Co-optimization of Power Allocation and Speed Trajectory for Autonomous HEVs in Off-Road Scenarios Considering Vehicle Dynamics

• Published in: IEEE transactions on vehicular technology 2024-07, Vol.73 (7), p.9878-9894
• Main Authors: Guo, Lingxiong, Nie, Shida, Liu, Hui, Wan, Hang, Liu, Rui, Zhang, Fawang, Xiang, Changle
• Format: Article
• Language: English
• Subjects: Active control; active safety module; Algorithms; autonomous HEV; Autonomous vehicles; Batteries; continuation/generalized minimal residual (C/GMRES) algorithm; Control stability; Controllers; Cooperative control; Cooperative optimization; Energy management; Intelligent vehicles; Lateral stability; Mechanical power transmission; Modules; Nonlinear control; nonlinear MPC; Nonlinear systems; off-road scenarios; Optimization; Optimization techniques; Path tracking; Performance prediction; Predictive control; Real time; Road traffic; Roads; Tracking control; Trajectory; Trajectory optimization; Vehicle dynamics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3384186

With the development of intelligent vehicle technologies, the traditional energy management is transitioning towards the cooperative optimization of velocity schedule and power allocation for autonomous HEVs, aiming to enhance energy efficiency further. Nonetheless, the task remains highly formidable due to the complex road characteristics and the large computational burdens involved. Consequently, the co-optimization technique in off-road scenarios has rarely received scholarly attention. This paper presents an integrated-optimization control framework with active safety module, a cooperative optimization controller, and a path tracking controller for an autonomous HEV. The active safety module deduces the restrictive relationship of both the environment and the vehicle's lateral stability on the power distribution and then determines a reasonable feasible region for cooperative optimization. Besides, the model predictive control (MPC)-based control strategies are designed to achieve satisfying control performance for the co-optimization and the path tracking with the stability constraint conditions. Additionally, a continuation/ generalized minimal residual (C/GMRES) algorithm is developed to mitigate the huge computation burden of the online nonlinear MPC controller, thereby benefiting from real-time control capability. Finally, simulation results show that the proposed control framework and strategy can achieve an efficient power split and path tracking efficacy while greatly reducing the computational burden in the complex off-road scenarios.