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 performance of electric vehicles (EVs) via vehicle-environment cooperation. In this paper, a cooperative power management 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 integrating 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 simulations 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.
Funding
National Natural Science Foundation of China (No. 52002046 and 52272395)
Chongqing Fundamental Research and Frontier Exploration Project (No. CSTC2019JCYJ-MSXMX0642)
Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201901539)
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
This paper was accepted for publication in Energy published by Elsevier. The final publication is available at https://doi.org/10.1016/j.energy.2023.127238. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/