehouse
The multiple capacitance prediction values and multiple residual capacity predicted values of battery to be measured, wherein, the multiple capacitance prediction value and described
Multiple residual capacity predicted values are corresponded；Fitting is weighted to the multiple capacitance prediction value according to default weight coefficient,
Obtain electric capacity match value；Using the measurement capacitance as desired value, the electric capacity match value is carried out by particle cluster algorithm excellent
Change, obtain optimization weight coefficient；Fitting is weighted to the multiple residual capacity predicted value according to the optimization weight coefficient,
Obtain the remaining capacity value of the battery to be measured.
9. the detection means of remaining battery capacity according to claim 8, it is characterised in that the microprocessor difference
Connect the switch module and data module, the switch module of the current measuring device and data module, institute of the dc source
State the switch module of voltage measuring apparatus and data module, the switch module of the internal resistance instrument and data module, described exchange resistance
The switch module and data module of anti-measuring instrument；
The detection means is also equipped with multigroup second binding post and multiple pull-switch modules, and the binding post of each group second is wrapped
Two the second binding posts are included, the binding post of each group second passes through corresponding pull-switch module and two first terminals
Son connection, all pull-switch modules are connected with the microprocessor, and the binding post of each group second is used to connect different respectively
The both positive and negative polarity of battery to be measured.
10. the detection means of remaining battery capacity according to claim 8, it is characterised in that also including with it is described micro-
The control terminal of processor connection, for sending the detection control signal of remaining battery capacity to the microprocessor, and
Receive the testing result for the remaining battery capacity that the microprocessor is returned.
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