Vehicle dynamic load estimation method based on CAN bus data

Abstract

The invention relates to a vehicle dynamic load estimation method based on CAN bus data. According to the method, key data such as the vehicle speed, the engine torque percentage, the reference torque, the rotating speed, the driving distance, the fuel consumption rate and gear information are obtained from a vehicle CAN bus, and noise and abnormal values are removed through filtering processing. And according to the vehicle state parameters, judging whether the vehicle is in a stable or non-stable driving condition, and further calculating the dynamic load by utilizing the dynamic model and the acquired data. Under the stable working condition, the load is estimated by combining the fuel consumption rate and the engine torque percentage; under the unstable working condition, the load is estimated according to the engine torque percentage, the rotating speed and the driving distance. According to the method, a proportionality coefficient is adjusted according to driving historical data, load estimation results under different working conditions are fused, and estimation precision and robustness are improved. The system does not need an additional sensor, is low in cost, is high in portability, is wide in adaptability, and is suitable for commercial vehicle load monitoring and management.

Description

Technical Field

[0001] This invention belongs to the field of vehicle engineering technology, and in particular relates to a method for estimating vehicle dynamic load based on CAN bus data. Background Technology

[0002] In recent years, my country's transportation industry has flourished, with commercial vehicles widely used in various transportation sectors. Real-time monitoring of cargo weight changes is crucial during commercial vehicle freight transport. Especially in enterprise logistics vehicle management, leveraging vehicle-to-everything (V2X) and big data technologies allows for the analysis of real-time dynamic load data, optimizing task scheduling and monitoring management of logistics vehicles and reducing resource waste. For government traffic management departments, high-precision real-time dynamic load data helps control overloading, extend road lifespan, and improve driving safety.

[0003] However, traditional static weighbridge measurement methods can no longer meet the needs of current social development, and real-time dynamic load measurement solutions have become an important requirement for industry development. The application scenarios for commercial vehicle load estimation are wide-ranging, not only in traffic management but also in vehicle technology development. Load parameters are indispensable calculation parameters when establishing vehicle lateral and longitudinal dynamic models. For example, automatic transmissions require load parameters to intelligently identify shift timing and speed; predictive cruise control, adaptive cruise control, and AEB technologies require load parameters to calculate acceleration, deceleration, and other parameters; LKA and EPS technologies require load parameters to determine important limiting parameters such as vehicle roll boundaries and angular velocity limits. Summary of the Invention

[0004] (a) Purpose of the invention To overcome the above shortcomings, the purpose of this invention is to provide a vehicle dynamic load estimation method based on CAN bus data, so as to solve the above technical problems.

[0005] (II) Technical Solution To achieve the above objectives, the technical solution provided in this application is as follows: A method for estimating vehicle dynamic load based on CAN bus data includes the following steps: Step 1: Obtain key data from the vehicle's CAN bus, including vehicle speed. Engine torque percentage Engine reference torque Engine speed Vehicle travel distance d, engine fuel consumption rate f, and vehicle gear information; Step 2: Filter the acquired raw data to remove noise and outliers; Step 3: Determine whether the vehicle is in a stable or unstable driving condition based on the vehicle's status parameters. Step 4: Calculate the vehicle's dynamic load based on the vehicle's dynamic model and the collected data.

[0006] Preferably, the operating condition identification conditions are as follows: Stable driving conditions include: vehicle speed The change does not exceed a preset threshold within the set time period. , The engine speed N does not change by more than a preset threshold within a set time period. , Engine torque percentage The change does not exceed a preset threshold within the set time period. .

[0007] If the vehicle does not meet the conditions for stable driving, it is determined that the vehicle is in an unstable driving condition.

[0008] Preferably, the load estimation under stable driving conditions in step 4: Under stable driving conditions, the vehicle's fuel consumption rate f and engine torque percentage are used. Combined with engine reference torque The formula for estimating the dynamic load of a vehicle is: ; in: ; It is the vehicle's unloaded weight; It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

[0009] Preferably, the load estimation under unstable driving conditions in step 4: Using the percentage of engine torque of the vehicle The dynamic load of the vehicle is estimated by combining the engine speed N with the vehicle's travel distance d, using the following formula: ; in: It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

[0010] Preferably, the proportional coefficient is adjusted based on the vehicle's driving history data. and This is to improve the accuracy of load estimation.

[0011] Preferably, the filtering process specifically includes: Step 21: Select a filtering algorithm, choosing either a low-pass filter or a median filter; Step 22 (1) The low-pass filter is an exponential moving average filter. The exponential moving average filter gives higher weight to the most recent data points. The formula is as follows: ; in: This is the filtered data; It is the raw data at time point t; It is the filtered data at time point t−1; It is a smoothing factor, typically taking values ​​between 0 and 1. <1; (2) The median filter removes outliers by calculating the median value within the data window. The formula is: ; in: This is the filtered data; ; Indicates taking the median value; Step 23 Outlier detection and handling, the specific formula is as follows: ; in: ; It is the mean of the data; ; ; .

[0012] Preferably, step 4 further includes fusing the load estimation results under stable and unstable driving conditions to improve the accuracy and robustness of load estimation, as shown in the formula: ; in: ; It is the dynamic load estimate under stable driving conditions; It is the dynamic load estimate under unstable driving conditions; It is the fusion weighting coefficient, which is dynamically adjusted according to the working condition identification results, and usually takes a value between 0 and 1.

[0013] Beneficial effects: 1. This invention fully utilizes the vehicle's existing CAN bus data, eliminating the need for additional weighing sensors or other specialized equipment, thereby significantly reducing hardware costs and the complexity and expense of vehicle modification. Simultaneously, by not relying on external sensors, it avoids the installation, calibration, and regular maintenance of sensors, saving maintenance time and labor costs, and reducing potential risks and repair costs due to sensor failure.

[0014] 2. This method can monitor the dynamic load of vehicles in real time, accurately estimating the load regardless of whether the vehicle is in a stable or unstable driving condition. This is superior to traditional static weighing methods, providing more timely and accurate load information. Furthermore, by fusing load estimation results from both stable and unstable driving conditions and incorporating a real-time update and feedback mechanism, this method can dynamically adjust the scaling factor, further improving the accuracy of load estimation and making the estimated load closer to the actual load. This provides more reliable data support for vehicle management and traffic management.

[0015] 3. This invention, by providing real-time dynamic load data, helps enterprises optimize logistics management and reduce costs, facilitates efficient enforcement of overloading regulations by the government, and ensures traffic safety. It also provides key parameters for intelligent vehicle technologies such as automatic transmission shifting and cruise control, improving vehicle performance. It is not dependent on specific vehicle models, can be widely adapted to commercial vehicles, requires no complex calibration or maintenance, and significantly enhances the adaptability and versatility of vehicle technology. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the invention.

[0017] This invention provides a method for estimating vehicle dynamic load based on CAN bus data, comprising the following steps: Step 1: Obtain key data from the vehicle's CAN bus, including vehicle speed. Engine torque percentage Engine reference torque Engine speed Vehicle travel distance d, engine fuel consumption rate f, and vehicle gear information; Step 2: Filter the acquired raw data to remove noise and outliers; Preferably, the filtering process specifically includes: Step 21: Select a filtering algorithm, choosing either a low-pass filter or a median filter; Step 22 (1) The low-pass filter is an exponential moving average filter. The exponential moving average filter gives higher weight to the most recent data points. The formula is as follows: ; in: This is the filtered data; It is the raw data at time point t; It is the filtered data at time point t−1; It is a smoothing factor, typically taking values ​​between 0 and 1. <1; (2) The median filter removes outliers by calculating the median value within the data window. The formula is: ; in: This is the filtered data; ; Indicates taking the median value; Step 23 Outlier detection and handling, the specific formula is as follows: ; in: ; It is the mean of the data; ; ; .

[0018] Step 3: Determine whether the vehicle is in a stable or unstable driving condition based on the vehicle's status parameters. Preferably, the operating condition identification conditions are as follows: Stable driving conditions include: vehicle speed The change does not exceed a preset threshold within the set time period. , The engine speed N does not change by more than a preset threshold within a set time period. , Engine torque percentage The change does not exceed a preset threshold within the set time period. .

[0019] If the vehicle does not meet the conditions for stable driving, it is determined that the vehicle is in an unstable driving condition.

[0020] Step 4: Calculate the vehicle's dynamic load based on the vehicle's dynamic model and the collected data.

[0021] Preferably, the load estimation under stable driving conditions in step 4: Under stable driving conditions, the vehicle's fuel consumption rate f and engine torque percentage are used. Combined with engine reference torque The formula for estimating the dynamic load of a vehicle is: ; in: ; It is the vehicle's unloaded weight; It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

[0022] Preferably, the load estimation under unstable driving conditions in step 4: Using the percentage of engine torque of the vehicle The dynamic load of the vehicle is estimated by combining the engine speed N with the vehicle's travel distance d, using the following formula: ; in: It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

[0023] Preferably, the proportional coefficient is adjusted based on the vehicle's driving history data. and This step is in the testing and adjustment phase, and can be tested and adjusted at intervals to improve the accuracy of load estimation.

[0024] Preferred, proportionality coefficient and The adjustments specifically include: ; ; in: and This is the adjusted proportional coefficient; It is the actual load value of the vehicle, which can be achieved through auxiliary means during testing, including weighing sensors, weighbridge weighing, or manual input by the user; β and β′ It is a feedback adjustment coefficient, which is preset according to the vehicle characteristics and usually takes a value between 0 and 1.

[0025] Preferably, step 4 further includes fusing the load estimation results under stable and unstable driving conditions to improve the accuracy and robustness of load estimation, as shown in the formula: ; in: ; It is the dynamic load estimate under stable driving conditions; It is the dynamic load estimate under unstable driving conditions; It is the fusion weighting coefficient, which is dynamically adjusted according to the working condition identification results, and usually takes a value between 0 and 1.

[0026] This invention provides a vehicle dynamic load estimation method based on CAN bus data. By acquiring key data from the vehicle's CAN bus and performing filtering, it can accurately determine the vehicle's driving conditions and calculate the vehicle's dynamic load accordingly. This method can estimate load not only under stable driving conditions using fuel consumption rate and engine torque percentage, but also under unstable driving conditions by combining engine torque percentage, engine speed, and vehicle distance traveled. Furthermore, by adjusting the scaling factor and fusing load estimation results from different operating conditions, the accuracy and robustness of load estimation are further improved. This method requires no additional hardware and has advantages such as low cost, strong portability, and wide adaptability, making it widely applicable to load monitoring and management of commercial vehicles.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating vehicle dynamic load based on CAN bus data, characterized in that, Includes the following steps: Step 1: Obtain key data from the vehicle's CAN bus, including vehicle speed. Engine torque percentage Engine reference torque Engine speed Vehicle travel distance d, engine fuel consumption rate f, and vehicle gear information; Step 2: Filter the acquired raw data to remove noise and outliers; Step 3: Determine whether the vehicle is in a stable or unstable driving condition based on the vehicle's status parameters. Step 4: Calculate the vehicle's dynamic load based on the vehicle's dynamic model and the collected data.

2. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, The operating condition identification conditions are as follows: Stable driving conditions include: vehicle speed The change does not exceed a preset threshold within the set time period. , The engine speed N does not change by more than a preset threshold within a set time period. , Engine torque percentage The change does not exceed a preset threshold within the set time period. . If the vehicle does not meet the conditions for stable driving, it is determined that the vehicle is in an unstable driving condition.

3. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, Load estimation under stable driving conditions in step 4: Under stable driving conditions, the vehicle's fuel consumption rate f and engine torque percentage are used. Combined with engine reference torque The formula for estimating the dynamic load of a vehicle is: ； in: This is a dynamic load estimate; It is the vehicle's unloaded mass; It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

4. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, Load estimation under unstable driving conditions in step 4: Using the percentage of engine torque of the vehicle The dynamic load of the vehicle is estimated by combining the engine speed N with the vehicle's travel distance d, using the following formula: ； in: It is a proportional coefficient, which is preset according to the characteristics of the vehicle.

5. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, Adjust the scaling factor based on the vehicle's driving history data. and This is to improve the accuracy of load estimation.

6. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, The filtering process specifically includes: Step 21: Select a filtering algorithm, choosing either a low-pass filter or a median filter; Step 22 (1) The low-pass filter is an exponential moving average filter. The exponential moving average filter gives higher weight to the most recent data points. The formula is as follows: ； in: This is the filtered data; It is the raw data at time point t; It is the filtered data at time point t−1; It is a smoothing factor, typically taking values ​​between 0 and 1. <1; (2) The median filter removes outliers by calculating the median value within the data window. The formula is: ； in: This is the filtered data; It is the raw data from time point t−i to t+i; Indicates taking the median value; Step 23 Outlier detection and handling, the specific formula is as follows: ； in: It is the raw data at time point t; It is the mean of the data; It is the standard deviation of the data; This is the threshold coefficient, which is usually set to 3; Replace outliers with the mean or median, using the following formula: 。 7. The vehicle dynamic load estimation method based on CAN bus data according to claim 1, characterized in that, Step 4 further includes fusing the load estimation results under stable and unstable driving conditions to improve the accuracy and robustness of load estimation. The formula is as follows: ； in: This is the final dynamic load estimate; It is the dynamic load estimate under stable driving conditions; It is the dynamic load estimate under unstable driving conditions; It is the fusion weighting coefficient, which is dynamically adjusted according to the working condition identification results, and usually takes a value between 0 and 1.

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