Method and device for judging non-dormancy abnormity of vehicle control unit and vehicle

Abstract

The invention relates to the technical field of new energy automobile control, and discloses a vehicle control unit non-dormancy abnormity judgment method and device and a vehicle, and the method comprises the following steps: S1, determining the laying condition of the vehicle; s2, selecting a judgment signal corresponding to each domain controller, and obtaining a data frame number corresponding to the judgment signal; and S3, calculating the dormancy duration of the controller according to the number of data frames uploaded by each controller and the corresponding uploading period. In the invention, whether each controller is in a normal state or not is judged by accurately calculating the actual dormancy duration of each controller, specifically, after a high-voltage signal is disconnected, the data uploading behavior of the controller is monitored, the data uploading frame number is counted, and the actual dormancy duration of each controller is calculated in combination with the cloud data uploading period; and then the calculation result is compared with the preset normal dormancy duration, so that the system can quickly identify the controller which is in abnormal dormancy, and early warning is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle control, and in particular to a method for determining a non-sleep abnormality of a vehicle controller. Background Art

[0002] With the popularity of new energy vehicles, efficient management and energy consumption control of vehicle control systems have become important research directions. However, in the existing technology, after the high-voltage system of the vehicle is turned off, some controllers fail to enter the dormant state in time, resulting in continuous power consumption of the low-voltage power supply, which in turn increases the energy consumption of the entire vehicle. This unnecessary energy consumption not only shortens the battery life, but may also cause users to be dissatisfied with the reliability of the vehicle. At the same time, in order to ensure the power of the low-voltage power supply, some new energy vehicles will frequently use high-voltage battery packs for recharging, but frequent recharging will reduce the overall life of the battery pack and affect the long-term use experience of the vehicle.

[0003] At present, most of the problems in the market or during the test process are not known until the existence of the control non-sleep situation is known. The lack of initiative leads to passive processing after the abnormality occurs, and it is impossible to give early warning and take effective measures in time. The existing sleep state judgment methods are mostly based on simplified monitoring on the vehicle side, which is easily affected by factors such as data loss or incomplete upload. For example, when the TBOX (telecommunication control unit) data is not fully uploaded, it may lead to misjudgment of the controller's sleep state, thereby affecting the judgment accuracy of the vehicle system. The existing technology cannot effectively avoid the errors caused by data loss, which further increases the difficulty of maintaining the vehicle control system.

[0004] In addition, existing technologies lack large-scale analysis of vehicle group data, making it difficult to identify sleep anomalies with common characteristics, and failing to effectively optimize the efficiency of anomaly troubleshooting. Single-vehicle data analysis is difficult to cover a variety of operating conditions, and the controller's sleep performance may vary significantly under different environmental conditions. The lack of a global judgment standard based on environmental conditions not only limits the speed of troubleshooting, but also increases the difficulty for engineers in locating anomalies, reducing the system's maintenance efficiency. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for determining a vehicle controller is not in sleep mode, which solves the problems mentioned in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for determining an abnormality of a vehicle controller not being in sleep mode comprises the following steps:

[0007] S1. Determine the parking condition of the vehicle;

[0008] S2. Select the judgment signal corresponding to each domain controller, and obtain the number of data frames corresponding to the judgment signal;

[0009] S3. Calculate the controller's sleep duration based on the number of data frames uploaded by each controller and the corresponding upload cycle:

[0010] S4. Compare the calculation result of S3 with the predefined normal sleep duration to identify abnormal controllers:

[0011] S5. Count the number of sleep anomalies of each vehicle. If multiple vehicles have sleep anomalies on the same controller for multiple times, an early warning is issued.

[0012] Preferably, the method further includes steps for avoiding misjudgment, and the specific steps are as follows:

[0013] a. Power detection when not in sleep mode: When the vehicle is in a standby state and has not yet entered the sleep mode, the low-voltage power supply continues to discharge, and the power should continue to decrease. By calculating the time difference between the previous and next two frames and multiplying it by the current consumption, the theoretical power consumption value during this period is obtained. If this theoretical power consumption value is much greater than the low-voltage power supply power difference between the previous and next two frames, the sleep time T at this time is recorded. sleep ;

[0014] b. Count the number of data frames during the idle condition:

[0015] If there is a sleep time T calculated in step a sleep , then the time T from the start of the shelving condition is counted start To sleep time T sleep The number of data frames uploaded between

[0016] If there is no T sleep , then the time T from the start of the shelving condition is counted start Until the suspension end time T end The number of uploaded data frames between

[0017] c. Comparison and error determination: Based on the comparison results of the theoretical power consumption value and the actual power change in step a, if the difference between the two exceeds the preset threshold, it is determined that TBOX may have an abnormality of incomplete data upload during this period; if the difference does not exceed the threshold, it is confirmed whether the vehicle has entered a dormant state.

[0018] Preferably, the step of calculating the sleep duration of the controller in step S3 is:

[0019] a. Determine whether the controller is in sleep mode by monitoring whether it is still uploading data after the high-voltage signal is disconnected. If the controller is uploading data, it means that the controller is not in sleep mode;

[0020] b. Count the number of data frames from each controller from uploading data to stopping uploading data;

[0021] c. The sleep time of each controller is obtained by multiplying the number of data frames by the data upload cycle of the cloud platform.

[0022] Preferably, in step S4, the statistics of the number of sleep exceptions include hierarchical statistics of different categories of exceptions to distinguish high-frequency exceptions from low-frequency exceptions, and the grading standard of the hierarchical statistics is based on the frequency of occurrence and impact of the exceptions, and the exceptions are divided into three categories: slight exceptions, moderate exceptions and severe exceptions.

[0023] Preferably, in step S4, the details of the normal sleep duration comparison are as follows:

[0024] a. Determine the standard upload cycle as the upload time interval for each data frame;

[0025] b. Calculate the actual sleep time from the start of data uploading to the stop of uploading by multiplying the number of data frames by the standard upload cycle;

[0026] c. Compare with the normal sleep time. If the actual sleep time is significantly longer than the normal sleep time, it is determined that the controller may have a sleep abnormality. If the actual sleep time is consistent with the normal sleep time, it is determined that the controller has no sleep abnormality.

[0027] Preferably, in step S5, big data analysis is further carried out to extract the sleep behavior pattern of each controller in the vehicle historical data, identify potential common sleep abnormality problems, and verify whether the sleep abnormality is affected by external conditions by comparing the sleep performance of each controller under different environmental conditions.

[0028] Preferably, the step S2 further includes performing a preliminary screening of the judgment signals of each controller after the vehicle enters the idle condition to exclude controllers that have entered sleep mode.

[0029] A vehicle controller non-sleep abnormality judgment device, according to a vehicle controller non-sleep abnormality judgment method according to any one of claims 1 to 7, characterized in that it comprises:

[0030] a. A status monitoring unit, used to determine the parking condition of the vehicle and the judgment signal of each domain controller;

[0031] b. A data processing unit, used to calculate the sleep time of the controller and compare it with the pre-defined normal sleep time;

[0032] c. Abnormality statistics unit, used to count the number of sleep abnormalities of each vehicle and issue an early warning when multiple sleep abnormalities of the same controller occur in multiple vehicles;

[0033] d. False positive avoidance module, used to perform power detection, data frame statistics and error judgment to ensure the accuracy of judgment.

[0034] A whole vehicle, having the function of the whole vehicle controller non-sleep abnormal judgment method according to any one of claims 1 to 7, characterized in that a plurality of domain controllers are configured in the whole vehicle, which can monitor and judge the sleep state of each controller in real time, and upload data by connecting to a cloud platform to realize timely warning and processing of abnormal states.

[0035] The present invention provides a method for determining an abnormality of a vehicle controller not being in sleep mode. The method has the following beneficial effects:

[0036] 1. The present invention determines whether each controller is in a normal state by accurately calculating the actual sleep time of each controller. Specifically, after the high-voltage signal is disconnected, the data upload behavior of the controller is monitored, the number of frames uploaded is statistically counted, and the actual sleep time of each controller is calculated in combination with the cycle of cloud data upload. This calculation result is then compared with the preset normal sleep time. The system can quickly identify abnormally dormant controllers to facilitate early warning, thereby improving the overall performance of the vehicle and user experience.

[0037] 2. The present invention detects the low-voltage power consumption of the vehicle in a non-sleeping state and calculates the theoretical power consumption of the controller, thereby effectively avoiding the situation of incomplete TBOX data upload, reducing the misjudgment caused by data upload loss, improving the accuracy of sleep state judgment, making vehicle data more credible, and enhancing the stability of the system.

[0038] 3. Use cloud-based big data analysis to collect statistics on the sleep time requirements of each controller to form a comprehensive controller sleep status database, and analyze the standard sleep time of each controller through long-term accumulated data. Based on statistical analysis, controllers with abnormal frequency exceeding the threshold are marked as potential fault points, which speeds up engineers' troubleshooting and improves the accuracy of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0040] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example:

[0043] Please refer to the attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a method for determining that a vehicle controller is not in sleep mode, comprising the following steps:

[0044] S1. Determine the parking condition of the vehicle;

[0045] S2. Select the judgment signal corresponding to each domain controller, and obtain the number of data frames corresponding to the judgment signal;

[0046] S3. Calculate the controller's sleep duration based on the number of data frames uploaded by each controller and the corresponding upload cycle:

[0047] S4. Compare the calculation result of S3 with the predefined normal sleep duration to identify abnormal controllers:

[0048] S5. Count the number of sleep anomalies of each vehicle. If multiple vehicles have sleep anomalies on the same controller for multiple times, an early warning is issued.

[0049] Specifically, by comprehensively analyzing the controller sleep state of the vehicle under idle conditions, the aim is to identify and determine the sleep time of each domain controller after the high-voltage signal is disconnected. The specific steps include determining the idle condition, selecting relevant controller signals, calculating the number of uploaded data frames and the sleep time, and comparing them with the normal sleep time preset by the enterprise, so as to find abnormally dormant controllers. This method not only improves the monitoring accuracy of the controller sleep state, but also provides reliable data support for subsequent troubleshooting.

[0050] It also includes steps to avoid false positives. The specific steps are as follows:

[0051] a. Power detection when not in sleep mode: When the vehicle is in a standby state and has not yet entered the sleep mode, the low-voltage power supply continues to discharge, and the power should continue to decrease. By calculating the time difference between the previous and next two frames and multiplying it by the current consumption, the theoretical power consumption value during this period is obtained. If this theoretical power consumption value is much greater than the low-voltage power supply power difference between the previous and next two frames, the sleep time T at this time is recorded. sleep ;

[0052] b. Count the number of data frames during the idle condition:

[0053] If there is a sleep time T calculated in step a sleep , then the time T from the start of the shelving condition is counted start To sleep time T sleep The number of data frames uploaded between

[0054] If there is no T sleep , then the time T from the start of the shelving condition is counted start Until the suspension end time Tend The number of uploaded data frames between

[0055] c. Comparison and error determination: Based on the comparison results of the theoretical power consumption value and the actual power change in step a, if the difference between the two exceeds the preset threshold, it is determined that TBOX may have an abnormality of incomplete data upload during this period; if the difference does not exceed the threshold, it is confirmed whether the vehicle has entered a dormant state.

[0056] Specifically, during the implementation process, taking into account the possibility of data loss when TBOX is uploading data, by monitoring the power changes of the low-voltage power supply and comparing the theoretical power consumption with the actual power difference, it is possible to effectively determine whether the controller has entered the sleep state. This measure not only improves the accuracy of the judgment, but also reduces the risk of misjudgment due to data loss, ensuring the reliability of the sleep state judgment.

[0057] The steps for calculating the sleep duration of the controller in step S3 are:

[0058] a. Determine whether the controller is in sleep mode by monitoring whether it is still uploading data after the high-voltage signal is disconnected. If the controller is uploading data, it means that the controller is not in sleep mode;

[0059] b. Count the number of data frames from each controller from uploading data to stopping uploading data;

[0060] c. The sleep time of each controller is obtained by multiplying the number of data frames by the data upload cycle of the cloud platform.

[0061] Specifically, whether each controller is in a normal state is determined by accurately calculating its actual sleep time. Specifically, after the high-voltage signal is disconnected, the controller's data upload behavior is monitored, the number of frames uploaded is statistically counted, and the actual sleep time of each controller is calculated in combination with the cloud data upload cycle. This calculation result is then compared with the preset normal sleep time. This allows for quick identification of abnormally dormant controllers for early warning, which improves the vehicle's overall performance and user experience.

[0062] In step S4, the statistics of the number of sleep exceptions include hierarchical statistics of different categories of exceptions to distinguish high-frequency exceptions from low-frequency exceptions. The grading standard of the hierarchical statistics is based on the frequency and impact of the exceptions, and the exceptions are divided into three categories: slight exceptions, medium exceptions and severe exceptions.

[0063] Specifically, by setting grading standards, anomalies are divided into three categories: minor, moderate, and severe, which helps engineers quickly identify common problems, improves the efficiency of exception handling, and provides a clear direction for subsequent optimization measures.

[0064] In step S4, the details of the normal sleep duration comparison are as follows:

[0065] a. Determine the standard upload cycle as the upload time interval for each data frame;

[0066] b. Calculate the actual sleep time from the start of data uploading to the stop of uploading by multiplying the number of data frames by the standard upload cycle;

[0067] c. Compare with the normal sleep time. If the actual sleep time is significantly longer than the normal sleep time, it is determined that the controller may have a sleep abnormality. If the actual sleep time is consistent with the normal sleep time, it is determined that the controller has no sleep abnormality.

[0068] Specifically, based on the overall description of step S4, the core idea of ​​sleep time calculation and abnormality comparison is provided, and then refined on this basis to concretize the calculation method to ensure the accuracy and operability of each step. The two constitute a progressive relationship from the overall overview to the detailed steps. The specific calculation steps in the comparison process are described in detail, and the calculation method of the actual sleep time is further refined. It also emphasizes how to use the standard upload cycle specified by the enterprise or cloud platform for calculation and comparison to determine whether the controller has a sleep abnormality. It provides operational calculation and judgment steps to ensure the accuracy of the comparison process.

[0069] In step S4, when the number of sleep abnormalities exceeds the preset frequency, the system automatically triggers an early warning and sends an abnormality report to the engineer. The abnormality report includes the specific controller identification, the number of abnormalities, the timestamp and the deviation information from the normal sleep time.

[0070] Specifically, when the number of sleep exceptions exceeds the preset frequency, an early warning is automatically triggered and a detailed exception report is sent to the engineer, which improves the timeliness of fault response. It also helps engineers quickly locate problems by providing specific controller identification, number of exceptions, timestamps and other information, reducing processing delays caused by information asymmetry.

[0071] In step S5, big data analysis is further carried out to extract the sleep behavior pattern of each controller in the vehicle historical data, identify potential common sleep abnormality problems, and verify whether the sleep abnormality is affected by external conditions by comparing the sleep performance of each controller under different environmental conditions.

[0072] Specifically, through in-depth analysis of vehicle historical data and extraction of the sleep behavior patterns of each controller, potential common sleep abnormality problems can be identified. This process includes summarizing and comparing the sleep data of the vehicle in different time periods and different usage environments (such as temperature, humidity, terrain, etc.), so as to discover the sleep abnormality frequency and pattern of the controller under specific conditions, and quickly identify which controllers are more prone to sleep abnormalities under specific environmental conditions, thereby providing a basis for subsequent technical improvements and optimizations.

[0073] In step S2, after the vehicle enters the idle state, preliminary screening is performed on the judgment signals of each controller to exclude controllers that have entered sleep mode.

[0074] Specifically, after the vehicle enters the idle condition, a preliminary screening is performed on the judgment signals of each controller to exclude controllers that have entered sleep mode, thereby ensuring the accuracy of subsequent data analysis and avoiding misjudgment of dormant controllers.

[0075] A vehicle controller non-sleep abnormality judgment device, according to a vehicle controller non-sleep abnormality judgment method according to any one of claims 1 to 7, characterized in that it comprises:

[0076] a. A status monitoring unit, used to determine the parking condition of the vehicle and the judgment signal of each domain controller;

[0077] b. A data processing unit, used to calculate the sleep time of the controller and compare it with the pre-defined normal sleep time;

[0078] c. Abnormality statistics unit, used to count the number of sleep abnormalities of each vehicle and issue an early warning when multiple sleep abnormalities of the same controller occur in multiple vehicles;

[0079] d. False positive avoidance module, used to perform power detection, data frame statistics and error judgment to ensure the accuracy of judgment.

[0080] Specifically, through the coordinated work of the above units, the vehicle controller non-sleep abnormality judgment device can achieve comprehensive monitoring, accurate judgment and timely warning of the vehicle controller, thereby effectively improving the safety and reliability of the vehicle.

[0081] A whole vehicle, having the function of the whole vehicle controller non-sleep abnormal judgment method according to any one of claims 1 to 7, characterized in that a plurality of domain controllers are configured in the whole vehicle, which can monitor and judge the sleep state of each controller in real time, and upload data by connecting to a cloud platform to realize timely warning and processing of abnormal states.

[0082] Specifically, through the implementation of the above specific functions, the whole vehicle can accurately determine the sleep abnormality of the controller.

[0083] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining an abnormality of a vehicle controller not being in sleep mode, characterized in that: The following steps are involved: S1. Determine the parking condition of the vehicle; S2. Select the judgment signal corresponding to each domain controller, and obtain the number of data frames corresponding to the judgment signal; S3. Calculate the controller's sleep duration based on the number of data frames uploaded by each controller and the corresponding upload cycle: S4. Compare the calculation result of S3 with the predefined normal sleep duration to identify abnormal controllers: S5. Count the number of sleep anomalies of each vehicle. If multiple vehicles have sleep anomalies on the same controller for multiple times, an early warning is issued.

2. A method for determining a vehicle controller not sleeping abnormally according to claim 1, characterized in that: It also includes steps to avoid false positives. The specific steps are as follows: a. Power detection when not in sleep mode: When the vehicle is in a standby state and has not yet entered the sleep mode, the low-voltage power supply continues to discharge, and the power should continue to decrease. By calculating the time difference between the previous and next two frames and multiplying it by the current consumption, the theoretical power consumption value during this period is obtained. If this theoretical power consumption value is much greater than the low-voltage power supply power difference between the previous and next two frames, the sleep time T at this time is recorded. sleep ; b. Count the number of data frames during the idle condition: If there is a sleep time T calculated in step a sleep , then the time T from the start of the shelving condition is counted start To sleep time T sleep The number of data frames uploaded between If there is no T sleep , then the time T from the start of the shelving condition is counted start Until the suspension end time T end The number of uploaded data frames between c. Comparison and error determination: Combined with the comparison results of the theoretical power consumption value and the actual power change in step a, if the difference between the two exceeds the preset threshold, it is determined that the TBOX may have an abnormality of incomplete data upload during this period; If the difference does not exceed the threshold, it is determined whether the vehicle enters a dormant state.

3. A method for determining a vehicle controller not sleeping abnormally according to claim 2, characterized in that: The steps for calculating the sleep duration of the controller in step S3 are as follows: a. Determine whether the controller is in sleep mode by monitoring whether it is still uploading data after the high-voltage signal is disconnected. If the controller is uploading data, it means that the controller is not in sleep mode; b. Count the number of data frames from each controller from uploading data to stopping uploading data; c. The sleep time of each controller is obtained by multiplying the number of data frames by the data upload cycle of the cloud platform.

4. The method for determining the abnormality of a vehicle controller not being in sleep mode according to claim 1, characterized in that: In step S4, the statistics of the number of sleep exceptions include hierarchical statistics of different categories of exceptions to distinguish high-frequency exceptions from low-frequency exceptions. The grading standard of the hierarchical statistics is based on the frequency of occurrence and impact of the exceptions, and the exceptions are divided into three categories: slight exceptions, medium exceptions and severe exceptions.

5. The method for determining the abnormality of a vehicle controller not being in sleep state according to claim 1, characterized in that: In step S4, the details of the normal sleep duration comparison are as follows: a. Determine the standard upload cycle as the upload time interval for each data frame; b. Calculate the actual sleep time from the start of data uploading to the stop of uploading by multiplying the number of data frames by the standard upload cycle; c. Compare with the normal sleep time. If the actual sleep time is significantly longer than the normal sleep time, it is determined that the controller may have a sleep abnormality. If the actual sleep time is consistent with the normal sleep time, it is determined that the controller has no sleep abnormality.

6. The method for determining the abnormality of a vehicle controller not being in sleep state according to claim 1, characterized in that: In step S5, big data analysis is further carried out to extract the sleep behavior pattern of each controller in the vehicle historical data, identify potential common sleep abnormality problems, and verify whether the sleep abnormality is affected by external conditions by comparing the sleep performance of each controller under different environmental conditions.

7. A vehicle controller non-sleep abnormality judgment method according to claim 1, characterized in that: The step S2 further includes performing a preliminary screening of the judgment signals of each controller after the vehicle enters the idle state to exclude controllers that have entered sleep mode.

8. A vehicle controller non-sleep abnormality judgment device, according to a vehicle controller non-sleep abnormality judgment method according to any one of claims 1 to 7, characterized in that: include: a. A status monitoring unit, used to determine the parking condition of the vehicle and the judgment signal of each domain controller; b. A data processing unit, used to calculate the sleep time of the controller and compare it with the pre-defined normal sleep time; c. Abnormality statistics unit, used to count the number of sleep abnormalities of each vehicle and issue an early warning when multiple sleep abnormalities of the same controller occur in multiple vehicles; d. False positive avoidance module, used to perform power detection, data frame statistics and error judgment to ensure the accuracy of judgment.

9. A vehicle having the function of the vehicle controller non-sleep abnormality judgment method according to any one of claims 1 to 7, characterized in that: The vehicle is equipped with a variety of domain controllers, which can monitor and determine the sleep status of each controller in real time, and upload data by connecting to the cloud platform to achieve timely warning and processing of abnormal conditions.

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