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Road slope estimation based on acceleration adaptive interactive multiple model algorithm for commercial vehicles
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AI
DOI：10.1016/j.ymssp.2022.109733.png)
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摘要
En 中文
Road slope is an important external variable in vehicle dynamic control systems. However, it is a challenging problem to estimate road slope accurately for commercial vehicles due to the coupling problem among the mass, road slope and pitch angle. To solve this problem, a novel road slope estimation scheme with the correction of pitch angle is proposed. First, the coupling problem between road slope and body pitch is analyzed from the perspective of sensor signals. Next, the driving conditions are divided into gentle and strong scenarios, abstracted to the smooth driving model (SDM) and intensive driving model (IDM), respectively. SDM alone cannot guarantee accuracy under intensive scenarios, while IDM alone converges slowly under smooth driving scenarios. The acceleration adaptive interactive multiple model (AAIMM) algorithm is then designed to combine the models and determine which model is the most appropriate under different driving intensities. At last, the sensor-less pitch angle correction strategy based on the suspension deformation model is presented and the particle swarm optimization (PSO) algorithm is used to optimize the suspension stiffness off-line. The simulations and road tests indicate the effectiveness and accuracy of the proposed road slope estimation scheme.
Keyword:
Road slope
Extended Kalman filter (EKF)
Interactive multiple model (IMM)
Pitch angle
Particle swarm optimization (PSO)
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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