Fuzzy optimized conditioned-barrier nonlinear control of electric vehicle for grid to vehicle & vehicle to grid applications

Abstract

Plug-in electric vehicle (PEV) is the long-term eco-friendly solution to conventional fossil fuel based vehicles. In this study, a novel nonlinear barrier conditioned super-twisting sliding mode controller (BC ST-SMC) is proposed for the control of the PEV. The conventional super-twisting sliding mode control (ST-SMC) can only cater for the disturbances with known bounds and exhibits wind-up effects in the presence of saturated inputs which deteriorates the closed-loop performance of the system. The proposed BC-ST SMC solves these problems by providing anti-windup and un-bounded disturbance rejection. Lyapunov stability criterion is used to prove the asymptotic stability of the system. The PEV comprises of two subsystems namely: bidirectional charging unit (BCU) and hybrid energy storage system (HESS). The BCU comprises of a totem pole power converter between the PEV and grid which operates in two modes of grid to vehicle (G2V) and vehicle to grid (V2G). The components of the HESS are the battery and regenerative fuel-cell (RFC) (i.e. fuel-cell (FC) with an electrolyzer). The electrolyzer generates hydrogen which is required for the FC. Fuzzy-logic based energy management system (EMS) is adopted to maintain the power balance using state of charge (SoC) of the battery and electrolyzer as inputs. Furthermore, the controller gains are tuned using multi-objective genetic algorithm (MOGA) and multi-objective grey wolf optimizer (MOGWO) using integral absolute derivative of control input (IADU) with integral square of the error (ISE) as a fitness function. The simulations are performed on MATLAB® Simulink (2022b) which proves the superior performance of BC ST-SMC over conventional nonlinear controllers in the presence of saturated inputs. Finally, hardware-in-loop (HIL) based experimental results verify the real-time control of the proposed PEV.