Effective Wheelbase Estimation for Multi-Axle Vehicle Control
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Solution Overview
Problem
Existing methods for estimating the effective wheelbase of vehicles with multiple axles are inaccurate, leading to suboptimal vehicle control and conservative design, affecting stability, maneuverability, load distribution, and overall performance.
Innovation Solution
A computer system uses multiple candidate estimations based on various models, including force, slip, friction, and vertical load, to calculate a weighted average of the effective wheelbase, utilizing machine learning for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple candidate estimations using different models are obtained and combined, then measurement precision of effective wheelbase is improved, but device complexity increases
Solution Approach 1:
The estimation system is segmented into multiple independent candidate estimation models, each handling specific aspects of wheelbase estimation. This allows the complex estimation problem to be divided into manageable sub-problems that can be solved separately and then combined, improving overall accuracy while maintaining systematic organization.
Solution Approach 2:
Multiple candidate estimations from different models are merged into a single comprehensive estimation. The system combines results from various estimation approaches (such as geometric models, force-based models, and sensor-based models) to produce a more accurate and reliable effective wheelbase measurement than any single model could achieve alone.
2Reliability
If conservative design is used due to inaccurate effective wheelbase estimation, then reliability is maintained, but productivity of vehicle control is reduced
Solution Approach 1:
The system implements feedback mechanisms where the estimated effective wheelbase is continuously monitored and used to adjust vehicle control parameters. This closed-loop approach allows the system to maintain reliability by validating estimates against actual vehicle behavior while simultaneously improving productivity by optimizing control actions based on accurate real-time information.
Solution Approach 2:
The system dynamically changes control parameters based on the estimated effective wheelbase value. By adjusting control gains, stability thresholds, and maneuverability parameters according to the accurate wheelbase estimation, the system achieves both reliability (through appropriate safety margins) and productivity (through optimized control responsiveness).
Data Source
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AI summary
A computer system (900) comprising processing circuitry (902) configured to estimate an effective wheelbase (30) of a vehicle unit (100) is provided. The effective wheelbase (30) of the vehicle unit (100) is a distance between a first position (10c) of a first coupling point or a first axle group (1) of the vehicle unit (100), to a second position (20c) of a second axle group (2) of the vehicle unit (100). The processing circuitry (902) is configured to obtain multiple candidate estimations of the effective wheelbase (30). The multiple candidate estimations have been estimated using different estimation models. The processing circuitry (902) is configured to estimate the effective wheelbase (30) based on the multiple candidate estimations.