Advanced nonlinear controller for hybrid energy storage system integrated with three-phase induction motor in hybrid electric vehicles

Internal combustion engines and electric propulsion systems are combined in hybrid electric vehicles to improve fuel economy and lower pollutants. It is still very difficult to optimize the energy management plan in order to balance the power flow between the engine and the electric motor. This study introduces an advanced optimized nonlinear controller for a hybrid energy storage system, integrated with a three-phase induction motor in hybrid electric vehicles. The system includes a fuel cell, battery, supercapacitor, and a combination of photoelectrochemical and photovoltaic cells. These energy sources are interconnected through a motor, a DC–AC inverter, and a DC–DC power converter. To ensure precise monitoring of source currents, accurate DC bus regulation, and overall stability of the closed-loop system, a condition-based integral terminal supertwisting sliding mode nonlinear controller is proposed. A genetic algorithm is employed to fine-tune the controller’s gain parameters to enhance the system’s performance. An adaptive neuro-fuzzy Inference System is used to track the hybrid photoelectrochemical and photovoltaic cells (HPEVs) maximum power point. Using MATLAB/Simulink experimental data from the extra-urban driving cycle, the performance of the optimized HESS is verified. The simulation findings and controller-in-the-loop experimental data are compared to confirm the efficacy of the suggested solution. The findings show that the suggested nonlinear controller greatly lowers errors and enhances the dynamic system’s overall performance.