LAUSR

The optimal charge and discharge management of the energy storage system (ESS) has a pivotal role in development of the energy management system for the plug-in hybrid electric vehicles (PHEV). This research aims to estimate a near optimal trajectory of ESS state of charge (SOC) to be employed in traffic-based adaptive PHEV control strategy to enhance fuel economy in various driving cycles. In the first step, several driving cycles are developed based on the real-world driving data. Experimental map-based Pontryagin's minimum principle control strategy is designed to extract the optimal SOC trajectories for different cycles. The achieved optimal data is then used to create the database employed for training a Nero-Fuzzy system. The SOC trajectories, estimated under the light of the obtained fuzzy inference system, are employed as the reference SOC profiles in adaptive equivalent consumption minimization strategy. The simulation results reveal that the proposed approach provides near optimal SOC trajectory and improves the equivalent fuel economy in various driving cycles. Moreover, this adaptive near optimal control strategy could be applied on-line as it doesn't require the knowledge of the entire driving cycle, and only the trip length and the average speed should be known a priori.