A low power management method for truck OBD diagnostic devices

Abstract

The application discloses a kind of low-power management methods for truck OBD diagnostic equipment, comprising the following steps: S1, comprehensive acquisition engine speed, ignition signal, battery voltage and vehicle body vibration information, accurately distinguish vehicle is in running state, short parking state and long-term off state;S2, according to the vehicle state distinguished in S1, automatically switch the power consumption mode of corresponding state;S3, adopt the multi-source wake-up mechanism of event trigger and timing trigger combination, check battery voltage acquisition and health;S4, battery voltage is detected by MCU ADC and external comparator redundancy;S5, call data management module before hibernation, save fault code and diagnostic log that are not uploaded to nonvolatile memory, and adopt power-off protection and redundancy storage mechanism;The method realizes the intelligent switching between the three power consumption modes of normal diagnosis, shallow hibernation and deep hibernation of diagnostic equipment, and enters forced deep hibernation when battery voltage is lower than threshold, to avoid vehicle battery depletion.

Description

Technical Field

[0001] This invention belongs to the field of truck OBD diagnostic technology, specifically relating to a low-power management method for truck OBD diagnostic equipment. Background Technology

[0002] On-Board Diagnostics (OBD) devices are widely used in vehicle fault code reading, operational status monitoring, and maintenance early warning systems. In truck environments, vehicles are often parked with the engine off for extended periods. If the OBD device continues to operate, it can cause the vehicle battery to continuously discharge, leading to battery depletion or even preventing the vehicle from starting.

[0003] Most current on-board diagnostic (OBD) devices are installed in vehicles year-round, with relatively fixed operating modes. Their core function is to acquire vehicle operating information such as engine, emissions, and fault codes through the OBD interface and upload or store the data. However, existing OBD devices exhibit the following typical characteristics: 1. Continuous power supply: Most devices continue to consume a high amount of power even after the vehicle is turned off, which puts a long-term burden on the battery.

[0004] 2. Simple sleep mechanism: Some products only enter low power mode when there is no communication for a long time, but the wake-up mechanism is simple and lacks awareness of the actual operating status of the vehicle.

[0005] 3. Lack of battery protection: When the battery voltage is too low, traditional OBD devices will still consume current, which can easily cause the battery to run out of power and affect the vehicle's ignition.

[0006] 3. Insufficient sleep logic: The existing solution cannot distinguish the vehicle status (such as ignition, engine off, short-term parking and long-term idling), resulting in unreasonable low power management.

[0007] 4. Incomplete wake-up conditions: Most devices rely on a single signal (such as the ignition wire). If the signal is abnormal, the device cannot be woken up normally.

[0008] 5. Lack of battery protection: Existing OBD equipment lacks real-time monitoring of battery voltage, posing a risk of battery depletion due to over-discharge.

[0009] 6. Insufficient data security: Ineffective data caching and storage during hibernation switching may result in the loss of diagnostic data. Summary of the Invention

[0010] The purpose of this invention is to provide a low-power management method for truck OBD diagnostic equipment. By introducing a multi-level power state machine, a battery voltage protection mechanism, and a multi-source wake-up mechanism, the device can intelligently switch between different vehicle states, ensuring the integrity of diagnostic data while effectively reducing power consumption and preventing battery depletion.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a low-power management method for truck OBD diagnostic equipment, comprising the following steps: S1. Collect engine speed, ignition signal, battery voltage and vehicle vibration information to establish a multi-parameter fusion logic judgment method to accurately distinguish whether the vehicle is in running state, short-term parking state, or long-term engine-off state. S2. Based on the vehicle status distinguished in S1, automatically switch the power consumption mode of the corresponding status. S3. A multi-source wake-up mechanism combining event triggering and timed triggering is used to check battery voltage acquisition and health. S4. Redundant detection of battery voltage via MCU ADC and external comparator to prevent battery depletion; S5. Before the device goes into sleep mode, call the data management module to save any unuploaded fault codes (DTCs) and diagnostic logs to non-volatile memory (Flash / EEPROM), and adopt power failure protection and redundant storage mechanisms to avoid data loss due to power failure. S6. Set delay and hysteresis logic to avoid rapid switching of power consumption mode due to frequent vehicle start-stop.

[0012] Preferably, in step S1, a multi-parameter fusion logic determination method is established by comprehensively collecting engine speed, ignition signal, battery voltage, and vehicle vibration information, as detailed below: S11. Preprocess engine speed, ignition signal, battery voltage and vehicle vibration information; S12. Extract features from the preprocessed information and then use the PCA algorithm to reduce the dimensionality of the extracted features. S13. Construct a hierarchical decision model using machine learning algorithms.

[0013] Preferably, the information preprocessing includes data cleaning, data filtering, and data normalization; Data cleaning: Remove outliers, missing values, and duplicate values ​​during the data collection process to ensure data quality.

[0014] Data filtering: Filtering the acquired data, such as using a low-pass filter to remove high-frequency noise and improve data smoothness.

[0015] Data normalization: Min-Max normalization or Z-Score normalization is used to normalize engine speed, ignition signal, battery voltage and vehicle vibration data.

[0016] Preferably, the Min-Max standardized formula is as follows: ; In the formula: The original data values, The minimum value in the original data. The maximum value in the original data. These are the standardized data values, ranging from [0,1].

[0017] Preferably, the feature extraction includes time-domain features, frequency-domain features, and correlation features; Time-domain characteristics: mean, variance, maximum, minimum, and peak values ​​of engine speed, ignition signal, battery voltage, and vehicle vibration data; Frequency domain features: Perform frequency domain analysis on vehicle vibration data to extract frequency domain features.

[0018] Preferably, the machine learning algorithm includes linear regression and logistic regression algorithms: The formula for the linear regression algorithm is as follows: ; In the formula: The dependent variable represents the vehicle's state (such as running, parked, etc.). The independent variables are engine speed, ignition signal, battery voltage, and vehicle vibration parameters, respectively. These are model parameters, representing the degree of influence of the independent variable on the dependent variable. The error term represents the portion that the model cannot explain; Logistic Regression Algorithm: ; In the formula: The probability that the dependent variable is 1 represents the probability that the vehicle is in a certain state (such as running).

[0019] Preferably, the power consumption mode that automatically switches to the corresponding state in step S2 is as follows: When in operation, the device is in normal diagnostic mode (RUN), the MCU runs at full speed, and supports diagnostic and communication functions; When the vehicle stops briefly for more than 5 minutes, the device enters a light sleep mode (LIGHT_SLEEP), where the MCU operates at a low frequency and only maintains necessary communication monitoring. When the shutdown time exceeds 2 hours, it enters deep sleep mode (DEEP_SLEEP), shutting down most peripherals and retaining only the RTC or voltage comparator to achieve an ultra-low standby current of less than 100μA.

[0020] Preferably, the event triggering includes situations such as ignition signal changes, CAN bus activity, sudden changes in battery voltage, and vehicle vibration; The timed trigger is generated periodically by the RTC, which generates an interrupt.

[0021] Compared with the prior art, the beneficial effects of the present invention are: This method combines engine speed, ignition signal, battery voltage, and vehicle vibration information to enable the diagnostic equipment to intelligently switch between three power consumption modes: normal diagnosis, shallow sleep, and deep sleep.

[0022] This method employs a wake-up strategy that combines event-triggered and timed-triggered methods, and enters forced deep sleep when the battery voltage is below a threshold, thereby effectively preventing the vehicle battery from running out of power.

[0023] This method also includes fault data storage protection before hibernation and anti-jitter switching mechanism to improve the reliability and stability of the system.

[0024] This method can significantly reduce the power consumption of truck OBD diagnostic equipment while ensuring the integrity of diagnostic functions, thereby improving the practicality and safety of the equipment. Attached Figure Description

[0025] Figure 1 A flowchart illustrating the vehicle status recognition process; Figure 2 A schematic diagram of the overall process for hierarchical power consumption management; Figure 3 This is a flowchart illustrating the multi-source wake-up mechanism. Figure 4 This is a flowchart illustrating the battery protection mechanism. Figure 5 This is a flowchart illustrating the data storage and protection mechanism. Figure 6 A flowchart illustrating the anti-jitter switching strategy. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention provides a technical solution: a low-power management method for truck OBD diagnostic equipment, comprising the following mechanism: Please see Figure 1 The vehicle status recognition module performs vehicle status recognition. By comprehensively collecting engine speed, ignition signal, battery voltage and vehicle vibration information, a multi-parameter fusion logic judgment method is established to accurately distinguish whether the vehicle is in running state, short-term parking state, or long-term engine-off state. Step 11: Preprocess the engine speed, ignition signal, battery voltage, and vehicle vibration information; This includes data cleaning, data filtering, and data normalization; Data cleaning: Remove outliers, missing values, and duplicate values ​​during the data collection process to ensure data quality.

[0028] Data filtering: Filtering the acquired data, such as using a low-pass filter to remove high-frequency noise and improve data smoothness.

[0029] Data normalization: Min-Max normalization or Z-Score normalization is used to normalize engine speed, ignition signal, battery voltage and vehicle vibration data; The formula for Min-Max standardization is as follows: ; In the formula: The original data values, The minimum value in the original data. The maximum value in the original data. These are the standardized data values, ranging from [0,1].

[0030] Step 12: Extract features from the preprocessed information, and then use the PCA algorithm to reduce the dimensionality of the extracted features; Time-domain characteristics: mean, variance, maximum, minimum, and peak values ​​of engine speed, ignition signal, battery voltage, and vehicle vibration data; Frequency domain features: Perform frequency domain analysis on vehicle vibration data and extract frequency domain features; Correlation characteristics: Analyze the correlation between parameters (such as the Pearson correlation coefficient between engine speed and vehicle vibration) to capture the cooperative variation pattern; The specific dimensionality reduction process is as follows: The engine speed, ignition signal, battery voltage, and vehicle vibration data are centrally processed by subtracting the mean value from each parameter.

[0031] Find the covariance matrix: Calculate the covariance matrix among the four parameters to represent the correlation between the parameters.

[0032] The formula for calculating the covariance matrix is ​​as follows: ; In the formula: This represents the sample covariance of variables X and Y, measuring the degree of linear correlation between the two variables. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and the larger the absolute value, the stronger the correlation. For variable X, the first The observation value (e.g., the first observation value) (Engine speed measured in this test) The first variable Y The observation value (e.g., the first observation value) (Battery voltage measured this time) The index of the observation is 1 to 1. , Let X be the sample mean of variable X. The sample mean of variable Y (all (average).

[0033] Eigenvalue decomposition: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors.

[0034] Eigenvalue decomposition: In the formula: A is the covariance matrix, and U and V are orthogonal matrices. It is a diagonal matrix, and the elements on the diagonal are the eigenvalues.

[0035] Principal component selection: Principal components are selected based on the magnitude of the eigenvalues. Usually, the first few eigenvectors with larger eigenvalues ​​are selected as principal components.

[0036] Data projection: Projecting the original data onto selected principal components to achieve dimensionality reduction.

[0037] Step 13: Construct a hierarchical decision model using machine learning algorithms.

[0038] Layered decision logic design First layer: Rapid screening of ignition signals If the ignition signal is valid (e.g., voltage > threshold), it is directly determined to be in operation.

[0039] If invalid, proceed to the second level of judgment.

[0040] Second layer: Joint analysis of engine speed and vibration If the rotational speed is greater than 0 and the vibration is significant (e.g., acceleration amplitude is greater than the threshold), it is determined to be in operation.

[0041] If the rotation speed is 0 but the vibration is slight or the battery voltage is stable, the vehicle will briefly stop.

[0042] If the rotation speed is 0 and there is no vibration, proceed to the third level of judgment.

[0043] Third layer: Comprehensive judgment based on engine shutdown time and battery voltage Record the duration of engine shutdown. If the duration exceeds the threshold and the battery voltage does not drop abnormally (e.g., voltage drop <5%), it is determined to be a long-term engine shutdown state.

[0044] If the engine shutdown time does not exceed the threshold but the battery voltage drops abnormally, an alarm will be triggered and the system will be marked as a potential fault condition. The machine learning algorithms include linear regression and logistic regression algorithms: The formula for the linear regression algorithm is as follows: ; In the formula: The dependent variable represents the vehicle's state (such as running, parked, etc.). The independent variables are engine speed, ignition signal, battery voltage, and vehicle vibration parameters, respectively. These are model parameters, representing the degree of influence of the independent variable on the dependent variable. The error term represents the portion that the model cannot explain; Logistic Regression Algorithm: ; In the formula: The probability that the dependent variable is 1 represents the probability that the vehicle is in a certain state (such as running).

[0045] Please see Figure 2 The hierarchical power consumption management mechanism is as follows: based on the different vehicle states, the power consumption mode of the corresponding state is automatically switched. When in operation, the device is in normal diagnostic mode (RUN), the MCU runs at full speed, and supports diagnostic and communication functions; When the vehicle stops briefly for more than 5 minutes, the device enters a light sleep mode (LIGHT_SLEEP), where the MCU operates at a low frequency and only maintains necessary communication monitoring. When the shutdown time exceeds 2 hours, it enters deep sleep mode (DEEP_SLEEP), shutting down most peripherals and retaining only the RTC or voltage comparator to achieve an ultra-low standby current of less than 100μA. This mechanism ensures that energy consumption is minimized while meeting diagnostic needs.

[0046] Please see Figure 3 Multi-source wake-up strategy A multi-source wake-up mechanism combining event triggering and timed triggering is used to check battery voltage acquisition and health. Events that trigger events include changes in ignition signals, CAN bus activity, sudden changes in battery voltage, and vehicle vibration. The timed trigger is triggered by the RTC generating periodic interrupts, for example, waking up once every 6 hours, for battery voltage acquisition and health checks; The dual wake-up protection mechanism can effectively prevent devices from remaining in a dormant state for an extended period due to abnormal conditions, thereby improving system reliability.

[0047] Please see Figure 4 Battery protection mechanism Battery protection has been added to the low-power management system. The system redundantly detects the battery voltage using the MCU ADC and an external comparator. When the voltage drops below 11.8V, the device immediately enters a forced deep sleep state to prevent battery depletion. Furthermore, this redundant detection mechanism improves the accuracy of voltage measurement and system safety.

[0048] Please see Figure 5 Data storage and protection mechanisms Before the device goes into sleep mode, the data management module is invoked to save any unuploaded fault codes (DTCs) and diagnostic logs to non-volatile memory (Flash / EEPROM). Power-off protection and redundant storage mechanisms are used to prevent data loss due to power failure. When the vehicle is restarted, the system can fully restore the previous diagnostic data, ensuring the continuity and reliability of diagnostic information.

[0049] Please see Figure 6 Anti-shake switching strategy Configure delay and hysteresis logic to avoid rapid switching of power consumption modes due to frequent vehicle start-stop.

[0050] For example, if the power is off for less than 5 minutes, it remains in RUN mode; if it is off for more than 5 minutes, it enters shallow sleep mode; and if it is off for more than 2 hours, it enters deep sleep mode. This strategy effectively prevents frequent switching of power consumption modes caused by short-term parking or waiting at intersections, thereby extending the equipment's lifespan and improving system stability.

[0051] In summary: 1. Intelligent power consumption management: Through state machine switching, the device can dynamically adjust the power consumption mode according to the vehicle's operating status, reducing the burden on the battery.

[0052] 2. Multi-source wake-up mechanism: Supports multiple wake-up methods such as ignition signal, CAN bus, RTC, and voltage surge, avoiding problems caused by the failure of a single signal.

[0053] 3. Battery protection: Voltage threshold protection is set to prevent battery depletion and ensure vehicle ignition capability.

[0054] 4. Data integrity guarantee: Automatically save diagnostic data and logs before entering hibernation to avoid data loss due to power failure.

[0055] 5. High applicability: It can be widely used in vehicle diagnostic systems for trucks, buses and other vehicles with high requirements for battery protection and low power consumption operation.

[0056] The multi-core embedded device program upgrade method and system proposed in this invention have multiple beneficial effects, including significantly improving upgrade efficiency, ensuring system stability, enhancing upgrade security, simplifying operation procedures, and supporting remote upgrades. These advantages make this invention widely applicable and of significant practical value in the field of multi-core embedded devices, such as dual-ARM core BMS systems and emergency start-up power supplies.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-power management method for truck OBD diagnostic equipment, characterized in that, Includes the following steps: S1. Comprehensively collect engine speed, ignition signal, battery voltage and vehicle vibration information, establish a multi-parameter fusion logic judgment method, and accurately distinguish whether the vehicle is in running state, short-term parking state and long-term engine off state. S2. Based on the vehicle status distinguished in S1, automatically switch the power consumption mode of the corresponding status. S3. A multi-source wake-up mechanism combining event triggering and timed triggering is used to check battery voltage acquisition and health. S4. Redundant detection of battery voltage via MCU ADC and external comparator to prevent battery depletion; S5. Before the device goes into sleep mode, call the data management module to save any unuploaded fault codes and diagnostic logs to non-volatile memory, and adopt power failure protection and redundant storage mechanisms to avoid data loss due to power failure. S6. Set delay and hysteresis logic to avoid rapid switching of power consumption mode due to frequent vehicle start-stop.

2. The low-power management method for truck OBD diagnostic equipment according to claim 1, characterized in that, In step S1, engine speed, ignition signal, battery voltage, and vehicle vibration information are comprehensively collected to establish a multi-parameter fusion logic determination method, as detailed below: S11. Preprocess engine speed, ignition signal, battery voltage and vehicle vibration information; S12. Extract features from the preprocessed information and then use the PCA algorithm to reduce the dimensionality of the extracted features. S13. Construct a hierarchical decision model using machine learning algorithms.

3. The low-power management method for truck OBD diagnostic equipment according to claim 2, characterized in that, The information preprocessing includes data cleaning, data filtering, and data normalization; Data cleaning: removing outliers, missing values, and duplicate values ​​during the data collection process.

4. Data normalization: Use Min-Max normalization or Z-Score normalization to normalize engine speed, ignition signal, battery voltage and vehicle vibration data.

5. A low-power management method for truck OBD diagnostic equipment according to claim 3, characterized in that, The formula for Min-Max standardization is as follows: ； In the formula: The original data values, The minimum value in the original data. The maximum value in the original data. These are the standardized data values, ranging from [0,1].

6. A low-power management method for truck OBD diagnostic equipment according to claim 2, characterized in that, The feature extraction includes time-domain features, frequency-domain features, and correlation features; Time-domain characteristics: mean, variance, maximum, minimum, and peak values ​​of engine speed, ignition signal, battery voltage, and vehicle vibration data; Frequency domain features: Perform frequency domain analysis on vehicle vibration data to extract frequency domain features.

7. A low-power management method for truck OBD diagnostic equipment according to claim 2, characterized in that, The machine learning algorithms include linear regression and logistic regression algorithms: The formula for the linear regression algorithm is as follows: ； In the formula: The dependent variable represents the vehicle's state (such as running, parked, etc.). The independent variables are engine speed, ignition signal, battery voltage, and vehicle vibration parameters, respectively. These are model parameters, representing the degree of influence of the independent variable on the dependent variable. The error term represents the portion that the model cannot explain; Logistic Regression Algorithm: ； In the formula: The probability that the dependent variable is 1 represents the probability that the vehicle is in a certain state.

8. A low-power management method for truck OBD diagnostic equipment according to claim 1, characterized in that, The power consumption mode that automatically switches to the corresponding state in S2 is as follows: When in operation, the device is in normal diagnostic mode, the MCU runs at full speed, and supports diagnostic and communication functions; When the vehicle stops briefly for more than 5 minutes, the device enters a shallow sleep mode, and the MCU operates at a low frequency, only maintaining necessary communication monitoring. When the shutdown time exceeds 2 hours, it enters deep sleep mode, shutting down most peripherals and retaining only the RTC or voltage comparator to achieve an ultra-low standby current of less than 100μA.

9. A low-power management method for truck OBD diagnostic equipment according to claim 1, characterized in that, The events triggered include changes in ignition signals, CAN bus activity, sudden changes in battery voltage, and vehicle vibration. The timed trigger is generated periodically by the RTC, which generates an interrupt.

Images