Optimized nonlinear controller for fuel cell, supercapacitor, battery, hybrid photoelectrochemical and photovoltaic cells based hybrid electric vehicles

Hybrid electric vehicles are getting popular due to the depletion of fossil fuel and natural gas reserves. It is important to utilize renewable energy sources to avoid the horrific effects of global warming and greenhouse gas emissions. This study presents an optimized nonlinear controller for a hybrid energy storage system (HESS) of fuel cell, battery, supercapacitor, and hybrid photoelectrochemical and photovoltaic cells (HPEV) based hybrid electric vehicles. All these energy sources are connected to a DC-DC power converter followed by a DC-AC inverter and a motor. Lyapunov based nonlinear controller is proposed to achieve tight DC bus regulation, good tracking of sources current, and global asymptotic stability of the closed loop system. The gain parameters of the proposed nonlinear controller are optimized using the grey wolf optimization (GWO) algorithm for performance improvement. Maximum power point tracking of HPEV is performed using an artificial neural network. Experimental data from the extra-urban driving cycle is used to demonstrate the performance of the proposed optimized HESS using Matlab/Simulink. To validate the performance of the proposed system the simulation results are compared with hardware in the loop experimental results. It can be observed from the simulation results that the proposed GWO optimized nonlinear controller decreases errors, and enhances the performance of the dynamical system.