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A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution
Qunyi Chu1, Wen Sun2, Yuanjian Zhang3
1Arts and Sciences, New York University Shanghai, Shanghai 200122, China.
Sensors (Basel, Switzerland)
|November 11, 2022
Summary
This study introduces a machine learning approach for accurate truck braking force distribution. It enhances vehicle state parameter estimation, improving braking strategy performance.
Area of Science:
- Automotive Engineering
- Machine Learning Applications
- Control Systems
Background:
- Accurate estimation of vehicle state parameters is crucial for effective braking force distribution in trucks.
- The nonlinear dynamics during braking make precise parameter estimation challenging, impacting distribution strategy accuracy.
Purpose of the Study:
- To propose a machine learning-based method for accurate vehicle state parameter estimation.
- To enhance the braking force distribution strategy for trucks by providing a reliable data foundation.
Main Methods:
- Data processing of collected vehicle information.
- Feature screening using Random Forest for dimensionality reduction.
- Online estimation of vehicle state parameters via an offline-trained Generalized Regression Neural Network (GRNN).
Main Results:
- Successful implementation of a four-wheel braking force distribution strategy using estimated parameters.
- Validation of the proposed method through joint simulations in MATLAB/Simulink and TruckSim.
Conclusions:
- The proposed machine learning method effectively estimates vehicle state parameters for improved braking force distribution.
- The study provides a robust data-driven approach for optimizing truck braking systems.
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