Cooperative power management for range extended electric vehicle based on internet of vehicles

The dramatic progress in internet of vehicles (IoVs) inspires further development in electrified transportation, and abundant information exchanged in IoVs can be infused into vehicles to promote the controlling perfor-mance of electric vehicles (EVs) via vehicle-environment cooperation. In this paper, a cooperative power man-agement strategy (PMS) is advanced for the range extended electric vehicle (REEV). To this end, the studied REEV is accurately modelled first, laying an efficient platform for strategy design. Based on the advanced framework of IoVs, the cooperative PMS is meticulously developed via incorporating the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. The brand-new SL-eECMS achieves preferable balance between the optimal effect and instant implementation capability through inte-grating the improved quantum particle swarm optimization (iQPSO), and ANFIS grasps future driving status macroscopically, offering the predicted charging request for online charge management. The substantial simu-lations and hardware-in-the-loop (HIL) test manifest that the proposed cooperative PSMS can coherently and efficiently manage power flow within power sources in the REEV, highlighting its anticipated preferable performance.

Automated summary

In this paper, a cooperative power management strategy is proposed for range extended electric vehicles (REEVs), utilizing vehicle-environment cooperation and abundant information exchanged in the internet of vehicles (IoVs). The strategy integrates the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. Simulation results and hardware-in-the-loop (HIL) test demonstrate the effectiveness and efficiency of the proposed strategy in managing power flow within power sources in the REEV.