Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation hybrid electric vehicles (HEVs) are proposed in this article. is adopted based on the enhancement a model predictive (MPC) by artificial neural network (ANN) approach. MPC-based ANN (NNMPC) to control speed HEVs simulation system experimental HIL test systems. established assess performance NNMPC velocity environment. real-time environment implemented through low-cost approach such as integration Arduino Mega 2560 host Lenovo PC with Core i7 @ 3.4 GHz processor. compared proportional–integral (PI) controller, classical MPC, two different settings methodology verify efficiency NNMPC. obtained results show distinct behavior good transient response, minimum error steady state, robustness against parameter perturbation.