Tractor-Trailer Axle Load Estimation via Vehicle Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In vehicle combinations without axle load sensors, accurately determining axle loads is crucial for setting correct brake pressures, but existing methods lack efficient solutions for estimating these loads without direct measurement.
Innovation Solution
A method and control unit that utilize a vehicle model to process input variables such as load information, torque, brake pressure, and incline angle to estimate front and rear axle loads, enabling precise control of the braking system by simulating axle load conditions and accounting for factors like rolling and wind resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If axle load sensors are installed on individual axles to measure axle loads directly, then measurement precision of axle loads is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the physical axle load measurement system through a vehicle model that calculates and estimates axle loads based on other measurable parameters (brake pressures, torques, speeds). This model-based approach replicates the function of physical sensors without requiring additional hardware, thereby maintaining measurement precision while reducing device complexity
Solution Approach 2:
The patent introduces intermediate parameters (brake pressures, wheel speeds, torques) as mediators to indirectly determine axle loads. Instead of directly measuring axle loads with sensors, the system uses these intermediate measurements combined with a vehicle model to calculate the desired axle load information, avoiding the need for direct sensor installation on axles
2Device complexity
If a vehicle model is used to estimate axle loads without sensors, then device complexity is reduced, but measurement precision of axle loads may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the estimated axle loads from the vehicle model are continuously refined using actual brake pressure measurements and wheel speed data. The system compares model predictions with actual sensor readings from available sensors (brake pressures, wheel speeds) and adjusts the estimation accordingly, ensuring high precision without requiring axle load sensors
Solution Approach 2:
The patent transforms the measurement problem by changing from direct measurement of axle loads to indirect calculation using multiple other parameters (brake pressures, torques, wheel speeds, vehicle mass). By utilizing multiple input parameters in the vehicle model, the system achieves accurate axle load estimation through parameter transformation rather than direct sensing
3Reliability
If brake pressure control is optimized using accurate axle load information, then braking reliability is improved, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The patent makes the existing vehicle model serve multiple functions: it not only estimates axle loads for brake pressure distribution but also provides insights into vehicle dynamics, mass distribution, and braking performance. This multi-functionality allows the same processing unit to optimize braking reliability without requiring separate dedicated systems for each function, thereby improving reliability without proportionally increasing complexity
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The invention relates to a method (900) for operating a tractor-trailer unit (100) consisting of a tractor (104) and a trailer (106). In a reading step (902) of said method, input variables (108) on the tractor (104) and the trailer (106) are read. The input variables (108) include payload data (306) on a payload (mload, mtrailer) of the tractor-trailer unit (100), torque data (308) on a torque (M) acting on a powertrain of the tractor (104), brake pressure data (310) on brake pressures (P1, P2, P3, Ptrl) provided in the tractor-trailer unit (100), and gradient angle data (312) on a gradient angle (α) of a roadway below the tractor-trailer unit (100). In a determination step (904), output variables (110) are determined using the input variables (108) and a processing rule (314). The output variables (110) include front axle load data (316) on an axle load (m) acting on a front axle of the tractor (104) as well as rear axle data (318) on an axle load (m) acting on a rear axle of the tractor (104). In a utilization step (906), the output variables (110) are used for controlling a brake system of the tractor-trailer unit (100) in order to operate the tractor-trailer unit (100).