A method and system for onboard dynamic load limit self-detection using OBDII diagnostics

Abstract

This invention discloses a vehicle dynamic load limit self-detection method and system for OBDII diagnostics, belonging to the field of automotive fault diagnosis technology. The method includes the following steps: (1) detecting the torque during the vehicle's acceleration period under both unloaded and loaded conditions, and recording the data; (2) verifying the integrity of the detection data and establishing a calculation model for the vehicle's load capacity; (3) establishing a system of equations based on the calculation model to solve for the estimated values ​​of the torque coefficient and friction coefficient; (4) performing error verification on the estimated values ​​of the torque coefficient and friction coefficient; (5) inputting the qualified estimated values ​​of the torque coefficient and friction coefficient into the calculation model to calculate the overload situation during vehicle operation in real time and make a judgment. This invention obtains the state information data of the vehicle's power system and uses a calculation model to calculate the vehicle's overload situation in real time, achieving high detection efficiency and low cost.

Description

Technical Field

[0001] This invention belongs to the field of automotive fault diagnosis technology, and more specifically, relates to an on-board dynamic load limit self-testing method and system for OBDII diagnostics. Background Technology

[0002] Due to the daily maintenance and management needs of high-grade highways, the dynamic load limit detection of vehicles is currently mainly supervised by highway management departments. The primary method is to install a limited number of vehicle load capacity detection devices at highway toll booths. While it's possible to detect vehicle overloading while the highway is still in operation, and foreign detection methods offer higher accuracy, achieving within 10% overloading at low speeds with a 95% confidence level, they require specific testing conditions, specialized equipment, and cannot provide real-time detection. However, domestic dynamic load limit detection technology still faces many problems, such as low measurement accuracy, excessively large testing stations, high equipment costs, and inconvenience in construction and maintenance. Especially for logistics management companies, overloading is often only detected after being ordered to rectify by highway management departments, a reactive approach where existing methods cannot meet the needs of ordinary logistics management companies.

[0003] Therefore, there is an urgent need for a low-cost, high-efficiency, and high-real-time detection method for vehicle load limit detection. Summary of the Invention

[0004] This application provides a method and system for on-board dynamic load limit self-detection in OBDII diagnostics. This application uses real-time acquisition of vehicle power and acceleration data, and models the data using dynamic principles, thereby performing real-time online estimation of the vehicle's load.

[0005] Firstly, this application provides a self-testing method for on-board dynamic load limit in OBDII diagnostics, comprising the following steps:

[0006] (1) Detect the torque during the vehicle's starting and acceleration periods under both unloaded and loaded conditions, and record the data;

[0007] (2) Verify the integrity of the test data and establish a calculation model for the vehicle mass M;

[0008] (3) Establish a set of equations based on the calculation model and solve for the estimated values ​​of torque coefficient and friction coefficient;

[0009] (4) Perform error verification on the estimated values ​​of torque coefficient and friction coefficient;

[0010] (5) Input the estimated values ​​of qualified torque coefficient and friction coefficient into the calculation model, calculate the overload situation of the vehicle in real time, and make a judgment.

[0011] A further technical solution, wherein step (1) includes:

[0012] When the car is unloaded, the unloaded mass is M0. The engine torque output values ​​of the three acceleration periods are measured as the first set of sampling data: the initial stage 7-27KM / h, the middle stage 27-47KM / h, and the high stage 47-67KM / h. The detection interval ΔT is 1 second, and the acceleration time is recorded at the same time.

[0013] When the vehicle is loaded, the unloaded mass is M1. The engine torque output values ​​during three acceleration periods—7-27 km / h, 27-47 km / h in the middle range, and 47-67 km / h in the high range—are measured as the second set of sampling data. The detection interval ΔT is 1 second, and the acceleration time is recorded simultaneously.

[0014] A further technical solution is that the data integrity in step (2) includes: acceleration start time, acceleration end time, engine torque sampling value during acceleration period, and the number of detections N during acceleration period. Verify the integrity of each set of data; if complete, proceed to the next step; otherwise, return to step (1).

[0015] In a further technical solution, the calculation model for the vehicle load mass M in step (2) is as follows:

[0016]

[0017] Where T0 is the initial acceleration time, V0 is the initial velocity at the start time T0, T1 is the acceleration end time, V(T1) is the velocity at the acceleration end time T1, T(t) is the torque at time t, k is the torque coefficient, μ is the friction coefficient, g is the gravitational acceleration, and the acceleration time T = T1 - T0.

[0018] With time discretization, the calculation model for the vehicle's onboard mass M is as follows:

[0019]

[0020] Where N is the number of torque detections during acceleration, Δt is the torque detection interval, and ti is the torque detection time.

[0021] A further technical solution, the specific method of step (3) is as follows:

[0022] The two sets of data from step (1) – unloaded and loaded – are used interchangeably in the calculation model. Specifically, the sampling data from one acceleration period of each set of data is used in the mass calculation model to construct a system of two linear equations. Each system of equations has two equations and two unknowns, k and μ, and a total of nine systems of equations are established. The calculated values ​​of the torque coefficient and friction coefficient are solved for the nine sets of equations: k1 and μ1, k2 and μ2, ..., k9 and μ9.

[0023] The average of the calculated values ​​is taken as the estimated value, thus obtaining the estimated value of the torque coefficient. and the estimated value of the coefficient of friction

[0024] Taking the initial acceleration equations as an example, the method for estimating the torque coefficient and friction coefficient is as follows:

[0025]

[0026] Among them, T M0 (ti) represents the torque detected during the initial acceleration under no-load conditions, T M1 (ti) represents the torque detected during the initial acceleration under load, T 01 and T 00 T represents the start and end times of the acceleration phase under no-load conditions. 11 and T 10 To accelerate the start and end times of the initial stage when under load.

[0027] A further technical solution is that the method for verifying the error of the estimated values ​​of torque coefficient and friction coefficient in step (4) is as follows: the square of the mass calculation error d 2 and threshold squared t 2 Compare the results; if the threshold condition is met:

[0028] d 2 ≤t 2 (Formula 5)

[0029] The estimated values ​​of torque coefficient and friction coefficient are then acceptable.

[0030] Among them, the mass calculation error For dimensionless standard parameters, M1, M2, and M3 are estimated values ​​of the torque coefficient. and the estimated value of the coefficient of friction The calculated mass values ​​are obtained by substituting the three sets of unloaded mass acceleration data M0 into the calculation model in step 2), where M4, M5, and M6 are estimated values ​​of the torque coefficient. The estimated value of the friction coefficient and the acceleration data of the three sets of loaded mass M1 are substituted into the calculation model in step 2) to obtain the calculated mass value. n = 6 is the number of samples randomly collected, which satisfies the judgment that the value of n is not less than 4.

[0031] Threshold alarm parameters are determined using a random sampling method. Based on the fact that sensor data acquisition errors follow a normal distribution with mean λ = 0 and standard deviation σ = 1, the threshold squared t in the random sampling method is... 2 It can be obtained through the following formula:

[0032]

[0033] In the formula, α is the prior probability that the threshold is not exceeded, σ is the standard deviation of the Gaussian distribution, and the parameter dimension m takes the value of 1.

[0034] A further technical solution, the specific method of step (5) is as follows:

[0035] The torque coefficient and friction coefficient that have passed the error inspection are substituted into the calculation model to perform load limit detection, calculate real-time load data, obtain the vehicle load M based on the calculation model, and determine whether it is less than the vehicle load limit Mmax value. If it is less, the detection continues; if it is greater, a load limit alarm signal is issued.

[0036] Secondly, this application discloses an on-board dynamic load limit self-detection system for OBDII diagnostics, comprising:

[0037] The 5G T-BOX lower-level machine communicates with the vehicle ECU system via the OBDII bus to obtain the status information data of the vehicle power system and transmits the data to the load limit detection module;

[0038] The load limit detection module establishes a calculation model for the vehicle's on-board mass M and calculates the vehicle's overload status in real time.

[0039] The alarm module communicates with the load limit detection module via 5G communication and receives the vehicle overload alarm signal sent by the load limit detection module.

[0040] Beneficial effects

[0041] 1. The on-board dynamic load limit self-testing method for OBDII diagnostics proposed in this invention can be tested at any time while the vehicle is running, without the need to go to a dedicated fixed testing equipment. It is low-cost, space-saving, and can be tested anytime and anywhere with high testing efficiency.

[0042] 2. This invention can upload abnormal load alarm data to the logistics company's vehicle diagnostic management platform in real time as needed. The diagnostic management platform can also query the vehicle's operating status, thereby enabling logistics companies to monitor the operating data of their commercial vehicles in real time. It can detect not only vehicle load limits but also vehicle speed, location, and other operating data, as well as engine status and other diagnostic data, greatly improving the vehicle operation management level of logistics companies.

[0043] 3. This invention uses real-time acquisition of vehicle power and acceleration data and modeling based on dynamic principles to estimate vehicle load in real time, resulting in high detection efficiency and high data accuracy.

[0044] 4. This invention employs a random sampling method for threshold alarm parameter determination, which is highly sensitive to data and helps improve the accuracy of the judgment. The calculation model of this invention uses two sets of data of different qualities interchangeably, resulting in weaker data correlation and improving the accuracy of the solution. Attached Figure Description

[0045] Figure 1 This is a flowchart of an on-board dynamic load limit self-detection method for OBDII diagnostics according to the present invention;

[0046] Figure 2 This is a framework diagram of an on-board dynamic load limit self-detection system for OBDII diagnostics according to the present invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0048] Example 1

[0049] like Figure 1 As shown, an on-board dynamic load limit self-testing method for OBDII diagnostics includes the following steps:

[0050] (1) Detect the torque during the vehicle's starting and acceleration periods under both unloaded and loaded conditions, and record the data;

[0051] (2) Verify the integrity of the test data and establish a calculation model for the vehicle mass M;

[0052] (3) Establish a set of equations based on the calculation model and solve for the estimated values ​​of torque coefficient and friction coefficient;

[0053] (4) Perform error verification on the estimated values ​​of torque coefficient and friction coefficient;

[0054] (5) Input the estimated values ​​of qualified torque coefficient and friction coefficient into the calculation model to calculate the overload situation of the vehicle in real time and make a judgment.

[0055] Step (1) includes:

[0056] When the car is unloaded, the unloaded mass is M0. The engine torque output values ​​of the three acceleration periods are measured as the first set of sampling data: the initial stage 7-27KM / h, the middle stage 27-47KM / h, and the high stage 47-67KM / h. The detection interval ΔT is 1 second, and the acceleration time is recorded at the same time.

[0057] When the vehicle is loaded, the unloaded mass is M1. The engine torque output values ​​during three acceleration periods—7-27 km / h, 27-47 km / h in the middle range, and 47-67 km / h in the high range—are measured as the second set of sampling data. The detection interval ΔT is 1 second, and the acceleration time is recorded simultaneously.

[0058] The data integrity in step (2) includes: acceleration start time, acceleration end time, engine torque sampling value during acceleration period, and the number of detections N during acceleration period. Verify the integrity of each set of data; if complete, proceed to the next step; otherwise, return to step (1).

[0059] In step (2), the calculation model for the vehicle's on-board mass M is as follows:

[0060]

[0061] Where T0 is the initial acceleration time, V0 is the initial velocity at the start time T0, T1 is the acceleration end time, V(T1) is the velocity at the acceleration end time T1, T(t) is the torque at time t, k is the torque coefficient, μ is the friction coefficient, g is the gravitational acceleration, and the acceleration time T = T1 - T0.

[0062] With time discretization, the calculation model for the vehicle's onboard mass M is as follows:

[0063]

[0064] Where N is the number of torque detections during acceleration, Δt is the torque detection interval, and ti is the torque detection time.

[0065] The specific method for step (3) is as follows:

[0066] The two sets of data from step (1) – unloaded and loaded – are used interchangeably in the calculation model. Specifically, the sampling data from one acceleration period of each set of data is used in the mass calculation model to construct a system of two linear equations. Each system of equations has two equations and two unknowns, k and μ, and a total of nine systems of equations are established. The calculated values ​​of the torque coefficient and friction coefficient are solved for the nine sets of equations: k1 and μ1, k2 and μ2, ..., k9 and μ9.

[0067] The average of the calculated values ​​is taken as the estimated value, thus obtaining the estimated value of the torque coefficient. And the estimated value of the coefficient of friction;

[0068] Taking the initial acceleration equations as an example, the method for estimating the torque coefficient and friction coefficient is as follows:

[0069]

[0070] Among them, T M0 (ti) represents the torque detected during the initial acceleration under no-load conditions, T M1 (ti) represents the torque detected during the initial acceleration under load, T 01 and T 00 T represents the start and end times of the acceleration phase under no-load conditions.11 and T 10 To accelerate the start and end times of the initial stage when under load.

[0071] The method for verifying the estimated values ​​of torque coefficient and friction coefficient in step (4) is as follows: The squared mass calculation error d... 2 and threshold squared t 2 Compare the results; if the threshold condition is met:

[0072] d 2 ≤t 2 (Formula 5)

[0073] The estimated values ​​of torque coefficient and friction coefficient are then acceptable.

[0074] Among them, the mass calculation error For dimensionless standard parameters, M1, M2, and M3 are estimated values ​​of the torque coefficient. and the estimated value of the coefficient of friction The calculated mass values ​​are obtained by substituting the three sets of unloaded mass acceleration data M0 into the calculation model in step 2), where M4, M5, and M6 are estimated values ​​of the torque coefficient. The estimated value of the friction coefficient and the acceleration data of the three sets of loaded mass M1 are substituted into the calculation model in step 2) to obtain the calculated mass value. n = 6 is the number of samples randomly collected, which satisfies the judgment that the value of n is not less than 4.

[0075] Threshold alarm parameters are determined using a random sampling method. Based on the fact that sensor data acquisition errors follow a normal distribution with mean λ = 0 and standard deviation σ = 1, the threshold squared t in the random sampling method is... 2 It can be obtained through the following formula:

[0076]

[0077] In the formula, α is the prior probability that the threshold is not exceeded, σ is the standard deviation of the Gaussian distribution, and the parameter dimension m takes the value of 1.

[0078] The specific method for step (5) is as follows:

[0079] The torque coefficient and friction coefficient, which have passed the error inspection, are substituted into the calculation model to perform load limit detection, calculate real-time load data, and determine the vehicle load M based on the calculation model, and whether it is less than the vehicle load limit M. max If the value is less than the limit, continue the detection; if it is greater than the limit, issue a load limit alarm signal.

[0080] like Figure 2 As shown, an OBDII diagnostic vehicle dynamic load limit self-detection system includes...

[0081] The 5G T-BOX lower-level machine communicates with the vehicle ECU system via the OBDII bus to obtain the status information data of the vehicle power system and transmits the data to the load limit detection module;

[0082] The load limit detection module establishes a calculation model for the vehicle's on-board mass M and calculates the vehicle's overload status in real time.

[0083] The alarm module communicates with the load limit detection module via 5G communication and receives the vehicle overload alarm signal sent by the load limit detection module.

[0084] The above-mentioned vehicle-mounted dynamic load limit self-detection method and system can be used to detect vehicles at any time while they are running, without the need to go to a dedicated fixed-point detection equipment, thus achieving high detection efficiency.

Claims

1. A self-detection method for onboard dynamic load limits using OBDII diagnostics, characterized in that: Includes the following steps: (1) Detect the torque during the vehicle's starting and acceleration periods under both unloaded and loaded conditions, and record the data; (2) Verify the integrity of the test data and establish a calculation model for the vehicle mass M; (3) Establish a set of equations based on the calculation model and solve for the estimated values ​​of torque coefficient and friction coefficient; (4) Perform error verification on the estimated values ​​of torque coefficient and friction coefficient; (5) Input the estimated values ​​of qualified torque coefficient and friction coefficient into the calculation model, calculate the overload situation of the vehicle in real time, and make a judgment. The specific method for step (3) is as follows: The two sets of data from step (1) – unloaded and loaded – are used interchangeably in the calculation model. Specifically, the sampling data from one acceleration period of each set of data is used in the mass calculation model to construct a system of two linear equations. Each system of equations has two equations and two unknowns, k and μ, and a total of nine systems of equations are established. The calculated values ​​of the torque coefficient and friction coefficient are solved for the nine sets of equations: k1 and μ1, k2 and μ2, ..., k9 and μ9. The average of the calculated values ​​is taken as the estimated value, thus obtaining the estimated value of the torque coefficient. and the estimated value of the coefficient of friction 2. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: Step (1) includes: When the car is unloaded, the unloaded mass is M0. The engine torque output values ​​of the three acceleration periods are measured as the first set of sampling data: the initial stage 7-27KM / h, the middle stage 27-47KM / h, and the high stage 47-67KM / h. The detection interval ΔT is 1 second, and the acceleration time is recorded at the same time. When the vehicle is loaded, the load mass is M1. The engine torque output values ​​during three acceleration periods—7-27 km / h, mid-range 27-47 km / h, and high-range 47-67 km / h—are measured as the second set of sampling data. The detection interval ΔT is 1 second, and the acceleration time is recorded simultaneously.

3. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: The data integrity in step (2) includes: acceleration start time, acceleration end time, engine torque sampling value during acceleration period, and the number of detections N during acceleration period.

4. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: In step (2), the calculation model for the vehicle's on-board mass M is as follows: Where T0 is the initial acceleration time, V0 is the initial velocity at the start time T0, T1 is the acceleration end time, V(T1) is the velocity at the acceleration end time T1, T(t) is the torque at time t, k is the torque coefficient, μ is the friction coefficient, g is the gravitational acceleration, and the acceleration time T = T1 - T0. With time discretization, the calculation model for the vehicle's onboard mass M is as follows: Where N is the number of torque detections during acceleration, Δt is the torque detection interval, and ti is the torque detection time.

5. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: Taking the initial acceleration equations as an example, the method for estimating the torque coefficient and friction coefficient is as follows: Among them, T M0 (ti) represents the torque detected during the initial acceleration under no-load conditions, T M1 (ti) represents the torque detected during the initial acceleration under load, T 01 and T 00 T represents the start and end times of the acceleration phase under no-load conditions. 11 and T 10 To accelerate the start and end times of the initial stage when under load.

6. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: The method for verifying the estimated values ​​of torque coefficient and friction coefficient in step (4) is as follows: The squared mass calculation error d... 2 and threshold squared t 2 Compare the results; if the threshold condition is met: d 2 ≤t 2 (Formula 5) The estimated values ​​of torque coefficient and friction coefficient are then acceptable. Among them, the mass calculation error For dimensionless standard parameters, M1, M2, and M3 are estimated values ​​of the torque coefficient. and the estimated value of the coefficient of friction The calculated mass values ​​are obtained by substituting the three sets of unloaded mass acceleration data M0 into the calculation model in step 2), where M4, M5, and M6 are estimated values ​​of the torque coefficient. The estimated value of the friction coefficient and the acceleration data of the three sets of loaded mass M1 are substituted into the calculation model in step 2) to obtain the calculated mass value. n = 6 is the number of samples randomly collected, which satisfies the judgment that the value of n is not less than 4. Threshold alarm parameters are determined using a random sampling method. Based on the fact that sensor data acquisition errors follow a normal distribution with mean λ = 0 and standard deviation σ = 1, the threshold squared t in the random sampling method is... 2 It can be obtained through the following formula: In the formula, α is the prior probability that the threshold is not exceeded, σ is the standard deviation of the Gaussian distribution, and the parameter dimension m takes the value of 1.

7. The on-board dynamic load limit self-detection method for OBDII diagnostics according to claim 1, characterized in that: The specific method for step (5) is as follows: The torque coefficient and friction coefficient, which have passed the error inspection, are substituted into the calculation model to perform load limit detection, calculate real-time load data, and determine the vehicle load M based on the calculation model, and whether it is less than the vehicle load limit M. max If the value is less than the limit, continue the detection; if it is greater than the limit, issue a load limit alarm signal.

8. An OBDII diagnostic vehicle dynamic load limit self-detection system implementing the OBDII diagnostic vehicle dynamic load limit self-detection method according to any one of claims 1-7, characterized in that: include The 5G T-BOX lower-level machine communicates with the vehicle ECU system via the OBDII bus to obtain the status information data of the vehicle power system and transmits the data to the load limit detection module; The load limit detection module establishes a calculation model for the vehicle's on-board mass M and calculates the vehicle's overload status in real time. The alarm module communicates with the load limit detection module via 5G communication and receives the vehicle overload alarm signal sent by the load limit detection module.

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