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In this paper, the authors propose adaptive neuro fuzzy inference system (ANFIS) algorithm, based on extreme learning machine (ELM) concepts for designing a controller for electric vehicle to grid (V2G) integration. First, learning speed and accuracy of the proposed algorithm is checked and second the transient response of the ELM-ANFIS (e-ANFIS) based controller is analyzed. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Thus, even with an involvement of a large number of plug-in hybrid electric vehicles (PHEV), a control technique for their charge and discharge pattern can be easily designed. To study the computational performance and transient response of the e-ANFIS based controller, it is compared with conventional ANFIS based controller. To implement the vehicle to grid integration concept, IEEE 33 bus radial distribution system is modelled in MATLAB environment.

ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration