Battery Temperature Abnormality Determination Method, Device, Equipment and Storage Medium

Abstract

The present invention belongs to the technical field of new energy vehicle battery temperature monitoring, and specifically relates to a method, device, equipment and storage medium for determining battery temperature anomalies. The method is applied to a battery temperature anomaly determination device, which includes a processor and an execution module. The method includes: the processor receives temperature data collected by a battery sensor group within a first preset time; according to the temperature data, the processor determines that the battery may have a temperature anomaly, and performs arithmetic processing on the temperature data according to a preset algorithm to obtain a processing result; according to the processing result, the processor determines the cause of the anomaly to obtain a determination result; according to the determination result, the execution module wakes up a preset safety determination program to determine battery thermal runaway and reports an emergency to the cloud. By performing calculation processing and determination on abnormal temperature signals, it can accurately distinguish whether the battery has a real temperature anomaly or an observation system anomaly, and eliminate false alarms and missed alarms.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery temperature monitoring for new energy vehicles, and particularly relates to a method, device, equipment and storage medium for determining battery temperature anomalies. Background Art

[0002] With the popularization of new energy vehicles, the safety requirements for new energy vehicles are also getting higher and higher. Enterprises need to establish a monitoring platform for the operation safety status of new energy vehicle products, strengthen the analysis and mining of vehicle operation data, apply advanced safety warning methods, and improve the safety warning ability of new energy vehicles. Achieving accurate monitoring and warning of the battery safety status is a key indicator for establishing a vehicle safety monitoring platform.

[0003] Chinese Patent CN103730700A discloses a method for determining and handling faults in a sampling wire harness of a power battery system. By determining whether the temperature exceeds a sampling threshold, it is determined whether there is a fault in the temperature sampling wire harness, and the vehicle end alarm is used to remind the customer to check and repair. Chinese Patent CN111258787A discloses a method for identifying abnormal NTC temperature sampling values based on a battery pack. By determining whether the temperature rise rate, the temperature difference between the same modules, and the temperature difference between the same battery packs exceed the threshold, it is determined whether there is an abnormality in the NTC sampling, and the abnormal NTC data is discarded and marked to avoid abnormal battery functions.

[0004] The above methods can avoid vehicle malfunctions in normal states, but have certain limitations in 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, and the current technology may misidentify it as a sampling abnormality. Therefore, it is necessary to determine and handle signal abnormalities, otherwise there may be a large number of false alarms and missed alarms. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, equipment and storage medium for determining battery temperature anomalies, which are used to calculate, process and determine abnormal temperature signals, accurately distinguish whether the battery has a real temperature anomaly or an observation system anomaly, and eliminate false alarms and missed alarms.

[0006] To achieve the above technical purpose, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for determining battery temperature anomalies, which is applied to a device for determining battery temperature anomalies. The device for determining battery temperature anomalies includes a processor and an execution module. The processor is used to process the temperature signals of the battery temperature sensor group and determine whether the battery has a real temperature anomaly. The method includes:

[0008] The processor receives the temperature data collected by the battery sensor group within a first preset time;

[0009] Based on the temperature data, the processor determines that the battery may have an abnormal temperature;

[0010] Perform arithmetic processing on the temperature data according to a preset algorithm to obtain a processing result;

[0011] Based on the processing result, the processor determines the cause of the abnormality to obtain a determination result;

[0012] Based on the determination result, the execution module wakes up a preset safety determination program, determines that the battery has thermal runaway, and reports an emergency to the cloud.

[0013] Combined with the first aspect, in some alternative embodiments, based on the temperature data, the processor determines that the battery may have an abnormal temperature, including:

[0014] The temperature data has been higher than the preset normal temperature range of the battery but has not exceeded the preset over-temperature threshold of the battery, and the processor determines that the battery may have an abnormal temperature;

[0015] Or, the temperature data has been lower than the preset normal temperature range of the battery, and the processor determines that the battery may have an abnormal temperature;

[0016] Or, the temperature continues to rise and exceeds the over-temperature threshold of the battery, and the processor determines that the battery may have an abnormal temperature;

[0017] Or, the temperature data is sometimes high and sometimes low, and the processor determines that the battery may have an abnormal temperature.

[0018] Combined with the first aspect, in some alternative embodiments, performing arithmetic processing on the temperature data according to a preset algorithm to obtain a processing result, including;

[0019] Sort the temperature data of the temperature sensor group according to the chronological order, calculate the median temperature at the same moment, and calculate the difference ΔT between the temperature displayed by each temperature sensor and the median temperature at each moment i,n , based on the difference ΔT i,n Calculate the average temperature difference ΔT of each temperature sensor within the first preset time i .

[0020] Combined with the first aspect, in some alternative embodiments, performing arithmetic processing on the temperature data according to a preset algorithm to obtain a processing result, including;

[0021] Calculate the temperature rise rate k of all temperature sensors within the first preset time i , based on the temperature rise rate k iCalculate the median temperature rise K within the first preset time, and calculate the chaos degree S within the first preset time according to the median temperature rise K and the temperature data i 。

[0022] Combined with the first aspect, in some alternative embodiments, according to the processing result, the processor determines the cause of the abnormality to obtain a determination result, including:

[0023] The temperature data has always been higher than the preset normal battery temperature range, and the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, but the chaos degree S i is less than the preset chaos degree safety threshold, and it is determined that the battery temperature sensor is abnormal;

[0024] Or, the temperature data has always been lower than the preset normal battery temperature range, and the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, but the chaos degree S i is less than the chaos degree safety threshold, and it is determined that the battery temperature sensor is abnormal;

[0025] Or, the temperature continues to rise and exceeds the battery over-temperature threshold, and the temperature rise rate k i is greater than the preset temperature rise rate threshold, but the chaos degree S i is less than the chaos degree safety threshold, and it is determined that there may be an abnormal heat source and the battery has a risk of thermal runaway;

[0026] Or, the temperature data is sometimes high and sometimes low. When the temperature is high, the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold; when the temperature is low, the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold, and it is determined that the battery temperature sensor is abnormal or the battery has a thermal runaway, and the battery has a risk of thermal runaway.

[0027] Combined with the first aspect, in some alternative embodiments, according to the determination result, the execution module wakes up the safety determination program, determines the battery thermal runaway, and reports the abnormal information to the cloud, including:

[0028] Determine that the sensor is abnormal, and the execution module records the fault information, determines it as a non-safety risk, and reports it to the cloud;

[0029] Determine that the battery has a risk of thermal runaway, and the execution module wakes up the preset safety determination program for further determination, determines the battery thermal runaway, and reports an emergency to the cloud.

[0030] In combination with the first aspect, in some alternative embodiments, the method includes:

[0031] During the process of the processor processing the temperature data, the temperature signals are sorted in chronological order, and the number of the arranged temperature signals does not match the number of sensors. The processor determines that the data parsing is incorrect, and the execution module records the abnormal signals and reports them to the cloud for processing.

[0032] In a second aspect, an embodiment of the present application further provides a battery temperature abnormality determination device, which is applied to a battery temperature abnormality determination device. The battery temperature abnormality determination device includes a processor and an execution module. The processor is used to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature abnormality. The device includes:

[0033] A processing unit: configured to receive the temperature data collected by the temperature sensor group within a first preset time, preliminarily determine the temperature condition of the battery according to the temperature data, and when the temperature data reflects that the battery temperature is abnormal, determine the cause of the abnormality according to a preset algorithm;

[0034] An execution unit: configured to report to the cloud when the processor determines that the sensor is abnormal, or wake up a preset safety determination program to determine battery thermal runaway and report an emergency to the cloud when the processor determines that the battery has a risk of thermal runaway.

[0035] In a third aspect, an embodiment of the present application further provides a battery temperature abnormality determination device, including a processor, an execution module and a memory. The processor is used to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature abnormality. A computer program is stored in the memory. When the computer program is executed by the processor or the execution module, the battery temperature abnormality determination device executes the above method.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer executes the above method.

[0037] The invention adopting the above technical solution has the following advantages:

[0038] In the present invention, the processor receives the temperature data collected by the battery temperature sensor group. The processor preliminarily determines that the collected temperature data is abnormal, and according to a preset algorithm, calculates the average temperature difference ΔT of each temperature sensor within a first preset time i and the chaos degree S iAnalyze and judge abnormal situations. The execution module determines to wake up a preset safety judgment program according to the judgment result and reports the result to the cloud. Further process the abnormal data to analyze whether it is a real temperature anomaly or an observation system anomaly, so as to report a more accurate result to the cloud, and then the cloud informs the engineer to make a timely response. Brief Description of the Drawings

[0039] The present invention can be further illustrated by the non-limiting embodiments given in the drawings;

[0040] Figure 1 It is a block diagram of a battery temperature anomaly determination device provided by an embodiment of the present application.

[0041] Figure 2 It is a schematic flowchart of a battery temperature anomaly determination method provided by an embodiment of the present application.

[0042] Figure 3 It is a block diagram of a battery temperature anomaly determination device provided by an embodiment of the present application.

[0043] The main component symbols are explained as follows:

[0044] 10. Battery temperature anomaly determination device; 11. Processor; 12. Execution module; 200. Battery temperature anomaly determination device; 210. Processing unit; 220. Execution unit. Detailed Embodiments

[0045] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. It should be noted that in the description of the drawings or the specification, similar or identical parts use the same drawing numbers, and the implementation manners not shown or described in the drawings are the forms known to those of ordinary skill in the art. In addition, the directional terms mentioned in the embodiments, such as "upper", "lower", "top", "bottom", "left", "right", "front", "rear", etc., are only references to the directions of the drawings and are not used to limit the protection scope of the present invention.

[0046] As Figure 1 shown, an embodiment of the present application provides a battery temperature anomaly determination device 10. The battery temperature anomaly determination device 10 may include a processor 11, an execution module 12 and a storage module. The processor 11 is used to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature anomaly.

[0047] In this embodiment, the processor 11 is electrically connected to the temperature sensor group, collects the temperature data of the temperature sensor group within the first preset time, compares it with the preset normal battery temperature range. If it is different from the normal battery temperature range, the battery temperature data is abnormal. According to the preset algorithm, the battery temperature data is further processed to determine whether the battery has a real temperature anomaly. When it is determined that there is a real temperature anomaly, the execution module 12 wakes up the preset safety determination program to determine that the battery has a real temperature anomaly and reports an emergency to the cloud. Further processing of the abnormal data to analyze whether it is a real temperature anomaly or an observation system anomaly makes the result reported to the cloud more accurate, and the cloud then informs the engineer to make a timely response.

[0048] The storage module stores a computer program. When the computer program is executed by the processor 11 or the execution module 12, the battery temperature anomaly determination device 10 can execute the corresponding steps in the following braking control method.

[0049] As Figure 2 shown, the present application also provides a battery temperature anomaly determination method, wherein the battery temperature anomaly determination method may include the following steps:

[0050] Step 110: The processor 11 receives the temperature data collected by the battery sensor group within the first preset time;

[0051] Step 120: According to the temperature data, the processor 11 determines that the battery may have a temperature anomaly;

[0052] Step 130: According to the preset algorithm, the temperature data is processed by arithmetic to obtain a processing result;

[0053] Step 140: According to the processing result, the processor 11 determines the cause of the anomaly to obtain a determination result;

[0054] Step 150: According to the determination result, the execution module 12 wakes up the preset safety determination program to determine battery thermal runaway and reports an emergency to the cloud.

[0055] In this embodiment, the processor 11 collects the temperature data within the first preset time, analyzes and judges the battery temperature situation fed back by the temperature data. When the processor 11 determines that the temperature is abnormal, according to the preset algorithm, the temperature data is processed. When it is determined that the battery has a real temperature anomaly, the execution module 12 wakes up the preset safety determination program to determine that the battery has a real temperature anomaly and reports an emergency to the cloud.

[0056] As an optional implementation manner, according to the temperature data, the processor 11 determines that the battery may have a temperature anomaly, including:

[0057] The temperature data has always been higher than the preset normal temperature range of the battery but has not exceeded the preset over-temperature threshold of the battery, and the processor 11 determines that the battery may have a temperature anomaly;

[0058] Alternatively, the temperature data has always been lower than the preset normal temperature range of the battery, and the processor 11 determines that the battery may have a temperature anomaly;

[0059] Alternatively, the temperature continuously rises and exceeds the over-temperature threshold of the battery, and the processor 11 determines that the battery may have a temperature anomaly;

[0060] Alternatively, the temperature data is sometimes high and sometimes low, and the processor 11 determines that the battery may have a temperature anomaly.

[0061] In this embodiment, the temperature data is compared with the normal temperature range of the battery. Whether the temperature data is continuously too high, too low, or fluctuates between high and low, the processor 11 determines that the battery may have a temperature anomaly; in addition, when the temperature continuously rises and exceeds the over-temperature threshold of the battery, the processor 11 will also determine that the battery may have a temperature anomaly.

[0062] As an alternative implementation, according to a preset algorithm, the temperature data is processed by calculation to obtain a processing result, including:

[0063] Sort the temperature data of the temperature sensor group according to the chronological order, calculate the median temperature at the same moment, and calculate the difference ΔT between the temperature displayed by each temperature sensor at each moment and the median temperature i,n , according to the difference ΔT i,n calculate the average temperature difference ΔT of each temperature sensor within the first preset time i .

[0064] In this embodiment, since time has monotonicity, the collected temperature data is sorted according to the time order, the median temperature at the same moment is calculated, and the difference ΔT between the temperature displayed by each temperature sensor at each moment and the median temperature is calculated i,n :

[0065] ΔT i,n = T i,n -Med(T 1,n , T 2,n …, T n,n )

[0066] where T i,n represents the temperature of the i-th temperature sensor in the temperature sensor group at the n-th moment;

[0067] Further calculate the average temperature difference ΔT of each temperature sensor within the first preset time i :

[0068] ΔT i = Ave(ΔT i,1 , ΔT i,,2 ,..., ΔT i,n )

[0069] The stability of the temperature data is measured by calculating the average temperature difference. When a real temperature anomaly occurs in the battery, the temperature fluctuates greatly and the average temperature difference changes significantly.

[0070] As an alternative implementation, according to a preset algorithm, the temperature data is processed to obtain a processing result, including:

[0071] Calculating the temperature rise rate k of all temperature sensors within the first preset time i , and according to the temperature rise rate k i Calculating the median temperature rise K within the first preset time, and calculating the chaos degree S within the first preset time according to the median temperature rise K and the temperature data i .

[0072] In this embodiment, the temperature rise rate k is obtained by integrating the temperature with respect to time i :

[0073]

[0074] Calculating the median temperature rise K within the first preset time according to the temperature rise rate ki:

[0075] K = Med(k1, k2…, k n )

[0076] Calculating the chaos degree S of each time point within the first preset time according to the median temperature rise K and the temperature data i,n :

[0077]

[0078] where α is the chaos degree threshold coefficient; the chaos degrees of each time point within the first preset time are accumulated and summed to obtain the chaos degree S within the first preset time i :

[0079]

[0080] The stability of the temperature data is measured by calculating the chaos degree within the first preset time. When a real temperature anomaly occurs in the battery, the temperature fluctuates greatly and the chaos degree value is also larger.

[0081] As an alternative implementation, according to the processing result, the processor determines the cause of the anomaly to obtain a determination result, including:

[0082] The temperature data has always been higher than the preset normal temperature range of the battery, and the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, but the degree of disorder S i is less than the preset safety threshold of the degree of disorder, it is determined that the battery temperature sensor is abnormal;

[0083] Or, the temperature data has always been lower than the preset normal temperature range of the battery, and the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, but the degree of disorder S i is less than the safety threshold of the degree of disorder, it is determined that the battery temperature sensor is abnormal;

[0084] Or, the temperature continues to rise and exceeds the over-temperature threshold of the battery, and the temperature rise rate k i is greater than the preset temperature rise rate threshold, but the degree of disorder S i is less than the safety threshold of the degree of disorder, it is determined that there may be an abnormal heat source and the battery has a risk of thermal runaway;

[0085] Or, the temperature data is sometimes high and sometimes low. When the temperature is high, the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, and the degree of disorder S i exceeds the safety threshold of the degree of disorder; when the temperature is low, the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, and the degree of disorder S i exceeds the safety threshold of the degree of disorder, it is determined that the battery temperature sensor is abnormal or the battery has a thermal runaway, and the battery has a risk of thermal runaway.

[0086] In this embodiment, the comprehensive degree of disorder S i , the average temperature difference ΔT i and the temperature rise rate ki are used to further analyze and determine the temperature data. When both the degree of disorder S i exceeds the safety threshold of the degree of disorder and the average temperature difference ΔT i is lower than the minimum temperature difference threshold or higher than the maximum temperature difference threshold, it is determined that the battery may have a real temperature anomaly and there is a risk of thermal runaway;

[0087] Or, the battery temperature rises abnormally, the battery temperature exceeds the over-temperature threshold of the battery, and the temperature rise rate k i is greater than the preset temperature rise rate threshold. Even if the degree of disorder S i is less than the safety threshold of the degree of disorder at this time, at this time, the processor determines that there may be an abnormal heat source, the battery may have a real temperature anomaly, and there is a risk of thermal runaway; in other cases, the processor 11 determines that the sensor is abnormal.

[0088] As an optional implementation, according to the determination result, the execution module 12 wakes up the safety determination program, determines that the battery is thermally runaway, and reports abnormal information to the cloud, including:

[0089] If the sensor is abnormal, the execution module 12 records the fault information, determines it as a non-safety risk, and reports it to the cloud;

[0090] If it is determined that the battery has a thermal runaway risk, the execution module 12 wakes up a preset safety determination program, makes further determinations, determines that the battery has thermal runaway, and reports the emergency to the cloud.

[0091] In this embodiment, the execution module classifies and processes abnormal situations. When it is determined that the sensor is abnormal, the execution module records the fault information and reports it. When it is determined that the battery has a risk of thermal runaway, the execution module wakes up the preset safety determination program, further analyzes and determines the battery condition, and determines that the battery has a real temperature abnormality and poses a safety risk. The execution module reports the emergency to the cloud, and the cloud notifies engineers and other technical personnel to issue early warnings and conduct investigations. The abnormal temperature data of the temperature sensor group is further analyzed and processed to eliminate false reports of dangerous situations and avoid omissions. Only when it is determined that the battery has a real risk of thermal runaway will the execution module 12 wake up the safety determination program to determine the battery condition, which greatly saves system computing power.

[0092] As an optional implementation, the method includes:

[0093] The processor 11 sorts the temperature signals in chronological order during the temperature data processing. The number of sorted temperature signals does not match the number of sensors. The processor 11 determines that the data analysis is wrong, and the execution module 12 records the abnormal signal and reports it to the cloud for processing.

[0094] In this embodiment, when the processor 11 analyzes and processes the temperature data, the temperature data will be sorted in chronological order. During the sorting process, the number of temperature signals may not match the number of sensors. At this time, the processor 11 determines that it is a data parsing error and there is no security risk.

[0095] like Figure 3 As shown, the present application also provides a battery temperature abnormality determination device 200, which includes at least one software function module that can be stored in the form of software or firmware in a storage module or solidified in the operating system (OS) of the battery temperature abnormality determination device 10. The processor 11 is used to execute the executable module 12 stored in the storage module, such as the software function module and computer program included in the battery temperature abnormality determination device 200.

[0096] The battery temperature anomaly determination device 200 includes a processing unit 210 and an execution unit 220. The functions of each unit can be as follows:

[0097] The processing unit 210: is configured to receive the temperature data collected by the temperature sensor group within a first preset time, preliminarily determine the temperature condition of the battery according to the temperature data, and when the temperature data reflects that the battery temperature is abnormal, determine the cause of the abnormality according to a preset algorithm;

[0098] The execution unit 220: is configured to report to the cloud when the processor 11 determines that the sensor is abnormal, or wake up a preset safety determination program when the processor 11 determines that there is a risk of thermal runaway of the battery, determine the thermal runaway of the battery, and report an emergency to the cloud.

[0099] In this embodiment, the storage module may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the storage module can be used to store a preset normal temperature range of the battery, an over-temperature threshold of the battery, an average temperature difference ΔT i , a temperature rise rate k i , a chaos degree S i , a preset maximum temperature difference threshold, a preset chaos degree safety threshold, a preset minimum temperature difference threshold, and a preset safety determination program.

[0100] It can be understood that Figure 1 the structure of the battery temperature anomaly determination device 10 shown in Figure 1 is only a schematic structural diagram, and the battery temperature anomaly determination device 10 may further include more components than those shown in Figure 1 Each component shown in

[0101] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described battery temperature anomaly determination device 10 and battery temperature anomaly determination device 200 can refer to the corresponding processes of each step in the foregoing method, and will not be elaborated here too much.

[0102] This application embodiment also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer is caused to execute the battery temperature anomaly determination method as described in the above embodiment.

[0103] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an 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 flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0104] In summary, the embodiments of the present application provide a method, device, equipment and storage medium for determining abnormal battery temperature. In this solution, the processor 11 initially analyzes the 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 determine whether the battery has a real temperature abnormality and a risk of thermal runaway. The execution module 12 wakes up a preset safety determination program according to the determination result, further determines the battery situation, and reports an emergency to the cloud. By further analyzing and processing the abnormal temperature data of the temperature sensor group, false alarms of dangerous situations are excluded and missed alarms are avoided. Only when it is determined that the battery has a real risk of thermal runaway, the execution module 12 will wake up the safety determination program to determine the battery situation, greatly saving the system computing power.

[0105] In the embodiments provided by the present application, it should be understood that the disclosed devices, systems and methods can also be implemented in other ways. The device, system and method embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the drawings show the possible architectures, functions and operations of systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment or a part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, the functional modules in various embodiments of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0106] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining abnormal battery temperature, characterized in that: Applied to a battery temperature anomaly determination device, the battery temperature anomaly determination device includes a processor and an execution module. The processor is used to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature anomaly. The method includes: The processor receives the temperature data collected by the battery sensor group within a first preset time; Based on the temperature data, the processor determines that the battery may have a temperature anomaly; According to a preset algorithm, the temperature data is processed arithmetically to obtain a processing result; Based on the processing result, the processor determines the cause of the anomaly to obtain a determination result; Based on the determination result, the execution module wakes up a preset safety determination program to determine battery thermal runaway and reports an emergency to the cloud; Among them, according to a preset algorithm, the temperature data is processed arithmetically to obtain a processing result, including; Sort the temperature data of the temperature sensor group according to the chronological order, calculate the median temperature at the same moment, and calculate the difference ΔT between the temperature displayed by each temperature sensor and the median temperature at each moment. i,n , according to the difference ΔT i,n Calculate the average temperature difference ΔT of each temperature sensor within the first preset time. i ; Among them, based on the processing result, the processor determines the cause of the anomaly to obtain a determination result, including: The temperature data has always been higher than the preset normal temperature range of the battery, and the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, but the entropy S i is less than the preset entropy safety threshold, it is determined that the battery temperature sensor is abnormal. The entropy is used to measure the stability of the temperature data. When the battery has a temperature anomaly, the greater the temperature fluctuation, the greater the value of the entropy; Alternatively, the temperature data has been continuously lower than the preset normal temperature range of the battery, and the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, but the degree of disorder S i is less than the safety threshold of the degree of disorder, it is determined that the battery temperature sensor is abnormal; Alternatively, the temperature continues to rise and exceeds the preset battery overtemperature threshold, and the temperature rise rate k i is greater than the preset temperature rise rate threshold, but the chaos degree S i is less than the chaos degree safety threshold, it is determined that there may be an abnormal heat source and the battery has a risk of thermal runaway; Alternatively, the temperature data fluctuates. When the temperature is high, the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold; when the temperature is low, the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold. It is determined that the battery temperature sensor is abnormal or the battery has a thermal runaway, and the battery has a risk of thermal runaway.

2. The method according to claim 1, characterized in that: Based on the temperature data, the processor determines that the battery may have a temperature anomaly, including: The temperature data has always been higher than the preset normal battery temperature range but has not exceeded the preset battery over-temperature threshold, and the processor determines that the battery may have a temperature anomaly; Or, the temperature data has always been lower than the preset normal battery temperature range, and the processor determines that the battery may have a temperature anomaly; Or, the temperature continues to rise and exceeds the battery over-temperature threshold, and the processor determines that the battery may have a temperature anomaly; Or, the temperature data is sometimes high and sometimes low, and the processor determines that the battery may have a temperature anomaly.

3. The method according to claim 1, characterized in that: According to a preset algorithm, the temperature data is processed arithmetically to obtain a processing result, including; Calculate the temperature rise rate k of all temperature sensors within the first preset time i , according to the temperature rise rate k i Calculate the median temperature rise K within the first preset time. Calculate the chaos degree S within the first preset time according to the median temperature rise K and the temperature data i .

4. The method according to claim 1, wherein: Based on the determination result, the execution module wakes up the safety determination program to determine battery thermal runaway and reports anomaly information to the cloud, including: When it is determined that the sensor is abnormal, the execution module records the fault information, determines it as a non-safety risk, and reports it to the cloud; When it is determined that the battery has a risk of thermal runaway, the execution module wakes up a preset safety determination program for further determination, determines battery thermal runaway, and reports an emergency to the cloud.

5. The method according to claim 1, characterized in that: The method includes: During the process of the processor processing the temperature data, the temperature signals are sorted in chronological order, and the number of temperature signals arranged does not match the number of sensors. The processor determines that the data parsing is incorrect, and the execution module records the abnormal signals and reports them to the cloud for processing.

6. A battery temperature anomaly determination device, characterized in that: Applied to a battery temperature anomaly determination device, the battery temperature anomaly determination device includes a processor and an execution module. The processor is used to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature anomaly. The device includes: A processing unit: used to receive the temperature data collected by the temperature sensor group within a first preset time, initially determine the temperature condition of the battery based on the temperature data, and when the temperature data reflects a battery temperature anomaly, determine the cause of the anomaly according to a preset algorithm; Execution unit: When the processor determines that the sensor is abnormal, report to the cloud, or when the processor determines that there is a risk of thermal runaway of the battery, wake up a preset safety determination program to determine the thermal runaway of the battery and report an emergency to the cloud; Wherein, the processing unit is further configured to: Sort the temperature data of the temperature sensor group according to the chronological order, calculate the median temperature at the same moment, and calculate the difference ΔT between the temperature displayed by each temperature sensor and the median temperature at each moment. i,n , according to the difference ΔT i,n Calculate the average temperature difference ΔT of each temperature sensor within the first preset time. i ; Wherein, the processing unit is further configured to: The temperature data has always been higher than the preset normal temperature range of the battery, and the average temperature difference ΔT i is greater than the preset maximum temperature difference threshold, but the entropy S i is less than the preset entropy safety threshold, and it is determined that the battery temperature sensor is abnormal. The entropy is used to measure the stability of the temperature data. When the battery has a temperature anomaly, the greater the temperature fluctuation, the greater the value of the entropy; Alternatively, the temperature data has been continuously lower than the preset normal temperature range of the battery, and the average temperature difference ΔT i is less than the preset minimum temperature difference threshold, but the degree of disorder S i is less than the safety threshold of the degree of disorder, and it is determined that the battery temperature sensor is abnormal; Alternatively, the temperature continues to rise and exceeds the preset battery over-temperature threshold, and the temperature rise rate k i is greater than the preset temperature rise rate threshold, but the degree of disorder S i is less than the degree of disorder safety threshold, it is determined that there may be an abnormal heat source and the battery has a risk of thermal runaway; Alternatively, the temperature data fluctuates, and when the temperature is high, the average temperature difference ΔT i is greater than a preset maximum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold; when the temperature is low, the average temperature difference ΔT i is less than a preset minimum temperature difference threshold, and the chaos degree S i exceeds the chaos degree safety threshold, it is determined that the battery temperature sensor is abnormal or the battery has a thermal runaway, and the battery has a risk of thermal runaway.

7. A battery temperature abnormality determination device, characterized in that: It includes a processor, an execution module and a memory. The processor is configured to process the temperature signals of the battery temperature sensor group to determine whether the battery has a real temperature abnormality. The computer program is stored in the memory. When the computer program is executed by the processor or the execution module, the battery temperature abnormality determination device executes the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium. When the computer program runs on the computer, the computer executes the method according to any one of claims 1-5.

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