Design & modelling of a hybrid AI-driven MPPT controller for electric vehicle

This article provides an intelligent Hybrid Maximum Power Point Tracking (MPPT) controller for Photo Voltaic (PV) systems which combines Particle Swarm Optimization (PSO) with an adaptive neuro-fuzzy inference system (ANFIS). The dynamic tracking performance and efficiency of existing MPPT techniques such as Incremental Conductance (INC) and Perturb and Observe (P&O)deteriorate in rapidly varying operating conditions. Therefore, the proposed ANFIS based MPPT technique employs neural network based learning along with fuzzy logic inference for modeling the non-linear relationship among environmental factors and Maximum Power Point (MPP). Moreover, PSO is incorporated to optimize some of the most important ANFIS parameters such as membership functions and rule base. The resultant PSO-ANFIS MPPT controller exhibits fast tracking response (0.02 s), less steady state oscillation and high efficiency (98.59%) compared to individual ANFIS, P&O and other existing MPPT algorithms.Experimental results validate its effectiveness, demonstrating strong suitability for modern PV applications, including electric vehicle systems.