CN115184808A - Battery thermal runaway risk detection method, device, equipment and computer storage medium - Google Patents
Battery thermal runaway risk detection method, device, equipment and computer storage medium Download PDFInfo
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Abstract
The application discloses a battery thermal runaway risk detection method, a device, equipment and a computer storage medium, wherein a first state parameter data of a first battery at a first moment, a second state parameter data of the first battery in a first time and a first distribution interval of a second state parameter data of a second battery in the first time are obtained, a second distribution interval of a second state parameter data of a third battery in the first time is determined according to the first state parameter data and a preset state parameter threshold, a second safety index is determined according to the second state parameter data, the first distribution interval and the second distribution interval, the first safety index and the second safety index are subjected to weighted fusion, and a thermal runaway risk detection result of the first battery is obtained. According to the embodiment of the application, the thermal runaway risk of the battery can be detected based on the state parameter data of the battery, and the battery thermal runaway can be prevented conveniently and timely.
Description
Technical Field
The application belongs to the technical field of battery safety detection, and particularly relates to a battery thermal runaway risk detection method, device and equipment and a computer storage medium.
Background
The problem of thermal runaway of the battery refers to the problem that when the battery is used in charging and discharging, heat is generated due to various internal electrochemical reactions, and due to the fact that a good heat dissipation system is not available, heat is accumulated in the battery, chain reaction is caused, a large amount of heat and harmful gases are dissipated, and adverse phenomena such as performance deterioration, smoke generation, fire explosion and the like occur.
The thermal runaway problem of the battery can seriously affect the performance of the battery, and even explosion, combustion and other dangers can occur in serious conditions, so that a method for evaluating the thermal runaway risk of the battery is urgently needed so as to prevent the thermal runaway of the battery in time.
Disclosure of Invention
The embodiment of the application provides a battery thermal runaway risk detection method, device and equipment and a computer storage medium, which can detect the thermal runaway risk of a battery based on the operation parameters of the battery and are convenient for preventing the thermal runaway of the battery in time.
In a first aspect, an embodiment of the present application provides a method for detecting a risk of thermal runaway of a battery, including:
acquiring first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time length, a first distribution interval of second state parameter data of a second battery in the first time length and a second distribution interval of second state parameter data of a third battery in the first time length, wherein the second battery is other normal batteries with the same type as the first battery, and the third battery is other fault batteries with the same type as the first battery;
determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold;
determining a second safety index of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval;
and performing weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
As a possible implementation manner, the determining a first safety index of the first battery according to the first state parameter data and the preset state parameter threshold includes:
for each first state parameter in the first state parameter data, determining a plurality of state parameter thresholds corresponding to the first state parameter from a plurality of preset state parameter thresholds;
determining a plurality of threshold intervals according to a plurality of state parameter thresholds, wherein different threshold intervals represent different states of the parameter, and one threshold interval corresponds to one safety index;
determining a target threshold interval to which a parameter value of the first state parameter belongs;
taking a safety index corresponding to the target threshold interval as a safety index of the first state parameter;
and taking the safety index of each first state parameter in the first state parameter data as the first safety index of the first battery.
As a possible implementation manner, the determining, by the second state parameter data, the second safety index of the first battery according to the second state parameter data, the first distribution interval, and the second distribution interval includes:
for each item of second state parameter in the second state parameter data, determining a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval, wherein the boundary value comprises a safety boundary value and a fault boundary value;
determining a safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value;
and taking the safety indexes of the second state parameters in the second state parameter data as second safety indexes of the first battery.
As a possible implementation manner, determining a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval includes:
determining an alternative safety boundary value corresponding to the second state parameter according to the first distribution interval;
determining an alternative fault boundary value corresponding to the second state parameter according to the second distribution interval;
determining whether a historical boundary value corresponding to the second state parameter exists, wherein the historical boundary value comprises a historical safety boundary value and a historical fault boundary value;
if the historical boundary value corresponding to the second state parameter does not exist, taking the alternative safety boundary value and the alternative fault boundary value as the boundary value corresponding to the second state parameter;
and if the historical boundary value corresponding to the second state parameter is determined to exist, updating the historical safety boundary value and the historical fault boundary value according to the alternative safety boundary value and the alternative fault boundary value respectively, and taking the updated historical safety boundary value and the updated historical fault boundary value as the boundary value corresponding to the second state parameter.
As a possible implementation manner, determining the safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value includes:
determining whether a parameter value of the second state parameter is close to a safety margin value or a fault margin value;
if the parameter value of the second state parameter is close to the safety boundary value, taking the first preset safety index as the safety index of the second state parameter;
and if the parameter value of the second state parameter is close to the fault boundary value, taking a second preset safety index as the safety index of the second state parameter.
As a possible implementation manner, determining a safety index of a comprehensive parameter according to a parameter value of the second state parameter and a corresponding boundary value includes:
determining a target interval consisting of the safety boundary value and the fault boundary value;
and determining the safety index of the second state parameter by adopting a linear interpolation scoring mode based on the target interval and the parameter value of the second state parameter.
As a possible implementation manner, the weighted fusion manner is linear weighted fusion, cross fusion or predictive fusion.
As a possible implementation manner, the at least one first status parameter includes:
total voltage, current, temperature, remaining charge, health, internal resistance, rate of temperature rise, cell voltage, cell temperature, and/or pressure.
As a possible implementation, the at least one second state parameter includes:
the maximum voltage in the first time period, the minimum voltage in the first time period, the charging frequency, the discharging frequency, the first-time fitting slope of the internal resistance and/or the first-time fitting slope of the health degree.
In a second aspect, an embodiment of the present application further provides a battery thermal runaway risk detection device, including:
the battery management system comprises an acquisition unit, a storage unit and a management unit, wherein the acquisition unit is used for acquiring first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time length, a first distribution interval of second state parameter data of a second battery in the first time length and a second distribution interval of second state parameter data of a third battery in the first time length, the second battery is other normal batteries with the same type as the first battery, and the third battery is other fault batteries with the same type as the first battery;
the first index unit is used for determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold;
the second indicator unit is used for determining a second safety indicator of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval;
and the fusion unit is used for performing weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
In a third aspect, an embodiment of the present application further provides a battery thermal runaway risk detection device, where the device includes: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of battery thermal runaway risk detection as described in any one of the first aspects.
In a fourth aspect, an embodiment of the present application further provides a computer storage medium, where computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by a processor, the method for detecting a risk of thermal runaway of a battery according to any one of the first aspect is implemented.
In a fifth aspect, the present application further provides a computer program product, where instructions of the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method for detecting a risk of thermal runaway of a battery according to any one of the first aspect.
According to the battery thermal runaway risk detection method, the battery thermal runaway risk detection device, the battery thermal runaway risk detection equipment and the computer storage medium, first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time duration and a first distribution interval of second state parameter data of a plurality of second batteries in the first time duration are obtained, a second distribution interval of second state parameter data of a third battery in the first time duration is obtained, the second battery is other normal batteries with the same type as the first battery, the third battery is other fault batteries with the same type as the first battery, a first safety index of the first battery is determined according to the first state parameter data and a preset state parameter threshold, a second safety index of the first battery is determined according to the second state parameter data and the first distribution interval and the second distribution interval, and the first safety index and the second safety index are subjected to weighted fusion to obtain a thermal runaway risk detection result of the first battery. According to the embodiment of the application, the thermal runaway risk of the battery can be detected based on the state parameter data of the battery, the battery thermal runaway is prevented conveniently and timely, the detection is performed based on the first state parameter data and the second state parameter data, the parameter dimensionality for risk detection is increased, and the accuracy of a risk detection result is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for detecting a risk of thermal runaway of a battery according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a battery thermal runaway risk detection device according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a battery thermal runaway risk detection device according to still another embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of, and not restrictive on, the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.
Thermal runaway belongs to a serious failure of a battery, and is extremely harmful to the battery. Most of the existing schemes for detecting the thermal runaway risk are diagnosis/early warning methods for the thermal runaway caused by a specific fault/cause and developed based on experimental data, however, laboratories are difficult to truly reflect the complex working conditions of practical application, and the robustness of the methods is insufficient.
Some schemes start from big data of a using end, but the characteristic parameter dimension proposed by the existing scheme is narrow, the actual thermal runaway causes are many, and the thermal runaway characteristics caused by different causes are different, so that the coverage of the current technology is insufficient.
In order to solve the prior art problems, embodiments of the present application provide a method, an apparatus, a device, and a computer storage medium for detecting a risk of thermal runaway of a battery. Massive operation data in the actual use process of the battery are collected through the cloud platform, a large number of characteristic parameters are analyzed and constructed by combining charge and discharge test data of a large number of normal batteries and abnormal batteries accumulated in a laboratory, a battery application safety boundary is found, and then the thermal runaway risk of the battery is evaluated.
First, a method for detecting a battery thermal runaway risk provided by the embodiment of the present application is described below.
Fig. 1 shows a schematic flow chart of a method for detecting a risk of thermal runaway of a battery according to an embodiment of the present application. As shown in fig. 1, the method for detecting a risk of thermal runaway of a battery provided in an embodiment of the present application may include the following steps:
s11, acquiring first state parameter data of the first battery at a first moment, second state parameter data of the first battery in a first time length, a first distribution interval of second state parameter data of the second battery in the first time length and a second distribution interval of second state parameter data of the third battery in the first time length.
The second battery is other normal batteries with the same type as the first battery, namely, batteries which do not have faults in other batteries with the same type as the first battery.
The third battery is the other failed battery with the same type as the first battery, namely the battery which has thermal runaway failure in the other batteries with the same type as the first battery.
In one example, the first time and the first duration may be set according to actual requirements, for example, the first time may be the current time, the first duration may be one week or one month, and the like. Preferably, the acquired second state parameter data of the first battery may be second state parameter data within a first time period closest to the first time.
S12, determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold value.
And S13, determining a second safety index of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval.
And S14, carrying out weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
In one example, the manner of weighted fusion may be linear weighted fusion, cross fusion, or predictive fusion.
The method for detecting the risk of the thermal runaway of the battery includes the steps of obtaining first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time duration, and a first distribution interval of the second state parameter data of a plurality of second batteries in the first time duration, obtaining a second distribution interval of the second state parameter data of a third battery in the first time duration, determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold value, determining a second safety index of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval, and conducting weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery. According to the embodiment of the application, the thermal runaway risk of the battery can be detected based on the state parameter data of the battery, the battery thermal runaway is prevented conveniently and timely, the detection is performed based on the first state parameter data and the second state parameter data, the parameter dimensionality for risk detection is increased, and the accuracy of a risk detection result is improved.
Specific implementations of the above steps are described below.
In some embodiments, the first state parameter data may comprise parameter values of at least one first state parameter, wherein the at least one first state parameter may comprise: total voltage, current, temperature, remaining charge, health, internal resistance, rate of temperature rise, cell voltage, cell temperature and/or pressure, etc.
In one example, in the battery operation process, first state parameter data of a battery may be collected through a battery management system and the like, and the collected first state parameter data is uploaded to a cloud platform for storage. The first state parameter data may be associated with the battery identification and acquisition time when stored for subsequent lookup. Based on this, in S11, first state parameter data of the first battery at the first time may be acquired from the cloud platform.
Furthermore, when the operation data is stored in the cloud platform, preprocessing such as data cleaning can be performed first, and situations such as message abnormality (for example, the message length is not consistent with the set) caused by abnormal transmission, extreme abnormality of battery data (which means that the battery data exceeds the range of the sensor) and the like are processed, wherein the processing mode includes deletion and interpolation, and then the preprocessed operation data is stored. Therefore, the accuracy of the first state parameter data stored in the cloud platform can be guaranteed, and the accuracy of the first state parameter data acquired from the cloud platform subsequently can be further guaranteed.
In some embodiments, the second state parameter data may comprise parameter values of at least one second state parameter, wherein the at least one second state parameter may comprise: the maximum voltage in the first time period, the minimum voltage in the first time period, the charging frequency, the discharging frequency, the first-time fitting slope of the internal resistance, the first-time fitting slope of the health degree and/or the like.
When the second state parameter data of the first battery in the first duration is acquired in S11, the first state parameter data of the first battery in the first duration may be acquired from the cloud platform, and then the second state parameter data in the first duration may be calculated based on the acquired first state parameter data in the first duration. The specific calculation method is the existing mature technology, and is not described too much here.
In one example, the number of the second batteries may be one or more (including two) in S11. Based on this, when the first distribution interval of the second state parameter data of the second batteries in the first time period is obtained in S11, the second state parameter data of the second batteries in the first time period may be obtained for each second battery, and then the first distribution interval may be determined based on the second state parameter data of all the second batteries.
In one example, the second state parameter data for the second battery over the first duration may be obtained in the same manner as the second state parameter data for the first battery.
In another example, the second battery may be a laboratory battery and its corresponding second state parameter data has been recorded during the experiment. Based on the above, the second state parameter data of the second battery in the first time period can be obtained from the experimental data.
Further, since the second state parameter data may include a plurality of second state parameters, the first distribution interval of the second state parameter data should include the first distribution interval corresponding to each second state parameter. For the first distribution interval corresponding to each second state parameter, the first distribution interval may be determined according to the parameter value of the second state parameter in the second state parameter data of all the second batteries.
In one example, when the first distribution interval of the second state parameter is determined according to the parameter values of the second state parameter in the second state parameter data of all the second batteries, the maximum value and the minimum value of all the parameter values may be determined, and then the interval composed of the maximum value and the minimum value may be taken as the first distribution interval of the second state parameter.
Similarly, the manner of acquiring the second distribution interval of the second state parameter data of the third battery in the first duration in S11 is similar to the manner of acquiring the first interval, and is not described herein again.
In some embodiments, the preset state parameter threshold may be multiple, and a plurality of state parameter thresholds corresponding to a plurality of first state parameters may be included. The preset state parameter threshold value can be given by a cell research and development expert according to an electrochemical principle or test data. Based on this, in S12, when the first safety index of the first battery is determined, the safety indexes of the first state parameters in the first state parameter data may be respectively determined, and then the safety indexes of the first state parameters in the first state parameter data are all used as the first safety indexes of the first battery.
In one example, for each first state parameter in the first state parameter data, when determining the safety index of the first state parameter, multiple state parameter thresholds corresponding to the first state parameter may be determined from multiple preset state parameter thresholds, multiple threshold intervals are then determined according to the multiple state parameter thresholds, a target threshold interval to which a parameter value of the first state parameter belongs is determined, and the safety index corresponding to the target threshold interval is used as the safety index of the first state parameter. Wherein, different threshold intervals represent different states of the parameter, and one threshold interval corresponds to one safety index.
For example, a first state parameter corresponds to three state parameter thresholds, namely, threshold 1, threshold 2 and threshold 3, and four threshold intervals are determined as (∞, threshold 1], (threshold 1, threshold 2], (threshold 2, threshold 3) and (threshold 3, plus infinity) according to the three state parameter thresholds, where the parameter state corresponding to the first interval is no anomaly, the corresponding safety index is Sx11, the parameter state corresponding to the second interval is slight anomaly, the corresponding safety index is Sx12, the parameter state corresponding to the third interval is moderate anomaly, the corresponding safety index is Sx13, the parameter state corresponding to the fourth interval is severe anomaly, and the corresponding safety index is Sx14.
In one example, the safety metric may be a score.
Based on the above manner, the first safety index of the first battery can be determined.
In some embodiments, when the second safety index of the first battery is determined in S13, the safety indexes of the second state parameters in the second state parameter data may be determined respectively, and then the safety indexes of the second state parameters in the second state parameter data are all used as the second safety indexes of the first battery.
In one example, when determining the safety index of each second state parameter in the second state parameter data, the boundary value corresponding to the second state parameter may be determined according to the first distribution interval and the second distribution interval, where the boundary value includes a safety boundary value and a fault boundary value, and then the safety index of the second state parameter may be determined according to the parameter value of the second state parameter and the corresponding boundary value.
Further, as can be seen from the foregoing description of S11, the first distribution interval of the second state parameter data of the second battery in the first time period may include a first distribution interval corresponding to each second state parameter, and the second distribution interval of the second state parameter data of the third battery in the first time period may include a second distribution interval corresponding to each second state parameter. Based on this, when determining the corresponding boundary value according to the first distribution interval and the second distribution interval for each second state parameter, the first distribution interval and the second distribution interval corresponding to the second state parameter may be determined first, and then the boundary value may be determined according to the first distribution interval and the second distribution interval corresponding to the first distribution interval and the second distribution interval.
Furthermore, when the boundary value corresponding to the second state parameter is determined according to the first distribution interval and the second distribution interval corresponding to the second state parameter, the safety boundary value corresponding to the normal battery can be determined according to the first distribution interval because the first distribution interval is determined according to the second state parameter data of the normal battery, and the fault boundary value corresponding to the fault battery can be determined according to the second distribution interval because the second distribution interval is determined according to the second state parameter data of the fault battery.
In one example, when the safety boundary value of the second state parameter is determined according to the first distribution interval corresponding to the second state parameter, the maximum value or the minimum value of the first distribution interval corresponding to the second state parameter may be selected as the candidate safety boundary value according to the characteristic of the second state parameter. For example, if the second state parameter is the highest voltage within the first time period, the maximum value of the corresponding first distribution interval may be selected as the candidate safety boundary value, and if the second state parameter is the lowest voltage within the first time period, the minimum value of the corresponding first distribution interval may be selected as the candidate safety boundary value.
Further, before or after the candidate safety boundary value is determined, it may be determined whether a historical safety boundary value of the second state parameter is currently stored, if it is determined that the historical safety boundary value does not exist, the candidate safety boundary value is directly used as the safety boundary value of the second state parameter, if it is determined that the historical safety boundary value exists, the candidate safety boundary value and the historical safety boundary value are compared, the historical safety boundary value is updated based on a preset first updating condition according to a comparison result, and then the updated historical safety boundary value is used as the safety boundary value of the second state parameter.
In one example, the first update condition may be set in advance according to the characteristic of the second state parameter, and the first update condition may be different for different second state parameters. For example, if the candidate safety margin value is greater than the historical safety margin value, the historical safety margin value is updated to the candidate safety margin value according to the first update condition when the candidate safety margin value of the highest voltage within the first time period is determined to be greater than the historical safety margin value.
In one example, when determining the fault boundary value of the second state parameter according to the second distribution interval corresponding to the second state parameter, the maximum value or the minimum value of the second distribution interval corresponding to the second state parameter may be selected as the fault boundary value according to the characteristic of the second state parameter, and the selected maximum value is usually opposite to the selected maximum value of the safety boundary value, which depends on the size and distribution relationship of the characteristics of the normal battery and the fault battery, so as to ensure that the boundary value may cover all the batteries. For example, if the second state parameter is the highest voltage within the first time period, the minimum value of the corresponding second distribution interval may be selected as the fault boundary value, and if the second state parameter is the lowest voltage within the first time period, the maximum value of the corresponding second distribution interval may be selected as the safety boundary value.
Further, before or after the candidate fault boundary value is determined, it may be determined whether a historical fault boundary value of the second state parameter is currently stored, if it is determined that the historical fault boundary value does not exist, the candidate fault boundary value is directly used as the fault boundary value of the second state parameter, if it is determined that the historical fault boundary value exists, the candidate fault boundary value and the historical fault boundary value are compared, the historical fault boundary value is updated according to a comparison result based on a preset second update condition, and then the updated historical fault boundary value is used as the fault boundary value of the second state parameter.
In one example, the second update condition may be set in advance according to a characteristic of the second state parameter, and the second update condition may be different for different second state parameters. For example, if the second update condition corresponding to the second state parameter, i.e., the highest voltage within the first time period, may be that the candidate fault boundary value is smaller than the historical fault boundary value, the historical fault boundary value is updated to the candidate fault boundary value when it is determined that the candidate fault boundary value of the highest voltage within the first time period is smaller than the historical fault boundary value according to the second update condition.
After the boundary value corresponding to the second state parameter is determined based on the above manner, the safety index of the second state parameter may be determined based on the determined boundary value and the parameter value of the second state parameter.
In one example, when determining the safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value, it may be determined whether the parameter value of the second state parameter is close to the safety boundary value or close to the fault boundary value, and if it is determined that the parameter value of the second state parameter is close to the safety boundary value, the first preset safety index is used as the safety index of the second state parameter, and if it is determined that the parameter value of the second state parameter is closer to the fault boundary value, the second preset safety index is used as the safety index of the second state parameter. The first preset safety index and the second preset safety index may be scores, the first preset safety index is not equal to the second preset safety index, for example, the first preset safety index may be 100, the second preset safety index may be 0, the first preset safety index may also be 0, the second preset safety index is 100, and the size relationship between the first preset safety index and the second preset safety index is not specifically limited in the embodiment of the present application.
When the boundary value that the parameter value of the second state parameter is close to is determined, a first difference absolute value between the parameter value of the second state parameter and the safety boundary value and a second difference absolute value between the parameter value of the second state parameter and the fault boundary value can be calculated, then the first difference absolute value and the second difference absolute value are compared, if the first difference absolute value is smaller than the second difference absolute value, the parameter value of the second state parameter is determined to be close to the safety boundary value, and if the parameter value of the second state parameter is not close to the fault boundary value, the parameter value of the second state parameter is determined to be close to the fault boundary value.
In another example, when determining the safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value, a target interval composed of the safety boundary value and the fault boundary value may be determined first, and then the safety index of the second state parameter may be determined by using a linear interpolation scoring method based on the target interval and the parameter value of the second state parameter. The format of the target interval may be [ safety boundary value, fault boundary value ], or [ fault boundary value, safety boundary value ], and the safety indexes of the two boundary values of the target interval may be set to 0 and 100, respectively, based on which the safety index of the second state parameter may be obtained by interpolation.
Through the mode, a large amount of running data in the actual use process of the battery is collected, and a large amount of accumulated relevant test data of a large amount of normal batteries and fault batteries are combined, so that a large amount of characteristic parameters are analyzed and constructed, the boundary value of battery application is found out, the thermal runaway risk of the battery is detected, the accuracy of a detection result is ensured, and the quantitative evaluation of the safety state of the battery is realized.
Based on the battery thermal runaway risk detection method provided by the embodiment, correspondingly, the application further provides a specific implementation manner of the battery thermal runaway risk detection device. Please see the examples below.
Referring to fig. 2 first, the device for detecting a risk of thermal runaway of a battery provided in an embodiment of the present application includes the following units:
an obtaining unit 201, configured to obtain first state parameter data of a first battery at a first time, second state parameter data of the first battery in a first time period, a first distribution interval of second state parameter data of a second battery in the first time period, and a second distribution interval of second state parameter data of a third battery in the first time period, where the second battery is another normal battery having the same model as the first battery, and the third battery is another faulty battery having the same model as the first battery;
a first index unit 202, configured to determine a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold;
the second index unit 203 is used for determining a second safety index of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval;
and the fusion unit 204 is configured to perform weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
The battery thermal runaway risk detection device obtains first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time, and a first distribution interval of the second state parameter data of a plurality of second batteries in the first time, and a second distribution interval of the second state parameter data of a third battery in the first time, wherein the second battery is other normal batteries with the same type as the first battery, the third battery is other fault batteries with the same type as the first battery, a first safety index of the first battery is determined according to the first state parameter data and a preset state parameter threshold, a second safety index of the first battery is determined according to the second state parameter data, the first distribution interval and the second distribution interval, and the first safety index and the second safety index are weighted and fused to obtain a thermal runaway risk detection result of the first battery. According to the method and the device, the thermal runaway risk of the battery can be detected based on the state parameter data of the battery, the battery thermal runaway is prevented conveniently and timely, meanwhile, the detection is performed based on the first state parameter data and the second state parameter data, the parameter dimensionality for risk detection is increased, and the accuracy of a risk detection result is improved.
As a possible implementation manner, the first state parameter data includes parameter values of at least one first state parameter, the preset state parameter threshold is multiple, and the first indicator unit 202 is configured to:
for each first state parameter in the first state parameter data, determining a plurality of state parameter thresholds corresponding to the first state parameter from a plurality of preset state parameter thresholds;
determining a plurality of threshold intervals according to a plurality of state parameter thresholds, wherein different threshold intervals represent different states of the parameter, and one threshold interval corresponds to one safety index;
determining a target threshold interval to which a parameter value of the first state parameter belongs;
taking a safety index corresponding to the target threshold interval as a safety index of the first state parameter;
and taking the safety index of each first state parameter in the first state parameter data as the first safety index of the first battery.
As a possible implementation manner, the second state parameter data includes at least one parameter value of the second state parameter, and the second index unit 203 is configured to:
for each second state parameter in the second state parameter data, determining a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval, wherein the boundary value comprises a safety boundary value and a fault boundary value;
determining the safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value;
and taking the safety indexes of the second state parameters in the second state parameter data as second safety indexes of the first battery.
As a possible implementation manner, the determining, by the second indicator unit 203, a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval includes:
determining an alternative safety boundary value corresponding to the second state parameter according to the first distribution interval;
determining an alternative fault boundary value corresponding to the second state parameter according to the second distribution interval;
determining whether a historical boundary value corresponding to the second state parameter exists, wherein the historical boundary value comprises a historical safety boundary value and a historical fault boundary value;
if the historical boundary value corresponding to the second state parameter does not exist, taking the alternative safety boundary value and the alternative fault boundary value as the boundary value corresponding to the second state parameter;
and if the historical boundary value corresponding to the second state parameter is determined to exist, updating the historical safety boundary value and the historical fault boundary value according to the alternative safety boundary value and the alternative fault boundary value respectively, and taking the updated historical safety boundary value and the updated historical fault boundary value as the boundary value corresponding to the second state parameter.
As a possible implementation manner, the determining, by the second indicator unit 203, the safety indicator of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value includes:
determining whether a parameter value of the second state parameter is close to a safety margin value or a fault margin value;
if the parameter value of the second state parameter is close to the safety boundary value, taking the first preset safety index as the safety index of the second state parameter;
and if the parameter value of the second state parameter is close to the fault boundary value, taking the second preset safety index as the safety index of the second state parameter.
As a possible implementation manner, the determining, by the second indicator unit 203, the safety indicator of the comprehensive parameter according to the parameter value of the second state parameter and the corresponding boundary value includes:
determining a target interval consisting of a safety boundary value and a fault boundary value;
and determining the safety index of the second state parameter by adopting a linear interpolation scoring mode based on the target interval and the parameter value of the second state parameter.
As a possible implementation manner, the weighted fusion manner is linear weighted fusion, cross fusion or predictive fusion.
As a possible implementation, the at least one first status parameter includes:
total voltage, current, temperature, remaining charge, health, internal resistance, rate of temperature rise, cell voltage, cell temperature, and/or pressure.
As a possible implementation, the at least one second state parameter includes:
the maximum voltage in the first time period, the minimum voltage in the first time period, the charging frequency, the discharging frequency, the first-time fitting slope of the internal resistance and/or the first-time fitting slope of the health degree.
Fig. 3 shows a schematic hardware structure diagram of a battery thermal runaway risk detection device provided by an embodiment of the application.
The battery thermal runaway risk detection apparatus may include a processor 301 and a memory 302 having stored thereon computer program instructions.
Specifically, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 302 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory 302 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to a method according to an aspect of the present disclosure.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement any one of the battery thermal runaway risk detection methods in the above-described embodiments.
In one example, the battery thermal runaway risk detection device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiment of the present application.
In addition, in combination with the battery thermal runaway risk detection method in the foregoing embodiment, the embodiment of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the battery thermal runaway risk detection methods in the above embodiments.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps, after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (13)
1. A battery thermal runaway risk detection method is characterized by comprising the following steps:
acquiring first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time length, a first distribution interval of second state parameter data of a second battery in the first time length and a second distribution interval of second state parameter data of a third battery in the first time length, wherein the second battery is other normal batteries with the same type as the first battery, and the third battery is other fault batteries with the same type as the first battery;
determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold;
determining a second safety index of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval;
and performing weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
2. The method according to claim 1, wherein the first status parameter data includes parameter values of at least one first status parameter, the preset status parameter threshold is plural, and the determining the first safety indicator of the first battery according to the first status parameter data and the preset status parameter threshold includes:
for each first state parameter in the first state parameter data, determining a plurality of state parameter thresholds corresponding to the first state parameter from a plurality of preset state parameter thresholds;
determining a plurality of threshold intervals according to the state parameter thresholds, wherein different threshold intervals represent different states of the parameter, and one threshold interval corresponds to one safety index;
determining a target threshold interval to which the parameter value of the first state parameter belongs;
taking a safety index corresponding to the target threshold interval as a safety index of the first state parameter;
and taking the safety index of each first state parameter in the first state parameter data as the first safety index of the first battery.
3. The method of claim 1, wherein the second state parameter data comprises parameter values of at least one second state parameter, and wherein determining the second safety metric for the first battery based on the second state parameter data and the first distribution interval and the second distribution interval comprises:
for each second state parameter in the second state parameter data, determining a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval, wherein the boundary value comprises a safety boundary value and a fault boundary value;
determining a safety index of the second state parameter according to the parameter value of the second state parameter and the corresponding boundary value;
and taking the safety index of each second state parameter in the second state parameter data as a second safety index of the first battery.
4. The method according to claim 3, wherein the determining a boundary value corresponding to the second state parameter according to the first distribution interval and the second distribution interval comprises:
determining an alternative safety boundary value corresponding to the second state parameter according to the first distribution interval;
determining an alternative fault boundary value corresponding to the second state parameter according to the second distribution interval;
determining whether a historical boundary value corresponding to the second state parameter exists, wherein the historical boundary value comprises a historical safety boundary value and a historical fault boundary value;
if the historical boundary value corresponding to the second state parameter does not exist, taking the alternative safety boundary value and the alternative fault boundary value as the boundary value corresponding to the second state parameter;
and if the historical boundary value corresponding to the second state parameter is determined to exist, updating the historical safety boundary value and the historical fault boundary value according to the alternative safety boundary value and the alternative fault boundary value respectively, and taking the updated historical safety boundary value and the updated historical fault boundary value as the boundary values corresponding to the second state parameter.
5. The method according to claim 3, wherein determining the safety measure of the second state parameter based on the parameter value of the second state parameter and the corresponding boundary value comprises:
determining whether a parameter value of the second state parameter is close to the safety margin value or close to the fault margin value;
if the parameter value of the second state parameter is close to the safety boundary value, taking a first preset safety index as the safety index of the second state parameter;
and if the parameter value of the second state parameter is close to the fault boundary value, taking a second preset safety index as the safety index of the second state parameter.
6. The method according to claim 3, wherein said determining a safety measure for said combined parameter from said parameter value for said second state parameter and said corresponding boundary value comprises:
determining a target interval consisting of the safety limit value and the fault limit value;
and determining the safety index of the second state parameter by adopting a linear interpolation scoring mode based on the target interval and the parameter value of the second state parameter.
7. The method of claim 1, wherein the weighted fusion is linear weighted fusion, cross fusion or predictive fusion.
8. The method of claim 2, wherein the at least one first status parameter comprises:
total voltage, current, temperature, remaining charge, health, internal resistance, rate of temperature rise, cell voltage, cell temperature, and/or pressure.
9. The method of claim 3, wherein the at least one second state parameter comprises:
the maximum voltage in the first time period, the minimum voltage in the first time period, the charging frequency, the discharging frequency, the internal resistance one-time fitting slope and/or the health degree one-time fitting slope.
10. A battery thermal runaway risk detection apparatus, comprising:
the battery management system comprises an acquisition unit, a storage unit and a management unit, wherein the acquisition unit is used for acquiring first state parameter data of a first battery at a first moment, second state parameter data of the first battery in a first time length, a first distribution interval of second state parameter data of a second battery in the first time length and a second distribution interval of second state parameter data of a third battery in the first time length, the second battery is other normal batteries with the same type as the first battery, and the third battery is other fault batteries with the same type as the first battery;
the first index unit is used for determining a first safety index of the first battery according to the first state parameter data and a preset state parameter threshold;
a second indicator unit, configured to determine a second safety indicator of the first battery according to the second state parameter data and the first distribution interval and the second distribution interval;
and the fusion unit is used for performing weighted fusion on the first safety index and the second safety index to obtain a thermal runaway risk detection result of the first battery.
11. A battery thermal runaway risk detection apparatus, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the battery thermal runaway risk detection method of any of claims 1-9.
12. A computer storage medium having computer program instructions stored thereon, which when executed by a processor, implement the battery thermal runaway risk detection method of any of claims 1-9.
13. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the battery thermal runaway risk detection method of any of claims 1-9.
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