A vehicle power battery spontaneous combustion risk assessment analysis method and detection equipment

Abstract

The present invention provides a vehicle power battery spontaneous combustion risk assessment and analysis method and detection equipment, which belongs to the field of vehicle device technology, and specifically includes: based on temperature data and changes in temperature data at different times, when it is determined that an electric vehicle has a suspected spontaneous combustion risk, a preset time period is divided into multiple sub-periods, and the temperature anomaly coefficients and temperature anomaly sub-periods of different sub-periods are determined by matching the temperature data in different sub-periods with the operating data of the power battery, and the spontaneous combustion risk of the power battery of the electric vehicle is determined based on the distribution data of the temperature anomaly sub-periods in the preset time period and the temperature anomaly coefficient, thereby improving the accuracy of the assessment and analysis of the spontaneous combustion risk of the power battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle devices, and in particular relates to a vehicle power battery spontaneous combustion risk assessment and analysis method and detection equipment. Background Art

[0002] As the installed capacity of on-board power batteries of electric vehicles becomes larger and larger, the risk of accidents caused by spontaneous combustion accidents becomes increasingly greater. Therefore, how to evaluate and analyze the risk of spontaneous combustion of power batteries of electric vehicles has become a technical problem that needs to be solved urgently.

[0003] In order to evaluate the risk of spontaneous combustion of the power battery of an electric vehicle, the invention patent application CN202211659060.5 "An electric vehicle power battery safety system" analyzes whether there is any abnormality in the operation of the battery pack based on the operation data of the battery pack, the smoke data and temperature data of the measurement point, which can timely avoid the spontaneous combustion of the vehicle and improve the accuracy of the spontaneous combustion risk detection. However, there are the following technical problems:

[0004] When an electric vehicle is driving, the output current of the battery pack is not constant. Instead, it changes with the output of the electric vehicle's motor. The size of the output current is related to the risk of spontaneous combustion of the battery pack. Therefore, if a detection strategy for the spontaneous combustion risk of the electric vehicle's power battery cannot be generated based on the output current of the battery pack, the timeliness of the detection and processing of the spontaneous combustion risk cannot be improved.

[0005] In response to the above technical problems, the present invention provides a vehicle power battery spontaneous combustion risk assessment and analysis method and detection equipment. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for evaluating and analyzing the risk of spontaneous combustion of a vehicle power battery is provided.

[0008] A vehicle power battery spontaneous combustion risk assessment and analysis method, specifically comprising:

[0009] S1 determines the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, obtains temperature data of the power battery of the electric vehicle at different times within the preset period;

[0010] S2, when it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and the changes in the temperature data at different times, divide the preset time period into a plurality of sub-time periods;

[0011] S3 determines the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery;

[0012] S4 determines the risk of spontaneous combustion of the power battery of the electric vehicle based on the distribution data of the temperature abnormality sub-period within the preset period and the temperature abnormality coefficient.

[0013] The beneficial effects of the present invention are:

[0014] Based on the temperature data and the changes in temperature data at different times, it is determined whether the electric vehicle has a suspected risk of spontaneous combustion. This takes into account both the abnormal temperature data of the electric vehicle's power battery and the abnormal temperature changes of the power battery caused by the changes in temperature data. This enables the screening of power batteries with suspected spontaneous combustion risks from the two perspectives of abnormal temperature and abnormal temperature changes, and also lays the foundation for differentiated detection of spontaneous combustion risks.

[0015] The spontaneous combustion risk of the power battery of the electric vehicle is determined based on the distribution data of the temperature anomaly sub-periods within the preset time period and the temperature anomaly coefficient, thereby realizing an accurate assessment of the spontaneous combustion risk of the power battery from the perspective of the temperature anomaly within the preset time period, avoiding the accuracy of the spontaneous combustion risk assessment results caused by a single consideration of the temperature anomaly at a certain moment, and improving the accuracy and reliability of the spontaneous combustion risk assessment results.

[0016] A further technical solution is that the preset time period is determined according to an average driving speed of the electric vehicle at a current driving mileage, wherein the faster the average driving speed is, the shorter the preset time period is.

[0017] A further technical solution is that the output current is determined according to the analysis result of the operation data of the electric vehicle.

[0018] A further technical solution is that the method for determining the temperature anomaly coefficient of the sub-period is:

[0019] Determining the remaining capacity and current of the power battery in the sub-period based on the operating data of the power battery in the sub-period;

[0020] Determining a matching temperature range of the power battery in the sub-period based on the remaining capacity and current of the power battery in the sub-period as constraint conditions;

[0021] Based on the matching temperature range of the power battery in the sub-period, the moment when the power battery is not within the matching temperature range in the sub-period is determined and taken as the abnormal temperature moment, and the temperature abnormality coefficient of the sub-period is determined according to the number ratio of the abnormal temperature moments.

[0022] A further technical solution is that the matching temperature range of the power battery in the sub-period is determined according to the historical temperature data of the power battery under the constraint conditions, and the temperature range corresponding to the historical temperature data under the constraint conditions is used as the matching temperature range.

[0023] A further technical solution is that the temperature anomaly coefficient of the sub-period ranges from 0 to 1, wherein when the temperature anomaly coefficient of the sub-period is greater than a preset anomaly threshold, the sub-period is determined to be a temperature anomaly sub-period.

[0024] A further technical solution is that the method for determining the spontaneous combustion risk of the power battery of the electric vehicle is:

[0025] The spontaneous combustion risk of the power battery of the electric vehicle is determined based on the distribution data of the temperature abnormality sub-period within the preset period and the temperature abnormality coefficient.

[0026] In a second aspect, the present invention provides a detection device, which adopts the above-mentioned vehicle power battery spontaneous combustion risk assessment and analysis method, specifically comprising:

[0027] Current analysis module, sub-period division module, temperature anomaly assessment module, spontaneous combustion risk assessment module;

[0028] The current analysis module is responsible for determining the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, the temperature data of the power battery of the electric vehicle at different times within the preset period are obtained;

[0029] The sub-period division module is responsible for dividing the preset time period into a plurality of sub-periods when it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and the changes in the temperature data at different times;

[0030] The temperature anomaly evaluation module is responsible for determining the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery;

[0031] The spontaneous combustion risk assessment module is responsible for determining the spontaneous combustion risk of the power battery of the electric vehicle based on the distribution data of the temperature abnormality sub-period within the preset time period and the temperature abnormality coefficient.

[0032] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0035] Figure 1 It is a flow chart of a vehicle power battery spontaneous combustion risk assessment and analysis method;

[0036] Figure 2 It is a flow chart for determining the need for an assessment and analysis of the risk of spontaneous combustion of electric vehicles;

[0037] Figure 3 It is a flow chart for determining whether electric vehicles have a suspected risk of spontaneous combustion;

[0038] Figure 4 is a flow chart of a method for determining a temperature anomaly coefficient of a sub-period;

[0039] Figure 5 It is a framework diagram of a detection device. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0041] As the output of the electric vehicle's motor changes, the output current of the battery pack will also change accordingly. The size of the output current is related to the risk of spontaneous combustion of the battery pack. Therefore, it is necessary to generate a detection strategy for the spontaneous combustion risk of the electric vehicle's power battery based on the output current of the battery pack.

[0042] Determine whether an electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis: determine the moment when the output current of the electric vehicle within a preset time period is greater than a preset current using the output current at different moments, and use this moment as the screening current moment; when the number of screening current moments within the preset time period accounts for more than 0.6, it is determined that the electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis.

[0043] Determine whether the electric vehicle has a suspected risk of spontaneous combustion: determine the moment when the temperature is greater than the preset temperature threshold based on the temperature data at different moments, and use it as the temperature abnormality moment; according to the change of the temperature data at different moments, perform the temperature change amount between different adjacent moments, and determine the temperature abnormality change moment based on the temperature change amount between different adjacent moments; when the number of temperature abnormal moments accounts for more than 0.5 or the number of temperature abnormality change moments accounts for more than 0.5, it is determined that the electric vehicle has a suspected risk of spontaneous combustion.

[0044] Temperature anomaly coefficient of sub-period: Taking the remaining capacity and current of the power battery in the sub-period as constraints, determine the corresponding matching temperature range of the power battery in the sub-period. Based on the matching temperature range of the power battery in the sub-period, determine the moment when the power battery is not within the matching temperature range in the sub-period, and take it as the abnormal temperature moment. Determine the temperature anomaly coefficient of the sub-period by the proportion of the number of abnormal temperature moments.

[0045] Temperature anomaly sub-period: a sub-period in which the temperature anomaly coefficient is greater than the preset anomaly coefficient.

[0046] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a vehicle power battery spontaneous combustion risk assessment and analysis method is provided, which specifically includes:

[0047] S1 determines the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, obtains temperature data of the power battery of the electric vehicle at different times within the preset period;

[0048] S2, when it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and the changes in the temperature data at different times, divide the preset time period into a plurality of sub-time periods;

[0049] S3 determines the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery;

[0050] S4 determines the risk of spontaneous combustion of the power battery of the electric vehicle based on the distribution data of the temperature abnormality sub-period within the preset period and the temperature abnormality coefficient.

[0051] Furthermore, the preset period is determined according to an average driving speed of the electric vehicle at a current driving distance, wherein the faster the average driving speed is, the shorter the preset period is.

[0052] Specifically, the output current is determined according to the analysis result of the operation data of the electric vehicle.

[0053] It is understandable that if Figure 2 As shown, it is determined that the electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis, specifically including:

[0054] Determine the moment when the output current of the electric vehicle within a preset period is greater than a preset current by using the output current at different moments, and use it as the current screening moment;

[0055] Acquire the number of screening current moments within the preset time period, and determine the number of screening current moments within different unit time lengths within the preset time period according to the distribution data of the screening current moments;

[0056] The target unit duration is determined based on the proportion of the number of screened current moments within different unit durations, and whether the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion is determined based on the proportion of the number of target unit durations.

[0057] Furthermore, the target unit duration is a unit duration in which the proportion of the number of screening current moments is greater than the proportion of the number of preset moments.

[0058] It can be understood that when the target unit duration quantity ratio is within the preset duration quantity ratio range, it is determined that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion.

[0059] It should be noted that the determination of the risk of spontaneous combustion of the electric vehicle requires an assessment and analysis, specifically including:

[0060] Determine the moment when the output current of the electric vehicle within a preset period is greater than a preset current by using the output current at different moments, and use it as the current screening moment;

[0061] Whether the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion is determined based on the number of screening current moments within the preset time period.

[0062] Optionally, determining that the electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis specifically includes:

[0063] S11: determining an average value of the output current of the electric vehicle at different times within a preset period of time based on the output current at different times, and determining a basic risk coefficient of the electric vehicle based on the average value of the output current at different times within the preset period of time;

[0064] Optionally, the above step S11 includes the following contents:

[0065] S111 determines the average value of the output current of the electric vehicle at different times within a preset period of time based on the output current at different times. When the average value of the output current at different times within the preset period of time is greater than the preset current, it is determined that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion. When the average value of the output current at different times within the preset period of time is not greater than the preset current, the process proceeds to step S112.

[0066] S112 obtains the output current of the electric vehicle at different times within a preset period of time. When the output current at different times within the preset period of time is less than the preset current, it is determined that the electric vehicle does not need to be evaluated and analyzed for the risk of spontaneous combustion. When there is a moment within the preset period of time when the output current is not greater than the preset current, the process proceeds to step S113.

[0067] S113 determines the basic risk coefficient of the electric vehicle based on the average value of the output current at different times within a preset period of time, and proceeds to step S12.

[0068] S12 determines the moment when the output current of the electric vehicle in a preset period is greater than the preset current based on the output current at different moments, and uses it as the screening current moment, and determines the division risk coefficient in different unit time lengths according to the number proportion of the screening current moments in different unit time lengths and the output current at different moments;

[0069] Optionally, the above step S12 includes steps S121-S124, specifically:

[0070] S121 determines the moment when the output current of the electric vehicle within a preset period is greater than the preset current based on the output current at different moments, and uses it as the screening current moment. When the number of the screening current moments is greater than the number of the preset moments, it is determined that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion. When the number of the screening current moments is not greater than the number of the preset moments, it proceeds to step S122.

[0071] S122 determines the target unit duration based on the proportion of the number of screening current moments within the unit duration. When the number of the target unit duration is greater than the preset duration, it is determined that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion. When the number of the target unit duration is not greater than the preset duration, the process proceeds to step S123.

[0072] S123 determines the division risk coefficients in different unit time lengths according to the proportion of the number of screening current moments in different unit time lengths and the output currents at different moments. When there is a unit time length with a division risk coefficient greater than the preset risk coefficient, the process proceeds to step S124. When there is no unit time length with a division risk coefficient greater than the preset risk coefficient, the process proceeds to step S13.

[0073] S124 When the number of unit time lengths whose risk coefficient is greater than the preset risk coefficient is greater than the preset time length, it is determined that the electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis; when the number of unit time lengths whose risk coefficient is greater than the preset risk coefficient is not greater than the preset time length, proceed to step S13.

[0074] S13 determines the comprehensive risk coefficient of the electric vehicle based on the divided risk coefficients within different unit time periods and the basic risk coefficient, and determines whether the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion according to the comprehensive risk coefficient of the electric vehicle.

[0075] Further, the temperature data of the power battery at different times within a preset time period includes the temperature at different times, which is specifically determined according to the analysis result of the monitoring data of the temperature monitoring device of the power battery.

[0076] Specifically, Figure 3 As shown, it is determined that the electric vehicle has a suspected risk of spontaneous combustion, including:

[0077] Determine the time when the temperature is greater than a preset temperature threshold based on the temperature data at different times, and take it as the temperature abnormality time;

[0078] According to the change of temperature data at different moments, the temperature change amount between different adjacent moments is calculated, and the time of abnormal temperature change is determined based on the temperature change amount between different adjacent moments;

[0079] Whether the electric vehicle has a suspected risk of spontaneous combustion is determined based on the number of abnormal temperature moments and abnormal temperature change moments.

[0080] Further, when the number of abnormal temperature moments or the number of abnormal temperature change moments does not meet the requirements, it is determined that the electric vehicle has a suspected risk of spontaneous combustion.

[0081] Optionally, determining that the electric vehicle has a suspected risk of spontaneous combustion specifically includes:

[0082] Determine an average value of the temperature data at different times based on the temperature data at different times, and use the average value of the temperature data to determine the average temperature;

[0083] According to the change of temperature data at different moments, the temperature change between different adjacent moments is calculated, and the average change is determined according to the average value of the temperature change between different adjacent moments;

[0084] Based on the average temperature, it is determined whether the electric vehicle has a suspected risk of spontaneous combustion by using an average variation.

[0085] Furthermore, when the average temperature or the average variation does not meet the requirements, it is determined that the electric vehicle has a suspected risk of spontaneous combustion.

[0086] It should also be noted that the determination of the suspected spontaneous combustion risk of the electric vehicle includes:

[0087] S21 determines the moment when the temperature is greater than a preset temperature threshold based on the temperature data at different moments, and takes it as the temperature abnormal moment, and determines the temperature abnormality coefficient according to the number ratio of the temperature abnormal moments and the temperature data at different temperature abnormal moments;

[0088] S22: according to the change of temperature data at different moments, the temperature change amount between different adjacent moments is calculated, and the temperature abnormal change moment is determined based on the temperature change amount between different adjacent moments, and the temperature abnormal change coefficient is determined according to the number proportion of the temperature abnormal change moments and the change amount of different temperature abnormal change moments;

[0089] S23 determines a comprehensive abnormality coefficient based on the temperature abnormality coefficient and the temperature variation abnormality coefficient, and uses the comprehensive abnormality coefficient to determine whether the electric vehicle has a suspected risk of spontaneous combustion.

[0090] Furthermore, the preset time period is divided into a plurality of sub-time periods, specifically including:

[0091] The preset time period is divided into a plurality of sub-time periods according to a preset duration.

[0092] It can be understood that the operating data of the power battery includes the remaining capacity and current of the power battery.

[0093] Specifically, Figure 4 As shown, the method for determining the temperature anomaly coefficient of the sub-period is:

[0094] Determining the remaining capacity and current of the power battery in the sub-period based on the operating data of the power battery in the sub-period;

[0095] Determining a matching temperature range of the power battery in the sub-period based on the remaining capacity and current of the power battery in the sub-period as constraint conditions;

[0096] Based on the matching temperature range of the power battery in the sub-period, the moment when the power battery is not within the matching temperature range in the sub-period is determined and taken as the abnormal temperature moment, and the temperature abnormality coefficient of the sub-period is determined according to the number ratio of the abnormal temperature moments.

[0097] Further, the matching temperature range of the power battery in the sub-period is determined according to the historical temperature data of the power battery under the constraint conditions, and the temperature range corresponding to the historical temperature data under the constraint conditions is used as the matching temperature range.

[0098] It can be understood that the temperature anomaly coefficient of the sub-period ranges from 0 to 1, wherein when the temperature anomaly coefficient of the sub-period is greater than a preset anomaly threshold, the sub-period is determined to be a temperature anomaly sub-period.

[0099] Optionally, the method for determining the temperature anomaly coefficient of the sub-period is:

[0100] Based on the operation data of the power battery in the sub-period, determining the temperature data of the power battery in the sub-period, when there is no moment in the sub-period when the temperature data is greater than the preset temperature limit value: determining that the sub-period does not belong to the temperature abnormality sub-period;

[0101] When there is a moment in which the temperature data is greater than the preset temperature limit value within the sub-period:

[0102] The basic temperature anomaly coefficient is determined based on the number of moments when the temperature data in the sub-period is greater than the preset temperature limit value and the temperature data at different moments. When the basic temperature anomaly coefficient meets the requirements:

[0103] Determine the remaining capacity and current of the power battery in the sub-period, and determine the matching temperature range of the power battery in the sub-period with the remaining capacity and current of the power battery in the sub-period as constraints;

[0104] When it is determined, based on the matching temperature range of the power battery in the sub-period, that there is no moment in the sub-period when the power battery is not within the matching temperature range, the sub-period is determined not to be a sub-period of abnormal temperature;

[0105] When it is determined that the power battery is not within the matching temperature range at a time point in the sub-period:

[0106] The moment when the power battery is not within the matching temperature range in the sub-period is regarded as an abnormal temperature moment. When the number of abnormal temperature moments in the sub-period is less than the preset number of moments:

[0107] Obtain the deviation between the temperature data of different abnormal temperature moments in the sub-period and the endpoint of the adjacent matching temperature range, and use it as the temperature deviation. When there is no abnormal temperature moment with a temperature deviation greater than the preset deviation:

[0108] It is determined that the sub-period does not belong to the abnormal temperature sub-period;

[0109] When there is an abnormal temperature moment with a temperature deviation greater than the preset deviation or the number of abnormal temperature moments in a sub-period is not less than the preset number of moments or the basic temperature anomaly coefficient meets the requirements:

[0110] The temperature anomaly coefficient of the sub-period is determined by the proportion of the number of the abnormal temperature moments and the temperature deviations of different abnormal temperature moments.

[0111] Furthermore, the method for determining the spontaneous combustion risk of the power battery of the electric vehicle is:

[0112] The spontaneous combustion risk of the power battery of the electric vehicle is determined based on the distribution data of the temperature abnormality sub-period within the preset period and the temperature abnormality coefficient.

[0113] Spontaneous combustion risk of the power battery of the electric vehicle: Based on the distribution data of the temperature anomaly sub-periods within the preset time period and the temperature anomaly coefficient, an AI model is used to determine the spontaneous combustion risk of the power battery of the electric vehicle, wherein the AI ​​model is constructed using a BP neural network, etc.

[0114] Embodiment 2 In the second aspect, as Figure 5 As shown, the present invention provides a detection device, which adopts the above-mentioned vehicle power battery spontaneous combustion risk assessment and analysis method, specifically including:

[0115] Current analysis module, sub-period division module, temperature anomaly assessment module, spontaneous combustion risk assessment module;

[0116] The current analysis module is responsible for determining the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, the temperature data of the power battery of the electric vehicle at different times within the preset period are obtained;

[0117] The sub-period division module is responsible for dividing the preset time period into a plurality of sub-periods when it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and the changes in the temperature data at different times;

[0118] The temperature anomaly evaluation module is responsible for determining the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery;

[0119] The spontaneous combustion risk assessment module is responsible for determining the spontaneous combustion risk of the power battery of the electric vehicle based on the distribution data of the temperature abnormality sub-period within the preset time period and the temperature abnormality coefficient.

[0120] Optionally, the above step S21 includes steps S211-S213, which are specifically:

[0121] S211 determines the moment when the temperature is greater than the preset temperature threshold based on the temperature data at different moments, and takes it as the temperature abnormal moment. When the number of temperature abnormal moments accounts for a greater proportion than the number of preset moments, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the number of temperature abnormal moments accounts for a less than the number of preset moments, the process proceeds to step S212.

[0122] S212 determines the temperature abnormality moments within the preset temperature range based on the temperature data at the temperature abnormality moments. When the number of temperature abnormality moments within the preset temperature range does not meet the requirement, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the number of temperature abnormality moments within the preset temperature range meets the requirement, the process proceeds to step S213.

[0123] S213 determines the temperature anomaly coefficient by the proportion of the number of temperature anomaly moments and the temperature data at different temperature anomaly moments. When the temperature anomaly coefficient does not meet the requirements, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the temperature anomaly coefficient meets the requirements, it proceeds to step S22.

[0124] Optionally, the above step S22 includes the following contents:

[0125] S221: Calculate the temperature variation between different adjacent moments according to the variation of temperature data at different moments. When the average value of the temperature variation between different adjacent moments does not meet the requirement, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the average value of the temperature variation between different adjacent moments meets the requirement, the process proceeds to step S222.

[0126] S222 determines the abnormal temperature change moment based on the temperature change amount between different adjacent moments. When the number of abnormal temperature change moments does not meet the requirement, the process proceeds to step S223. When the number of abnormal temperature change moments meets the requirement, the process proceeds to step S224.

[0127] S223 determines the ratio of the number of abnormal temperature change moments according to the number of abnormal temperature change moments. When the ratio of the number of abnormal temperature change moments does not meet the requirement, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the ratio of the number of abnormal temperature change moments meets the requirement, the process proceeds to step S224.

[0128] S224 determines the temperature change abnormality coefficient by the proportion of the number of temperature abnormality change moments and the change amount at different temperature abnormality change moments. When the temperature change abnormality coefficient does not meet the requirements, it is determined that the electric vehicle has a suspected risk of spontaneous combustion. When the temperature change abnormality coefficient meets the requirements, proceed to step S23.

[0129] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0130] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A vehicle power battery spontaneous combustion risk assessment and analysis method, characterized in that: Specifically include: Determining the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, obtaining temperature data of the power battery of the electric vehicle at different times within the preset period; When it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and changes in the temperature data at different times, the preset time period is divided into a plurality of sub-time periods; Determine the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery; Determining the risk of spontaneous combustion of the power battery of the electric vehicle based on the distribution data of the temperature anomaly sub-period within the preset period and the temperature anomaly coefficient; Determine that the electric vehicle needs to undergo a spontaneous combustion risk assessment and analysis, specifically including: Determine the moment when the output current of the electric vehicle within a preset period is greater than a preset current by using the output current at different moments, and use it as the current screening moment; Acquire the number of screening current moments within the preset time period, and determine the number of screening current moments within different unit time lengths within the preset time period according to the distribution data of the screening current moments; The target unit duration is determined based on the proportion of the number of screened current moments within different unit durations, and whether the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion is determined based on the proportion of the number of target unit durations.

2. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The preset period is determined according to an average driving speed of the electric vehicle in the current driving mileage, wherein the faster the average driving speed is, the shorter the preset period is.

3. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The output current is determined according to an analysis result of the operation data of the electric vehicle.

4. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The target unit duration is a unit duration in which the proportion of the number of screening current moments is greater than the proportion of the number of preset moments.

5. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: When the target unit duration quantity ratio is within a preset duration quantity ratio range, it is determined that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion.

6. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The preset time period is divided into a plurality of sub-time periods, specifically including: The preset time period is divided into a plurality of sub-time periods according to a preset duration.

7. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The operating data of the power battery includes the remaining capacity and current of the power battery.

8. The vehicle power battery spontaneous combustion risk assessment and analysis method according to claim 1, characterized in that: The method for determining the temperature anomaly coefficient of the sub-period is: Determining the remaining capacity and current of the power battery in the sub-period based on the operating data of the power battery in the sub-period; Determining a matching temperature range of the power battery in the sub-period based on the remaining capacity and current of the power battery in the sub-period as constraint conditions; Based on the matching temperature range of the power battery in the sub-period, the moment when the power battery is not within the matching temperature range in the sub-period is determined and taken as the abnormal temperature moment, and the temperature abnormality coefficient of the sub-period is determined according to the number ratio of the abnormal temperature moments.

9. A detection device, using a vehicle power battery spontaneous combustion risk assessment and analysis method according to any one of claims 1 to 8, characterized in that: Specifically include: Current analysis module, sub-period division module, temperature anomaly assessment module, spontaneous combustion risk assessment module; The current analysis module is responsible for determining the output current of the electric vehicle at different times within a preset period, and when determining that the electric vehicle needs to be evaluated and analyzed for the risk of spontaneous combustion based on the output current at different times, the temperature data of the power battery of the electric vehicle at different times within the preset period are obtained; The sub-period division module is responsible for dividing the preset time period into a plurality of sub-periods when it is determined that the electric vehicle has a suspected risk of spontaneous combustion based on the temperature data and the changes in the temperature data at different times; The temperature anomaly evaluation module is responsible for determining the temperature anomaly coefficients of different sub-periods and the temperature anomaly sub-periods according to the matching of the temperature data in different sub-periods with the operation data of the power battery; The spontaneous combustion risk assessment module is responsible for determining the spontaneous combustion risk of the power battery of the electric vehicle based on the distribution data of the temperature abnormality sub-period within the preset time period and the temperature abnormality coefficient.

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