Abstract: It is greatly important to accurately estimate the driving state for the longitudinal and lateral stability control of a distributed drive electric vehicle (DDEV). Noise adaptive extended kalman filtering (NAEKF) algorithm is adopted to estimate the driving state of DDEV in this paper. The algorithm makes full use of the realtime statistical information of observed signal. By monitoring the dynamic change of innovations and residuals of filter, the state noise variance and measurement noise variance are constantly corrected, with filter gain and the proportions of predicted and observed state values in state values filtered adjusted, hence improving state estimation accuracy. Finally the algorithm adopted is verified by simulation with vehicle dynamics software veDYNA. Results show that compared with EKF, the algorithm adopted can effectively overcome the problem of inaccuracy in prior statistical information and the inaccuracy in estimation caused by complex conditions, with a mean error of state estimation not more than 27%, a root mean square error not more than 26%, and a rather small peak relative error.

Key words: distributed driving, running state, noise adaptive, extended kalman filter, distributed drive, driving state, noise adaptiveextended kalman filtering