Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach

The rapid growth of electrification, automation and connectivity in the transport industries puts forward higher requirements on control strategies to improve energy efficiency, traffic safety and driving comfort. Intense efforts have developed energy management strategies (EMS) in car-following scenarios for hybrid electric vehicles (HEVs) by adopting model predictive control (MPC). However, the computational complex online optimization intrinsic to MPC hinders its real-time implementation. This paper is thus proposed to develop a framework of energy-saving controller for HEVs based on explicit MPC, taking advantage of its online computational efficiency, to enable real-time control. To achieve this, the constrained finite-time optimization control (CFTOC) problems of car-following control and energy management strategy for a hybrid electric vehicle are formulated separately. The two problems are then shifted to explicit MPC by precomputing the explicit solutions offline and the control laws are coupled together to form the control framework. Numerical simulations show that the proposed controller can improve the energy efficiency, driving safety and comfort while reduce the online computational costs. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller. •The constrained finite-time optimization problem of car-following and energy management for the HEV is built separately.•Piece-wise affine model of the EMS is derived by using the PSO algorithm.•Explicit model predictive control theory is utilized by precomputing the control laws offline.•The control framework is developed by coupling the two EMPC algorithms.•Real-time performance is evaluated through hardware-in-the-loop experiments.