Abstract
Wheel force transducers (WFTs) are widely used in the car and commercial vehicle industry to determine forces of vehicles in operation, relevant for multiple tasks within the development process of the manufacturers (Sankarganesh and Pawar, 2015). Due to the uncertainty and randomness of the wheel forces, it is crucial to design a suitable real-time filter for the WFT to obtain the high-accuracy forces data. Considering the diversity of the wheel forces, models for different dynamic ranges are established. And a two-step adaptive filter is proposed in this paper. In the first step, the wheel forces’ actual dynamic is evaluated by the interacting multiple model (IMM) algorithm, then a suitable model is chosen to match the evaluated dynamic of the wheel force for data denoising in the second step. Simulations in different driving conditions are carried out to demonstrate the effectiveness of the proposed filter. Furthermore, the results of data test show that the adaptive filter can be successfully used in practice to get high-accuracy wheel forces.
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