This study proposes a new method for vehicle sideslip angle estimation utilizing the competitively priced sensor fusion using in-vehicle sensors and low-cost standalone global positioning system (GPS). To estimate unmeasurable vehicle states, vehicle sideslip angle and tire cornering stiffness, an interacting multiple model (IMM) Kalman filter is proposed that combines two extended Kalman filters (EKFs), each including kinematic and dynamic equations of vehicle lateral velocity. To properly combine the outputs of these model-based EKFs, a weighted probability of each model based on the stochastic process is designed, which reflects the characteristics of each of the kinematic and dynamic equations in real-time. Also, the observability of the proposed estimation algorithm is checked by observability functions of nonlinear systems. The estimation performance in various driving scenarios is verified using an experimental vehicle, and its superiority is confirmed through a comparative study. The proposed algorithm makes the following main contributions for estimating the vehicle sideslip angle: 1) the high optimality of estimation results and 2) the accurate estimation of tire cornering stiffness.

This paper introduces a new torque vectoring (TV) algorithm with regards to enhancing the cornering performance of the electronic-four-wheel drive (e-4WD) vehicles. The proposed TV algorithm aims to distribute the in-wheel motor (IWM) torques to the left and right wheels assisting the vehicle in following the driver's intended trajectories. The proposed TV algorithm first included a yaw rate controller reference for neutral-steering which leads to the improvement of vehicle cornering agility. Thereafter, in-vehicle sensors and a standalone global positioning system (GPS) combined with a smooth sliding mode controller (SMC) were used based on a vehicle bicycle model to generate desired yaw moment values to track the reference. Finally, a novel torque distribution algorithm using the Daisy-chaining allocation, which is suitable for redundant actuator configuration, was utilized. Noteworthy, the Daisy-chaining allocation algorithm takes into account a torque operation area with both the T-N curve of IWM and the tire friction circle. In this manner, based on the vehicle bicycle model and using the CARSIM interface, the simulations, evaluation and verifications of the proposed control technique were done. Thereafter, considering a real-car based experiments with various driving scenarios, the effectiveness of the proposed TV algorithm was evaluated. It was confirmed that some evaluation factors in terms of the cornering performance were improved. The following main contributions make the proposed TV algorithm be a meaningful solution to enhance the cornering performance of e-4WD vehicles: 1) improvement of both smoothness and convergence rate of control action, 2) a practical and intuitive way to distribute IWM torques, and 3) high applicability to mass-produced vehicles.

This paper proposes a path tracking controller to assist the electronic-four-wheel drive (e-4WD) vehicle in following the desired path profile. It utilizes only readily available in-vehicle sensors and a standalone global positioning system (GPS). Noteworthy, both the lateral distance error and the heading angle error between the desired path profile and the vehicle are calculated using only a standalone GPS. Therefore, the proposed algorithm can be easily applied to mass-produced e-4WD vehicles. A model predictive control (MPC) is selected as the controller type, which derives optimal control input considering both state and input constraints of in-wheel motor (IWM) in the e-4WD vehicle. Due to the advantage of this MPC predicting the vehicle's future dynamic behavior in advance, it is possible to output a more preemptive and stable yaw moment for vehicle path tracking. Finally, a weighted least square (WLS) allocation that is suitable for redundant actuator configuration is utilized to distribute the optimal IWM torques to the front left and right wheels of the e-4WD vehicle. The major contributions of the proposed control algorithm are as follows. 1) It is the first time in this paper that the MPC is applied to path tracking of e-4WD vehicles. 2) The condensed constraint matrix of the proposed MPC has a structure that can be easily applied to the linear programming. Using the CarSim simulation and the real-car based experiment, the effectiveness of the proposed MPC algorithm is verified by a comparative analysis. Thus, the high path tracking accuracy of the proposed algorithm is confirmed.

This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety's sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach.