Multi-Input Model Predictive Speed Control Of Lean-Burn Natural Gas Engine In Range-Extended Electric Vehicles

This study presents a multi-input model predictive speed control of lean-burn natural gas engine in a range extender to improve the responsiveness and anti-disturbance ability of the system. Lean combustion is recognized as an effective strategy to improve the economy and emission performance of the natural gas engine. However, the engine torque is insufficient compared with a normal combustion mode, which will result in a degraded anti-disturbance and low response performance in an engine speed tracking system in the range extender. In order to improve the performance of the lean-burn gas engine speed system, the air-fuel ratio (AFR) additional torque is introduced as another input of the system besides the throttle valve to increase the engine transient output torque, where the AFR additional torque is produced by setting AFR deviation from the nominal value. A multi-input model predictive control (MPC) controller is designed to handle the multiple inputs and constraints, guaranteeing that the AFR returns to the nominal value at steady state for efficiency and emissions, and an improved fast MPC algorithm is proposed to reduce the computation effort of the MPC strategy. Moreover, the proposed controller is evaluated by co-simulations on the platform of Matlab/Simulink with GT-Power and experiments on a range extender. (C) 2021 Elsevier Ltd. All rights reserved.