Cascaded driving states estimation method of distributed drive electric vehicle based on PIO and STF algorithm

A vehicle state estimation method based on PIO and STF algorithm is proposed to achieve accurate and reliable state estimation of DDEV. Considering the presence of unknown input in the EDWM, the PIO method was used to reconstruct the unknown input of the system and establish the dynamic equation of longitudinal tire force. A LTFO was designed by combining PIO and EKF algorithm, achieving the estimation of longitudinal force with low-cost electric drive sensor information as inputs. A cascaded estimation strategy based on STF algorithm is proposed, which treats the longitudinal force estimation result as a virtual sensor measurement value and serves as the input of the STF, thus achieving full state estimation of vehicle driving. The results show that the designed estimation method can track the trend of vehicle state changes in real time and has high estimation accuracy.