Real-time road slope estimation based on adaptive extended Kalman filter algorithm with in-vehicle data

The accurate and real-time knowledge of road slope is a key of vehicle dynamic control while running on the road. In order to obtain accurate road slope considering the random disturbance in complex driving circumstance, an algorithm based on adaptive extended Kalman filter (AEKF) is proposed to estimate the road slope in real time in this paper. Firstly, the nonlinear vehicle longitudinal dynamics is established and transformed into discrete state space system to implement the recursive estimation. Then the innovation-based adaptive tuning part is designed to estimate time-varying process noise covariance and measurement noise covariance. Finally, the simulations are conducted to verify the algorithm through CarSim platform. The results are in good agreement with the robustness of the AEKF method for the unknown time varying disturbances, which is better than the existing regular extended Kalman filter algorithm.