Abstract
Vehicle mass is an important parameter for motion control of intelligent vehicles, but is hard to directly measure using normal sensors. Therefore, accurate estimation of vehicle mass becomes crucial. In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced. In machine learning method, a feedforward neural network (FFNN) is used to learn the relationship between vehicle mass and other state parameters, namely longitudinal speed and acceleration, driving or braking torque, and wheel angular speed. In dynamics-based method, recursive least square (RLS) with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass. According to the reliability of each method under different conditions, these two methods are fused using fuzzy logic. Simulation tests under New European Driving Cycle (NEDC) condition are carried out. The simulation results show that the estimation accuracy of the fusion method is around 97%, and that the fusion method performs better stability and robustness compared with each single method.
Highlights
Accurate acquisition of vehicle state parameters is the basis of high-quality motion control of intelligent vehicles
(4) Simulation tests under New European Driving Cycle (NEDC) are carried out, with the results validating that the proposed fusion method shows significant improvement with respect to estimation accuracy, stability and robustness compared with each single estimation method
6 Conclusion In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is proposed for intelligent vehicles
Summary
Accurate acquisition of vehicle state parameters is the basis of high-quality motion control of intelligent vehicles. Dynamics-based methods may obtain accurate vehicle mass estimation results when the vehicle is under large and persistent excitation, but the estimation accuracy is difficult to ensure when the vehicle is under small excitation. Torabi et al [13] firstly collected the dynamic information of the vehicle under various conditions as the training set, and trained the feedforward neural network to realize simultaneous estimation of vehicle mass and road slope. Considering the advantages and limitations of the methods above, a vehicle mass estimation method based on fusion of ML and vehicle dynamic model is proposed in this paper in order to improve the estimation effect. (4) Simulation tests under New European Driving Cycle (NEDC) are carried out, with the results validating that the proposed fusion method shows significant improvement with respect to estimation accuracy, stability and robustness compared with each single estimation method.
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