Estimation of Vehicle Mass and Road Slope for Commercial Vehicles Utilizing an Interacting Multiple-Model Filter Method Under Complex Road Conditions

Precise and real-time estimation of vehicle mass and road slope plays a pivotal role in attaining accurate vehicle control. Currently, road slope estimation predominantly emphasizes longitudinal slopes, with limited research on intricate slopes that include both longitudinal roads and continuous turning up-and-down slopes. To address the limitations in existing road slope estimation research, this paper puts forward a novel joint-estimation approach for vehicle mass and road slope. Vehicle mass is initially estimated via M-estimation and recursive least squares with a forgetting factor (FFRLS). A road slope estimate approach, which utilizes interacting multiple models (IMM) and cubature Kalman filtering (CKF), is proposed for complex road slope scenarios. This algorithm integrates kinematic and dynamic vehicle models within the multi-model (MM) ensemble of the IMM filter. The kinematic vehicle model is appropriate for longitudinal road gradients, whereas the dynamic vehicle model is better suited for continuous turning up-and-down slope conditions. The IMM filter employs a stochastic process to weight the appropriate vehicle model according to the driving conditions. Consequently, the weights assigned by the IMM filter enable the algorithm to adaptively select the most suitable vehicle model, leading to more accurate slope estimates under complex conditions compared to single-model-based algorithms. Simulations were carried out using Matlab/Simulink2020-Trucksim2020 to verify the effectiveness of the proposed estimation approach. The results demonstrate that, compared with existing methods, the proposed estimation approach has achieved an improvement in the precision of evaluating vehicle mass and road gradient, thus confirming its superiority.