###### Abstract  
The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm's fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm's results are superior to the traditional SCKF algorithm's results. distributed drive; interacting multiple model; square root cubature Kalman filter; state estimation; vehicle system +
Footnote †: journal: Applied sciences  
18 October 2021  
12 November 2021  
**Problems:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Problems:**MDP1 stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. conventional vehicles, effectively improving the vehicle's sensing capability and making real-time observation of vehicle dynamics parameters easier [2]. Accurate vehicle state estimation results directly improve the sensing capability of intelligence vehicles, which is important for path tracking, trajectory tracking, and active safety controller design of intelligence vehicles. [PERSON] et al. proposed a nonlinear tire stiffness observer for the real-time tire stiffness estimation problem, and the Kalman-Bucy filter was used for lateral velocity estimation and a constrained MPC path tracking controller based on accurate state parameter information was proposed to improve the effectiveness of path tracking [3,4]. [PERSON] et al. designed a robust tracking control method based on the estimation results provided by the state observer [5]. [PERSON] et al. designed a chassis controller integrated with four-wheel independent steering system and a direct yaw moment control system integrated with MPC based on the vehicle state estimation results provided by the UKF observer, which fully improved the stability of the vehicle [6]. A large number of studies based on kinematic and vehicle dynamics models have been conducted on vehicle state estimation. Tire-road conditions and vehicle parameters have no effect on kinematic-based state estimation, but the integration of sensor measurements generates cumulative errors that lead to large deviations in the results, so the method is only applicable to steady-state situations. The state estimation based on the dynamics model is also divided into two categories. The first is the linear two-degree-of-freedom (2-DOF) vehicle model, which is derived from the linear tire model and has a high estimation accuracy in conditions with small tire slip angle and is widely used to estimate the yaw rate and sideslip angle [7,8]. However, when the vehicle is operating in extreme conditions, the steering angle is large, the tire slip angle increases, and the tire enters the nonlinear region, the linear tire model does not accurately reflect the real vehicle dynamics. As a result of the large difference between the estimated and real values, the vehicle controller receives the result and makes an incorrect decision, resulting in serious consequences.