All-in-One Control Framework for Distributed Drive Electric Buses Path Tracking Subject to Uncertain Crosswind and Varied Passenger Mass

This article proposes an all-in-one control framework for distributed drive electric buses (DDEBs) that can effectively attenuate the effects of uncertain crosswind and varied passenger mass in path tracking, while minimizing the electricity consumption of battery. To track the desired path and ensure driving stability, a robust tube-based model predictive control method is proposed considering lateral, yaw and roll motions. Simultaneously, for the longitudinal motion, a robust sliding mode control method is designed to achieve accurate tracking of longitudinal velocity. Meanwhile, the uncertain crosswind and varied passenger mass faced by the DDEBs are decoupled into external disturbances and their effects on the system are eliminated in both controllers. Then, to cope with energy optimization problem in path tracking, a torque distribution controller is constructed by formulating the vehicle stability margin, tracking dynamic requirements and energy efficiency as constrained weighted least squares problem. Finally, a Trucksim-Simulink co-simulation is performed, where the simulation results demonstrated that the proposed control framework is robust and effective, and can achieve better energy saving effect compared to the original control methods.

Automated summary

This article proposes an all-in-one control framework for distributed drive electric buses (DDEBs) that can effectively attenuate the effects of uncertain crosswind and varied passenger mass in path tracking, while minimizing the electricity consumption of battery. The control framework includes robust tube-based model predictive control for lateral, yaw and roll motions, and robust sliding mode control for longitudinal motion. It also decouples the uncertain crosswind and varied passenger mass into external disturbances and formulates an energy optimization problem as a constrained weighted least squares problem. The proposed control framework is demonstrated to be robust and effective in a Trucksim-Simulink co-simulation, achieving better energy saving effect compared to the original control methods.