On Slope Attitude Angle Estimation for Mass-Production Range-Extended Electric Vehicles Based on the Extended Kalman Filter Approach

Since vehicle attitude cannot be readily measured, this paper designs a state observer based on the information available on the CAN bus. The attitude angle estimated in this way is not only robust in practical applications but can also replace an IMU sensor for accurate remaining fuel range prediction under complex driving conditions. The primary innovation of this work is the development of an extended Kalman filter (EKF)-based estimation of the vehicle pitch attitude angle and its deployment in real-world vehicle systems. Firstly, a vehicle longitudinal model considering the suspension dynamics is established, followed by a model-based extended Kalman filter (EKF) design. Then, the EKF algorithm is verified by a co-simulation using Simulink and CarSim of typical working conditions. Numerical tests indicate the effectiveness of the EKF algorithm, with the estimation error being below 0.5°. Finally, the proposed EKF is engineered to range-extended NETA electrical vehicles and applied for reliable remaining fuel range prediction. The mass-production application proves that the EKF observer can respond to changes in body pitch motion stably and rapidly, and the estimated error is less than 1.5°.