Abstract:
The paper introduces a control strategy for a plug-in hybrid electric vehicle (PHEV)
system, integrating a photovoltaic-based RES with battery and supercapacitor-based
ESS. The objective is to efficiently utilize the PV system using a neural network to
determine its maximum power points. To ensure robust control, the paper designs both
a robust nonlinear higher-order super twisting sliding mode controller and an integral
terminal sliding mode controller.
One notable aspect of the proposed system is the implementation of a current-fed con verter for the PV and supercapacitor. Unlike SCR-based converters, current-fed con verters prevent device failure and core saturation, making them more reliable. This
characteristic contributes to the overall robustness of the system. The integration of the
neural network, along with the designed controllers and current-fed converter, enhances
the efficiency and reliability of the plug-in hybrid electric vehicle system. This research
holds promise for developing advanced and resilient energy management solutions for
sustainable transportation systems in the future. Additionally, a power bi-directional
converter is employed for the battery in the EV system. This converter enables the
battery to both charge and discharge power as required by the system which facilitates
the energy flow between the battery and the rest of the system, allowing efficient energy
management and utilization within the PHEVs. The integration of ANN for PV with
the above mentioned converters and controllers along with improved-gray wolf optimiza tion represents the novelty of this work. The paper confirms global asymptotic stability
of the control method using Lyapunov stability analysis. It further demonstrates the
proposed system’s behavior through a hardware-in-the-loop experiment.
