A Time-delay Neural Network of Sideslip Angle Estimation for In-wheel Motor Drive Electric Vehicles

In this paper, a time-delay neural network (TDNN) is proposed to estimate the sideslip angle under extreme maneuvers for in-wheel-motor-drive electric vehicles (IWMD EVs). The principle component analysis (PCA) method is first utilized for data preprocessing. Then a time delay module is introduced into the neural network model to improve its robustness. The estimated sideslip angle is further filtered by the Kalman filter. Finally, the proposed estimation scheme is verified via the comprehensive hardware-in-loop (HIL) tests, in which the proposed method can achieve high estimation accuracy.