Sideslip angle soft-sensor based on neural network left inversion for multi-wheel independently driven electric vehicles

Effective estimation of vehicle states such as the yaw rate and the sideslip angle is important for vehicle stability control. Unfortunately the devices are very expensive to measure the sideslip angle directly and are not suitable for ordinary vehicle. Therefore, it must be estimated. A novel sideslip angle soft-sensor using neural network left inversion (NNLI) is presented for the in-wheel motor driven electric vehicle (EV). The innovation of the presented algorithm is not only little concerned with reference model parameters identification, but also uses the characteristic of the in-wheel motor driven EV. Longitudinal acceleration, lateral acceleration, yaw rate, longitudinal velocity, steering angle, the torque of in-wheel motor which can be acquired by ordinary sensors are used as inputs. Co-simulations are carried out to demonstrate the effectiveness of the proposed soft-sensor with Simulink and CarSim.