Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios. (15th January 2023)

• Record Type: Journal Article
• Authors: Chen, Zheng; Wu, Simin; Shen, Shiquan; Liu, Yonggang; Guo, Fengxiang; Zhang, Yuanjian
• Is Part Of: Energy. Volume 263:Part F(2023)
• Publication Date: 2023-01-15
• Subjects: Eco-driving -- Velocity planning -- Energy management strategy -- Gaussian process -- Double delayed Q-learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126060
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed for automated and connected PHEV to co-optimize vehicle velocity and energy management in urban driving scenarios. In the upper layer, the external traffic disturbance and powertrain characteristics are integrated into velocity planning via a Gaussian process (GP) model and a desired acceleration, respectively. In the power allocation layer, a double delayed Q-learning (DDQL) algorithm is employed to instantaneously optimize the power allocation for powertrain system based on the planned velocity. The feasibility and energy-saving effect of the proposed co-optimization strategy is verified through a traffic-in-the-loop simulator under various urban driving scenarios. The simulation results demonstrate that the integration of traffic lights, powertrain characteristics and speed prediction of preceding vehicle into velocity planning of PHEV can make vehicle velocity smoother, so as to improve fuel economy, driving comfort and traffic efficiency. As coupled with DDQL algorithm, our proposed co-optimization strategy can reach 97.31% energy economy of typical DP-based strategy but in a real-time framework. Highlights: Co-optimization strategy of velocity planning and powertrain control is developed. An effective and efficient method for solving the co-optimization is designed. Dynamic traffic restrictions and powertrain dynamics are sufficiently considered. DDQL algorithm is explored to optimally and instantaneously allocate power of PHEV.