Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information

Abstract

The simplified lateral dynamic models of front and rear tires are proposed with lateral tire force information in this paper. The regression models of the recursive least squares (RLS) with forgetting factors and constraints are constructed based on simplified lateral dynamic models for estimating tire cornering stiffness. In addition, a nonlinear observer of sideslip angle is designed for four-in-wheel-motor-driven electric vehicles (FIWMD-EVs) with the estimated information on tire cornering stiffness. Sideslip angle can be estimated through a first-order Stirling's interpolation filter (DD1-filter) and a firstorder low-pass filter. The reliability, feasibility, effectiveness, and practicality of simplified lateral dynamic models are verified by contrast simulation experiments. The simplified lateral dynamic models of front and rear tires are not influenced on the change of sideslip angle, nor influenced by each other. Moreover, it can result in improving computation speed that computational burden is reduced after simplifying lateral dynamic models. With the estimated information above mentioned, sideslip angle is also estimated well. The estimated information on tire cornering stiffness and sideslip angle is benefit to the design of lateral stability control system, which can make vehicle adapt to different road conditions and control the steering motion attitude of vehicle in future works.

Improved adaptive lane‐keeping control for four‐wheel steering vehicles without lateral velocity measurements

“…We assumed that the vehicle parameters (e.g. mass and cornering stiffness) could vary by 30% from the parameter values shown in . Under this assumption, the parameter α _ was set as 14 so Relation was satisfied.…”

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

“…The accurate derivation of torques and rotating angular speeds of each IWMEV wheel can be easily achieved without increasing any sensor, and the obtained information perception range from IWMEVs is larger than that from traditional vehicles; these two advantages provide the basis for accurate vehicle state estimation. With regard to the vehicle state estimation of IWMEVs, Wang et al used UKF to estimate the sideslip angle when tire forces are obtained with virtual lateral tire force sensors, assuming that a linear relationship exists between lateral tire force and tire slip angle when vehicle lateral acceleration is less than 0.3 g. Lian et al not only estimated the lateral tire forces with the recursive least squares algorithm, but they also estimated the sideslip angle with EKF, in which tire cornering stiffness was considered in the design of the nonlinear observer of the sideslip angle. Jin and Yin proposed a novel method to estimate lateral tire–road forces and the vehicle sideslip angle by utilizing real‐time measurements.…”

Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

“…When there is model error and unknown time-varying noise in the system, new observation data only plays a small role in correcting state estimation. Therefore, error may accumulate to cause large filter error and even divergence [14][15][16][17]. Based on it, this work introduced limited-memory filter based on traditional EKF to increase the role of new observation data and reduce the harmful effects of old measurement data on filter.…”