Manchester Metropolitan University's Research Repository

    Low-cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle

    Essa, Mohamed El-Sayed M, Lotfy, Joseph Victor W, Abd-Elwahed, M Essam K, Rabie, Khaled ORCID logoORCID: https://orcid.org/0000-0002-9784-3703, ElHalawany, Basem M and Elsisi, Mahmoud (2023) Low-cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle. Electronics, 12 (4). p. 971. ISSN 2079-9292

    Published Version
    Available under License Creative Commons Attribution.

    Download (7MB) | Preview


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

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Actions (login required)

    View Item View Item