Priority-driven Multi-objective Model Predictive Control for Integrated Motion Control and Energy Management of Hybrid Electric Vehicles

The integrated motion control and intelligent energy management problems of hybrid electric vehicles (HEVs) can be divided into the sub-problems of fuel consumption minimization, battery electrical and thermal management, and trajectory tracking. The HEVs generally pose different requirements on the underlying control objectives concerning different working scenarios (e.g., acceleration, cruise, and brake cycles). In this paper, two priority-driven multi-objective model predictive control (MoMPC) approaches are developed, which facilitate a flexible design of predictive control by dynamically prioritizing independent (potentially conflicting) objectives. In addition, the practical constraints on the system dynamics, the battery, and the powertrain components are explicitly taken into account and imposed on the MoMPC problems. Simulation studies are conducted to assess the performance of the proposed controllers on two standard driving cycles. The results underscore the controllers' capacity to maintain motion control performance and reduce energy consumption while ensuring the battery operates within safe temperature, current, and state of charge boundaries.