Robust adaptive nonlinear control of plugin hybrid electric vehicles for vehicle to grid and grid to vehicle power flow with hybrid energy storage system.

Plugin hybrid electric vehicles (PHEVs) can solve the concerns of toxic gases emissions from fossil fuel. The PHEV under consideration consists of an on-board smart charger and a hybrid energy storage system (HESS) composed of the battery as a primary power source and an ultracapacitor (UC) as a secondary power source conjoined with two DC-DC bidirectional buck-boost converters. The on-board charging unit comprises of an AC-DC boost rectifier and DC-DC buck converter. The entire system's state model has been derived. An adaptive supertwisting sliding mode controller (AST-SMC) has been proposed for the unitary power factor correction at the grid side, tight voltage regulation of the charger and the DC bus, adaptation of time varying parameters and tracking of the currents involving the variation in the load profile. A genetic algorithm has been applied for optimizing the cost function of the controller gains. Key results refers reduction of chattering, adaptation of parametric variations, nonlinearities and external disturbances of the dynamical system. HESS results presents negligible convergence time and overshoots/undershoots even at the transients, and no steady state error. For the driving mode, the switching between dynamic and static behaviors and for parking mode, vehicle to grid (V2G) and grid to vehicle (G2V) operations have been proposed. In order to make nonlinear controller intelligent to achieve the V2G and G2V functionality, a state of charge based high-level controller has also been proposed. A standard Lyapunov stability criteria has been used to ensure asymptotic stability of the entire system. The proposed controller has been compared with sliding mode control (SMC) and finite time synergetic control (FTSC) by the simulation results using MATLAB/Simulink. Also, the hardware in loop setup has been used to validate the performance in real-time.