Co-Optimization of Speed Planning and Power Management for Automated HEVs in Unstructured Road Scenarios

With the development of intelligent and connected vehicle technology, the simultaneous optimization of powertrain performance and vehicle motions provides an unprecedented perspective to further explore the energy-saving potential of hybrid electric vehicles (HEVs). However, it is still very challenging because of the complex driving environment and large computational burdens, and hence, the co-optimization strategy under unstructured scenarios has rarely been studied. For unstructured road driving scenarios, this article proposes a hierarchical optimal control system combining speed planning and energy management for an autonomous HEV. In the first layer, a vehicle stability constraints system is designed with consideration of both external road characteristics and vehicle dynamics to limit the longitudinal speed, preventing the vehicle from excessive yaw, sideslip, and rollover during a complex, unstructured road trip. Then, the improved gray wolf optimizer is designed to efficiently generate optimal speed and state-of-charge (SOC) trajectories to guide the vehicle motion behavior and power split while ensuring driver safety. In the second layer, a mechanical implementation system is applied to fulfill the control system framework. Finally, the performance of the proposed co-optimization is comprehensively verified in terms of fuel economy, vehicle maneuverability, computational effectiveness, and adaptability. Compared with the sequential optimization, the improvements by co-optimization range from 9.38% to 13.03% in fuel economy and from 0.31% to 3.15% in vehicle maneuverability under different test cycles while ensuring real-time capability.