CN108226693A - Method and apparatus for detecting short circuit in battery in real time, and computer-readable storage medium - Google Patents
Method and apparatus for detecting short circuit in battery in real time, and computer-readable storage medium Download PDFInfo
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- CN108226693A CN108226693A CN201711364956.XA CN201711364956A CN108226693A CN 108226693 A CN108226693 A CN 108226693A CN 201711364956 A CN201711364956 A CN 201711364956A CN 108226693 A CN108226693 A CN 108226693A
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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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
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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 invention discloses a real-time battery internal short circuit detection method, a real-time battery internal short circuit detection device and a computer readable storage medium. The real-time battery internal short circuit detection method comprises the following steps: s10, acquiring battery temperature data and battery current data of the battery; s20, calculating a battery standard value according to the battery temperature data and the battery current data; s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value; and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value. The real-time battery internal short circuit detection method can obtain a thermal parameter comparison value according to a battery thermal parameter identification standard formula, judge whether the battery generates heat abnormally or not according to the comparison value in real time, and is accurate and efficient.
Description
Technical field
The present invention relates to battery detecting field, more particularly to a kind of real-time battery internal short-circuit detection method, real-time battery
Internal short-circuit detection device and computer readable storage medium.
Background technology
Traditional lithium ion battery is as Vehicular dynamic battery in use, may job failure or safety problem.Wherein
One common safety problem, that is, battery internal short-circuit.The internal short-circuit of lithium-ion-power cell is generally referred to due in power battery
The phenomenon that portion's generation current loop, the abnormal electric discharge of initiation and abnormal heat production.The abnormal heat production of internal short-circuit may lead to power
There is a situation where thermal runaway, on fire, explosion etc. are dangerous for battery.
Therefore, power battery internal short-circuit must obtain effective prevention and control.Method the most direct is exactly internal short-circuit detection.It passes
The battery internal short-circuit detection method of the power of system can only be detected the battery under off working state, and can not be to " after manufacture
The situation of Car Battery installation " and the driving operating mode of " have external load/have electric current output " are accurately detected.
Invention content
Based on this, it is necessary to be asked for what traditional battery internal short-circuit detection method can not be detected for driving operating mode
Topic, provides a kind of real-time battery internal short-circuit detection method, real-time battery internal short-circuit detection device and computer readable storage medium.
A kind of real-time battery internal short-circuit detection method, including step：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
The battery standard value includes battery average temperature value and battery maximum temperature value in one of the embodiments,
The step S20 includes：
S210 takes the average acquisition battery average temperature value T according to the battery temperature dataavg；
S220 chooses the maximum value in the battery temperature data as the battery maximum temperature value Tmax。
The battery thermal parameter includes the equivalent heat production internal resistance parameter of battery in one of the embodiments, and battery Entropy Changes produces
Thermal parameter, the step S30 include：
S310 obtains Cell Experimentation An temperature data, Cell Experimentation An current data；
S320 according to the battery temperature experimental data and the Cell Experimentation An current data of battery, is produced according to battery
Thermal model calculates the battery thermal parameter and distinguishes normal formula, and the battery heat production model is：
Wherein, M is battery quality, unit kg；CpFor battery specific heat capacity, unit Jkg-1·K-1；For battery temperature
Derivatives of the T to the time is spent, unit is DEG C s-1；H is battery to the average heat transfer coefficient of environment, unit Wm2·K-1；A is
The average heat dissipation area of battery, unit m2；T is battery temperature value, and unit is DEG C；T∞For environment temperature, unit is DEG C；I is
Cell current value, unit A；RΩRepresent the equivalent heat production internal resistance parameter of the battery, unit Ω；TKTo be scaled Kelvin
Battery temperature, unit K, TK=T+273.15；UTRepresent the battery Entropy Changes heat production parameter, unit VK-1。
In one of the embodiments, before the S320 steps, further include：
S311 based on the battery heat production model, carries out the battery temperature experimental data, Cell Experimentation An current data
Noise reduction process.
The method of the noise reduction process includes the recursive filtering method with forgetting factor in one of the embodiments,.
The battery temperature experimental data includes the battery temperature of time acquisition at equal intervals in one of the embodiments,
Angle value T, the Cell Experimentation An current data include the cell current value I of time acquisition at equal intervals.
The thermal parameter fiducial value includes battery average equivalent heat production internal resistance thermal parameter in one of the embodiments,
RΩ,avg, battery mean entropy sell of one's property thermal parameter UT, the worst equivalent heat production internal resistance thermal parameter R of batteryΩ,maxWith the worst Entropy Changes heat production of battery
Parameter UT,max, further included after the step S320：
Step S330, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to the electricity
The equivalent heat production internal resistance parameter R in pondΩ, the battery average equivalent heat production internal resistance thermal parameter R is calculated respectivelyΩ,avgAnd the battery
Worst equivalent heat production internal resistance thermal parameter RΩ,max；
Step S340, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to the electricity
Pond Entropy Changes heat production parameter UT, the battery mean entropy is calculated respectively sells of one's property thermal parameter UT,avg, the worst Entropy Changes heat production ginseng of battery
Number UT,max。
The step S40 includes in one of the embodiments,：
S410 obtains abnormal heat production factor Y by the fiducial valueT；
S420 is by the normal heat production factor YTWith predetermined threshold value ΛTCompare to determine the whether abnormal heat production of the battery pack.
The abnormal heat production factor Y in one of the embodiments,TPass throughIt obtains.
The predetermined threshold value Λ in one of the embodiments,TIncluding at least the first outlier threshold ΛT1With the second abnormal threshold
Value ΛT2, the second outlier threshold ΛT2Abnormal rank be more than the first outlier threshold ΛT1Abnormal rank.
A kind of real-time battery internal short-circuit detection device, including real-time battery internal short-circuit detection device and computer, falls into a trap
The computer program that calculation machine includes memory, processor and storage on a memory and can run on a processor, the processing
Device uses real-time battery internal short-circuit detection method when performing the computer program, the method includes：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
A kind of computer readable storage medium, is stored thereon with computer program, can be used when which is executed by processor
In perform the method the step of.
Real-time battery internal short-circuit detection method provided by the invention includes step：S10 obtains the battery temperature number of battery
According to, battery current data；S20 calculates battery standard value by the battery temperature data, the battery current data；S30,
According to the battery standard value, distinguish that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；S40, according to the physochlaina infudibularis
Number fiducial value judges the whether abnormal heat production of the battery.The real-time battery internal short-circuit detection method can be according to battery thermal parameter
Distinguish that normal formula obtains thermal parameter fiducial value, and judges the whether abnormal heat production of the battery, precise and high efficiency by comparing value in real time.
Description of the drawings
Fig. 1 is the flow chart of real-time battery internal short-circuit detection method provided in an embodiment of the present invention；
Fig. 2 is the design sketch that real-time battery internal short-circuit detection method provided in an embodiment of the present invention carries out recursive filtering；
Fig. 3 is reduced and temperature anomaly elevated view extremely for internal short-circuit cell voltage provided in an embodiment of the present invention；
Fig. 4 is the battery thermal parameter identification result schematic diagram provided in an embodiment of the present invention based on heat production model；
Fig. 5 is Outlier factor Y provided in an embodiment of the present inventionTIt is classified schematic diagram；
Fig. 6 is real-time battery internal short-circuit structure of the detecting device figure provided in an embodiment of the present invention；
Reference sign：
Real-time battery internal short-circuit detection device 10, real-time battery internal short-circuit detection device 11, computer 12, memory 100,
Processor 200, computer program 300
Specific embodiment
In order to which the goal of the invention, technical solution and the technique effect that make the present invention are more clearly understood, below in conjunction with attached drawing pair
Specific embodiments of the present invention are described.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention,
It is not intended to limit the present invention.
Refer to Fig. 1, the embodiment of the present invention provides a kind of real-time battery internal short-circuit detection method, short in the real-time battery
Road detection method includes step：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
In step slo, the battery temperature data, the battery current data can be time interior acquisition at equal intervals
Battery temperature data information, battery current data in driving.The battery management system of vehicle can control multiple batteries
The battery pack of control.The battery temperature data and the battery current data can be acquired by battery management system.It is described
Vehicular battery management system can acquire the battery temperature data of each battery, the battery current number in the battery pack
According to.
In step S20, the battery standard value can be by the battery temperature data, the battery current number
According to calculation processing obtain can reflect the battery temperature data, the battery current data global feature numerical value.At it
In middle one embodiment, the battery standard value can be by the battery temperature data, the battery current data meter
The temperature averages of obtained battery, the current average of battery.The battery standard value is alternatively by the battery
Median battery temperature value that temperature data, the battery current data are calculated, median cell current value etc..
In step 30, the battery thermal parameter can be the thermal parameter in battery heat production model.The battery thermal parameter
Can be multiple.The battery thermal parameter distinguishes that normal formula can be obtained by the battery heat production model.By the battery mark
Quasi- value brings the battery thermal parameter into and distinguishes that the thermal parameter fiducial value can be calculated in normal formula.
In step s 40, calculation processing can be carried out to the thermal parameter fiducial value to obtain judging battery operating mode
Result of calculation.The whether abnormal heat production of the battery can be judged according to the result of calculation.It in one of the embodiments, can be with
By by the result of calculation compared with default empirical value, so as to judge the whether abnormal heat production of the battery.Further, it is described
Default empirical value can be multiple empirical values with different danger classes.
The present invention can obtain the battery temperature data of battery, battery current data in real time；It is in real time obtained by described
Battery temperature data, the battery current data obtained in real time calculate battery standard value in real time；According to the battery standard value,
Distinguish that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；So as to judge the battery according to the thermal parameter fiducial value
Whether abnormal heat production.The real-time battery internal short-circuit detection method can detect the heat production of battery in real time during vehicle driving
Situation, and judge whether battery has the situation of internal short-circuit according to the heat production situation of battery.The real-time battery internal short-circuit detection side
Method provides effective scheme for the internal short-circuit fault detect under power battery driving operating mode.
The battery standard value includes battery average temperature value and battery maximum temperature value in one of the embodiments,.
The step S20 includes：
S210 takes the average acquisition battery average temperature value T according to the battery temperature dataavg；
S220 chooses the maximum value in the battery temperature data as the battery maximum temperature value Tmax。
The battery average temperature value TavgCan be：
Wherein TiCan be that n times are acquired altogether for the current value of single battery ith acquisition in interval time.
The battery maximum temperature value TmaxCan be：
Wherein, N be for single battery collecting temperature value number, TiTemperature value for ith acquisition.
The battery thermal parameter includes the equivalent heat production internal resistance parameter of battery in one of the embodiments, and battery Entropy Changes produces
Thermal parameter, the step S30 include：
S310 obtains battery temperature experimental data, Cell Experimentation An current data；
S320 according to the battery temperature experimental data and the Cell Experimentation An current data of battery, is produced according to battery
Thermal model calculates the battery thermal parameter and distinguishes normal formula, and the battery heat production model is：
Wherein, M is battery quality, unit kg；CpFor battery specific heat capacity, unit Jkg-1·K-1；For battery temperature
Derivatives of the T to the time is spent, unit is DEG C s-1；H is battery to the average heat transfer coefficient of environment, unit Wm2·K-1；A is
The average heat dissipation area of battery, unit m2；T is battery temperature value, and unit is DEG C；T∞For environment temperature, unit is DEG C；I is
Cell current value, unit A；RΩRepresent the equivalent heat production internal resistance parameter of the battery, unit Ω；TKTo be scaled Kelvin
Battery temperature, unit K, TK=T+273.15；UTRepresent the battery Entropy Changes heat production parameter, unit VK-1。
Parameter identification method based on the battery heat production model meets formula (16)-(25).
Formula (16) is the fundamental formular of the parameter identification method based on model, and wherein z represents observed quantity, according to filtered
Model formation (15), in the problem of present invention, z meets formula (17)；Represent signal input quantity,It is a column vector, has
Two componentsWithI.e.Reference formula (15),Meet formula (18),Meet formula (19)；θ represents to be identified
Battery thermal parameter, θ also have there are two component θ1And θ2, i.e. θ=[θ1,θ2]T, with reference to formula (15), θ1Meet formula (20), θ2Meet formula
(21)。
θ1=RΩ (20)
θ2=UT (21)
K moment corresponding physical quantity, such as z are represented using subscript kkRepresent the observed quantity at k moment,Represent the k moment
Signal observed quantity, θkRepresent the parameter identification result at k moment.zk,And θkMeet formula (22).
But in fact, due to θkIt is to be obtained by parameter identification, can only be obtained by the estimates of parameters of last moment
θk-1And zkEstimated value zk *：
Define the evaluated error ε at k momentkFor：
Then k moment parameter θskRecurrence identification equation be：
Wherein Pkθ is recognized to be used for recurrencekSecond-order matrix.PkIt can be obtained by steepest descent method, be definite value positive definite pair
Angle symmetrical matrix.It can also be obtained by recurrent least square method.
Pass through the equivalent heat production internal resistance parameter R of the batteryΩWith the battery Entropy Changes heat production parameter UTIt can reflect battery sheet
Matter heat production information.Pass through the equivalent heat production internal resistance parameter R of the batteryΩWith the battery Entropy Changes heat production parameter UTIt is and interior for detecting
Short circuit.
In one of the embodiments, before the S320 steps, further include：
S311 based on the battery heat production model, carries out the battery temperature experimental data, Cell Experimentation An current data
Noise reduction process.The noise reduction process can include limit filtration method, middle position value filtering method, digital averaging filtering method etc..By right
The battery temperature experimental data, Cell Experimentation An current data, which carry out noise reduction process, can improve the battery temperature experiment number
According to the resolution ratio of, Cell Experimentation An current data, computational accuracy and computational efficiency can be improved.
The noise reduction process method includes the recursive filtering method with forgetting factor in one of the embodiments,.It is described to pass
γ can be denoted as by returning the forgetting factor of filtering, and calculating process is as follows：
The export vector of initial data is defined as { x1, x2, x3, x4}.The value data at k moment is represented using subscript k,
That is x1,kRepresent k moment x1Numerical value.
x1=T (4)
x2=T-T∞ (5)
x3=I2 (6)
x4=ITK (7)
The recursive filtering with forgetting factor γ is carried out, filtered vector is defined as y1, y2, y3, y4。y1, y2, y3, y4By
{x1, x2, x3, x4Obtain after recursive filtering, the formula that recursive filtering uses is (8)-(11), and wherein subscript k represents the k moment
Data, k=0 represent initial time.
y1,0=x1,0,y1,k=γ y1,k-1+x1,k, k=1,2,3...N (8)
y2,0=x2,0,y2,k=γ y2,k-1+x2,k, k=1,2,3...N (9)
y3,0=x3,0,y3,k=γ y3,k-1+x3,k, k=1,2,3...N (10)
y4,0=x4,0,y4,k=γ y4,k-1+x4,k, k=1,2,3...N (11)
Filtered data y1DerivativeIt can be asked for by formula (12), i.e.,：
And haveI.e.It can reflectSituation of change.And：
So, since the signal before filtering meets：
I.e. filtered signal still can meet the form of model equation (3).
Fig. 2 is referred to, since actual samples signal is there are noise, and temperature sampling resolution ratio is relatively low, direct-on-line meter
Calculate derivatives of the battery temperature T to the timeData fluctuations are larger so that next step based on mould
The result of calculation of the parameter identification of type also fluctuates larger, can not obtain stable detection result.And pass through passing with forgetting factor
After returning filtering, y is calculated1DerivativeData are just relatively smooth, reflect the true trend that battery temperature rise rate changes over time.
The battery temperature experimental data includes the battery temperature of constant duration acquisition in one of the embodiments,
Angle value T.The Cell Experimentation An current data includes the cell current value I of constant duration acquisition.
The thermal parameter fiducial value includes battery average equivalent heat production internal resistance thermal parameter in one of the embodiments,
RΩ,avg, battery mean entropy sell of one's property thermal parameter UT, the worst equivalent heat production internal resistance thermal parameter R of batteryΩ,maxWith the worst Entropy Changes heat production of battery
Parameter UT,max, further included after the step S320：
Step S330, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to the electricity
The equivalent heat production internal resistance parameter R in pondΩ, the battery average equivalent heat production internal resistance thermal parameter R is calculated respectivelyΩ,avgAnd the battery
Worst equivalent heat production internal resistance thermal parameter RΩ,max；
Step S340, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to the electricity
Pond Entropy Changes heat production parameter UT, the battery mean entropy is calculated respectively sells of one's property thermal parameter UT,avg, the worst Entropy Changes heat production ginseng of battery
Number UT,max。
In step S330, by the battery average temperature value Tavg, the battery maximum temperature value TmaxRespectively as formula
(3) T and T inK, bring the equivalent heat production internal resistance parameter R of the battery intoΩ, respectively obtain average equivalent heat production internal resistance thermal parameter
RΩ,avgAnd the worst equivalent heat production internal resistance thermal parameter R of batteryΩ,max。
In step S340, by the battery average temperature value Tavg, the battery maximum temperature value TmaxRespectively as formula
(3) T and T inKBring the battery Entropy Changes heat production parameter U intoT, the battery mean entropy is calculated respectively sells of one's property thermal parameter UT,avg,
The worst Entropy Changes heat production parameter U of batteryT,max。
The step S40 includes in one of the embodiments,：
S410 obtains abnormal heat production factor Y by the fiducial valueT；
S420 is by the normal heat production factor YTWith predetermined threshold value ΛTCompare to determine the whether abnormal heat production of the battery pack.
In step S410, the exception heat production factor YTCan with the abnormal heat production situation of quantitative assessment current battery,
Also can quantitative assessment battery pack heat production degree of irregularity.Different threshold value Λ can be setTCome to abnormal heat production degree
It is classified, improves the reasonability of comprehensive descision internal short-circuit exception heat production.The exception heat production factor YTThe electricity can be passed through
Pond average equivalent heat production internal resistance thermal parameter, the battery mean entropy sell of one's property thermal resistance in the worst equivalent heat production of thermal parameter, the battery
Parameter and the worst Entropy Changes heat production parameter of the battery are calculated and are obtained.
The abnormal heat production factor Y in one of the embodiments,TPass throughIt obtains.It is described
Abnormal heat production factor YTY can also be passed throughT1=| RΩ,max-RΩ,avg|+|UT,max-UT,avg| it obtains.
The predetermined threshold value Λ in one of the embodiments,TIncluding at least the first outlier threshold ΛT1With the second abnormal threshold
Value ΛT2.The second outlier threshold ΛT2Abnormal rank be more than the first outlier threshold ΛT1Abnormal rank.Wherein
In one embodiment, the first outlier threshold ΛT1Can be prompting value, indicating battery has begun heat production.When described default
Threshold value ΛTReach the second outlier threshold ΛT2Afterwards, it can be understood as battery heat production is abnormal, needs to stop the use of battery.
In one of the embodiments, based on formula (3), M=0.75kg, Cp=1100Jkg-1·K-1, h=15W
m2·K-1, A=0.02m2.It is controllably touched containing internal short-circuit inside the composition battery pack that multiple batteries are connected, wherein certain batteries
Element is sent out, and more serious internal short-circuit is triggered in 3598s.
Fig. 3 is referred to, after internal short-circuit triggering, abnormal rise occurs in the temperature in battery pack.Abnormal single battery temperature
The climbing speed of Tmax is far above mean temperature TavgClimbing speed.The voltage V of abnormal single batteryminIt is gradually deviated from battery pack
Average voltage Vavg。
Fig. 4 is referred to, the battery of battery for recognizing acquisition in the step S330 and the step S340 is averaged
Equivalent heat production internal resistance thermal parameter RΩ,avgAnd the worst equivalent heat production internal resistance thermal parameter R of batteryΩ,max, the battery is averaged
Entropy Changes heat production parameter UT,avg, the worst Entropy Changes heat production parameter U of batteryT,maxBy the influence of noise very little of signal sampling.And
After internal short-circuit failure occurs, the worst equivalent heat production internal resistance thermal parameter R of batteryΩ,maxWith the worst Entropy Changes heat production parameter U of batteryT,max
Deviate considerably from battery average equivalent heat production internal resistance thermal parameter RΩ,avgThermal parameter U is sold of one's property with the battery mean entropyT,avg, for sentencing
Abnormal heat-producing malfunction caused by determining internal short-circuit is more reliable.
Fig. 5 is referred to, can heat production rank be divided by 1-5 five by the abnormal heat production factor in one of the embodiments,
A grade.YT<2 be 0 grade (not abnormal), 2≤YT<2.5 be 1 grade of exception, 2.5≤YT<3 be 2 grades, 3≤YT<3.5 be 3 grades, 3.5
≤YT<4 be 4 grades, YT>4 be 5 grades.After 3598s triggers battery internal short-circuit, the apparent rapid increase of Outlier factor, and trend list
It adjusts, can be used for judging the abnormal heat production state of battery.In one of the embodiments, it is considered that YT>(time is when 2.5
4000s), the heat production of battery exception clearly (more than 150%) bigger than normal heat production, should be determined as abnormal heat production and
Doubtful internal short-circuit state.Referring now to Fig. 3, only 6 DEG C of the difference of battery maximum temperature and mean temperature uses conventional methods, this
When can't judge that battery is abnormal.
In an embodiment of the present invention, the battery heat production judged by above-mentioned real-time battery internal short-circuit detection method is abnormal
(YT>2.5) 4000s (accumulating time 402s) is appeared in.In fact, using identical test condition, this battery is from internal short-circuit
The used time for being triggered to generation severe thermal runaway is about 2963s.Pass through what is detected based on real-time battery internal short-circuit detection method
Heat production is abnormal, and 2561s (42mi n41s) is advanced by relative to the final thermal runaway that occurs.Therefore, the real-time battery internal short-circuit inspection
Survey method is capable of the situation of Accurate Prediction battery internal short-circuit.
Fig. 6 is referred to, the embodiment of the present invention also provides a kind of real-time battery internal short-circuit detection device 10.The real-time battery
Internal short-circuit detection device 10 includes real-time battery internal short-circuit detection device 11 and computer 12.Its Computer 12 includes memory
100th, processor 200 and it is stored in the computer program 300 that can be run on memory 100 and on processor 200.The processing
Device 200 performs the real-time battery internal short-circuit detection method, the method includes：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
The embodiment of the present invention also provides a kind of computer readable storage medium.It is stored on the computer readable storage medium
There is computer program.The step of can be used for the real-time battery internal short-circuit detection method when program is executed by processor.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
It is completed by computer program or the relevant hardware of instruction, the program can be stored in a computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, each implementation provided herein
Any reference to memory, storage, database or other media used in example, may each comprise non-volatile and/or easy
The property lost memory.Nonvolatile memory may include read-only memory (ROM), programming ROM (PROM), electrically programmable ROM
(EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include random access memory (RAM)
Or external cache.By way of illustration and not limitation, RAM is available in many forms, such as static state RAM (SRAM),
It is dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhanced SDRAM (ESDRAM), same
Walk link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) directly RAM (RDRAM), direct memory bus
Dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Embodiment described above only expresses the several embodiments of the present invention, and description is more specific and detailed, but simultaneously
Cannot the limitation to the scope of the claims of the present invention therefore be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention
Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (12)
1. a kind of real-time battery internal short-circuit detection method, which is characterized in that including step：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
2. battery internal short-circuit detection method in real time as described in claim 1, which is characterized in that the battery standard value includes electricity
Pond average temperature value and battery maximum temperature value, the step S20 include：
S210 takes the average acquisition battery average temperature value T according to the battery temperature dataavg；
S220 chooses the maximum value in the battery temperature data as the battery maximum temperature value Tmax。
3. battery internal short-circuit detection method in real time as claimed in claim 2, which is characterized in that the battery thermal parameter includes electricity
The equivalent heat production internal resistance parameter in pond and battery Entropy Changes heat production parameter, the step S30 include：
S310 obtains Cell Experimentation An temperature data, Cell Experimentation An current data；
S320, according to the battery temperature experimental data and the Cell Experimentation An current data of battery, according to battery heat production mould
Type calculates the battery thermal parameter and distinguishes normal formula, and the battery heat production model is：
Wherein, M is battery quality, unit kg；CpFor battery specific heat capacity, unit Jkg-1·K-1；For T pairs of battery temperature
The derivative of time, unit are DEG C s-1；H is battery to the average heat transfer coefficient of environment, unit Wm2·K-1；A is battery
Average heat dissipation area, unit m2；T is battery temperature value, and unit is DEG C；T∞For environment temperature, unit is DEG C；I is battery electricity
Flow valuve, unit A；RΩRepresent the equivalent heat production internal resistance parameter of the battery, unit Ω；TKTo be scaled the battery of Kelvin
Temperature, unit K, TK=T+273.15；UTRepresent the battery Entropy Changes heat production parameter, unit VK-1。
4. battery internal short-circuit detection method in real time as claimed in claim 3, which is characterized in that before the S320 steps,
It further includes：
Based on the battery heat production model, noise reduction is carried out to the battery temperature experimental data, Cell Experimentation An current data by S311
Processing.
5. battery internal short-circuit detection method in real time as claimed in claim 4, which is characterized in that the method packet of the noise reduction process
Include the recursive filtering method with forgetting factor.
6. battery internal short-circuit detection method in real time as claimed in claim 3, which is characterized in that the battery temperature experimental data
Include the battery temperature value T of constant duration acquisition, the Cell Experimentation An current data includes constant duration acquisition
The cell current value I.
7. battery internal short-circuit detection method in real time as claimed in claim 3, which is characterized in that the thermal parameter fiducial value includes
Battery average equivalent heat production internal resistance thermal parameter RΩ,avg, battery mean entropy sell of one's property thermal parameter UT, thermal resistance in the worst equivalent heat production of battery
Parameter RΩ,maxWith the worst Entropy Changes heat production parameter U of batteryT,max, further included after the step S320：
Step S330, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to described battery etc.
Imitate heat production internal resistance parameter RΩ, the battery average equivalent heat production internal resistance thermal parameter R is calculated respectivelyΩ,avgAnd the battery is worst
Equivalent heat production internal resistance thermal parameter RΩ,max；
Step S340, according to the battery average temperature value TavgWith the battery maximum temperature value Tmax, according to the battery entropy
Sell of one's property thermal parameter UT, the battery mean entropy is calculated respectively sells of one's property thermal parameter UT,avg, the worst Entropy Changes heat production parameter of battery
UT,max。
8. battery internal short-circuit detection method in real time as claimed in claim 7, which is characterized in that the step S40 includes：
S410 obtains abnormal heat production factor Y by the fiducial valueT；
S420 is by the normal heat production factor YTWith predetermined threshold value ΛTCompare to determine the whether abnormal heat production of the battery pack.
9. battery internal short-circuit detection method in real time as claimed in claim 8, which is characterized in that the exception heat production factor YTIt is logical
It crossesIt obtains.
10. battery internal short-circuit detection method in real time as claimed in claim 8, which is characterized in that the predetermined threshold value ΛTAt least
Including the first outlier threshold ΛT1With the second outlier threshold ΛT2, the second outlier threshold ΛT2Abnormal rank be more than described the
One outlier threshold ΛT1Abnormal rank.
11. a kind of real-time battery internal short-circuit detection device, including real-time battery internal short-circuit detection device (11) and computer (12),
Its Computer (12) is including memory (100), processor (200) and is stored on memory (200) and can be in processor
(200) computer program (300) run on, which is characterized in that the processor (200) performs the computer program
(300) real-time battery internal short-circuit detection method is used when, the method includes：
S10 obtains battery temperature data, the battery current data of battery；
S20 calculates battery standard value by the battery temperature data, the battery current data；
S30 according to the battery standard value, distinguishes that normal formula obtains thermal parameter fiducial value according to battery thermal parameter；
S40 judges the whether abnormal heat production of the battery according to the thermal parameter fiducial value.
12. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The step of can be used for any one of perform claim requirement 1-10 the methods during execution.
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