Optimal Energy Efficient Control of Pure Electric Vehicle Power System Based on Dynamic Traffic Information Flow

To improve the energy efficiency and driving condition adaptability of pure electric vehicles (EVs) in a complex traffic environment, simulation conditions that can dynamically update traffic information based on measured data were designed. Next, to increase the pure EV's driving range, an energy efficient optimal control framework based on dynamic traffic information flow was proposed, which includes the traffic information layer, the target planning layer, and the prediction and control layer. In the traffic information layer, the vehicle receives and updates the traffic information data. The time-domain information and distance domain information are converted in the target planning layer and the optimal state-of-charge (SOC) reference trajectory is planned through the remote cloud computing system. The traffic information is predicted in the prediction and control layer, and an SOC scroll tracking control method is proposed to continuously control the power system to achieve the goal of optimal energy consumption and economic driving. Finally, under a variety of real road simulation conditions with dynamic traffic information, we verify that the economic performance of the proposed control framework is equivalent to that of the one based on dynamic programming algorithms and has the potential for online real-time control.