Tractor-Trailer Mass Estimation Using Kalman Filter
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Solution Overview
Problem
Autonomous or semi-autonomous vehicles face challenges in accurately estimating the mass of trailers due to the inefficiency and high cost of equipping trailers with sensors for mass and payload measurement.
Innovation Solution
A system and method utilizing a model-free Kalman filter that estimates the combined tractor-trailer mass using airbag suspension pressure data, longitudinal force, and GPS/IMU data without additional sensors on the trailer, employing a control system to compute static and dynamic mass estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed on the trailer to measure mass and payload, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the mass measurement function from the trailer and relocates it to the tractor. By using sensors already installed on the tractor (accelerometers, force sensors) and processing data through a Kalman filter, the system obtains trailer mass information without adding any sensors to the trailer itself, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent introduces a Kalman filter as an intermediary computational system that processes data from existing tractor sensors to indirectly measure trailer mass. This intermediary algorithm acts as a virtual sensor, deriving trailer mass information from tractor dynamics without requiring physical sensors on the trailer
2Measurement precision
If additional sensors are added to the trailer, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The measurement function is extracted from the trailer and performed using the tractor's existing sensor suite. This eliminates the need to manufacture trailers with integrated mass sensors, thereby reducing trailer manufacturing costs while maintaining measurement precision through sophisticated data processing
Solution Approach 2:
The tractor serves itself by using its own existing sensors and computational resources to measure the trailer's mass. The tractor's control system processes its own sensor data to derive trailer parameters, eliminating the need for separate trailer-based measurement systems and reducing overall system cost
3Device complexity
If static mass estimation is used, then device complexity is reduced, but reliability of real-time control decreases
Solution Approach 1:
The patent transitions from static mass estimation to dynamic mass estimation by continuously processing real-time sensor data from the tractor during operation. The Kalman filter dynamically updates mass estimates based on current acceleration, force, and motion data, providing reliable real-time mass information without significantly increasing device complexity
Solution Approach 2:
The system implements feedback through the Kalman filter, which continuously compares predicted mass values with actual sensor measurements and adjusts estimates accordingly. This feedback mechanism ensures high reliability of real-time mass estimates while keeping the system relatively simple by using standard control theory approaches
Data Source
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AI summary
Provided are systems, methods and computer program code for estimating a current mass of a vehicle and a trailer.