CN110161414A - A kind of power battery thermal runaway on-line prediction method and system - Google Patents
A kind of power battery thermal runaway on-line prediction method and system Download PDFInfo
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- CN110161414A CN110161414A CN201910509963.7A CN201910509963A CN110161414A CN 110161414 A CN110161414 A CN 110161414A CN 201910509963 A CN201910509963 A CN 201910509963A CN 110161414 A CN110161414 A CN 110161414A
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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
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Abstract
The invention discloses a kind of power battery thermal runaway on-line prediction method and system.This method comprises: the voltage value according to battery cell each in power battery calculates the voltage deviation matrix at each moment；Voltage value includes the voltage data of battery cell T from the T-M moment to current time；According to the voltage rating and the variation Increment Matrix of corresponding last moment at each moment of voltage deviation matrix, battery cell, the variation Increment Matrix at each moment is calculated；The variation that variation Increment Matrix according to each moment calculates current time T increases rate matrix；The variation growth rate that the variation of the mileage travelled of current automobile, the temperature averages of Current Temperatures probe and current time T increases the corresponding each monomer of rate matrix is input in thermal runaway monomer prediction model, power battery thermal runaway prediction result is obtained.The present invention realizes in real vehicle environment to the on-line prediction of power battery thermal runaway, improves precision of prediction.
Description
Technical field
The present invention relates to battery thermal runaway electric powder predictions, more particularly to a kind of power battery thermal runaway on-line prediction
Method and system.
Background technique
The advantages that lithium ion battery is high by its specific energy, specific power is big and long service life is made extensively by electric car
With, but with the extensive use of the raising of power battery specific energy and ternary lithium ion battery, the safety of lithium ion battery is asked
Inscribe increasingly prominent, new-energy automobile safety accident in 2018 is up to 50, the main reason for wherein battery thermal runaway is accident.Battery
Thermal runaway accident is related to the injures and deaths and property loss of a large amount of personnel, thus battery thermal runaway be Development of Electric Vehicles during
The key problem for needing to solve.
Currently, being mainly to the research of battery thermal runaway: the internal-response of battery when probing into generation thermal runaway by testing
Mechanism and surface, and then propose the measure of prevention thermal runaway.Cell voltage that existing method is measured by laboratory, temperature
The characterization parameters such as degree accurately diagnose the abnormality of battery, but in real vehicle environment, the characteristic of battery is by various aspects
Factor influences, and is the complex working condition of a various factors coupling, thus existing method is difficult to apply to true electric car.
In order to ensure driving safety and avoid the incipient fault of electric vehicle, have in recent years some scholars propose battery therefore
The method of barrier prediction and health state evaluation, these methods mostly carry out one-dimensional assessment based on SOH, and SOH refers to battery health shape
State can generally be calculated with current maximum with the ratio of dosage and rated capacity, and carrying out prediction using SOH can be well reflected
Health status, degree of aging and the remaining life of battery, but can not diagnose and predict battery thermal runaway, overcharge, overdischarge,
The short-term failure such as battery short circuit.
Summary of the invention
Based on this, it is necessary to a kind of power battery thermal runaway on-line prediction method and system are provided, to realize in real vehicle ring
To the on-line prediction of power battery thermal runaway in border, precision of prediction is improved.
To achieve the above object, the present invention provides following schemes:
A kind of power battery thermal runaway on-line prediction method, comprising:
Obtain the mileage travelled of current automobile, each battery cell in the temperature averages and power battery of Current Temperatures probe
Voltage value；The voltage value includes the voltage data of battery cell T from the T-M moment to current time；One moment corresponding one
Frame data；
Voltage value according to each battery cell in the power battery calculates the voltage deviation matrix at each moment；The electricity
Pressure deviation matrix is made of multiple voltage deviation values；The corresponding voltage deviation value of one battery cell；
According to the voltage deviation matrix, the voltage of the voltage rating of battery cell and corresponding last moment at each moment
Offset increment matrix calculates the variation Increment Matrix at each moment；The variation Increment Matrix is inclined by multiple voltages
Increment is moved to constitute；The corresponding variation increment of one battery cell；
The variation that variation Increment Matrix according to each moment calculates current time T increases rate matrix；It is described
Variation increases rate matrix and is made of multiple variation growth rates；The corresponding variation of one battery cell increases
Rate；
The voltage of the mileage travelled of the current automobile, the temperature averages of Current Temperatures probe and current time T is inclined
The variation growth rate for moving the corresponding each monomer of growth rate matrix is input in thermal runaway monomer prediction model, obtains power
Battery thermal runaway prediction result.
Optionally, it is described by the mileage travelled of the current automobile, Current Temperatures probe temperature averages and it is current when
The variation growth rate for carving the corresponding each monomer of variation growth rate matrix of T is input to thermal runaway monomer prediction model
In, power battery thermal runaway prediction result is obtained, is specifically included:
The voltage of the mileage travelled of the current automobile, the temperature averages of Current Temperatures probe and current time T is inclined
The variation growth rate for moving the corresponding each monomer of growth rate matrix is input in thermal runaway monomer prediction model, and output is current
The thermal runaway prediction matrix of moment T；
Thermal runaway prediction matrix according to the current time T judges whether there is potential thermal runaway battery cell；
The serial number of potential thermal runaway battery cell is then transmitted to automobile by potential thermal runaway battery cell if it exists
Instrument board, new-energy automobile big data monitor supervision platform and vehicle maintenance platform, to realize monitoring and early warning；
Potential thermal runaway battery cell if it does not exist then judges whether to generate new data；
If generating new data, T=T+1 is enabled, and returns to the mileage travelled for obtaining current automobile, Current Temperatures spy
The voltage value of each battery cell in the temperature averages and power battery of needle；
If not generating new data, terminate.
Optionally, the voltage value according to each battery cell in the power battery calculates the voltage deviation at each moment
Matrix specifically includes:
Voltage value according to each battery cell in the power battery calculates the voltage median of each moment battery cell
Value；
According to position in the voltage value of each battery cell in the power battery and the voltage of each moment battery cell
Numerical value calculates the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t
The voltage deviation value at moment, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery
Voltage value of the monomer in t moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the sum of battery cell
Amount, Vm,tIndicate the voltage I d median of t moment battery cell.
Optionally, it is described according to the voltage deviation matrix, the voltage rating of battery cell and each moment it is corresponding on
The variation Increment Matrix at one moment calculates the variation Increment Matrix at each moment, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the voltage of t moment
Offset increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-
The variation increment at 1 moment, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery list
The voltage rating of body.
Optionally, the variation that the variation Increment Matrix according to each moment calculates current time T increases
Rate matrix, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell current
The variation growth rate of moment T, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of electricity
Variation growth rate of the pond monomer in t moment
The present invention also provides a kind of power battery thermal runaway on-line prediction systems, comprising:
Data acquisition module, for obtaining the temperature averages of the mileage travelled of current automobile, Current Temperatures probe and dynamic
The voltage value of each battery cell in power battery；The voltage value includes the voltage number of battery cell T from the T-M moment to current time
According to；One moment corresponding frame data；
First matrix computing module calculates each moment for the voltage value according to each battery cell in the power battery
Voltage deviation matrix；The voltage deviation matrix is made of multiple voltage deviation values；The corresponding voltage of one battery cell
Deviation；
Second matrix computing module, for according to the voltage deviation matrix, battery cell voltage rating and it is each when
The variation Increment Matrix for carving corresponding last moment calculates the variation Increment Matrix at each moment；The voltage is inclined
Increment Matrix is moved to be made of multiple variation increments；The corresponding variation increment of one battery cell；
Third matrix computing module calculates the electricity of current time T for the variation Increment Matrix according to each moment
Pressure offset increases rate matrix；The variation increases rate matrix and is made of multiple variation growth rates；One battery cell
A corresponding variation growth rate；
Prediction module, for by the temperature averages of the mileage travelled of the current automobile, Current Temperatures probe and currently
The variation growth rate that the variation of moment T increases the corresponding each monomer of rate matrix is input to thermal runaway monomer prediction mould
In type, power battery thermal runaway prediction result is obtained.
Optionally, the prediction module, specifically includes:
Prediction matrix acquiring unit, for by the temperature-averaging of the mileage travelled of the current automobile, Current Temperatures probe
The variation growth rate that the variation of value and current time T increase the corresponding each monomer of rate matrix is input to thermal runaway list
In body prediction model, the thermal runaway prediction matrix of current time T is exported；
First judging unit, for being judged whether there is potentially according to the thermal runaway prediction matrix of the current time T
Thermal runaway battery cell；
Serial number transmission unit, for potential thermal runaway battery cell if it exists, then by potential thermal runaway battery cell
Serial number be transmitted to the instrument board of automobile, new-energy automobile big data monitor supervision platform and vehicle maintenance platform, with realize monitoring and
Early warning；
Second judgment unit then judges whether to generate new data for potential thermal runaway battery cell if it does not exist；
Return unit if enabling T=T+1 for generating new data, and returns to the data acquisition module；
End unit, if terminating for not generating new data.
Optionally, the first matrix computing module, specifically includes:
Median computing unit calculates each moment electricity for the voltage value according to each battery cell in the power battery
The voltage I d median of pond monomer；
First matrix calculation unit, for according to each battery cell in the power battery voltage value and it is described each when
The voltage I d median for carving battery cell, calculates the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t
The voltage deviation value at moment, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery
Voltage value of the monomer in t moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the sum of battery cell
Amount, Vm,tIndicate the voltage I d median of t moment battery cell.
Optionally, the second matrix computing module, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the voltage of t moment
Offset increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-
The variation increment at 1 moment, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery list
The voltage rating of body.
Optionally, the third matrix computing module, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell current
The variation growth rate of moment T, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of electricity
Variation growth rate of the pond monomer in t moment
Compared with prior art, the beneficial effects of the present invention are:
The invention proposes a kind of power battery thermal runaway on-line prediction method and system.This method is based on time series point
The difference of the potential monomer of thermal runaway Yu normal monomer voltage curve has been analysed, it then will by the method that voltage deviation absolute value adds up
Historical data is coupled with online data, uses thermal runaway monomer prediction model later, is carried out to potential thermal runaway monomer pre-
It surveys.Compared to laboratory procedure, the state of real vehicle is predicted again after being analyzed using the data that real vehicle is run, more
Close to actual engineer application.It can be realized in real vehicle environment using method or system of the invention to power battery thermal runaway
On-line prediction, can occur thermal runaway a few days ago accurately to the potential monomer of thermal runaway carry out real-time online prediction, and
Precision of prediction is high.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is a kind of flow chart of the power battery thermal runaway on-line prediction method of the embodiment of the present invention 1；
Fig. 2 is the variation growth rate distribution map of the sometime each monomer of the embodiment of the present invention 2；
Fig. 3 is the amount of monomer frequency distribution diagram in 2 different voltages of embodiment of the present invention offset growth rate section；
Fig. 4 is the schematic diagram of 2 thermal runaway monomer prediction model of the embodiment of the present invention；
Fig. 5 is the prediction result figure of the difference M value in M >=3800 of the embodiment of the present invention 2.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Embodiment 1
Fig. 1 is a kind of flow chart of the power battery thermal runaway on-line prediction method of the embodiment of the present invention 1.
The power battery thermal runaway on-line prediction method of embodiment, comprising:
Step S1: the mileage travelled of current automobile, each in the temperature averages and power battery of Current Temperatures probe is obtained
The voltage value of battery cell；The voltage value includes the voltage data of battery cell T from the T-M moment to current time.
Wherein, an a moment corresponding frame data.
Step S2: the voltage value according to each battery cell in the power battery calculates the voltage deviation square at each moment
Battle array.
The voltage deviation matrix is made of multiple voltage deviation values；The corresponding voltage deviation value of one battery cell.
The step S2, specifically includes:
21: the voltage value according to each battery cell in the power battery calculates position in the voltage of each moment battery cell
Numerical value.
22: according in the voltage value of each battery cell in the power battery and the voltage of each moment battery cell
Bit value calculates the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t
The voltage deviation value at moment, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery
Voltage value of the monomer in t moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the sum of battery cell
Amount, Vm,tIndicate the voltage I d median of t moment battery cell.
Step S3: according to the voltage deviation matrix, the voltage rating of battery cell and corresponding upper a period of time at each moment
The variation Increment Matrix at quarter calculates the variation Increment Matrix at each moment.
The variation Increment Matrix is made of multiple variation increments；The corresponding voltage of one battery cell is inclined
Move increment.
The step S3, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the voltage of t moment
Offset increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-
The variation increment at 1 moment, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery list
The voltage rating of body.
Step S4: the variation growth rate square of current time T is calculated according to the variation Increment Matrix at each moment
Battle array.
The variation increases rate matrix and is made of multiple variation growth rates；The corresponding electricity of one battery cell
Pressure offset growth rate.
The step S4, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell current
The variation growth rate of moment T, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of electricity
Variation growth rate of the pond monomer in t moment
Step S5: by the mileage travelled of the current automobile, the temperature averages of Current Temperatures probe and current time T
The variation growth rate that variation increases the corresponding each monomer of rate matrix is input in thermal runaway monomer prediction model, is obtained
To power battery thermal runaway prediction result.
The step S5, specifically includes:
51: by the mileage travelled of the current automobile, the voltage of the temperature averages of Current Temperatures probe and current time T
The variation growth rate that offset increases the corresponding each monomer of rate matrix is input in thermal runaway monomer prediction model, and output is worked as
The thermal runaway prediction matrix of preceding moment T.
52: the thermal runaway prediction matrix according to the current time T judges whether there is potential thermal runaway battery cell.
The serial number of potential thermal runaway battery cell is then transmitted to automobile by potential thermal runaway battery cell if it exists
Instrument board, new-energy automobile big data monitor supervision platform and vehicle maintenance platform, to realize monitoring and early warning.
Potential thermal runaway battery cell if it does not exist then judges whether to generate new data；If generating new data,
T=T+1 is enabled, and returns to the temperature averages and power battery of the mileage travelled for obtaining current automobile, Current Temperatures probe
In each battery cell voltage value；If not generating new data, terminate.
Power battery thermal runaway on-line prediction method in the present embodiment, can be realized in real vehicle environment to power battery
The a few days ago accurately pre- to thermal runaway potential monomer progress real-time online of thermal runaway can occur for the on-line prediction of thermal runaway
It surveys, and precision of prediction is high.
A more detailed embodiment is provided below.
Embodiment 2
1, the selection of data
The data of the present embodiment come from new-energy automobile country big data platform, which can acquire and store new energy
All data when automobilism, including online data and off-line data.Data in the platform cover vehicle location, speed with
And the aspect of battery system state.The data of electric car include vehicle running state data, vehicle position data, vehicle in platform
Battery system status data, vehicular electric machine system state data, vehicle trouble alert data etc..The present embodiment is to new energy vapour
The data of the more automobiles and normal automotive that thermal runaway occurred are analyzed in vehicle country big data platform.Every two frame data it
Between time interval be 10s.
Data prediction step are as follows: (1) from platform transfer thermal runaway automobile occur the thermal runaway previous moon data with
And normal automotive one month data.(2) data are subjected to transcoding and segmentation, obtain readable form document.(3) according to GB/
Real-time information collection item in T32960 extracts dimension relevant to thermal runaway prediction, including battery cell voltage, probe temperature
Degree, current vehicle driving mileage, all data being distributed in temporal sequence.(4) exceptional value and sky are removed by threshold method
Value, obtains the valid data of automobile.
2, thermal runaway monomer prediction model
The reason of causing automobile batteries thermal runaway has very much, mainly based on machinery abuse and electrical abuse, wherein mechanical
Abuse includes that battery collides, and is squeezed, puncture etc., and electrically abusing mainly includes internal short-circuit, external short circuit, overcharge, over-discharge
Electricity etc..Most of thermal runaways are gradually spread, battery management system BMS can pass through prison since some or certain several monomers
The parameters such as temperature, the voltage of battery are surveyed to alarm to thermal runaway phenomenon, but due to battery temperature and electricity when thermal runaway occurs
Pressure rapidly rises, and BMS real time on-line monitoring is also difficult to avoid the generation of accident, therefore the identification to potential thermal runaway monomer at present
It is vital with prediction.Fortunately, thermal runaway can occur for method based on big data for the previous period to battery
Data are analyzed, and so as to predict the potential thermal runaway monomer of battery, can be carried out to it before thermal runaway generation
Prediction, avoids the generation of accident.
1) thermal runaway monomer prediction model constructs
The thermal runaway of battery is influenced by the factor of many aspects, and battery cell voltage is that the comprehensive of failure embodies.In order to
The potential thermal runaway failure of battery is studied, the thermal runaway previous moon each battery cell voltage is for statistical analysis to occurring, and passes through
Analysis is it is found that in the previous moon that thermal runaway occurs, and the fluctuation of thermal runaway incipient fault monomer voltage is than normal battery cell
It is bigger, and repeatedly there is the case where brownout, in the latter stage of electric discharge, thermal runaway incipient fault monomer voltage is repeatedly lower than
3.3V, and hold voltage to can reflect the size of battery SOC, therefore the potential monomer of the thermal runaway produces over-discharge in the latter stage of electric discharge
Electricity.In addition, the potential monomer voltage of thermal runaway is more slightly higher than normal monomer voltage in the latter stage of charging, illustrate the potential list of the thermal runaway
Body produces slight overcharge in the latter stage of charging, with the increase of charge and discharge cycles number, the battery electrochemical property by
Gradual change is poor.
For the degree of fluctuation of quantitative description battery cell voltage, battery cell voltage deviation in every frame data is calculated, is counted
Calculate formula are as follows:
ΔVi,t=Vi,t-Vmedian,t
In formula, Δ Vi,tFor the voltage deviation of i-th of monomer t frame data, unit V；Vi,tFor i-th of monomer t frame
The voltage of data, unit V, Vmedian,tFor the median of all monomer voltages in t frame data, unit V.
Voltage deviation in thermal runaway monomer and normal monomer one month is counted, by statistical data it is found that sending out
The heat previous moon out of control, the voltage deviation of normal monomer are generally kept between -0.1V -+0.1V, have preferable one
Cause property, and the voltage deviation of the potential monomer of thermal runaway repeatedly exceeds -0.1V-section+0.1V, hence it is evident that it is more inclined than other normal monomers
Difference is bigger, and the voltage deviation of monomer even repeatedly exceeds -0.5V, and voltage deviation presents and just neglects negative phenomenon suddenly.
From the above-mentioned analysis to battery cell voltage it can be concluded that the thermal runaway of battery cell can pass through the inclined of voltage
Difference reflects, and has the cumulative process to run down.By current time and historical juncture each battery cell voltage deviation
Absolute value has added up as the monomer current voltage offset increment, so that automobile current data is coupled with historical data, it is right
Potential thermal runaway monomer is identified.If current time data are T frame, each battery cell voltage offset increment is defined as follows:
In formula, Fi,TFor the variation increment of i-th of monomer T frame data, V0For battery cell voltage rating, unit
For V.
By above-mentioned formula it can be concluded that T-1 frame data correspond to moment each battery cell voltage offset increment:
In formula, Fi,T-1For the variation increment of the T-1 frame data of i-th of monomer
It can be finally obtained by both the above formula:
Automobile is every when generating the new data of a frame, can pass through the F in above-mentioned formulai,TWith Fi,T-1Relationship directly calculate
Fi,T, without using all monomer absolute value of the bias are carried out again it is cumulative by the way of, therefore will not be because of the increasing of car data amount
Add and increases calculation amount.
The variation increment that each battery cell of each moment is calculated according to above-mentioned formula, it is by analysis it is found that each
The variation increment at battery cell each moment and substantially linear variation at any time, and the potential monomer of thermal runaway is than normal monomer
The increase of variation increment is faster.
According to above-mentioned analysis it is found that can variation incremental rate curve to battery cell to carry out least-squares line quasi-
It closes, identifies potential thermal runaway monomer with the slope of fitting gained straight line.But as car data amount is continuously increased, minimum two
Multiply straight line fitting calculate the time be also continuously increased, therefore define material calculation M come limit participate in fitting historical data quantity.
When material calculation M indicates one frame new data of the every generation of vehicle, the historical data of variation increment fitting is participated in
Frame number.Material calculation is a constant, can be when designing BMS control strategy according to the computing capability of big data platform and BMS
It determines, guarantees the data the Fitting Calculation time less than the time interval between every two frame data of automobile, to realize online pre- in real time
It surveys.
When automobile one frame new data of every generation, the variation increment of current time data and preceding M frame data is carried out most
Small square law straight line fitting calculates the slope K of the variation incremental rate curve of each monomeri, it is defined as variation growth
Rate, calculation formula are as follows:
In formula, KiFor the variation growth rate of i-th of monomer, t is data frame number, Fi,tIt is i-th in t frame data
Monomer voltage offset increment.
Fig. 2 is the variation growth rate distribution map of the sometime each monomer of the embodiment of the present invention 2, and Fig. 3 is the present invention
2 different voltages of embodiment deviate the amount of monomer frequency distribution diagram in growth rate section.As seen from the figure, No. 125 thermal runaway monomers
Variation growth rate be greater than other monomers, and the variation growth rate of most of monomers in 0.375*10 (- 5) hereinafter,
Therefore the potential monomer of thermal runaway can be identified by the variation growth rate in each monomer of line computation.
Although battery cell voltage is that the comprehensive of failure embodies, the degree of aging of the failures such as battery thermal runaway and battery
Also there is relationship, cell degradation degree is indicated with current mileage travelled, in addition, temperature is the most apparent characterization parameter of thermal runaway,
Therefore each probe temperature is also used as to one of the judgment basis of potential thermal runaway monomer.The variation of each monomer is increased
The input of rate, current driving mileage, Current Temperatures probe temperature average value as neural network.
Since this model is to establish model based on the real vehicle data that thermal runaway automobile has occurred, with the actual conditions of automobile compared with
To meet, it is reason to believe that heat can occur for the potential thermal runaway monomer judged according to model maximum probability within a period of time later
Hardover failure should be safeguarded in time.Therefore this model only predicts whether certain monomer is the potential monomer of thermal runaway, without sending out it
Heat probability out of control is predicted.It will whether be potential thermal runaway monomer as the output of neural network, the potential heat of training is lost
Control monomer prediction model.Neural network training model such as Fig. 4.
2) thermal runaway monomer prediction algorithm process
According to the above-mentioned thermal runaway prediction model established to battery cell, propose that a kind of battery heat based on data-driven is lost
Prediction technique is controlled, core concept is to combine automobile current data with historical data, and the potential heat of real-time online identification is lost
Control monomer.If automobile current time is T frame data, step are as follows:
(1) online fitting data frame number M is set.
(2) the T frame data (current time data) for extracting automobile, calculate all battery cell voltages in T frame data
Median.
(3) absolute value for seeking the difference of each monomer voltage and voltage median, as current time voltage deviation matrix:
MT=(| V1,T-Vm,T|,…,|Vn,T-Vm,T|)
In formula, MTFor current time voltage deviation matrix, Vm,TFor the median of current time all battery cell voltages,
V1,T,V2,T,…,Vi,T,…,Vn,TFor each battery cell voltage.
(4) current time variation Increment Matrix is calculated according to formula:
In formula, NTFor current time variation Increment Matrix.
(5) (T-M) in variation Increment Matrix to T frame data is subjected to least square line fitting, obtained each
The current time variation of monomer increases rate matrix.
KT=(k1,T,…,kn,T)
In formula, KTIncrease rate matrix, k for current time variation1,T,k2,T,…,ki,T,…,kn,TFor each monomer
Variation growth rate.
(6) by the variation growth rate of each monomer, current driving mileage, Current Temperatures probe temperature average value is defeated
Enter into thermal runaway prediction model, judges potential thermal runaway monomer.
(7) when one frame new data of the every generation of automobile, (1)-(6) are repeated, are constantly recycled.
3, real vehicle data is verified
Total number of samples evidence, training data and the inspection data that real vehicle verifying is selected are as shown in table 1.
1 real vehicle verify data of table
Prediction matrix P is set to record the prediction case of battery thermal runaway:
In formula, pij(i=1,2 ..., t) (j=1,2 ..., n) indicate what i moment j battery cell was calculated according to model
The predicted value of monomer thermal runaway, if meeting the condition of thermal runaway early warning, by pijIt is denoted as 1, is otherwise denoted as 0.
The battery cell that thermal runaway occurs for this vehicle is No. 125 monomers, 5000 is set by material calculation M, by simulating, verifying
It as a result is later always early warning it is found that data when No. 125 potential thermal runaway monomer first time prediction occurrings are the 49111st frame
State, it can early warning is carried out to thermal runaway failure before 7 days；And remaining monomer is recorded without early warning.Demonstrate algorithm
Accuracy.
In order to study influence of the material calculation M to prediction result, to M=100,200,300 ..., 9900,10000 situations
The prediction result of the lower potential monomer of thermal runaway is compared.By to M=100,100,4000,10000 when corresponding prediction knot
The analysis of fruit, it is known that, it for the vehicle, as M < 3800, cannot accurately be predicted, the very few electricity for leading to fitting of data volume
Pressure offset growth rate is affected by voltage fluctuation, and some normal monomers are wrongly judged as the potential monomer of thermal runaway；M≥
When 3800, accurately the potential monomer of thermal runaway can be predicted.
Fig. 5 is the prediction result figure of the difference M value in M >=3800 of the embodiment of the present invention 2, and wherein ordinate indicates first
The secondary frame number n for predicting potential thermal runaway monomer, n is smaller, shows that prediction is more timely.As seen from the figure, n increases with the increase of M
Greatly, and growth trend is slower and slower, this is because participating in newly generated data accounting in the data of fitting with the increase of M and subtracting
It is small, cause the time for predicting the potential monomer of thermal runaway more to lag.In addition, M from 3800 rise to 10000 during, n from
48135 have risen to 51031, and between adjacent two frame data between be divided into 10s, therefore 8 hours have been lagged, compared to can be with
Predict within 7 days in advance potential thermal runaway monomer, it is not apparent for increasing prediction hysteresis quality caused by M.But it will increase due to increasing M
The calculating time of model, M should not choose it is excessive, by the formula of variation growth rate it is found that the calculation amount of multiplying be 2M+
2, the calculation amount of add operation is 5M+5.
The power battery thermal runaway on-line prediction method of the present embodiment, the power to the automobile previous moon that thermal runaway occurs
Battery cell data are analyzed, and the potential monomer of time series analysis thermal runaway and normal monomer voltage curve are primarily based on
Difference, and then influence of the deviation of voltage to battery overcharge overdischarge is analyzed, then added up by voltage deviation absolute value
Method couples historical data with current data, and method neural network based establishes thermal runaway monomer prediction model,
Potential thermal runaway monomer is predicted, finally model is verified using real vehicle data.It is verified, the power of the present embodiment
Battery thermal runaway on-line prediction method accurately can carry out real-time online prediction to the potential monomer of thermal runaway, be power battery
Online thermal runaway diagnosis provides certain mentality of designing and reference frame.
The power battery thermal runaway on-line prediction method of the present embodiment, has the following characteristics that
(1) automobile current data is coupled with historical data, predicts potential thermal runaway monomer in battery pack.
(2) real-time online failure predication, with the continuous iteration of the update of car data, with the increase of data volume, potential heat
The difference of the variation increment of the variation increment and other normal monomers of monomer out of control is increasing, therefore predicts accurately
It spends higher and higher.
(3) since the present embodiment need to only consider the deviation of each monomer voltage, identification automobile current state is not needed,
Can influence to avoid data frame losing to prediction result, improve the accuracy of prediction.
(4) the preferable and poor battery cell of consistency is judged by the size energy simultaneous quantitative of variation growth rate,
For the assessment providing method of battery consistency.
(5) the present embodiment predicts whether certain monomer is the potential monomer of thermal runaway, do not have to the probability of its thermal runaway into
Row prediction.
(6) the present embodiment can accurately carry out the thermal runaways such as over-charging of battery over-discharge failure caused by battery inconsistency
Prediction.
The present invention also provides a kind of power battery thermal runaway on-line prediction systems, comprising:
Data acquisition module, for obtaining the temperature averages of the mileage travelled of current automobile, Current Temperatures probe and dynamic
The voltage value of each battery cell in power battery；The voltage value includes the voltage number of battery cell T from the T-M moment to current time
According to；One moment corresponding frame data.
First matrix computing module calculates each moment for the voltage value according to each battery cell in the power battery
Voltage deviation matrix；The voltage deviation matrix is made of multiple voltage deviation values；The corresponding voltage of one battery cell
Deviation.
Second matrix computing module, for according to the voltage deviation matrix, battery cell voltage rating and it is each when
The variation Increment Matrix for carving corresponding last moment calculates the variation Increment Matrix at each moment；The voltage is inclined
Increment Matrix is moved to be made of multiple variation increments；The corresponding variation increment of one battery cell.
Third matrix computing module calculates the electricity of current time T for the variation Increment Matrix according to each moment
Pressure offset increases rate matrix；The variation increases rate matrix and is made of multiple variation growth rates；One battery cell
A corresponding variation growth rate.
Prediction module, for by the temperature averages of the mileage travelled of the current automobile, Current Temperatures probe and currently
The variation growth rate that the variation of moment T increases the corresponding each monomer of rate matrix is input to thermal runaway monomer prediction mould
In type, power battery thermal runaway prediction result is obtained.
As an alternative embodiment, the prediction module, specifically includes:
Prediction matrix acquiring unit, for by the temperature-averaging of the mileage travelled of the current automobile, Current Temperatures probe
The variation growth rate that the variation of value and current time T increase the corresponding each monomer of rate matrix is input to thermal runaway list
In body prediction model, the thermal runaway prediction matrix of current time T is exported；
First judging unit, for being judged whether there is potentially according to the thermal runaway prediction matrix of the current time T
Thermal runaway battery cell；
Serial number transmission unit, for potential thermal runaway battery cell if it exists, then by potential thermal runaway battery cell
Serial number be transmitted to the instrument board of automobile, new-energy automobile big data monitor supervision platform and vehicle maintenance platform, with realize monitoring and
Early warning；
Second judgment unit then judges whether to generate new data for potential thermal runaway battery cell if it does not exist；
Return unit if enabling T=T+1 for generating new data, and returns to the data acquisition module；
End unit, if terminating for not generating new data.
As an alternative embodiment, the first matrix computing module, specifically includes:
Median computing unit calculates each moment electricity for the voltage value according to each battery cell in the power battery
The voltage I d median of pond monomer；
First matrix calculation unit, for according to each battery cell in the power battery voltage value and it is described each when
The voltage I d median for carving battery cell, calculates the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t
The voltage deviation value at moment, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery
Voltage value of the monomer in t moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the sum of battery cell
Amount, Vm,tIndicate the voltage I d median of t moment battery cell.
As an alternative embodiment, the second matrix computing module, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the voltage of t moment
Offset increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-
The variation increment at 1 moment, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery list
The voltage rating of body.
As an alternative embodiment, the third matrix computing module, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell current
The variation growth rate of moment T, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of electricity
Variation growth rate of the pond monomer in t moment
Power battery thermal runaway on-line prediction system in the present embodiment, can be realized in real vehicle environment to power battery
The a few days ago accurately pre- to thermal runaway potential monomer progress real-time online of thermal runaway can occur for the on-line prediction of thermal runaway
It surveys, and precision of prediction is high.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (10)
1. a kind of power battery thermal runaway on-line prediction method characterized by comprising
Obtain the mileage travelled of current automobile, in the temperature averages and power battery of Current Temperatures probe each battery cell electricity
Pressure value；The voltage value includes the voltage data of battery cell T from the T-M moment to current time；One moment corresponding frame number
According to；
Voltage value according to each battery cell in the power battery calculates the voltage deviation matrix at each moment；The voltage is inclined
Poor matrix is made of multiple voltage deviation values；The corresponding voltage deviation value of one battery cell；
According to the voltage deviation matrix, the variation of the voltage rating of battery cell and corresponding last moment at each moment
Increment Matrix calculates the variation Increment Matrix at each moment；The variation Increment Matrix is increased by multiple variations
Amount is constituted；The corresponding variation increment of one battery cell；
The variation that variation Increment Matrix according to each moment calculates current time T increases rate matrix；The voltage
Offset increases rate matrix and is made of multiple variation growth rates；The corresponding variation growth rate of one battery cell；
The variation of the mileage travelled of the current automobile, the temperature averages of Current Temperatures probe and current time T is increased
The variation growth rate of the corresponding each monomer of long rate matrix is input in thermal runaway monomer prediction model, obtains power battery
Thermal runaway prediction result.
2. a kind of power battery thermal runaway on-line prediction method according to claim 1, which is characterized in that it is described will be described
The variation of the current mileage travelled of automobile, the temperature averages of Current Temperatures probe and current time T increases rate matrix pair
The variation growth rate for each monomer answered is input in thermal runaway monomer prediction model, obtains the prediction of power battery thermal runaway
As a result, specifically including:
The variation of the mileage travelled of the current automobile, the temperature averages of Current Temperatures probe and current time T is increased
The variation growth rate of the corresponding each monomer of long rate matrix is input in thermal runaway monomer prediction model, exports current time
The thermal runaway prediction matrix of T；
Thermal runaway prediction matrix according to the current time T judges whether there is potential thermal runaway battery cell；
The serial number of potential thermal runaway battery cell is then transmitted to the instrument of automobile by potential thermal runaway battery cell if it exists
Disk, new-energy automobile big data monitor supervision platform and vehicle maintenance platform, to realize monitoring and early warning；
Potential thermal runaway battery cell if it does not exist then judges whether to generate new data；
If generating new data, T=T+1 is enabled, and returns to the mileage travelled for obtaining current automobile, Current Temperatures probe
The voltage value of each battery cell in temperature averages and power battery；
If not generating new data, terminate.
3. a kind of power battery thermal runaway on-line prediction method according to claim 1, which is characterized in that described according to institute
The voltage value for stating each battery cell in power battery calculates the voltage deviation matrix at each moment, specifically includes:
Voltage value according to each battery cell in the power battery calculates the voltage I d median of each moment battery cell；
According to the voltage value of each battery cell in the power battery and the voltage I d median of each moment battery cell,
Calculate the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t moment
Voltage deviation value, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery cell in t
The voltage value at moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the total quantity of battery cell, Vm,tTable
Show the voltage I d median of t moment battery cell.
4. a kind of power battery thermal runaway on-line prediction method according to claim 3, which is characterized in that described according to institute
The variation Increment Matrix of voltage deviation matrix, the voltage rating of battery cell and corresponding last moment at each moment is stated,
The variation Increment Matrix at each moment is calculated, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the variation of t moment
Increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-1
The variation increment at quarter, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery cell
Voltage rating.
5. a kind of power battery thermal runaway on-line prediction method according to claim 4, which is characterized in that the foundation is every
The variation that the variation Increment Matrix at a moment calculates current time T increases rate matrix, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell in current time T
Variation growth rate, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of battery list
Variation growth rate of the body in t moment
6. a kind of power battery thermal runaway on-line prediction system characterized by comprising
Data acquisition module, for obtaining the mileage travelled of current automobile, the temperature averages and power electric of Current Temperatures probe
The voltage value of each battery cell in pond；The voltage value includes the voltage data of battery cell T from the T-M moment to current time；
One moment corresponding frame data；
First matrix computing module, for calculating the electricity at each moment according to the voltage value of each battery cell in the power battery
Press deviation matrix；The voltage deviation matrix is made of multiple voltage deviation values；The corresponding voltage deviation of one battery cell
Value；
Second matrix computing module, for according to the voltage deviation matrix, battery cell voltage rating and each moment pair
The variation Increment Matrix for the last moment answered calculates the variation Increment Matrix at each moment；The variation increases
Moment matrix is made of multiple variation increments；The corresponding variation increment of one battery cell；
Third matrix computing module, the voltage for calculating current time T for the variation Increment Matrix according to each moment are inclined
It moves and increases rate matrix；The variation increases rate matrix and is made of multiple variation growth rates；One battery cell is corresponding
One variation growth rate；
Prediction module, for by the temperature averages and current time T of the mileage travelled of the current automobile, Current Temperatures probe
Variation increase the variation growth rate of the corresponding each monomer of rate matrix and be input in thermal runaway monomer prediction model,
Obtain power battery thermal runaway prediction result.
7. a kind of power battery thermal runaway on-line prediction system according to claim 6, which is characterized in that the prediction mould
Block specifically includes:
Prediction matrix acquiring unit, for by the temperature averages of the mileage travelled of the current automobile, Current Temperatures probe and
It is pre- that the variation growth rate that the variation of current time T increases the corresponding each monomer of rate matrix is input to thermal runaway monomer
It surveys in model, exports the thermal runaway prediction matrix of current time T；
First judging unit loses for judging whether there is potential heat according to the thermal runaway prediction matrix of the current time T
Control battery cell；
Serial number transmission unit, for potential thermal runaway battery cell if it exists, then by the sequence of potential thermal runaway battery cell
It number is transmitted to the instrument board, new-energy automobile big data monitor supervision platform and vehicle maintenance platform of automobile, to realize monitoring and early warning；
Second judgment unit then judges whether to generate new data for potential thermal runaway battery cell if it does not exist；
Return unit if enabling T=T+1 for generating new data, and returns to the data acquisition module；
End unit, if terminating for not generating new data.
8. a kind of power battery thermal runaway on-line prediction system according to claim 6, which is characterized in that first square
Battle array computing module, specifically includes:
Median computing unit calculates each moment battery list for the voltage value according to each battery cell in the power battery
The voltage I d median of body；
First matrix calculation unit, for the voltage value and each moment electricity according to each battery cell in the power battery
The voltage I d median of pond monomer calculates the voltage deviation matrix at each moment
Wherein, MtIndicate the voltage deviation matrix of t moment, t ∈ [T-M, T], Δ V1,tIndicate first battery cell in t moment
Voltage deviation value, Δ Vn,tIndicate n-th of battery cell in the voltage deviation value of t moment, V1,tIndicate first battery cell in t
The voltage value at moment, Vn,tN-th of battery cell is indicated in the voltage value of t moment, n indicates the total quantity of battery cell, Vm,tTable
Show the voltage I d median of t moment battery cell.
9. a kind of power battery thermal runaway on-line prediction system according to claim 8, which is characterized in that second square
Battle array computing module, specifically:
Wherein, NtIndicate the variation Increment Matrix of t moment, F1,tIndicate first battery cell in the variation of t moment
Increment, Fn,tIndicate n-th of battery cell in the variation increment of t moment, F1,t-1Indicate first battery cell in t-1
The variation increment at quarter, Fn,t-1Indicate n-th of battery cell in the variation increment at t-1 moment, V0Indicate battery cell
Voltage rating.
10. a kind of power battery thermal runaway on-line prediction system according to claim 9, which is characterized in that the third
Matrix computing module, specifically:
KT=(k1,T,…,kn,T),
Wherein, KTIndicate that the variation of current time T increases rate matrix, k1,TIndicate first battery cell in current time T
Variation growth rate, kn,TIndicate n-th of battery cell in the variation growth rate of current time T, i-th of battery list
Variation growth rate of the body in t moment
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