The real-time and accurate estimation of vehicle states is the premise of vehicle intelligence development. However

the existing researches usually ignore the time-varying characteristics of tire cornering stiffness

and introducing linear tire model into vehicle model seriously affects the estimation accuracy of vehicle states under extreme conditions. An algorithm for estimating intelligent electric vehicle longitudinal speed

yaw rate and sideslip angle of vehicle mass center with tire cornering stiffness updated online is proposed. Based on the fuzzy adaptive extended Kalman filter (FAEKF)

the vehicle state estimation model is established. The fuzzy controller is used to adjust the Kalman gain matrix including the covariance of observation noise in EKF algorithm in real time to achieve the adaptive effect of the algorithm. Using the forgetting-factor recursive least square method (FFRLS)

the estimation model of tire cornering stiffness is established. A new FAEKF+FFRLS algorithm is proposed by combining the two algorithms in an embedded way

which can better realize the joint estimation and mutual correction of states and parameters. The algorithm is verified by co-simulation Trucksim and MATLAB/Simulink. The results show that compared with the standard EKF algorithm

the proposed state estimation algorithm has higher accuracy

better stability and robustness.