An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario

Nowadays, the comprehensive performance of plug-in hybrid electric vehicle (PHEV) is expected to be further improved with development of connected vehicle technology. However, the strong coupling and traffic flow uncertainty characteristics of connected scenario pose formidable challenge to existing energy management strategies (EMSs) in terms of optimization effect and computational efficiency. For comprehensively improving connected PHEV performances including energy saving, safety, traffic effi-ciency and computational efficiency, a multi-objective hierarchical EMS with less computational burden is proposed by incorporating resistance network (RN) triggered motion planning and alternating direction method of multipliers (ADMM) based convex torque optimization. Specifically, the RN method is employed to characterize and decouple the complex interaction relationship within connected scenario from internal mechanism perspective, enabling the velocity profile optimization issue that with fixed end time constraint and online correction mechanism for traffic flow uncertainty. According to the ve-locity profile optimized in cloud level, the convex formulation of model predictive control (MPC) based torque distribution problem is formulated in vehicle level, and an efficient ADMM algorithm is used for its solution, with the aim of satisfying energy saving and practical application requirements simulta-neously. Based on real connected information, the superiorities of proposed EMS are verified by both simulation and hardware-in-loop (HIL) experiment. (c) 2022 Published by Elsevier Ltd.

Automated summary

This study proposes a multi-objective hierarchical energy management strategy to improve the performance of connected plug-in hybrid electric vehicles (PHEVs). The strategy incorporates resistance network-triggered motion planning and convex torque optimization based on the alternating direction method of multipliers (ADMM), aiming to comprehensively optimize energy saving, safety, traffic efficiency, and computational efficiency.