Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles

Energy management is one of the key technologies to improve the energy efficiency of electrified vehicles. In the existing real-time powertrain control strategies, most studies focus on improving fuel economy based on high-fidelity powertrain models without adequately exploring the impact of model accuracy on computational efficiency and energy saving. To address this research gap, this paper proposes a hierarchical control framework to minimize the fuel consumption of a plug-in hybrid electric vehicle. Specifically, three main contributions are presented to distinguish our efforts from existing research. First, two types of powertrain models are used in the optimization framework. In the upper control layer, an approximated model is employed to generate the optimal reference state of charge trajectory using convex optimization. Then in the lower control layer, by integrating the equivalent consumption minimize strategy into the model predictive control framework, the fuel consumption is minimized in real-time by using a high-fidelity powertrain model. Second, optimization results from the other three real-time control strategies and two predictive energy management strategies are presented and analyzed to verify the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are discussed.