Benchmark Study on Real-time Energy Optimization of HEVs under Connected Environment⁎This work was supported in part by the National Nature Science Foundation of China under Grant 61903152, Grant U1864201, and Grant 61773009, in part by the Jilin Provincial Science Foundation of China under Grant 20200201162JC, Grant JJKH20200986KJ, Grant 20190302105GX, Grant 20200201285JC, and in part by the Funds for Joint Project of Jilin Province and Jilin University under Grant SXGJSF2017-2-1-1, in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control, and in part by the Interdisciplinary Integration and Innovation Project of JLU under Grant JLUXKJC2020202. Issue 10 (2021) - Gong, Xun; Wang, Jieyu; Ma, Baolin; Sun, Mengge; Wang, Yuhao; Deng, Nuo; Yan, Yongjun; Hu, Yunfeng; Chen, Hong
Abstract: Energy management of hybrid electric vehicles (HEVs) in the connected environment has attracted widespread attention. This article proposes a benchmark study on real-time two-layer hierarchical energy management framework for energy optimization of a connected power-split HEV. In the upper layer, Gaussian process (GP) is employed to predict the future velocity of the preceding vehicle, then the velocity predictor is combined with intelligent traffic information to plan an economical velocity trajectory of the ego vehicle. The lower layer incorporates the planned velocity over the receding horizon and applies the Pontryagin's Maximum Principle (PMP) based power management strategy to split the torques of the HEV. The effectiveness of the proposed energy management framework is evaluated on a high-fidelity simulator considering traffic-in-the-loop simulation. The simulation result has proven this framework can achieve good fuel economy while guarantees the constraints regarding driving safety and travel time.