CN110161414B - Power battery thermal runaway online prediction method and system - Google Patents
Power battery thermal runaway online prediction method and system Download PDFInfo
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- CN110161414B CN110161414B CN201910509963.7A CN201910509963A CN110161414B CN 110161414 B CN110161414 B CN 110161414B CN 201910509963 A CN201910509963 A CN 201910509963A CN 110161414 B CN110161414 B CN 110161414B
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- G—PHYSICS
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- 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]
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Abstract
The invention discloses a power battery thermal runaway online prediction method and system. The method comprises the following steps: calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; and inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery. The method and the device realize the online prediction of the thermal runaway of the power battery in the real vehicle environment and improve the prediction precision.
Description
Technical Field
The invention relates to the technical field of battery thermal runaway prediction, in particular to a power battery thermal runaway online prediction method and system.
Background
The lithium ion battery is widely used by the electric automobile by virtue of the advantages of high specific energy, large specific power, long service life and the like, but with the improvement of the specific energy of the power battery and the wide application of the ternary lithium ion battery, the safety problem of the lithium ion battery is increasingly prominent, and the safety accident of the new energy automobile reaches 50 in 2018, wherein the thermal runaway of the battery is the main reason of the accident. The thermal runaway accident of the battery involves casualties and property losses of a large number of people, so that the thermal runaway of the battery is a core problem to be solved in the development process of the electric automobile.
At present, the main researches on the thermal runaway of the battery are as follows: the internal reaction mechanism and the external characteristics of the battery when thermal runaway occurs are explored through experiments, and further, a measure for preventing the thermal runaway is provided. The existing method carries out accurate diagnosis on the abnormal state of the battery through characteristic parameters such as battery voltage, temperature and the like measured by a laboratory, but in a real vehicle environment, the characteristics of the battery are influenced by various factors and are a complex working condition of multi-factor coupling, so that the existing method is difficult to be applied to a real electric vehicle.
In order to ensure driving safety and avoid potential failures of electric vehicles, in recent years, some researchers have proposed methods for battery failure prediction and state of health assessment, which are based on one-dimensional assessment based on SOH, which is a state of health of a battery and is generally calculated by using a ratio of a current maximum available usage to a rated capacity, and prediction using SOH may well reflect a state of health, a degree of aging, and a remaining life of the battery, but may not diagnose and predict short-term failures such as thermal runaway, overcharge, overdischarge, and a short-term battery short-circuit of the battery.
Disclosure of Invention
Therefore, it is necessary to provide a power battery thermal runaway online prediction method and system to realize online prediction of the power battery thermal runaway in an actual vehicle environment and improve prediction accuracy.
In order to achieve the purpose, the invention provides the following scheme:
an online prediction method for thermal runaway of a power battery comprises the following steps:
acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; one moment corresponds to one frame data;
calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage deviation matrix is composed of a plurality of voltage deviation values; one battery monomer corresponds to one voltage deviation value;
calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; the voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment;
calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; the voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate;
and inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
Optionally, the step of inputting the current driving mileage of the vehicle, the temperature average value of the current temperature probe, and the voltage offset growth rate of each cell corresponding to the voltage offset growth rate matrix at the current time T into a thermal runaway cell prediction model to obtain a power battery thermal runaway prediction result includes:
inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset growth rate of each monomer corresponding to the voltage offset growth rate matrix at the current time T into a thermal runaway monomer prediction model, and outputting the thermal runaway prediction matrix at the current time T;
judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current moment T;
if the potential thermal runaway single battery exists, transmitting the serial number of the potential thermal runaway single battery to an instrument panel of the automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform so as to realize monitoring and early warning;
if no potential thermal runaway battery monomer exists, judging whether new data are generated;
if new data are generated, enabling T to be T +1, and returning to the step of obtaining the current driving mileage of the automobile, the average temperature value of the current temperature probe and the voltage value of each battery cell in the power battery;
and if no new data is generated, ending the process.
Optionally, the calculating a voltage deviation matrix at each moment according to the voltage value of each battery cell in the power battery specifically includes:
calculating the voltage median value of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery;
calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery and the voltage median value of each battery monomer at each moment
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tIndicates the voltage deviation value, DeltaV, of the first battery cell at the moment tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
Optionally, the step of calculating the voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery cell, and the voltage offset increment matrix at the last moment corresponding to each moment includes:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicates the increment of voltage deviation, V, of the nth cell at the time t-10Indicating the rated voltage of the battery cell.
Optionally, the step of calculating a voltage offset increase rate matrix at the current time T according to the voltage offset increment matrix at each time specifically includes:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
The invention also provides a power battery thermal runaway online prediction system, which comprises:
the data acquisition module is used for acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; one moment corresponds to one frame data;
the first matrix calculation module is used for calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage deviation matrix is composed of a plurality of voltage deviation values; one battery monomer corresponds to one voltage deviation value;
the second matrix calculation module is used for calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; the voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment;
the third matrix calculation module is used for calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; the voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate;
and the prediction module is used for inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
Optionally, the prediction module specifically includes:
the prediction matrix obtaining unit is used for inputting the driving mileage of the current automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into the thermal runaway monomer prediction model and outputting the thermal runaway prediction matrix at the current time T;
the first judgment unit is used for judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current time T;
the serial number transmission unit is used for transmitting the serial number of the potential thermal runaway battery monomer to an instrument panel of an automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform if the potential thermal runaway battery monomer exists so as to realize monitoring and early warning;
the second judgment unit is used for judging whether new data are generated or not if no potential thermal runaway single battery exists;
a returning unit, configured to, if new data is generated, make T equal to T +1, and return to the data obtaining module;
and an ending unit for ending if no new data is generated.
Optionally, the first matrix calculation module specifically includes:
the median calculating unit is used for calculating the voltage median of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery;
a first matrix calculation unit, configured to calculate a voltage deviation matrix at each time according to the voltage value of each battery cell in the power battery and the voltage median value of each battery cell at each time
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tIndicates the voltage deviation value, DeltaV, of the first battery cell at the moment tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
Optionally, the second matrix calculation module specifically includes:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicates the increment of voltage deviation, V, of the nth cell at the time t-10Indicating the rated voltage of the battery cell.
Optionally, the third matrix calculation module specifically includes:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a power battery thermal runaway online prediction method and system. The method analyzes the difference of the voltage curves of the thermal runaway potential monomer and the normal monomer based on the time sequence, then couples historical data and online data by a voltage deviation absolute value accumulation method, and then predicts the thermal runaway potential monomer by adopting a thermal runaway monomer prediction model. Compared with a laboratory research method, the method has the advantages that the actual vehicle running data is analyzed, and then the actual vehicle state is predicted, so that the method is closer to the actual engineering application. The method or the system can realize the online prediction of the thermal runaway of the power battery in the real-vehicle environment, can accurately perform the real-time online prediction on the thermal runaway potential monomer in the days before the thermal runaway occurs, and has high prediction precision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an online prediction method of thermal runaway of a power battery according to embodiment 1 of the present invention;
FIG. 2 is a graph showing the voltage offset increase rate of each cell at a certain time in example 2 of the present invention;
FIG. 3 is a graph showing the frequency distribution of the number of cells in different voltage offset increase rate intervals according to example 2 of the present invention;
FIG. 4 is a schematic diagram of a thermal runaway monomer prediction model in example 2 of the present invention;
FIG. 5 is a diagram of the prediction results of different values of M when M is not less than 3800 in example 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1
Fig. 1 is a flowchart of an online prediction method of thermal runaway of a power battery in embodiment 1 of the present invention.
The online prediction method for the thermal runaway of the power battery comprises the following steps:
step S1: acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery cell from the moment T-M to the current moment T.
Wherein one time corresponds to one frame of data.
Step S2: and calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery.
The voltage deviation matrix is composed of a plurality of voltage deviation values; one battery cell corresponds to one voltage deviation value.
The step S2 specifically includes:
21: and calculating the voltage median value of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery.
22: calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery and the voltage median value of each battery monomer at each moment
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tIndicates the voltage deviation value, DeltaV, of the first battery cell at the moment tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
Step S3: and calculating the voltage offset increment matrix of each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix of the last moment corresponding to each moment.
The voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment.
The step S3 specifically includes:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicates the increment of voltage deviation, V, of the nth cell at the time t-10Indicating the rated voltage of the battery cell.
Step S4: and calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time.
The voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate.
The step S4 specifically includes:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
Step S5: and inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
The step S5 specifically includes:
51: and inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model, and outputting the thermal runaway prediction matrix at the current time T.
52: and judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current moment T.
If the potential thermal runaway single battery exists, the serial number of the potential thermal runaway single battery is transmitted to an instrument panel of the automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform, so that monitoring and early warning are achieved.
If no potential thermal runaway battery monomer exists, judging whether new data are generated; if new data are generated, enabling T to be T +1, and returning to the step of obtaining the current driving mileage of the automobile, the average temperature value of the current temperature probe and the voltage value of each battery cell in the power battery; and if no new data is generated, ending the process.
The online prediction method for the thermal runaway of the power battery in the embodiment can realize online prediction of the thermal runaway of the power battery in a real-vehicle environment, can accurately perform real-time online prediction on a thermal runaway potential monomer in several days before the thermal runaway occurs, and has high prediction precision.
A more detailed embodiment is provided below.
Example 2
1. Data selection
The data of the embodiment come from a national big data platform of the new energy automobile, and the platform can collect and store various data including online data and offline data during the operation of the new energy automobile. The data in the platform covers aspects of vehicle position, speed, and battery system status. The data of the electric automobile in the platform comprises vehicle running state data, vehicle position data, vehicle battery system state data, vehicle motor system state data, vehicle fault alarm data and the like. The embodiment analyzes the data of a plurality of automobiles which are overheated and out of control and normal automobiles on the national big data platform of the new energy automobile. The time interval between every two frames of data is 10 s.
The data preprocessing steps are as follows: (1) and calling the data of one month before the thermal runaway of the thermal runaway automobile and the data of one month of a normal automobile from the platform. (2) And transcoding and dividing the data to obtain a readable form file. (3) According to the real-time information acquisition items in GB/T32960, extracting the dimensionality related to thermal runaway prediction, including cell voltage, probe temperature and current automobile driving mileage, and obtaining various data distributed according to a time sequence. (4) And removing abnormal values and null values by a threshold value method to obtain effective data of the automobile.
2. Thermal runaway monomer prediction model
There are many causes for thermal runaway of automotive batteries, mainly including mechanical abuse including battery collision, crush, puncture, etc., and electrical abuse including internal short circuit, external short circuit, overcharge, overdischarge, etc. Most thermal runaway starts from a certain monomer or a plurality of monomers and gradually diffuses, a battery management system BMS can alarm the thermal runaway phenomenon by monitoring parameters such as temperature and voltage of a battery, but because the temperature and the voltage of the battery rapidly rise when the thermal runaway happens, the occurrence of accidents is difficult to avoid by current BMS real-time online monitoring, and therefore the identification and prediction of potential thermal runaway monomers are of great importance. Fortunately, the method based on big data can analyze the data of the battery for a period of time before the thermal runaway occurs, so that the potential thermal runaway monomer of the battery can be predicted, the thermal runaway can be predicted before the occurrence of the thermal runaway, and the occurrence of accidents is avoided.
1) Construction of thermal runaway monomer prediction model
Thermal runaway of a battery is affected by a number of factors, and cell voltage is a comprehensive manifestation of failure. In order to research the potential thermal runaway faults of the battery, the statistical analysis is carried out on the voltage of each battery cell in one month before the thermal runaway occurs, and the analysis shows that the voltage fluctuation of the thermal runaway potential fault cell is larger than that of a normal battery cell in one month before the thermal runaway occurs, the situation that the voltage is too low is repeatedly caused, the voltage of the thermal runaway potential fault cell is repeatedly lower than 3.3V in the final stage of discharge, and the terminal voltage can reflect the SOC of the battery, so the thermal runaway potential cell is overdischarged in the final stage of discharge. In addition, the thermal runaway potential cell voltage was slightly higher than the normal cell voltage at the end of charging, indicating that the thermal runaway potential cell was slightly overcharged at the end of charging, and the electrochemical properties of the battery gradually deteriorated as the number of charge and discharge cycles increased.
In order to quantitatively describe the fluctuation degree of the cell voltage, the cell voltage deviation in each frame of data is calculated, and the calculation formula is as follows:
ΔVi,t＝Vi,t-Vmedian,t
in the formula,. DELTA.Vi,tIs the ith monomerVoltage deviation of t frame data, unit is V; vi,tThe voltage of the ith cell frame data is V, Vmedian,tThe median of all cell voltages in the t frame data is given in V.
The voltage deviation of the thermal runaway monomer and the normal monomer within one month is counted, and the statistical data shows that the voltage deviation of the normal monomer is generally kept between-0.1V and +0.1V and has better consistency in the previous month before the thermal runaway occurs, the voltage deviation of the thermal runaway potential monomer exceeds the range of-0.1V to +0.1V for many times and is obviously larger than the voltage deviation of other normal monomers, the voltage deviation of the monomer exceeds-0.5V for even many times, and the voltage deviation appears the phenomena of neglect, plus and minus.
From the above analysis of the cell voltage, it can be concluded that thermal runaway of the cell can be reflected by a deviation in the voltage and there is a progressively worsening process of accumulation. And accumulating the absolute values of the voltage deviation of each battery monomer at the current moment and the historical moment to be used as the current voltage deviation increment of the monomer, so that the current data and the historical data of the automobile are coupled, and the potential thermal runaway monomer is identified. And setting the current time data as a Tth frame, wherein the voltage offset increment of each battery monomer is defined as follows:
in the formula, Fi,TFor the voltage offset increment of the ith cell, Tth frame data, V0Is the rated voltage of the battery cell and has the unit of V.
The voltage deviation increment of each battery cell at the corresponding moment of the T-1 frame data can be obtained by the formula:
in the formula, Fi,T-1Increment of voltage offset for T-1 frame data of ith cell
The following two formulas can be obtained:
when the automobile generates a new frame of data, the new frame of data can pass through F in the formulai,TAnd Fi,T-1Directly calculating F from the relationship of (1)i,TAnd the method of accumulating the absolute values of all the monomer deviations is not needed, so that the calculated amount is not increased due to the increase of the data amount of the automobile.
The voltage offset increment of each battery cell at each moment is calculated according to the formula, and analysis shows that the voltage offset increment of each battery cell at each moment approximately linearly changes along with time, and the thermal runaway potential cell is increased faster than the normal cell voltage offset increment.
According to the analysis, the least square straight line fitting can be carried out on the voltage offset incremental curve of the battery cell, and the slope of the straight line obtained through fitting is used for identifying the potential thermal runaway cell. However, as the amount of automobile data increases, the calculation time of the least square straight line fitting also increases, so that the calculation step size M is defined to limit the number of history data participating in the fitting.
The calculated step size M represents the number of frames of historical data that participate in the voltage offset delta fitting each time a new frame of data is generated by the vehicle. The calculation step length is a constant and can be determined according to the calculation capacity of the big data platform and the BMS when the BMS control strategy is designed, and the data fitting calculation time is ensured to be smaller than the time interval between every two frames of data of the automobile, so that the online real-time prediction is realized.
When the automobile generates a new frame of data, the least square method straight line fitting is carried out on the voltage offset increment of the current time data and the previous M frames of data, and the slope K of the voltage offset increment curve of each monomer is calculatediDefined as the voltage offset increase rate, the calculation formula is as follows:
in the formula, KiThe voltage offset increase rate of the ith cell, t is the number of data frames, Fi,tIs the ith cell voltage offset increment in the t frame data.
Fig. 2 is a voltage offset increase rate distribution diagram of each cell at a certain time in example 2 of the present invention, and fig. 3 is a cell number frequency distribution diagram in different voltage offset increase rate intervals in example 2 of the present invention. As can be seen from the figure, the voltage deviation increase rate of the No. 125 thermal runaway monomer is larger than that of other monomers, and the voltage deviation increase rate of most monomers is below 0.375 by 10(-5), so that the thermal runaway potential monomer can be identified by calculating the voltage deviation increase rate of each monomer on line.
Although the voltage of the battery monomer is the comprehensive embodiment of the fault, the fault such as thermal runaway of the battery and the like are also related to the aging degree of the battery, the aging degree of the battery is represented by the current driving mileage, and in addition, the temperature is the most obvious characteristic parameter of the thermal runaway, so that the temperature of each probe is also used as one of judgment bases of potential thermal runaway monomers. And taking the voltage offset increase rate of each monomer, the current driving mileage and the average value of the current temperature probe temperature as the input of the neural network.
Because the model is established based on the real vehicle data of the thermal runaway vehicle, the model is in accordance with the actual condition of the vehicle, and the thermal runaway fault of the potential thermal runaway monomer judged according to the model is reasonably believed to occur probably within a period of time later, and the maintenance is required in time. Therefore, the model only predicts whether a monomer is a thermal runaway potential monomer, but does not predict the probability of thermal runaway. And (4) taking whether the monomer is the potential thermal runaway monomer as the output of the neural network, and training a potential thermal runaway monomer prediction model. The neural network training model is as shown in figure 4.
2) Thermal runaway monomer prediction algorithm process
According to the thermal runaway prediction model established for the battery monomer, a battery thermal runaway prediction method based on data driving is provided, and the core idea is to combine the current data and the historical data of the automobile and identify the potential thermal runaway monomer on line in real time. Setting the current moment of the automobile as the Tth frame data, and the steps are as follows:
(1) and setting the number M of online fitting data frames.
(2) And extracting the T-th frame data (current time data) of the automobile, and calculating the median of all the cell voltages in the T-th frame data.
(3) And solving the absolute value of the difference value between each single voltage and the median of the voltage to be used as a voltage deviation matrix at the current moment:
MT＝(|V1,T-Vm,T|,…,|Vn,T-Vm,T|)
in the formula, MTFor the current time voltage deviation matrix, Vm,TFor the median, V, of all cell voltages at the present moment1,T,V2,T,…,Vi,T,…,Vn,TFor each cell voltage.
(4) Calculating a current moment voltage offset increment matrix according to a formula:
in the formula, NTThe delta matrix is offset for the voltage at the present time.
(5) And performing least square linear fitting on data from (T-M) to T frame in the voltage offset increment matrix to obtain a current-time voltage offset increment rate matrix of each monomer.
KT＝(k1,T,…,kn,T)
In the formula, KTFor the current time voltage offset growth rate matrix, k1,T,k2,T,…,ki,T,…,kn,TThe voltage offset growth rate of each cell.
(6) And inputting the voltage deviation growth rate of each monomer, the current driving mileage and the average temperature value of the current temperature probe into a thermal runaway prediction model to judge the potential thermal runaway monomer.
(7) And (4) repeatedly executing the steps (1) to (6) when the automobile generates a new frame of data, and continuously circulating.
3. Real vehicle data verification
The total sample data, training data and test data for real vehicle validation are shown in table 1.
TABLE 1 real vehicle verification data
Setting a prediction matrix P to record the prediction situation of battery thermal runaway:
in the formula, pij(i is 1,2, …, t) (j is 1,2, …, n) represents the predicted value of the single thermal runaway calculated by the battery single body at the time j according to the model, if the condition of the thermal runaway early warning is met, p is addedijAnd recording as 1, otherwise, recording as 0.
The battery monomer generating the thermal runaway of the vehicle is a No. 125 monomer, the calculation step length M is set to 5000, and the simulation verification result shows that the data of the No. 125 potential thermal runaway monomer when early warning is generated for the first time is 49111 th frame, and then the data is in an early warning state all the time, namely the thermal runaway fault can be early warned before 7 days; and the rest monomers have no early warning record. The accuracy of the algorithm is verified.
In order to study the influence of the calculation step size M on the prediction results, the prediction results of the thermal runaway potential monomer in the case of M being 100, 200, 300, … and 9900,10000 are compared. By analyzing the corresponding prediction results when M is 100, 100, 4000 and 10000, it can be known that, for the vehicle, when M is less than 3800, the prediction cannot be accurately performed, the increase rate of the fitted voltage offset is greatly influenced by voltage fluctuation due to too small data amount, and some normal monomers are erroneously determined as potential thermal runaway monomers; when M is more than or equal to 3800, the thermal runaway potential monomer can be accurately predicted.
FIG. 5 is a graph of the prediction results of different values of M when M is greater than or equal to 3800 in example 2 of the present invention, where the ordinate represents the number of frames n for predicting a potential thermal runaway monomer for the first time, and a smaller n indicates a more timely prediction. It can be seen that n increases with increasing M and the trend of growth is slower because as M increases, the proportion of newly generated data in the data participating in the fitting decreases, resulting in a lag in predicting the thermal runaway potential monomer. In addition, in the process of increasing M from 3800 to 10000, n is increased from 48135 to 51031, the interval between two adjacent frames of data is 10s, so that the data lags 8 hours, and compared with the situation that a potential thermal runaway monomer can be predicted 7 days in advance, the prediction lag caused by increasing M is not obvious. However, increasing M increases the calculation time of the model, and M should not be selected too much, so that the calculation amount of multiplication is 2M +2 and the calculation amount of addition is 5M +5, as can be seen from the formula of the voltage offset increase rate.
The method for predicting the thermal runaway of the power battery on line analyzes the data of the power battery monomer of the automobile which has thermal runaway in the previous month, firstly analyzes the difference of a voltage curve of a thermal runaway potential monomer and a normal monomer based on a time sequence, further analyzes the influence of voltage deviation on overcharge and overdischarge of the battery, then couples historical data and current data by a voltage deviation absolute value accumulation method, establishes a thermal runaway monomer prediction model based on a neural network method, predicts the thermal runaway potential monomer, and finally verifies the model by using real vehicle data. Through verification, the power battery thermal runaway online prediction method can accurately perform real-time online prediction on the thermal runaway potential monomer, and provides a certain design idea and reference basis for online thermal runaway diagnosis of the power battery.
The power battery thermal runaway online prediction method of the embodiment has the following characteristics:
(1) and coupling the current data and the historical data of the automobile to predict the potential thermal runaway monomer in the battery pack.
(2) The real-time online fault prediction is iterated continuously along with the update of automobile data, and the voltage offset increment of a potential thermal runaway monomer is larger and larger than the voltage offset increment of other normal monomers along with the increase of data volume, so that the prediction accuracy is higher and higher.
(3) Because the embodiment only needs to consider the deviation of each single voltage, the current state of the automobile does not need to be identified, the influence of data frame loss on the prediction result can be avoided, and the prediction accuracy is improved.
(4) The battery monomer with better consistency and poorer consistency can be quantitatively judged at the same time according to the size of the voltage deviation increase rate, and a method is provided for evaluating the consistency of the battery.
(5) The embodiment predicts whether a monomer is a thermal runaway potential monomer, and does not predict the probability of thermal runaway.
(6) The embodiment can accurately predict the thermal runaway faults such as battery overcharge and overdischarge caused by the inconsistency of the batteries.
The invention also provides a power battery thermal runaway online prediction system, which comprises:
the data acquisition module is used for acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; one time corresponds to one frame data.
The first matrix calculation module is used for calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage deviation matrix is composed of a plurality of voltage deviation values; one battery cell corresponds to one voltage deviation value.
The second matrix calculation module is used for calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; the voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment.
The third matrix calculation module is used for calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; the voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate.
And the prediction module is used for inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
As an optional implementation manner, the prediction module specifically includes:
the prediction matrix obtaining unit is used for inputting the driving mileage of the current automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into the thermal runaway monomer prediction model and outputting the thermal runaway prediction matrix at the current time T;
the first judgment unit is used for judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current time T;
the serial number transmission unit is used for transmitting the serial number of the potential thermal runaway battery monomer to an instrument panel of an automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform if the potential thermal runaway battery monomer exists so as to realize monitoring and early warning;
the second judgment unit is used for judging whether new data are generated or not if no potential thermal runaway single battery exists;
a returning unit, configured to, if new data is generated, make T equal to T +1, and return to the data obtaining module;
and an ending unit for ending if no new data is generated.
As an optional implementation manner, the first matrix calculation module specifically includes:
the median calculating unit is used for calculating the voltage median of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery;
a first matrix calculation unit, configured to calculate a voltage deviation matrix at each time according to the voltage value of each battery cell in the power battery and the voltage median value of each battery cell at each time
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tRepresenting the first electricityVoltage deviation value, delta V, of the cell body at time tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
As an optional implementation manner, the second matrix calculation module specifically includes:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicates the increment of voltage deviation, V, of the nth cell at the time t-10Indicating the rated voltage of the battery cell.
As an optional implementation manner, the third matrix calculation module specifically includes:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
The power battery thermal runaway online prediction system in the embodiment can realize online prediction of the thermal runaway of the power battery in a real-vehicle environment, can accurately perform real-time online prediction on a thermal runaway potential monomer in a few days before the thermal runaway occurs, and has high prediction precision.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A power battery thermal runaway online prediction method is characterized by comprising the following steps:
acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; one moment corresponds to one frame data; m is more than 0 and less than T, and M is an integer;
calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage deviation matrix is composed of a plurality of voltage deviation values; one battery monomer corresponds to one voltage deviation value;
calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; the voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment;
calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; the voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate;
and inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
2. The power battery thermal runaway online prediction method according to claim 1, wherein the step of inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe, and the voltage deviation increase rate of each cell corresponding to the voltage deviation increase rate matrix at the current time T into a thermal runaway cell prediction model to obtain a power battery thermal runaway prediction result specifically comprises the steps of:
inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset growth rate of each monomer corresponding to the voltage offset growth rate matrix at the current time T into a thermal runaway monomer prediction model, and outputting the thermal runaway prediction matrix at the current time T;
judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current moment T;
if the potential thermal runaway single battery exists, transmitting the serial number of the potential thermal runaway single battery to an instrument panel of the automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform so as to realize monitoring and early warning;
if no potential thermal runaway battery monomer exists, judging whether new data are generated;
if new data are generated, enabling T to be T +1, and returning to the step of obtaining the current driving mileage of the automobile, the average temperature value of the current temperature probe and the voltage value of each battery cell in the power battery;
and if no new data is generated, ending the process.
3. The method for online predicting the thermal runaway of the power battery according to claim 1, wherein the calculating the voltage deviation matrix at each moment according to the voltage values of the battery cells in the power battery specifically comprises:
calculating the voltage median value of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery;
calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery and the voltage median value of each battery monomer at each moment
Mt＝(ΔV1,t,…,ΔVn,t)
＝(|V1,t-Vm,t|,…,|Vn,t-Vm,t|)，
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tIndicates the voltage deviation value, DeltaV, of the first battery cell at the moment tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
4. The online prediction method for the thermal runaway of the power battery according to claim 3, wherein the voltage offset increment matrix at each moment is calculated according to the voltage offset matrix, the rated voltage of the battery cell and the voltage offset increment matrix at the last moment corresponding to each moment, and specifically:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicates the nth cellIncrement of voltage offset of cell at time t-1, V0Indicating the rated voltage of the battery cell.
5. The online prediction method for the thermal runaway of the power battery according to claim 4, wherein the voltage offset increase rate matrix of the current time T is calculated according to the voltage offset increment matrix of each time, and specifically comprises the following steps:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
6. An online prediction system for thermal runaway of a power battery is characterized by comprising:
the data acquisition module is used for acquiring the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage value of each battery monomer in the power battery; the voltage value comprises voltage data of the battery monomer from T-M moment to current moment T; one moment corresponds to one frame data; m is more than 0 and less than T, and M is an integer;
the first matrix calculation module is used for calculating a voltage deviation matrix at each moment according to the voltage value of each battery monomer in the power battery; the voltage deviation matrix is composed of a plurality of voltage deviation values; one battery monomer corresponds to one voltage deviation value;
the second matrix calculation module is used for calculating a voltage offset increment matrix at each moment according to the voltage deviation matrix, the rated voltage of the battery monomer and the voltage offset increment matrix at the last moment corresponding to each moment; the voltage offset delta matrix is composed of a plurality of voltage offset deltas; one cell corresponds to one voltage offset increment;
the third matrix calculation module is used for calculating a voltage offset increase rate matrix of the current time T according to the voltage offset increment matrix of each time; the voltage offset growth rate matrix is composed of a plurality of voltage offset growth rates; one cell corresponds to one voltage offset increase rate;
and the prediction module is used for inputting the current driving mileage of the automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into a thermal runaway monomer prediction model to obtain a thermal runaway prediction result of the power battery.
7. The power battery thermal runaway online prediction system of claim 6, wherein the prediction module specifically comprises:
the prediction matrix obtaining unit is used for inputting the driving mileage of the current automobile, the temperature average value of the current temperature probe and the voltage offset increase rate of each monomer corresponding to the voltage offset increase rate matrix at the current time T into the thermal runaway monomer prediction model and outputting the thermal runaway prediction matrix at the current time T;
the first judgment unit is used for judging whether a potential thermal runaway battery monomer exists according to the thermal runaway prediction matrix at the current time T;
the serial number transmission unit is used for transmitting the serial number of the potential thermal runaway battery monomer to an instrument panel of an automobile, a new energy automobile big data monitoring platform and a vehicle maintenance platform if the potential thermal runaway battery monomer exists so as to realize monitoring and early warning;
the second judgment unit is used for judging whether new data are generated or not if no potential thermal runaway single battery exists;
a returning unit, configured to, if new data is generated, make T equal to T +1, and return to the data obtaining module;
and an ending unit for ending if no new data is generated.
8. The power battery thermal runaway online prediction system of claim 6, wherein the first matrix calculation module specifically comprises:
the median calculating unit is used for calculating the voltage median of each battery monomer at each moment according to the voltage value of each battery monomer in the power battery;
a first matrix calculation unit, configured to calculate a voltage deviation matrix at each time according to the voltage value of each battery cell in the power battery and the voltage median value of each battery cell at each time
Mt＝(ΔV1,t,…,ΔVn,t)
＝(|V1,t-Vm,t|,…,|Vn,t-Vm,t|)，
Wherein M istA voltage deviation matrix representing time T, T ∈ [ T-M, T]，ΔV1,tIndicates the voltage deviation value, DeltaV, of the first battery cell at the moment tn,tIndicates the voltage deviation value, V, of the nth battery cell at the time point t1,tRepresenting the voltage value, V, of the first cell at time tn,tRepresenting the voltage value of the nth battery cell at the moment t, n representing the total number of battery cells, Vm,tThe median value of the voltage of the battery cell at the time t is shown.
9. The power battery thermal runaway online prediction system of claim 8, wherein the second matrix calculation module is specifically:
wherein N istA delta matrix of voltage offsets representing time t, F1,tRepresenting the increment of the voltage offset of the first cell at time t, Fn,tIndicates the increment of voltage deviation of the nth cell at the time t, F1,t-1Representing the increment of the voltage offset of the first cell at time t-1, Fn,t-1Indicating the increment of voltage deviation of the nth battery cell at the time point of t-1，V0Indicating the rated voltage of the battery cell.
10. The power battery thermal runaway online prediction system of claim 9, wherein the third matrix calculation module specifically is:
KT＝(k1,T,…,kn,T)，
wherein, KTA voltage offset growth rate matrix, k, representing the current time T1,TRepresents the voltage offset increase rate, k, of the first cell at the current time Tn,TThe voltage deviation growth rate of the nth battery cell at the current time T and the voltage deviation growth rate of the ith battery cell at the time T are shown
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Families Citing this family (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110531195B (en) * | 2019-09-09 | 2021-06-01 | 河南理工大学 | Method for identifying magnetizing inrush current and internal fault of transformer |
| CN112644336B (en) * | 2019-10-11 | 2022-11-04 | 北京车和家信息技术有限公司 | Power battery thermal runaway prediction method and device |
| CN110794305B (en) * | 2019-10-14 | 2021-03-16 | 北京理工大学 | Power battery fault diagnosis method and system |
| CN110907833B (en) * | 2019-10-16 | 2021-01-29 | 北京理工大学 | Power battery fault diagnosis method based on sliding window |
| CN111913113A (en) * | 2020-07-14 | 2020-11-10 | 蜂巢能源科技有限公司 | Method and device for identifying short circuit in electric core, storage medium and electronic equipment |
| CN111766531B (en) * | 2020-07-31 | 2021-07-16 | 中国汽车工程研究院股份有限公司 | Accumulated risk modeling method for judging voltage consistency of battery pack |
| CN111948541A (en) * | 2020-08-13 | 2020-11-17 | 中国第一汽车股份有限公司 | Vehicle battery overvoltage prediction method and device, server and storage medium |
| CN112092675B (en) * | 2020-08-31 | 2022-03-25 | 长城汽车股份有限公司 | Battery thermal runaway early warning method, system and server |
| CN112363061A (en) * | 2020-11-11 | 2021-02-12 | 上海电享信息科技有限公司 | Thermal runaway risk assessment method based on big data |
| CN112784482A (en) * | 2021-01-15 | 2021-05-11 | 东软睿驰汽车技术（沈阳）有限公司 | Thermal runaway prediction method and device |
| US20220352737A1 (en) * | 2021-04-29 | 2022-11-03 | GM Global Technology Operations LLC | Thermal runaway prognosis by detecting abnormal cell voltage and soc degeneration |
| CN113533992B (en) * | 2021-07-26 | 2022-04-08 | 北京理工大学 | Lithium ion battery thermal runaway early warning method based on ultrasonic guided wave sensor |
| CN114966427A (en) * | 2021-08-03 | 2022-08-30 | 长城汽车股份有限公司 | Performance detection method, device and system of battery thermal management system |
| CN116068441A (en) * | 2021-10-29 | 2023-05-05 | 北汽福田汽车股份有限公司 | Power battery internal short circuit early warning method and device and vehicle |
| CN114509678B (en) * | 2022-02-10 | 2024-06-18 | 北京理工大学重庆创新中心 | Power battery thermal runaway prediction method based on big data |
| CN115221798B (en) * | 2022-09-19 | 2022-12-30 | 中南大学 | Battery thermal process space-time modeling prediction method, system, equipment and medium |
| CN115267589B (en) * | 2022-09-26 | 2023-01-06 | 陕西汽车集团股份有限公司 | Multi-parameter joint diagnosis method for battery faults of electric vehicle |
| CN116106757B (en) * | 2022-12-06 | 2024-10-01 | 北汽福田汽车股份有限公司 | Battery cell detection method and device, storage medium and electronic equipment |
| CN115863795B (en) * | 2022-12-06 | 2023-09-12 | 北汽福田汽车股份有限公司 | Data processing method, data processing device, vehicle, and storage medium |
| CN117339913A (en) * | 2023-10-31 | 2024-01-05 | 科立鑫(珠海)新能源有限公司 | Waste battery recovery system |
| CN117761542A (en) * | 2023-12-27 | 2024-03-26 | 江苏讯汇科技股份有限公司 | Battery cabinet thermal runaway early warning method based on battery internal temperature estimation |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103033759A (en) * | 2012-12-11 | 2013-04-10 | 上海市电力公司 | Storage battery thermal runway intelligent judgment method |
| CN105589046A (en) * | 2016-01-29 | 2016-05-18 | 金龙联合汽车工业（苏州）有限公司 | Detection and alarm method of power battery pack thermal runaway diffusion |
| CN106066457A (en) * | 2016-05-25 | 2016-11-02 | 烟台创为新能源科技有限公司 | A kind of battery thermal runaway detecting system and detection method thereof |
| CN107702801A (en) * | 2017-11-16 | 2018-02-16 | 南京工业大学 | Temperature detection-based thermal runaway early warning device and method for lithium ion battery |
| CN107861065A (en) * | 2017-09-30 | 2018-03-30 | 中国汽车技术研究中心 | Thermal runaway test method for power battery test |
| CN108446434A (en) * | 2018-02-07 | 2018-08-24 | 清华大学 | Prediction technique, device and the computer readable storage medium of power battery thermal runaway safety |
| CN108711893A (en) * | 2018-04-08 | 2018-10-26 | 江西优特汽车技术有限公司 | A kind of power battery thermal runaway early warning system and method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9577443B2 (en) * | 2010-06-03 | 2017-02-21 | C&C Power, Inc. | Battery system and management method |
-
2019
- 2019-06-13 CN CN201910509963.7A patent/CN110161414B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103033759A (en) * | 2012-12-11 | 2013-04-10 | 上海市电力公司 | Storage battery thermal runway intelligent judgment method |
| CN105589046A (en) * | 2016-01-29 | 2016-05-18 | 金龙联合汽车工业（苏州）有限公司 | Detection and alarm method of power battery pack thermal runaway diffusion |
| CN106066457A (en) * | 2016-05-25 | 2016-11-02 | 烟台创为新能源科技有限公司 | A kind of battery thermal runaway detecting system and detection method thereof |
| CN107861065A (en) * | 2017-09-30 | 2018-03-30 | 中国汽车技术研究中心 | Thermal runaway test method for power battery test |
| CN107702801A (en) * | 2017-11-16 | 2018-02-16 | 南京工业大学 | Temperature detection-based thermal runaway early warning device and method for lithium ion battery |
| CN108446434A (en) * | 2018-02-07 | 2018-08-24 | 清华大学 | Prediction technique, device and the computer readable storage medium of power battery thermal runaway safety |
| CN108711893A (en) * | 2018-04-08 | 2018-10-26 | 江西优特汽车技术有限公司 | A kind of power battery thermal runaway early warning system and method |
Non-Patent Citations (2)
| Title |
|---|
| 18650型锂离子电池火灾爆炸预警装置研究;杨赟 等;《消防科学与技术》;20180715;第37卷(第7期);第939-942页 * |
| Study of Effect Batteries Capacity on Probability of Thermal Runaway Occurrence;Nataliya N. Yazvinskaya 等;《International Journal of ELECTROCHEMICAL SCIENCE》;20160906;第8163-8168页 * |
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