Adoptive Super Twisting Sliding Mode Control based Nonlinear Control of Plugin Hybrid Electric Vehicle:

Abstract

Plugin hybrid electric vehicles (PHEVs) solve major issue of fossil fuel de- pendency, carbon gases emission and have far-reaching effects on the electric

transportation business. We assemble them from an on-board smart charger

to transfer the energy from grid to vehicle and a hybrid energy storage sys- tem (HESS). In this study HESS is composed of the battery as the primary

power source and ultracapacitor (UC) as a secondary power source, con- joined with two DC-DC bidirectional buck-boost converters. The on-board

charging unit comprises an AC-DC boost rectifier and DC-DC buck con- verter. The state model of the system has been taken into account. The

control objectives are ensuring unitary power factor at the grid side, tight voltage regulation of charger and DC bus, adapting varying parameters and

currents tracking despite the variation in load profile. To achieve the con- trol objectives, adaptive supertwisting sliding mode controller (AST-SMC)

has been proposed. For optimizing the cost function of controller gains, we have applied a genetic algorithm. For driving mode the switching between dynamic and static behavior and for parking mode, vehicle to grid (V2G) and grid to vehicle (G2V) operations are proposed. In order to make our nonlinear control intelligent for performing the V2G and G2V functionality, enhanced performance, and battery life the state of charge based high-level controller has been proposed. A standard Lyapunov stability criteria has

been verified for the system to ensure asymptotic stability. The compari- son of the proposed controller has been presented considering sliding mode

controller (SMC) and finite time synergetic controller (FTSC) as comparison controllers using MATLAB/Simulink. Also, the hardware in loop setup has been used to validate the performance in real-time.