CN107831443A - Battery system short trouble diagnostic method based on coefficient correlation - Google Patents
Battery system short trouble diagnostic method based on coefficient correlation Download PDFInfo
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
Abstract
The present invention proposes a kind of battery system short trouble diagnostic method based on coefficient correlation.This method uses the mathematical property of coefficient correlation, and eliminate battery cell equilibrium state and degree of aging difference influences to caused by fault diagnosis result, so as to accurately and reliably identify the voltage pulsation of short trouble early stage.This method only needs real-time voltage monomer voltage to measure, therefore does not need battery model, so as to save a large amount of modeling works early stage.In actual applications, it is necessary to a cycle signal be added in all voltage signals of collection, to ensure that this method will not send false alarm in battery standing.The position of fail battery can be judged the position of battery according to involved by abnormal coefficient correlation.
Description
Technical field：
The present invention relates to a kind of battery system short trouble diagnostic method based on coefficient correlation.
Background technology：
With extensive use of the lithium-ion battery systems in electric automobile, associated security incident takes place frequently, and it is pacified
Full property becomes the key factor for restricting electric automobile extensive development.In the safety evaluation of battery system, battery management
System serves vital effect.Electric automobile battery management system (battery management system,
BMS) cell voltage, temperature and electric current are sampled, and battery is obtained from gathered master data by respective algorithms
Important process state, include the nuclear charge state (state of charge, SOC) of battery, health status (state of
Health, SOH), power rating, equilibrium state, and safe condition.Wherein, the security of battery system not only concerns electronic
The reliability of automobilism, the security of the lives and property of user is more directly concerned, therefore, the on-line fault diagnosis of battery system are electricity
The important core of pond management system.The on-line fault diagnosis of battery system need to detect system exception in failure early period of origination,
To failure modes and fault location.Common electric fault includes：Overcharge, cross put, internal short-circuit and external short circuit.
In above-mentioned electric fault, described two short troubles due to its short time it is highly exothermic the characteristics of, become in the weight of fault diagnosis
Weight.
At present, conventional battery system short trouble diagnostic method can be divided into following three class：1. the diagnosis side based on threshold value
Method, mainly by being contrasted to cell voltage, electric current and temperature and the battery system operation limit, if institute's collection capacity exceedes
Working limit, then send safety alarm；2. the diagnostic method based on battery model, mainly by establishing complete, accurate battery
Model, input, the inside battery parameter or voltage output of synchronization are made prediction, and it is entered with reference to the electric current of battery
Row Closed loop track, amendment.If the parameter predicted there are larger remnants with actually measured parameter, safety alarm is sent；3. base
In the diagnostic method of battery cell voltage difference, when the voltage differences of cell are excessive, safety alarm is sent.Wherein, method
1. because its realization is simple, reliable operation, it is widely used among the fault diagnosis of battery system.However, because battery is short
The generally obstructed over-current sensor in the loop on road, current information missing；Short-circuit loop resistance value be insufficient to it is small in the case of, electricity
Pressure is no more than working limit；At failure initial stage, battery temperature rise unobvious, it is difficult to accurately caught by limited temperature sensor, because
This, there is significant deficiency in this method in the diagnosis of short trouble.2. method to input because that takes into account the electric current of battery, successfully
Eliminate drawbacks described above.But this method needs substantial amounts of previous work, including data platform foundation, data acquisition, data point
Analysis and model checking.Also, when the accuracy of model is not high enough to robustness, this method easily sends false alarm, influences
The normal operation of electric automobile.3. method is mainly used in the assessment of battery cell equilibrium state, but can also be applied to short circuit
Fault diagnosis, and excellent performance when the uniformity of monomer is high in battery system.However, as battery cell is in electric automobile
Aging in, great changes have taken place for the open-circuit voltage (open circuit voltage, OCV) of battery and internal resistance, battery
Output voltage larger difference in the case of normal work be present, so as to again result in false alarm.
The content of the invention：
In order to solve the above problems, the present invention proposes a kind of battery system short trouble diagnosis side based on coefficient correlation
Method, this method by the calculating to the coefficient correlation between battery cell voltage, dexterously evaded battery cell in SOC and
Inconsistency in SOH has reached quick, accurate, reliable short trouble diagnosis to the influence caused by short trouble.
The present invention adopts the following technical scheme that：A kind of battery system short trouble diagnostic method based on coefficient correlation, bag
Include following steps：
S1. the voltage of series-connected cell in battery system is gathered；
S2. designed periodic signal is added in the voltage gathered；
S3. the mobile coefficient correlation of adjacent numbering battery, including head and the tail battery are calculated；
S4. compare and try to achieve mobile coefficient correlation, and compared with predetermined threshold value, be such as less than threshold value, then send alarm；
S5. by analyzing the numbering of abnormal coefficient correlation, the position of failure judgement battery, alarm of being concurrently out of order.
Further, in the step S1, the collection of series-connected cell is completed and recorded by vehicle-mounted BMS.
Further, in the step S2, the periodic signal added can avoid phase relation between battery cell voltage
Number is zero theoretical limitation in battery standing, and the limitation is drawn by analysis below：
Two signals X, Y coefficient correlation are represented by
In formula, ziIt is Z signals in the sampling at i moment, μzFor the average value of Z signals, n is the number of samples of Z signals, related
The span of coefficient is [- 1,1], wherein 1 represents two signal perfect positive correlations, 0 represents that two signals are completely uncorrelated, -1
Represent two signal perfect negative correlations, between two signals the calculating of coefficient correlation following property can be pushed away to obtain by formula (1)：
rαX+β,Y=rX,Y (2)
In formula, α and β are constant.
Further, when adding separate Gaussian noise on signal X and Y respectively, formula (1) can be derived as：
In formula, N and M are separate Gaussian noise, and when battery is in static condition, battery cell voltage is equal to it
OCV, so as to which Section 1 is zero in formula (3) molecule, simultaneously as N and M are separate, Section 2 is zero in formula (3) molecule,
In this case, battery cell does not break down, direct coefficient correlation computing can cause false alarm；
Solution for this kind of false alarm is that identical periodic signal is separately added into measured voltage readings,
Concrete principle is derived by following formula：
In formula, A is the periodic signal that is added,For signal Z variance, when two batteries monomers are under static condition,
AndWhen, its coefficient correlation is equal to 1.
Further, in the step 3, mobile coefficient correlation is the coefficient correlation of the data in moving window, and its is specific
Computational methods derive from the varying type of formula (1)：
And it is rewritten as recursive form：
In formula, w is moving window size.
Further, in the step 4, according to the change of mobile coefficient correlation, anomalous variation is marked according to threshold method
Coefficient correlation, and send alarm.
Further, in the step 5, the position of battery cell corresponding to the coefficient correlation of anomalous variation, locking are passed through
Fail battery
The present invention has the advantages that：Battery system short trouble based on coefficient correlation proposed by the invention is examined
Disconnected method, in the case where battery pack has inconsistency, diagnoses and positions failure accurately and in time.
Brief description of the drawings：
Fig. 1 is the short trouble diagnostic method flow chart based on coefficient correlation.
Fig. 2 is that mobile coefficient correlation calculates schematic diagram.
Fig. 3 is experimental facilities connection figure.
Experimental data (a) monomer voltage in Fig. 4；(b) monomer voltage trouble point enlarged drawing；(c) monomer temperature.
Fig. 5 is the mobile coefficient correlation result of calculation of adjacent cell.
Fig. 6 is influence of the added square wave to mobile coefficient correlation result of calculation.
Embodiment：
The present invention is further illustrated below in conjunction with the accompanying drawings.
Battery system short trouble diagnostic method of the invention based on coefficient correlation, comprises the following steps：
S1. the voltage of series-connected cell (module) in battery system is gathered.
S2. designed periodic signal is added in the voltage gathered.
S3. the mobile coefficient correlation of adjacent numbering battery (module), including head and the tail battery (module) are calculated.
S4. compare and try to achieve mobile coefficient correlation, and compared with predetermined threshold value.Such as it is less than threshold value, then sends alarm.
S5. by analyzing the numbering of abnormal coefficient correlation, the position of failure judgement battery, alarm of being concurrently out of order.
It is embodied flow chart and sees Fig. 1.
Wherein in step 1, the collection of series-connected cell is completed and recorded by vehicle-mounted BMS.
Wherein in step 2, coefficient correlation is in battery between the periodic signal that is added helps avoid battery cell voltage
It is zero theoretical limitation during standing.The limitation can be drawn by analysis below.
Two signals X, Y coefficient correlation are represented by：
In formula, ziIt is Z signals in the sampling at i moment, μzFor the average value of Z signals, n is the number of samples of Z signals.It is related
The span of coefficient is [- 1,1], wherein 1 represents two signal perfect positive correlations, 0 represents that two signals are completely uncorrelated, -1
Represent two signal perfect negative correlations.The calculating of coefficient correlation can be pushed away to obtain following property by formula (1) between two signals：
rαX+β,Y=rX,Y (2)
In formula, α and β are constant.The formula is it is understood that when the either signal in two signals involved by coefficient correlation
Amplitude changes, or when being added any biasing, coefficient correlation is constant.As can be seen here, what coefficient correlation was assessed is two signals
The similarity of variation tendency, rather than the difference of its concrete numerical value.The property is that solve battery cell inconsistency to short-circuit event
The ideal tools that barrier diagnosis influences, because the inconsistency of monomer is concentrated mainly on battery balanced state and electricity in battery system
The difference of pond ageing state.The difference of both states can be characterized by SOC and SOH difference, and finally specifically be embodied respectively
The difference of biasing and change amplitude between battery cell voltage.When the two factors are excluded by the calculating of coefficient correlation
Afterwards, the result of fault diagnosis is influenceed inconsistency no longer between by monomer.
Ideally, when calculating the coefficient correlation of series-connected cell monomer of two section normal works, the value should be close to 1.
But when short trouble occurs for wherein one section monomer, the value will deviate from 1, so as to provide foundation for fault diagnosis.
When adding separate Gaussian noise on signal X and Y respectively, formula (1) can be derived as：
In formula, N and M are separate Gaussian noise.When battery is in static condition, battery cell voltage is equal to it
OCV, so as to which Section 1 is zero in formula (3) molecule.Simultaneously as N and M are separate, Section 2 is zero in formula (3) molecule.Cause
This, under this special case, even if battery cell does not break down, direct coefficient correlation computing can cause false alarm.
Solution for this kind of false alarm is that identical periodic signal is separately added into measured voltage readings.
Concrete principle is derived by following formula：
In formula, A is the periodic signal that is added,For signal Z variance.As shown in formula (4), when two batteries monomers exist
Under static condition, andWhen, its coefficient correlation is equal to 1, so as to avoid this method in such case
The decline of coefficient correlation caused by lower.
In the step 3, mobile coefficient correlation is the coefficient correlation of the data in moving window.Its circular comes
Come from the varying type of formula (1)：
And it is rewritten as recursive form：
In formula, w is moving window size.Step 3 requires to calculate the mobile coefficient correlation of adjacent monomer, including head and the tail battery
Mobile coefficient correlation, as shown in Figure 2.
In step 4, according to the change of mobile coefficient correlation, the coefficient correlation of anomalous variation is marked according to threshold method, concurrently
Go out alarm.
In step 5, by the position of battery cell corresponding to the coefficient correlation of anomalous variation, fail battery is locked.
1. voltage sample
As shown in figure 3, four section series connection ferric phosphate lithium cells are in parallel with dc source, DC load.A set of BMS systems are used for
Gather monomer voltage, temperature and the electric current of four batteries.At the same time, main frame preserves gathered data by dSPACE, and
Control dc source and DC load charge and discharge to battery pack, are made with simulating the normal of the battery in true road conditions
With.Operating mode of simulating in experiment is UDDS (urban dynamometer driving schedule) operating mode, the sampling period
For 0.1s.In normal battery operation, laboratory technician is short by the 4th batteries with the wire that a resistance is 0.36 Ω in 42.4s
Road, short circuit duration 1.3s, the phenomenon at initial stage of battery internal short-circuit under truth is simulated with this.Institute's collection voltages and temperature are as schemed
Shown in 4.
2. add periodic signal
Formula (4) requires that the variance of added periodic signal is much larger than the variance of measurement noise.In this experiment, measurement is made an uproar
The standard deviation of sound is 1mV, and it is 0 that the periodic signal added, which elects average value as, amplitude 3mV, and the cycle is the side in 2 sampling periods
Ripple.The periodic signal variance is 9 times of noise variance, and therefore, in the ideal case, in battery standing, it moves phase relation
Number should be 0.9.
3. calculate the mobile coefficient correlation of adjacent cell
In the calculating of this experiment, the size of moving window is 30 sampling periods.The mobile coefficient correlation of adjacent cell
Drawn by formula (6).Specific result of calculation is as shown in Figure 5.
4. the abnormal mobile coefficient correlation of mark
As shown in figure 5, r (3,4) and r (4,1) significantly decreases, should be labeled as abnormal.
5. position fail battery
Battery according to associated by coefficient correlation, it can be deduced that battery cell #4 is fail battery, because two appearance are different
Normal coefficient correlation is associated therewith.Its immediate cause is that short circuit occurs for monomer #4 as monomer #3 and monomer #1 normal works
Failure, and the decline of voltage is embodied in, make its voltage different from monomer #3 and monomer #1 voltage change trend, so as to cause it
Decline suddenly with monomer #3 and monomer #1 mobile coefficient correlation.
6. analysis of experimental results
Fig. 6 compared for influence of the added square wave to mobile coefficient correlation result of calculation.Can from monomer #3 voltage
Go out, in the standing stage that experiment starts, do not add monomer #3 and monomer the #4 mobile coefficient correlation of square wave close to 0, until
Gradually recover after having follow current input.As can be seen here, the periodic signal added in the present invention is to method of the present invention
Normal work serve vital effect.
In Fig. 4 (b), monomer #4 voltage declines suddenly in 42.4s, but the not below electric discharge of ferric phosphate lithium cell
Lower limit 2.5V, so short trouble can not be judged by voltage threshold method.Visible monomer #4 temperature is more other in Fig. 4 (c)
Monomer has rising, but it is only 0.3 DEG C to rise, and is difficult to catch in actual applications, so the short-circuit diagnostic method of temperature threshold
Failure.Described according to experiment, caused by wire of the short circuit by outside, so the failure is unobservable in current measurement.Cause
This, based on the diagnostic method of threshold value in the failure it is entirely ineffective.
In monomer voltage shown in Fig. 4 (a), monomer #2 voltage and the voltage of other monomers have obvious biasing, bias
For 22mV.In the application of actual electric car, the biasing can cause the false alarm of the short-circuit diagnostic method based on cell voltage difference.Together
When, because the short-circuit diagnostic method operand based on model is big, it is impossible to track the parameter of each battery cell, therefore this method exists
Also false alarm can be sent under the biasing.
, inconsistency be present in battery pack in the short trouble method based on coefficient correlation proposed by the invention
Under, diagnose accurately and in time and position failure.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, some improvement can also be made under the premise without departing from the principles of the invention, and these improvement also should be regarded as the present invention's
Protection domain.
Claims (7)
- A kind of 1. battery system short trouble diagnostic method based on coefficient correlation, it is characterised in that：Comprise the following stepsS1. the voltage of series-connected cell in battery system is gathered；S2. designed periodic signal is added in the voltage gathered；S3. the mobile coefficient correlation of adjacent numbering battery, including head and the tail battery are calculated；S4. compare and try to achieve mobile coefficient correlation, and compared with predetermined threshold value, be such as less than threshold value, then send alarm；S5. by analyzing the numbering of abnormal coefficient correlation, the position of failure judgement battery, alarm of being concurrently out of order.
- 2. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 1, it is characterised in that：It is described In step S1, the collection of series-connected cell is completed and recorded by vehicle-mounted BMS.
- 3. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 1, it is characterised in that：It is described In step S2, the periodic signal that is added can avoid the reason that coefficient correlation is zero in battery standing between battery cell voltage By limitation, the limitation is drawn by analysis below：Two signals X, Y coefficient correlation are represented by<mrow> <msub> <mi>r</mi> <mrow> <mi>X</mi> <mo>,</mo> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>In formula, ziIt is Z signals in the sampling at i moment, μzFor the average value of Z signals, n is the number of samples of Z signals, coefficient correlation Span be [- 1,1], wherein 1 represent two signal perfect positive correlations, 0 represent two signals it is completely uncorrelated, -1 represent Two signal perfect negative correlations, between two signals the calculating of coefficient correlation following property can be pushed away to obtain by formula (1)：rαX+β,Y=rX,Y (2)In formula, α and β are constant.
- 4. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 3, it is characterised in that：When point Not when adding separate Gaussian noise on signal X and Y, formula (1) can be derived as：<mrow> <msub> <mi>r</mi> <mrow> <mi>X</mi> <mo>+</mo> <mi>N</mi> <mo>,</mo> <mi>Y</mi> <mo>+</mo> <mi>M</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>M</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>M</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&mu;</mi> <mi>M</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>In formula, N and M are separate Gaussian noise, and when battery is in static condition, battery cell voltage is equal to its OCV, So as to which Section 1 is zero in formula (3) molecule, simultaneously as N and M are separate, Section 2 is zero in formula (3) molecule, in this feelings Under condition, battery cell does not break down, and direct coefficient correlation computing can cause false alarm；Solution for this kind of false alarm is that identical periodic signal is separately added into measured voltage readings, specifically Principle is derived by following formula：<mrow> <msub> <mi>r</mi> <mrow> <mi>X</mi> <mo>+</mo> <mi>N</mi> <mo>+</mo> <mi>A</mi> <mo>,</mo> <mi>Y</mi> <mo>+</mo> <mi>M</mi> <mo>+</mo> <mi>A</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msubsup> <mi>A</mi> <mi>i</mi> <mn>2</mn> </msubsup> </mrow> <mrow> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <msub> <mi>A</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <msub> <mi>N</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <msub> <mi>A</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <msub> <mi>M</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>=</mo> <mfrac> <msubsup> <mi>&sigma;</mi> <mi>A</mi> <mn>2</mn> </msubsup> <mrow> <msqrt> <mrow> <msubsup> <mi>&sigma;</mi> <mi>A</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>&sigma;</mi> <mi>N</mi> <mn>2</mn> </msubsup> </mrow> </msqrt> <msqrt> <mrow> <msubsup> <mi>&sigma;</mi> <mi>A</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>&sigma;</mi> <mi>M</mi> <mn>2</mn> </msubsup> </mrow> </msqrt> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>In formula, A is the periodic signal that is added,For signal Z variance, when two batteries monomers are under static condition, andWhen, its coefficient correlation is equal to 1.
- 5. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 1, it is characterised in that：In the step 3, mobile coefficient correlation is the coefficient correlation of the data in moving window, and its circular derives from The varying type of formula (1)：<mrow> <msub> <mi>r</mi> <mrow> <mi>X</mi> <mo>,</mo> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>n</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msqrt> <mrow> <mi>n</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msubsup> <mi>x</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <mi>n</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msubsup> <mi>y</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>And it is rewritten as recursive form：<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>P</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>Q</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>Q</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>R</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>R</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>S</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msubsup> <mi>y</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msubsup> <mi>y</mi> <mrow> <mi>i</mi> <mo>-</mo> <mi>w</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mrow> <mo>(</mo> <msub> <mi>r</mi> <mrow> <mi>X</mi> <mo>,</mo> <mi>Y</mi> </mrow> </msub> <mo>)</mo> </mrow> <mi>k</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>wP</mi> <mi>k</mi> </msub> <mo>-</mo> <msub> <mi>Q</mi> <mi>k</mi> </msub> <msub> <mi>R</mi> <mi>k</mi> </msub> </mrow> <mrow> <msqrt> <mrow> <msub> <mi>wS</mi> <mi>k</mi> </msub> <mo>-</mo> <msubsup> <mi>Q</mi> <mi>k</mi> <mn>2</mn> </msubsup> </mrow> </msqrt> <msqrt> <mrow> <msub> <mi>wT</mi> <mi>k</mi> </msub> <mo>-</mo> <msubsup> <mi>R</mi> <mi>k</mi> <mn>2</mn> </msubsup> </mrow> </msqrt> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>In formula, w is moving window size.
- 6. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 1, it is characterised in that：It is described In step 4, according to the change of mobile coefficient correlation, the coefficient correlation of anomalous variation is marked according to threshold method, and send alarm.
- 7. the battery system short trouble diagnostic method based on coefficient correlation as claimed in claim 1, it is characterised in that：It is described In step 5, by the position of battery cell corresponding to the coefficient correlation of anomalous variation, fail battery is locked.
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