Distributed drive electric vehicle with four in-wheel motors is widespread with various characteristics, such as performance potentials for independent wheel drive control and energy efficiency. However, in future, one of the biggest obstacles for its success in the automotive industry would be its limited energy storage. This paper proposes a hierarchical control method that involves a high-level motion controller that uses sliding mode control to calculate the total desired force and yaw moment and a low-level allocation controller in which an optimal energy-efficient control allocation scheme is presented to provide optimally distributed torques of four in-wheel motors in all the normal cases. A practicable motor energy efficiency model as a motor actuator is proposed by incorporating the electric motor efficiency map based on measured data into the motor efficiency experiment and a current closed-loop motor model. Moreover, both tracking performance and energy-saving are carried out in this research and evaluated via a co-simulation approach using MATLAB/Simulink and CarSim. A ramp maneuver at a constant speed and New European Driving Cycle and Urban Dynamometer Driving Schedule maneuvers have been conducted. To conclude, it is demonstrated that distributed drive electric vehicle with four in-wheel motors can reduce total power consumption and enhance tracking performance compared with a simple control allocation in which the torques are the fixed ratio distribution.
Differential braking and active steering have already been integrated to overcome their shortcomings. However, existing research mainly focuses on two-axle vehicles and controllers are mostly designed to use one control method to improve the other. Moreover, many experiments are needed to improve the robustness; therefore, these control methods are underutilized. This paper proposes an integrated control system specially designed for multi-axle vehicles, in which the desired lateral force and yaw moment of vehicles are determined by the sliding mode control algorithm. The output of the sliding mode control is distributed to the suitable wheels based on the abilities and potentials of the two control methods. Moreover, in this method, fewer experiments are needed, and the robustness and simultaneity are both guaranteed. To simplify the optimization system and to improve the computation speed, seven simple optimization subsystems are designed for the determination of control outputs on each wheel. The simulation results show that the proposed controller obviously enhances the stability of multi-axle trucks. The system improves 68% of the safe velocity, and its performance is much better than both differential braking and active steering. This research proposes an integrated control system that can simultaneously invoke differential braking and active steering of multi-axle vehicles to fully utilize the abilities and potentials of the two control methods. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.A) l i−1 − l v > 0, B bli ≥ 0, A bli < 0, δ i > 0 B) l i−1 − l v > 0, B bli > 0, A bli < 0, δ i = 0 C) l i−1 − l v = 0, δ i = 0 a4 B bri = 0, A bri = 0 (Cannot use this wheel to brake) B bli = 0, A bli = 0 (Cannot use this wheel to brake)
Vertical tire forces are essential for vehicle modelling and dynamic control. However, an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish. The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint. To simplify the design process and reduce the demand of the sensors, this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control. The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter (ATEKF). To adapt to the widely varying parameters, a sliding mode update is designed to make the ATEKF adaptive, and together with the use of an initial setting update and a vertical tire force adjustment, the overall system becomes more robust. In particular, the model aims to eliminate the effects of the over-constraint and the uneven weight distribution. The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation, and its performance is better than that of the treble extend Kalman filter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.