CN115730184A - Battery temperature abnormality determination method, device, equipment and storage medium - Google Patents
Battery temperature abnormality determination method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of new energy automobile battery temperature monitoring, and particularly relates to a battery temperature abnormity judgment method, device, equipment and storage medium. A battery temperature abnormity determination method is applied to a battery temperature abnormity determination device, the battery temperature abnormity determination device comprises a processor and an execution module, the processor is used for processing temperature signals of a battery temperature sensor group and determining whether real temperature abnormity occurs in a battery, and the method comprises the following steps: the processor receives temperature data collected by the battery sensor group within a first preset time; according to the temperature data, the processor judges that the temperature of the battery is abnormal possibly, and according to a preset algorithm, the temperature data is operated to obtain a processing result; according to the processing result, the processor judges the abnormal reason to obtain a judgment result; and according to the judgment result, the execution module wakes up a preset safety judgment program, determines the thermal runaway of the battery and reports the emergency situation to the cloud. And calculating and judging the abnormal temperature signal, accurately distinguishing whether the battery has real temperature abnormality or an observation system is abnormal, and eliminating the conditions of false alarm and missing alarm.
Description
Technical Field
The invention belongs to the technical field of new energy automobile battery temperature monitoring, and particularly relates to a battery temperature abnormity judgment method, device, equipment and storage medium.
Background
With the popularization of new energy vehicles, the safety requirements on the new energy vehicles are higher and higher, enterprises need to establish a new energy vehicle product operation safety state monitoring platform, analysis and mining of vehicle operation data are enhanced, an advanced safety early warning method is applied, the safety early warning capability of the new energy vehicles is improved, accurate monitoring and early warning of battery safety states are achieved, and the new energy vehicle safety early warning platform is a key index for establishing the vehicle safety monitoring platform.
Chinese patent CN103730700A discloses a method for judging and processing faults of a sampling wire harness by a power battery system, wherein whether the temperature of the sampling wire harness exceeds a sampling threshold value is judged, whether the temperature of the sampling wire harness has faults is determined, and a customer is reminded to check and maintain through vehicle-end alarm. Chinese patent CN111258787A discloses a method for identifying an abnormal NTC temperature sampling value based on a battery pack, which determines whether NTC sampling is abnormal by judging whether a temperature rise rate, a temperature difference of the same module and a temperature difference of the same battery pack exceed a threshold, discards and marks abnormal NTC data, and avoids battery function abnormality.
The method can avoid the malfunction of the vehicle in a normal state, but has certain limitation on the real-time identification of accidental safety faults; for example, when thermal runaway occurs, the temperature, temperature difference and temperature rise rate of the battery must exceed the threshold values, and the current technology may mistakenly identify sampling abnormality. Therefore, the signal abnormality needs to be judged and handled, otherwise, a large amount of risk false alarm and false alarm can occur.
Disclosure of Invention
The purpose of the invention is: the method, the device, the equipment and the storage medium are used for calculating, processing and judging abnormal temperature signals, accurately distinguishing whether the battery has real temperature abnormality or an observation system is abnormal, and eliminating the situations of false alarm and missing alarm.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, an embodiment of the present application provides a battery temperature abnormality determination method, which is applied to a battery temperature abnormality determination device, where the battery temperature abnormality determination device includes a processor and an execution module, where the processor is configured to process a temperature signal of a battery temperature sensor group and determine whether a real temperature abnormality occurs in a battery, and the method includes:
the processor receives temperature data collected by the battery sensor group within a first preset time;
according to the temperature data, the processor judges that the temperature of the battery is abnormal possibly;
according to a preset algorithm, carrying out operation processing on the temperature data to obtain a processing result;
according to the processing result, the processor judges the abnormal reason to obtain a judgment result;
and according to the judgment result, the execution module wakes up a preset safety judgment program, determines the thermal runaway of the battery and reports the emergency situation to the cloud.
With reference to the first aspect, in some optional embodiments, the determining, by the processor, that a temperature abnormality of the battery may occur according to the temperature data includes:
the temperature data is always higher than a preset normal temperature interval of the battery but not higher than a preset battery over-temperature threshold, and the processor judges that the battery is possibly abnormal in temperature;
or the temperature data is always lower than a preset normal temperature interval of the battery, and the processor judges that the temperature of the battery is abnormal possibly;
or the temperature continuously rises and exceeds the battery over-temperature threshold value, and the processor judges that the battery is possibly abnormal in temperature;
or the processor judges that the battery is possible to have temperature abnormality when the temperature data is high or low.
With reference to the first aspect, in some optional embodiments, the temperature data is subjected to operation processing according to a preset algorithm to obtain a processing result, including;
sorting the temperature data of the temperature sensor group according to the time sequence, calculating the median temperature at the same moment, and calculating the difference value delta T between the display temperature of all the temperature sensors at each moment and the median temperature i,n According to said difference Δ T i,n Calculating the temperature difference average value delta T of each temperature sensor in the first preset time i 。
With reference to the first aspect, in some optional embodiments, the temperature data is subjected to operation processing according to a preset algorithm to obtain a processing result, including;
calculating the temperature rise rate k of all the temperature sensors within the first preset time i According to said rate of temperature rise k i Calculating the temperature rise median K in the first preset time, and calculating the chaos S in the first preset time according to the temperature rise median K and the temperature data i 。
With reference to the first aspect, in some optional implementations, according to the processing result, the determining, by the processor, a cause of the abnormality to obtain a determination result includes:
the temperature data is always higher than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Greater than a preset maximum temperature difference threshold, but with said degree of disorder S i Judging that the battery temperature sensor is abnormal when the chaos degree is smaller than a preset chaos degree safety threshold;
or the temperature data is always lower than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Less than a preset minimum temperature difference threshold, but with said degree of disorder S i When the temperature is lower than the chaos degree safety threshold, judging that the battery temperature sensor is abnormal;
or the temperature continuously rises and exceeds the over-temperature threshold value of the battery, and the temperature rise rate k is i Greater than a preset temperature rise rate threshold, but with said degree of disorder S i If the temperature is lower than the chaos safety threshold, judging that an abnormal heat source possibly exists and the battery has a thermal runaway risk;
or, the temperature data is high and low, and the temperature difference average value delta T is high i Greater than a preset maximum temperature difference threshold, and the degree of disorder S i Exceeding a chaos safety threshold; at low temperature, the average value of the temperature difference is delta T i Less than a preset minimum temperature difference threshold, and the degree of disorder S i And if the chaos degree safety threshold is exceeded, judging that the battery temperature sensor is abnormal or the battery is in thermal runaway, and judging that the battery has the risk of thermal runaway.
With reference to the first aspect, in some optional embodiments, according to the determination result, the waking up, by the execution module, a safety determination program, determining that the battery is out of control due to heat, and reporting abnormal information to the cloud, includes:
judging that the sensor is abnormal, and recording fault information by the execution module, determining that the fault information is a non-safety risk, and reporting to a cloud;
and judging that the battery has a thermal runaway risk, awakening a preset safety judgment program by the execution module, further judging, determining the thermal runaway of the battery, and reporting an emergency to the cloud.
In combination with the first aspect, in some alternative embodiments, the method includes:
the processor sequences the temperature signals according to a time sequence in the temperature data processing process, the number of the temperature signals which are arranged is not consistent with the number of the sensors, the processor judges that the data analysis is wrong, and the execution module records abnormal signals and reports the abnormal signals to the cloud for processing.
In a second aspect, an embodiment of the present application further provides a battery temperature abnormality determination apparatus, which is applied to a battery temperature abnormality determination device, where the battery temperature abnormality determination device includes a processor and an execution module, the processor is configured to process a temperature signal of the battery temperature sensor group and determine whether a real temperature abnormality occurs in a battery, and the apparatus includes:
a processing unit: the temperature sensor group is used for receiving temperature data collected by the temperature sensor group within a first preset time, preliminarily judging the temperature condition of the battery according to the temperature data, and judging the reason of abnormality according to a preset algorithm when the temperature data reflects the abnormal temperature of the battery;
an execution unit: and the method is used for reporting to the cloud when the processor judges that the sensor is abnormal, or awakening a preset safety judgment program when the processor judges that the battery has a thermal runaway risk, judging the thermal runaway of the battery and reporting to the cloud to realize emergency.
In a third aspect, an embodiment of the present application further provides a battery temperature abnormality determination device, which includes a processor, an execution module, and a memory, where the processor is configured to process a temperature signal of the battery temperature sensor group, and determine whether a real temperature abnormality occurs in a battery, and the memory stores a computer program, and when the computer program is executed by the processor or the execution module, the battery temperature abnormality determination device is enabled to execute the method described above.
In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program runs on a computer, the computer is caused to execute the foregoing method.
The invention adopting the technical scheme has the following advantages:
the temperature data collected by the battery temperature sensor group is received by the processor, the processor preliminarily judges that the collected temperature data is abnormal, and the temperature difference average value delta T of each temperature sensor in the first preset time is calculated according to a preset algorithm i And degree of disorder S i And analyzing and judging the abnormal condition, determining to awaken a preset safety judgment program according to a judgment result by the execution module, and reporting the result to the cloud. The abnormal data is further processed, whether the analysis is real temperature abnormality or observation system abnormality is analyzed, the result reported to the cloud is more accurate, and the cloud informs the projectTeacher reacts in time.
Drawings
The invention is further illustrated by the non-limiting examples given in the accompanying drawings;
fig. 1 is a block diagram of a battery temperature abnormality determination apparatus according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a method for determining abnormal battery temperature according to an embodiment of the present disclosure.
Fig. 3 is a block diagram of a battery temperature abnormality determination apparatus according to an embodiment of the present application.
The main component symbols are as follows:
10. a battery temperature abnormality determination device; 11. a processor; 12. an execution module; 200. a battery temperature abnormality determination device; 210. a processing unit; 220. and an execution unit.
Detailed Description
The present invention will be described in detail with reference to the drawings and specific embodiments, wherein like reference numerals are used for similar or identical parts in the drawings or the description, and implementations not shown or described in the drawings are known to those of ordinary skill in the art. In addition, directional terms, such as "upper", "lower", "top", "bottom", "left", "right", "front", "rear", and the like, used in the embodiments are only directions referring to the drawings, and are not intended to limit the scope of the present invention.
As shown in fig. 1, the embodiment of the present application provides a battery temperature abnormality determination apparatus 10, where the battery temperature abnormality determination apparatus 10 may include a processor 11, an execution module 12, and a storage module, where the processor 11 is configured to process a temperature signal of the battery temperature sensor group to determine whether a real temperature abnormality occurs in the battery.
In this embodiment, the processor 11 is electrically connected to the temperature sensor group, collects temperature data of the temperature sensor group within a first preset time, compares the temperature data with a preset normal battery temperature interval, if the temperature data is different from the normal battery temperature interval, the battery temperature data is abnormal, further processes the battery temperature data according to a preset algorithm, determines whether the battery has a real temperature abnormality, and when the battery is determined to have the real temperature abnormality, the execution module 12 wakes up a preset safety determination program to determine that the battery has the real temperature abnormality, and reports an emergency to the cloud. And further processing the abnormal data, analyzing whether the data is the real temperature abnormality or the observation system abnormality, reporting the result to the cloud more accurately, and informing an engineer to the cloud to react in time.
The storage module stores therein a computer program that, when executed by the processor 11 or the execution module 12, enables the battery temperature abnormality determination apparatus 10 to execute the respective steps in the braking control method described below.
As shown in fig. 2, the present application also provides a battery temperature abnormality determination method, wherein the battery temperature abnormality determination method may include the steps of:
step 110: the processor 11 receives temperature data acquired by the battery sensor group within a first preset time;
step 120: according to the temperature data, the processor 11 judges that the temperature of the battery is abnormal;
step 130: according to a preset algorithm, carrying out operation processing on the temperature data to obtain a processing result;
step 140: according to the processing result, the processor 11 judges the reason of the abnormality to obtain a judgment result;
step 150: according to the judgment result, the execution module 12 wakes up a preset safety judgment program, determines that the battery is out of control thermally, and reports an emergency situation to the cloud.
In this embodiment, the processor 11 collects temperature data within a first preset time, and analyzes and judges a battery temperature condition fed back by the temperature data; when the processor 11 determines that the temperature is abnormal, the temperature data is processed according to a preset algorithm, when it is determined that the real temperature of the battery is abnormal, the execution module 12 wakes up a preset safety determination program to determine that the real temperature of the battery is abnormal, and an emergency is reported to the cloud.
As an alternative embodiment, the processor 11 determines that the battery may have a temperature abnormality according to the temperature data, and includes:
the temperature data is always higher than a preset normal temperature interval of the battery, but does not exceed a preset over-temperature threshold of the battery, and the processor 11 judges that the temperature of the battery is abnormal;
or, the temperature data is always lower than a preset normal temperature interval of the battery, and the processor 11 determines that the temperature of the battery is abnormal;
or, the temperature continuously rises and exceeds the battery over-temperature threshold, and the processor 11 determines that the battery may have abnormal temperature;
alternatively, the processor 11 may determine that the temperature of the battery is abnormal when the temperature data is high or low.
In this embodiment, the temperature data is compared with the normal temperature range of the battery, the temperature data is always too high or too low to fluctuate timely and at a high time, and the processor 11 determines that the temperature of the battery is abnormal; in addition, the temperature continues to rise and exceeds the battery over-temperature threshold, and the processor 11 may determine that the battery may have a temperature abnormality.
As an optional implementation manner, according to a preset algorithm, performing operation processing on the temperature data to obtain a processing result, including;
sorting the temperature data of the temperature sensor group according to the time sequence, calculating the median temperature at the same moment, and calculating the difference value delta T between the display temperature of all the temperature sensors at each moment and the median temperature i,n According to said difference DeltaT i,n Calculating the temperature difference average value delta T of each temperature sensor in the first preset time i 。
In this embodiment, since time is monotonous, the collected temperature data is sorted according to the time sequence, the median temperature at the same time is calculated, and the difference Δ T between the display temperature of all the temperature sensors and the median temperature at each time is calculated i,n ：
ΔT i，n ＝T i，n -Med(T 1，n ，T 2，n …，T n，n )
Wherein T is i,n Indicating a temperature of an ith one of the temperature sensors at time n;
further calculating the average temperature difference value delta T of each temperature sensor in the first preset time i ：
ΔT i ＝Ave(ΔT i,1 ,ΔT i,,2 ,...,ΔT i,n )
The stability of temperature data is measured by calculating the average value of the temperature difference, and when the real temperature of the battery is abnormal, the temperature fluctuation is large, and the change of the average value of the temperature difference is obvious.
As an optional implementation manner, according to a preset algorithm, performing operation processing on the temperature data to obtain a processing result, including;
calculating the temperature rise rate k of all the temperature sensors within the first preset time i According to said rate of temperature rise k i Calculating the temperature rise median K in the first preset time, and calculating the chaos S in the first preset time according to the temperature rise median K and the temperature data i 。
In this embodiment, the temperature rise rate k is obtained by integrating the temperature with respect to time i ：
Calculating the median K of the temperature rise within the first preset time according to the rate ki of the temperature rise:
K＝Med(k 1 ,k 2 …,k n )
calculating the chaos S of each time point in first preset time according to the temperature rise median K and the temperature data i,n ：
Wherein alpha is a chaos threshold coefficient; accumulating the chaos of each time point in a first preset timeAdding and summing to obtain the chaos S in the first preset time i ：
The stability of temperature data is measured by calculating the chaos degree in the first preset time, and when the real temperature of the battery is abnormal, the temperature fluctuation is large, and the chaos degree value is larger.
As an optional implementation manner, according to the processing result, the processor determines the cause of the abnormality to obtain a determination result, including:
the temperature data is always higher than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Greater than a preset maximum temperature difference threshold, but with said degree of disorder S i Judging that the battery temperature sensor is abnormal when the chaos degree is smaller than a preset chaos degree safety threshold;
or the temperature data is always lower than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Less than a preset minimum temperature difference threshold, but with said degree of disorder S i If the temperature is less than the chaos safety threshold, judging that the battery temperature sensor is abnormal;
or the temperature continuously rises and exceeds the over-temperature threshold value of the battery, and the temperature rise rate k is i Greater than a preset temperature rise rate threshold, but with said degree of disorder S i If the temperature is less than the chaos safety threshold, judging that an abnormal heat source possibly exists and the battery has a thermal runaway risk;
or, the temperature data is high and low, and the temperature difference average value delta T is high i Greater than a preset maximum temperature difference threshold, and the degree of disorder S i Exceeding a chaos safety threshold; at low temperature, the mean value of temperature difference is Delta T i Less than a preset minimum temperature difference threshold, and the degree of disorder S i And if the temperature of the battery exceeds the chaos safety threshold, judging that the battery temperature sensor is abnormal or the battery is in thermal runaway, and ensuring that the battery has the risk of thermal runaway.
In this embodiment, the degree of integration disorder S i Average value of temperature differenceΔT i And further analyzing and judging the temperature data by the temperature rise rate ki, and meeting the chaos degree S at the same time i Exceeding a safe threshold for misordering and a mean value of the temperature difference Δ T i When the temperature is lower than the lowest temperature difference threshold or higher than the highest temperature difference threshold, the fact that the battery is possibly abnormal in real temperature is judged, and the thermal runaway risk exists;
or the battery is abnormally heated, the temperature of the battery exceeds the battery over-temperature threshold value, and the temperature rise rate k i Greater than a predetermined temperature rise rate threshold, even at the same time, the degree of disorder S i When the temperature is lower than the chaos safety threshold, the processor judges that an abnormal heat source possibly exists, the battery possibly has real temperature abnormality and the risk of thermal runaway exists; in other cases, the processor 11 determines that the sensor is abnormal.
As an optional implementation manner, according to the determination result, the executing module 12 wakes up a safety determination program, determines that the battery is out of control due to heat, and reports the abnormal information to the cloud, including:
judging that the sensor is abnormal, and recording fault information by the execution module 12, determining that the fault information is a non-safety risk, and reporting to a cloud;
and judging that the battery has a thermal runaway risk, awakening a preset safety judgment program by the execution module 12, further judging, determining the thermal runaway of the battery, and reporting an emergency situation to the cloud.
In the embodiment, the execution module classifies and processes the abnormal conditions, and when the sensor is judged to be abnormal, the execution module records and reports the fault information; when the battery is judged to have the thermal runaway risk, the execution module wakes up a preset safety judgment program, further analyzes and judges the battery condition, determines that the battery has real temperature abnormity and has the safety risk, reports emergency to the cloud end, and the cloud end informs technicians such as engineers to perform early warning and troubleshooting. And further analyzing and processing abnormal temperature data of the temperature sensor group, eliminating false alarm of dangerous cases and avoiding missing alarm. Only when the battery is judged to have a real thermal runaway risk, the execution module 12 wakes up the safety judgment program to judge the battery condition, so that the system calculation force is greatly saved.
As an alternative embodiment, the method comprises:
the processor 11 sequences the temperature signals according to a time sequence in the temperature data processing process, the number of the temperature signals which are listed is not consistent with the number of the sensors, the processor 11 judges that the data analysis is wrong, and the execution module 12 records abnormal signals and reports the abnormal signals to the cloud for processing.
In this embodiment, in the process of analyzing and processing the temperature data, the processor 11 may sort the temperature data in a time sequence, and in the sorting process, a situation that the number of the temperature signals does not match the number of the sensors may occur, and at this time, the processor 11 determines that the data analysis is incorrect, and there is no safety risk.
As shown in fig. 3, the present application also provides a battery temperature abnormality determination apparatus 200, where the battery temperature abnormality determination apparatus 200 includes at least one software function module that can be stored in a memory module in the form of software or Firmware (Firmware) or solidified in an Operating System (OS) of the battery temperature abnormality determination device 10. The processor 11 is configured to execute an executable module 12 stored in the storage module, such as a software function module and a computer program included in the battery temperature abnormality determination apparatus 200.
The battery temperature abnormality determination device 200 includes a processing unit 210 and an execution unit 220, and functions of the units may be as follows:
the processing unit 210: the temperature sensor group is used for receiving temperature data acquired by the temperature sensor group within first preset time, preliminarily judging the temperature condition of the battery according to the temperature data, and judging the abnormal reason according to a preset algorithm when the temperature data reflects abnormal temperature of the battery;
the execution unit 220: the method and the device are used for reporting to the cloud when the processor 11 determines that the sensor is abnormal, or waking up a preset safety determination program when the processor 11 determines that the battery has a thermal runaway risk, determining the thermal runaway of the battery, and reporting to the cloud an emergency.
In this embodiment, the memory module can be, but is not limited to, a random access memory, a read only memory, a programmable read only memoryErasable programmable read only memory, electrically erasable programmable read only memory, and the like. In this embodiment, the storage module may be configured to store a preset normal temperature interval of the battery, an over-temperature threshold of the battery, and an average value Δ T of temperature differences i Rate of temperature rise k i Degree of disorder S i The method comprises the steps of setting a preset maximum temperature difference threshold value, a preset chaos degree safety threshold value, a preset minimum temperature difference threshold value and a preset safety judgment program.
It is to be understood that the structure of the battery temperature abnormality determination apparatus 10 shown in fig. 1 is merely a structural schematic diagram, and the battery temperature abnormality determination apparatus 10 may also include more components than those shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
It should be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the battery temperature abnormality determining device 10 and the battery temperature abnormality determining apparatus 200 described above may refer to the corresponding processes of the steps in the foregoing method, and will not be described in detail herein.
The embodiment of the application also provides a computer readable storage medium. The computer-readable storage medium has stored therein a computer program that, when run on a computer, causes the computer to execute the battery temperature abnormality determination method as described in the above-described embodiments.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by hardware, or by software plus a necessary general hardware platform, and based on such understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions to enable a computer device (which can be a personal computer or a network device, etc.) to execute the method described in the various implementation scenarios of the present application.
In summary, the embodiments of the present application provide a method, an apparatus, a device and a storage medium for determining battery temperature abnormality. In the scheme, the processor 11 performs preliminary analysis on data collected by the battery temperature sensor group, when the data is abnormal, the processor 11 further processes the abnormal temperature data according to a preset algorithm to judge whether the battery is abnormal in real temperature and has a thermal runaway risk, and the execution module 12 wakes up a preset safety judgment program according to a judgment result to further determine the battery condition and report an emergency situation to the cloud. And further analyzing and processing abnormal temperature data of the temperature sensor group, eliminating false alarm of dangerous cases and avoiding missing alarm. Only when the battery is judged to have a real thermal runaway risk, the execution module 12 wakes up the safety judgment program to judge the battery condition, so that the system calculation force is greatly saved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus, system, and method may be implemented in other ways. The apparatus, system, and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted 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-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A battery temperature abnormality determination method is characterized in that: the method is applied to the battery temperature abnormity determination equipment, the battery temperature abnormity determination equipment comprises a processor and an execution module, the processor is used for processing the temperature signals of the battery temperature sensor group and determining whether the real temperature abnormity occurs in the battery, and the method comprises the following steps:
the processor receives temperature data collected by the battery sensor group within a first preset time;
according to the temperature data, the processor judges that the temperature of the battery is abnormal possibly;
according to a preset algorithm, carrying out operation processing on the temperature data to obtain a processing result;
according to the processing result, the processor judges the abnormal reason to obtain a judgment result;
and according to the judgment result, the execution module wakes up a preset safety judgment program, determines the thermal runaway of the battery and reports the emergency situation to the cloud.
2. The method of claim 1, wherein: according to the temperature data, the processor judges that the battery may have temperature abnormity, and comprises the following steps:
the temperature data is always higher than a preset normal temperature interval of the battery but not higher than a preset battery over-temperature threshold, and the processor judges that the battery is possibly abnormal in temperature;
or the temperature data is always lower than a preset normal temperature interval of the battery, and the processor judges that the temperature of the battery is abnormal possibly;
or the temperature continuously rises and exceeds the battery over-temperature threshold, and the processor judges that the battery is possible to have abnormal temperature;
or the processor judges that the battery is possible to have temperature abnormality when the temperature data is high or low.
3. The method of claim 1, wherein: according to a preset algorithm, carrying out operation processing on the temperature data to obtain a processing result, including;
sorting the temperature data of the temperature sensor group according to the time sequence, calculating the median temperature at the same moment, and calculating the difference value delta T between the display temperature of all the temperature sensors at each moment and the median temperature i,n According to said difference DeltaT i,n Calculating the temperature difference average value delta T of each temperature sensor in the first preset time i 。
4. The method of claim 1, wherein: according to a preset algorithm, carrying out operation processing on the temperature data to obtain a processing result, including;
calculating the temperature rise rate k of all the temperature sensors within the first preset time i Calculating the median K of the temperature rise within the first preset time according to the rate ki of the temperature rise, and calculating the chaos S within the first preset time according to the median K of the temperature rise and the temperature data i 。
5. The method of claim 1, wherein: according to the processing result, the processor judges the reason of the abnormality to obtain a judgment result, and the judgment result comprises the following steps:
the temperature data is always higher than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Greater than a preset maximum temperature difference threshold, but with said degree of disorder S i Judging that the battery temperature sensor is abnormal when the chaos degree is smaller than a preset chaos degree safety threshold;
or the temperature data is always lower than a preset normal temperature interval of the battery, and the average temperature difference value delta T i Less than a preset minimum temperature difference threshold, but with said degree of disorder S i If the temperature is less than the chaos safety threshold, judging that the battery temperature sensor is abnormal;
or the temperature continuously rises and exceeds the battery over-temperature threshold value, theRate of temperature rise k i Greater than a preset temperature rise rate threshold, but with said degree of disorder S i If the temperature is less than the chaos safety threshold, judging that an abnormal heat source possibly exists and the battery has a thermal runaway risk;
or, the temperature data is high and low, and the temperature difference average value delta T is high i Greater than a preset maximum temperature difference threshold, and the degree of disorder S i Exceeding a chaos safety threshold; at low temperature, the mean value of temperature difference is Delta T i Less than a preset minimum temperature difference threshold, and the degree of disorder S i And if the temperature of the battery exceeds the chaos safety threshold, judging that the battery temperature sensor is abnormal or the battery is in thermal runaway, and ensuring that the battery has the risk of thermal runaway.
6. The method of claim 1, wherein: according to the judgment result, the execution module wakes up a safety judgment program, determines the thermal runaway of the battery and reports abnormal information to the cloud, and the method comprises the following steps:
judging that the sensor is abnormal, recording fault information by the execution module, determining that the fault information is a non-safety risk, and reporting to a cloud;
and judging that the battery has a thermal runaway risk, awakening a preset safety judgment program by the execution module, further judging, determining the thermal runaway of the battery, and reporting an emergency to the cloud.
7. The method of claim 1, wherein: the method comprises the following steps:
the processor sequences the temperature signals according to a time sequence in the temperature data processing process, the number of the temperature signals which are arranged is not consistent with the number of the sensors, the processor judges that the data analysis is wrong, and the execution module records abnormal signals and reports the abnormal signals to the cloud for processing.
8. A battery temperature abnormality determination device is characterized in that: the device is applied to the battery temperature abnormity determination equipment which comprises a processor and an execution module, wherein the processor is used for processing the temperature signal of the battery temperature sensor group and determining whether the real temperature abnormity occurs in the battery, and the device comprises:
a processing unit: the temperature sensor group is used for receiving temperature data collected by the temperature sensor group within a first preset time, preliminarily judging the temperature condition of the battery according to the temperature data, and judging the reason of abnormality according to a preset algorithm when the temperature data reflects the abnormal temperature of the battery;
an execution unit: and the method is used for reporting to the cloud when the processor judges that the sensor is abnormal, or awakening a preset safety judgment program when the processor judges that the battery has a thermal runaway risk, judging the thermal runaway of the battery and reporting to the cloud to realize emergency.
9. A battery temperature abnormality determination device characterized in that: the battery temperature abnormality detection device comprises a processor, an execution module and a memory, wherein the processor is used for processing the temperature signals of the battery temperature sensor group and judging whether the battery has real temperature abnormality or not, a computer program is stored in the memory, and when the computer program is executed by the processor or the execution module, the battery temperature abnormality judgment device is enabled to execute the method according to any one of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when run on a computer, causes the computer to carry out the method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117249921A (en) * | 2023-11-15 | 2023-12-19 | 宁德时代新能源科技股份有限公司 | Abnormality identification method for temperature sampling, related device, vehicle and storage medium |
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| CN117249921A (en) * | 2023-11-15 | 2023-12-19 | 宁德时代新能源科技股份有限公司 | Abnormality identification method for temperature sampling, related device, vehicle and storage medium |
| CN117249921B (en) * | 2023-11-15 | 2024-04-05 | 宁德时代新能源科技股份有限公司 | Abnormality identification method for temperature sampling, related device, vehicle and storage medium |
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