Abstract
Accurate and reliable vehicle state estimation results are very significant to the active safety, energy optimization, and the intelligent control of vehicles. In this paper, to improve the accuracy and adaptability of vehicle running state estimation, the vehicle running states fused estimation strategy is presented for in-wheel motor drive electric vehicle using the Kalman filters and tire force compensation method. The concept of electric drive wheel model (EDWM) is developed and deduced, and then, considering that the EDWM is a nonlinear model with an unknown input, the design concept of high-order sliding mode observer is used to construct the state space equation of longitudinal force. To improve the accuracy and the reliability of vehicle state estimation, an overall estimation strategy with information fusion and tire force compensation is designed, in which a weighted square-root cubature Kalman filter with an adaptive covariance matrix of measurement noise is developed for observer design. Finally, the simulations in CarSim-Simulink co-simulation model and experiments are carried out, and the effectiveness of the designed estimation strategy is validated.
Highlights
With the gradual increase of vehicle ownership, the environmental pollution problem caused by vehicle exhaust emissions is becoming more and more serious
WEIGHTED SQUARE-ROOT CUBATURE KALMAN FILTER (WCKF) The vehicle dynamics model can be expressed as the following discrete state space equations xk = f + wk−1 yk = h + vk where xk is the state vector of system, yk is the measurement vector of system. f () and h() are the nonlinear functions and represents the state transition matrix of state equation and measurement equation, respectively. wk and vk represents the system noise and measurement noise, respectively, and they are the uncorrelated white noise vectors of zero mean
This paper presents a novel design of vehicle running states fused estimation strategy using the Kalman filters and tire force compensation method
Summary
With the gradual increase of vehicle ownership, the environmental pollution problem caused by vehicle exhaust emissions is becoming more and more serious. With the gradual improvement of consumer demand for vehicle performance, the precision degree of vehicle is getting higher and higher, and the vehicle control system is becoming more and more complex correspondingly, which cause that the current vehicles are dependent on more accurate vehicle state estimation results [31]–[35] In this case, a series of improved Kalman filter methods have been widely studied and applied, in which the effect of estimation has been significantly improved [36]–[41]. In [42], the vehicle dynamics model has been established with the longitudinal, lateral, and vertical kinetic equations being considered, to obtain the estimation of vehicle sideslip angle, the extended Kalman filter and the recursive least squares algorithm is combined to design the observer and improve the estimation accuracy.
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