Real-time dynamic coordinated optimization control with near-global optimal learning for connected plug-in hybrid electric vehicles

With the application of connected technologies, such as vehicle-to-vehicle and vehicle-to-cloud communication in connected vehicles to obtain various traffic information, it is crucial to balance the optimality and computational burden of the energy management strategy for further improving the fuel economy of the connected plug-in hybrid electric vehicle. Another key factor affecting the improvement of fuel economy and control performance in connected plug-in hybrid electric vehicles is the fluctuation in driving torque resulting from the different response characteristics of the engine and motor during mode switching of the powertrain for the power distribution of the energy management strategy. To address these challenges, this paper proposes a novel real-time dynamic coordinated optimization control scheme that incorporates energy management at the upper layer and adaptive coordination at the lower layer for the connected plug-in hybrid electric vehicle in a vehicle-following scenario. Based on the offline optimal control rules extracted by the extreme learning machine, which possesses good generalization capabilities, the upper-layer guided model predictive control for energy management is implemented by applying the particle swarm optimization algorithm within variable horizons across different road sections. The bottom-layer adaptive fixed-time control scheme, equipped with a coordinated mechanism, is designed to address transient response deviations in the upper-layer results. The effectiveness and advantages of the proposed hierarchical scheme are validated through both the co-simulation platform and a hardware-in-loop test.