Efficient Motion Planning and Energy-Saving Coordinated Control for Intelligent Hybrid Electric Vehicles

Intelligent hybrid electric vehicles (IHEVs) represent a cutting-edge direction in the evolution of automotive technology and intelligent transportation systems. Aiming at improving the overall operational efficiency of intelligent hybrid electric vehicles and promoting the coordination between intelligent driving systems and hybrid power systems, this paper proposes a real-time hierarchical framework for efficient motion planning and energy-saving coordinated control. In this framework, the multi-step predictive control method based on heuristic reinforcement learning algorithm is developed at the motion planning layer to improve the overall performance of integrated path tracking and safety-oriented obstacle avoidance. Then, the double deep reinforcement learning method with accelerated gradient optimization is developed at the energy-saving control layer to maximize fuel economy while satisfying the power demands of motion planning. Through the virtual driving simulation and real-world scenario, the results show that the proposed hierarchical framework enables high-precision path tracking, safe and smooth obstacle avoidance, rapid driving, and superior energy efficiency.