Robust energy management technique for plug-in hybrid electric vehicle with traffic condition identification

In this paper proposes an efficient hybrid approach for optimal energy management of plug in hybrid electric vehicle (PHEV) with traffic conditions. The proposed hybrid system is joint operation of Atomic Orbital Search (AOS) and Recalling Enhanced Recurrent Neural Network (RERNN) and normally named as AOS-RERNN approach. The main purpose of the proposed approach is to control the energy through Internet of Vehicles (IoVs), which provides significant fuel economy of PHEV. Based on certain traffic condition the derived driving cycle-based parameter of the energy management is optimized by the AOS approach in online. The controlling thresholds are optimized by AOS to provide a set of optimal parameters. At last, the performance of the proposed system is executed on MATLAB/Simulink working platform compared with various existing methods. The proposed approach provides improved fuel economy than the existing approaches.