Nonlinear Control Design for Electric Propulsion in Electric Vehicles

This paper depicts a nonlinear Adaptive Neuro-Fuzzy Rule-based (ANF-RBC) control design for electric propulsion subsystem of electric vehicles with a fully active topology of LiBs/Electrochemical Double-Layer-Capacitors-hybrid electrical energy storage system (LiBs/ECDLSCs-HEESS). A hybrid electrical ESS is used to generate LiBs current reference, and then ANF-RBC nonlinear controller designed and simulated to decrease non-linearity generated by used LiBs/ECDLSCs-HEESS with two input membership functions and twenty-five Takagi Sugeno Fuzzy inference rules. To measure performance of ANF-RBC nonlinear control system design two outputs lithium-ion battery current and Direct Current bus voltage was used, hence ANF-RBC implementation becomes easier. Proposed ANF-RBC nonlinear controller performance studied to that of existing nonlinear state of art controllers used in electric vehicles with three different load conditions. To validate the effectiveness of the proposed nonlinear control scheme simulated in MAT-LAB/Simulink with a fuzzy tool kit and quantitatively with the integral absolute error under three load conditions.